https://infinity6.ai/assets/index-BtLVn5xc.js

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14 *
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28 *
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30 *
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c={mode:"hidden",children:r.children};return!(o&1)&&t.child!==a?(r=t.child,r.childLanes=0,r.pendingProps=c,t.deletions=null):(r=Oo(a,c),r.subtreeFlags=a.subtreeFlags&14680064),l!==null?i=Oo(l,i):(i=Is(i,o,n,null),i.flags|=2),i.return=t,r.return=t,r.sibling=i,t.child=r,r=i,i=t.child,o=e.child.memoizedState,o=o===null?G0(n):{baseLanes:o.baseLanes|n,cachePool:null,transitions:o.transitions},i.memoizedState=o,i.childLanes=e.childLanes&~n,t.memoizedState=K0,r}return i=e.child,e=i.sibling,r=Oo(i,{mode:"visible",children:r.children}),!(t.mode&1)&&(r.lanes=n),r.return=t,r.sibling=null,e!==null&&(n=t.deletions,n===null?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=r,t.memoizedState=null,r}function mk(e,t){return t=Tv({mode:"visible",children:t},e.mode,0,null),t.return=e,e.child=t}function lf(e,t,n,r){return r!==null&&X2(r),mc(t,e.child,null,n),e=mk(t,t.pendingProps.children),e.flags|=2,t.memoizedState=null,e}function m9(e,t,n,r,a,i,o){if(n)return t.flags&256?(t.flags&=-257,r=Mx(Error(ne(422))),lf(e,t,o,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(i=r.fallback,a=t.mode,r=Tv({mode:"visible",children:r.children},a,0,null),i=Is(i,a,o,null),i.flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,t.mode&1&&mc(t,e.child,null,o),t.child.memoizedState=G0(o),t.memoizedState=K0,i);if(!(t.mode&1))return lf(e,t,o,null);if(a.data==="$!"){if(r=a.nextSibling&&a.nextSibling.dataset,r)var l=r.dgst;return r=l,i=Error(ne(419)),r=Mx(i,r,void 0),lf(e,t,o,r)}if(l=(o&e.childLanes)!==0,Zn||l){if(r=an,r!==null){switch(o&-o){case 4:a=2;break;case 16:a=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:a=32;break;case 536870912:a=268435456;break;default:a=0}a=a&(r.suspendedLanes|o)?0:a,a!==0&&a!==i.retryLane&&(i.retryLane=a,Mi(e,a),ha(r,e,a,-1))}return yk(),r=Mx(Error(ne(421))),lf(e,t,o,r)}return a.data==="$?"?(t.flags|=128,t.child=e.child,t=P9.bind(null,e),a._reactRetry=t,null):(e=i.treeContext,xr=No(a.nextSibling),br=t,kt=!0,ia=null,e!==null&&($r[Fr++]=ki,$r[Fr++]=Si,$r[Fr++]=zs,ki=e.id,Si=e.overflow,zs=t),t=mk(t,r.children),t.flags|=4096,t)}function JP(e,t,n){e.lanes|=t;var r=e.alternate;r!==null&&(r.lanes|=t),B0(e.return,t,n)}function $x(e,t,n,r,a){var i=e.memoizedState;i===null?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=a)}function FO(e,t,n){var r=t.pendingProps,a=r.revealOrder,i=r.tail;if(Mn(e,t,r.children,n),r=Pt.current,r&2)r=r&1|2,t.flags|=128;else{if(e!==null&&e.flags&128)e:for(e=t.child;e!==null;){if(e.tag===13)e.memoizedState!==null&&JP(e,n,t);else if(e.tag===19)JP(e,n,t);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;e.sibling===null;){if(e.return===null||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(ft(Pt,r),!(t.mode&1))t.memoizedState=null;else switch(a){case"forwards":for(n=t.child,a=null;n!==null;)e=n.alternate,e!==null&&Dh(e)===null&&(a=n),n=n.sibling;n=a,n===null?(a=t.child,t.child=null):(a=n.sibling,n.sibling=null),$x(t,!1,a,n,i);break;case"backwards":for(n=null,a=t.child,t.child=null;a!==null;){if(e=a.alternate,e!==null&&Dh(e)===null){t.child=a;break}e=a.sibling,a.sibling=n,n=a,a=e}$x(t,!0,n,null,i);break;case"together":$x(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function nh(e,t){!(t.mode&1)&&e!==null&&(e.alternate=null,t.alternate=null,t.flags|=2)}function $i(e,t,n){if(e!==null&&(t.dependencies=e.dependencies),qs|=t.lanes,!(n&t.childLanes))return null;if(e!==null&&t.child!==e.child)throw Error(ne(153));if(t.child!==null){for(e=t.child,n=Oo(e,e.pendingProps),t.child=n,n.return=t;e.sibling!==null;)e=e.sibling,n=n.sibling=Oo(e,e.pendingProps),n.return=t;n.sibling=null}return t.child}function p9(e,t,n){switch(t.tag){case 3:MO(t),dc();break;case 5:lO(t);break;case 1:er(t.type)&&Oh(t);break;case 4:ak(t,t.stateNode.containerInfo);break;case 10:var r=t.type._context,a=t.memoizedProps.value;ft($h,r._currentValue),r._currentValue=a;break;case 13:if(r=t.memoizedState,r!==null)return r.dehydrated!==null?(ft(Pt,Pt.current&1),t.flags|=128,null):n&t.child.childLanes?$O(e,t,n):(ft(Pt,Pt.current&1),e=$i(e,t,n

40),e!==null?e.sibling:null);ft(Pt,Pt.current&1);break;case 19:if(r=(n&t.childLanes)!==0,e.flags&128){if(r)return FO(e,t,n);t.flags|=128}if(a=t.memoizedState,a!==null&&(a.rendering=null,a.tail=null,a.lastEffect=null),ft(Pt,Pt.current),r)break;return null;case 22:case 23:return t.lanes=0,OO(e,t,n)}return $i(e,t,n)}var RO,Q0,DO,LO;RO=function(e,t){for(var n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};Q0=function(){};DO=function(e,t,n,r){var a=e.memoizedProps;if(a!==r){e=t.stateNode,bs(Va.current);var i=null;switch(n){case"input":a=v0(e,a),r=v0(e,r),i=[];break;case"select":a=Nt({},a,{value:void 0}),r=Nt({},r,{value:void 0}),i=[];break;case"textarea":a=b0(e,a),r=b0(e,r),i=[];break;default:typeof a.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=Eh)}k0(n,r);var o;n=null;for(u in a)if(!r.hasOwnProperty(u)&&a.hasOwnProperty(u)&&a[u]!=null)if(u==="style"){var l=a[u];for(o in l)l.hasOwnProperty(o)&&(n||(n={}),n[o]="")}else u!=="dangerouslySetInnerHTML"&&u!=="children"&&u!=="suppressContentEditableWarning"&&u!=="suppressHydrationWarning"&&u!=="autoFocus"&&(Wd.hasOwnProperty(u)?i||(i=[]):(i=i||[]).push(u,null));for(u in r){var c=r[u];if(l=a!=null?a[u]:void 0,r.hasOwnProperty(u)&&c!==l&&(c!=null||l!=null))if(u==="style")if(l){for(o in l)!l.hasOwnProperty(o)||c&&c.hasOwnProperty(o)||(n||(n={}),n[o]="");for(o in c)c.hasOwnProperty(o)&&l[o]!==c[o]&&(n||(n={}),n[o]=c[o])}else n||(i||(i=[]),i.push(u,n)),n=c;else u==="dangerouslySetInnerHTML"?(c=c?c.__html:void 0,l=l?l.__html:void 0,c!=null&&l!==c&&(i=i||[]).push(u,c)):u==="children"?typeof c!="string"&&typeof c!="number"||(i=i||[]).push(u,""+c):u!=="suppressContentEditableWarning"&&u!=="suppressHydrationWarning"&&(Wd.hasOwnProperty(u)?(c!=null&&u==="onScroll"&&vt("scroll",e),i||l===c||(i=[])):(i=i||[]).push(u,c))}n&&(i=i||[]).push("style",n);var u=i;(t.updateQueue=u)&&(t.flags|=4)}};LO=function(e,t,n,r){n!==r&&(t.flags|=4)};function $u(e,t){if(!kt)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;t!==null;)t.alternate!==null&&(n=t),t=t.sibling;n===null?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;n!==null;)n.alternate!==null&&(r=n),n=n.sibling;r===null?t||e.tail===null?e.tail=null:e.tail.sibling=null:r.sibling=null}}function xn(e){var t=e.alternate!==null&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;a!==null;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags&14680064,r|=a.flags&14680064,a.return=e,a=a.sibling;else for(a=e.child;a!==null;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function f9(e,t,n){var r=t.pendingProps;switch(Z2(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return xn(t),null;case 1:return er(t.type)&&Th(),xn(t),null;case 3:return r=t.stateNode,pc(),bt(Jn),bt(Nn),ok(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),(e===null||e.child===null)&&(of(t)?t.flags|=4:e===null||e.memoizedState.isDehydrated&&!(t.flags&256)||(t.flags|=1024,ia!==null&&(rw(ia),ia=null))),Q0(e,t),xn(t),null;case 5:ik(t);var a=bs(nm.current);if(n=t.type,e!==null&&t.stateNode!=null)DO(e,t,n,r,a),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(t.stateNode===null)throw Error(ne(166));return xn(t),null}if(e=bs(Va.current),of(t)){r=t.stateNode,n=t.type;var i=t.memoizedProps;switch(r[La]=t,r[em]=i,e=(t.mode&1)!==0,n){case"dialog":vt("cancel",r),vt("close",r);break;case"iframe":case"object":case"embed":vt("load",r);break;case"video":case"audio":for(a=0;a<dd.length;a++)vt(dd[a],r);break;case"source":vt("error",r);break;case"img":case"image":case"link":vt("error",r),vt("load",r);break;case"details":vt("toggle",r);break;case"input":lP(r,i),vt("invalid",r);break;case"select":r._wrapperState={wasMultiple:!!i.multiple},vt("invalid",r);break;case"textarea":uP(r,i),vt("invalid",r)}k0(n,i),a=null;for(var o in i)if(i.hasOwnProperty(o)){var l=i[o];o==="children"?typeof l=="string"?r.textContent!==l&&(i.suppressHydrationWarning!==!0&&af(r.textContent,l,e),a=["children",l]):typeof l=="number"&&r.textContent!==""+l&&(i.suppressHydrationWarning!==!0&&af(r.textContent,l,e),a=["children",""+l]):Wd.hasOwnProperty(o)&&l!=null&&o==="onScroll"&&vt("scroll",r)}switch(n){case"input":Yp(r),cP(r,i,!0);break;case"textarea":Yp(r),dP(r);break;case"select":case"option":break;default:typeof i.onClick=="function"&&(r.onclick=Eh)}r=a,t.updateQueue=r,r!==null&&(t.flags|=4)}else{o=a.nodeType===9?a:a.ownerDocument,e==="http://www.w3.org/1999/xhtml"&&(e=pT(n)),e==="http://www.w3.org/1999/xhtml"?n==="script"?(e=o.createElement("div"),e.innerHTML="<script><\/script>

40",e=e.removeChild(e.firstChild)):typeof r.is=="string"?e=o.createElement(n,{is:r.is}):(e=o.createElement(n),n==="select"&&(o=e,r.multiple?o.multiple=!0:r.size&&(o.size=r.size))):e=o.createElementNS(e,n),e[La]=t,e[em]=r,RO(e,t,!1,!1),t.stateNode=e;e:{switch(o=S0(n,r),n){case"dialog":vt("cancel",e),vt("close",e),a=r;break;case"iframe":case"object":case"embed":vt("load",e),a=r;break;case"video":case"audio":for(a=0;a<dd.length;a++)vt(dd[a],e);a=r;break;case"source":vt("error",e),a=r;break;case"img":case"image":case"link":vt("error",e),vt("load",e),a=r;break;case"details":vt("toggle",e),a=r;break;case"input":lP(e,r),a=v0(e,r),vt("invalid",e);break;case"option":a=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},a=Nt({},r,{value:void 0}),vt("invalid",e);break;case"textarea":uP(e,r),a=b0(e,r),vt("invalid",e);break;default:a=r}k0(n,a),l=a;for(i in l)if(l.hasOwnProperty(i)){var c=l[i];i==="style"?gT(e,c):i==="dangerouslySetInnerHTML"?(c=c?c.__html:void 0,c!=null&&fT(e,c)):i==="children"?typeof c=="string"?(n!=="textarea"||c!=="")&&Vd(e,c):typeof c=="number"&&Vd(e,""+c):i!=="suppressContentEditableWarning"&&i!=="suppressHydrationWarning"&&i!=="autoFocus"&&(Wd.hasOwnProperty(i)?c!=null&&i==="onScroll"&&vt("scroll",e):c!=null&&F2(e,i,c,o))}switch(n){case"input":Yp(e),cP(e,r,!1);break;case"textarea":Yp(e),dP(e);break;case"option":r.value!=null&&e.setAttribute("value",""+$o(r.value));break;case"select":e.multiple=!!r.multiple,i=r.value,i!=null?zl(e,!!r.multiple,i,!1):r.defaultValue!=null&&zl(e,!!r.multiple,r.defaultValue,!0);break;default:typeof a.onClick=="function"&&(e.onclick=Eh)}switch(n){case"button":case"input":case"select":case"textarea":r=!!r.autoFocus;break e;case"img":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}t.ref!==null&&(t.flags|=512,t.flags|=2097152)}return xn(t),null;case 6:if(e&&t.stateNode!=null)LO(e,t,e.memoizedProps,r);else{if(typeof r!="string"&&t.stateNode===null)throw Error(ne(166));if(n=bs(nm.current),bs(Va.current),of(t)){if(r=t.stateNode,n=t.memoizedProps,r[La]=t,(i=r.nodeValue!==n)&&(e=br,e!==null))switch(e.tag){case 3:af(r.nodeValue,n,(e.mode&1)!==0);break;case 5:e.memoizedProps.suppressHydrationWarning!==!0&&af(r.nodeValue,n,(e.mode&1)!==0)}i&&(t.flags|=4)}else r=(n.nodeType===9?n:n.ownerDocument).createTextNode(r),r[La]=t,t.stateNode=r}return xn(t),null;case 13:if(bt(Pt),r=t.memoizedState,e===null||e.memoizedState!==null&&e.memoizedState.dehydrated!==null){if(kt&&xr!==null&&t.mode&1&&!(t.flags&128))rO(),dc(),t.flags|=98560,i=!1;else if(i=of(t),r!==null&&r.dehydrated!==null){if(e===null){if(!i)throw Error(ne(318));if(i=t.memoizedState,i=i!==null?i.dehydrated:null,!i)throw Error(ne(317));i[La]=t}else dc(),!(t.flags&128)&&(t.memoizedState=null),t.flags|=4;xn(t),i=!1}else ia!==null&&(rw(ia),ia=null),i=!0;if(!i)return t.flags&65536?t:null}return t.flags&128?(t.lanes=n,t):(r=r!==null,r!==(e!==null&&e.memoizedState!==null)&&r&&(t.child.flags|=8192,t.mode&1&&(e===null||Pt.current&1?Yt===0&&(Yt=3):yk())),t.updateQueue!==null&&(t.flags|=4),xn(t),null);case 4:return pc(),Q0(e,t),e===null&&Xd(t.stateNode.containerInfo),xn(t),null;case 10:return tk(t.type._context),xn(t),null;case 17:return er(t.type)&&Th(),xn(t),null;case 19:if(bt(Pt),i=t.memoizedState,i===null)return xn(t),null;if(r=(t.flags&128)!==0,o=i.rendering,o===null)if(r)$u(i,!1);else{if(Yt!==0||e!==null&&e.flags&128)for(e=t.child;e!==null;){if(o=Dh(e),o!==null){for(t.flags|=128,$u(i,!1),r=o.updateQueue,r!==null&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;n!==null;)i=n,e=r,i.flags&=14680066,o=i.alternate,o===null?(i.childLanes=0,i.lanes=e,i.child=null,i.subtreeFlags=0,i.memoizedProps=null,i.memoizedState=null,i.updateQueue=null,i.dependencies=null,i.stateNode=null):(i.childLanes=o.childLanes,i.lanes=o.lanes,i.child=o.child,i.subtreeFlags=0,i.deletions=null,i.memoizedProps=o.memoizedProps,i.memoizedState=o.memoizedState,i.updateQueue=o.updateQueue,i.type=o.type,e=o.dependencies,i.dependencies=e===null?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return ft(Pt,Pt.current&1|2),t.child}e=e.sibling}i.tail!==null&&$t()>hc&&(t.flags|=128,r=!0,$u(i,!1),t.lanes=4194304)}else{if(!r)if(e=Dh(o),e!==null){if(t.flags|=128,r=!0,n=e.updateQueue,n!==null&&(t.updateQueue=n,t.flags|=4),$u(i,!0),i.tail===null&&i.tailMode==="hidden"&&!o.alternate&&!kt)return xn(t),null}else 2*$t()-i.renderingStartTime>hc&&n!==1073741824&&(t.flags|=128,r=!0,$u(i,!1),t.lanes=4194304);i.isBackwards?(o.sibling=t.child,t.child=o):(n=i.last,n!==null?n.sibling=o:t.child=o,i.last=o)}return i.tail!==null?(t=i.tail,i.rendering=t,i.tail=t.sibling,i.renderingStartTime=$t(),t.sibling=null,n=Pt.current,ft(Pt,r?n&1|2:n&1),t):(xn(t),null);case 22:case 23:return vk(),r=t.memoizedState!==null,e!==null&&e.memoizedState!==null!==r&&(t.flags|=8192),r&&t.mode&1?fr&1073741824&&(xn(t),t.subtreeFlags&6&&(t.flags|=8192)):xn(t),null;case 24:return null;case 25:return null}throw Error(ne(156,t.tag))}function h9(e,t){switch(Z2(t),t.tag){case 1:return 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i=n.lanes|n.childLanes;if(rz(e,i),e===an&&(Wt=an=null,mn=0),!(n.subtreeFlags&2064)&&!(n.flags&2064)||uf||(uf=!0,XO(Ph,function(){return Vl(),null})),i=(n.flags&15990)!==0,n.subtreeFlags&15990||i){i=Br.transition,Br.transition=null;var o=tt;tt=1;var l=Ge;Ge|=4,pk.current=null,v9(e,n),UO(n,e),Uz(M0),_h=!!I0,M0=I0=null,e.current=n,y9(n),GL(),Ge=l,tt=o,Br.transition=i}else e.current=n;if(uf&&(uf=!1,bo=e,Uh=a),i=e.pendingLanes,i===0&&(Eo=null),ZL(n.stateNode),tr(e,$t()),t!==null)for(r=e.onRecoverableError,n=0;n<t.length;n++)a=t[n],r(a.value,{componentStack:a.stack,digest:a.digest});if(qh)throw qh=!1,e=ew,ew=null,e;return Uh&1&&e.tag!==0&&Vl(),i=e.pendingLanes,i&1?e===tw?Pd++:(Pd=0,tw=e):Pd=0,Go(),null}function Vl(){if(bo!==null){var e=CT(Uh),t=Br.transition,n=tt;try{if(Br.transition=null,tt=16>e?16:e,bo===null)var r=!1;else{if(e=bo,bo=null,Uh=0,Ge&6)throw Error(ne(331));var a=Ge;for(Ge|=4,he=e.current;he!==null;){var i=he,o=i.child;if(he.flags&16){var l=i.deletions;if(l!==null){for(var 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46 * @license lucide-react v0.462.0 - ISC
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48 * This source code is licensed under the ISC license.
49 * See the LICENSE file in the root directory of this source tree.
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51 * @license lucide-react v0.462.0 - ISC
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53 * This source code is licensed under the ISC license.
54 * See the LICENSE file in the root directory of this source tree.
55 */const TB=S.forwardRef(({color:e="currentColor",size:t=24,strokeWidth:n=2,absoluteStrokeWidth:r,className:a="",children:i,iconNode:o,...l},c)=>S.createElement("svg",{ref:c,...EB,width:t,height:t,stroke:e,strokeWidth:r?Number(n)*24/Number(t):n,className:OI("lucide",a),...l},[...o.map(([u,d])=>S.createElement(u,d)),...Array.isArray(i)?i:[i]]));/**
56 * @license lucide-react v0.462.0 - ISC
57 *
58 * This source code is licensed under the ISC license.
59 * See the LICENSE file in the root directory of this source tree.
60 */const xe=(e,t)=>{const n=S.forwardRef(({className:r,...a},i)=>S.createElement(TB,{ref:i,iconNode:t,className:OI(`lucide-${CB(e)}`,r),...a}));return n.displayName=`${e}`,n};/**
61 * @license lucide-react v0.462.0 - ISC
62 *
63 * This source code is licensed under the ISC license.
64 * See the LICENSE file in the root directory of this source tree.
65 */const Uv=xe("ArrowDown",[["path",{d:"M12 5v14",key:"s699le"}],["path",{d:"m19 12-7 7-7-7",key:"1idqje"}]]);/**
66 * @license lucide-react v0.462.0 - ISC
67 *
68 * This source code is licensed under the ISC license.
69 * See the LICENSE file in the root directory of this source tree.
70 */const Ws=xe("ArrowLeft",[["path",{d:"m12 19-7-7 7-7",key:"1l729n"}],["path",{d:"M19 12H5",key:"x3x0zl"}]]);/**
71 * @license lucide-react v0.462.0 - ISC
72 *
73 * This source code is licensed under the ISC license.
74 * See the LICENSE file in the root directory of this source tree.
75 */const _a=xe("ArrowRight",[["path",{d:"M5 12h14",key:"1ays0h"}],["path",{d:"m12 5 7 7-7 7",key:"xquz4c"}]]);/**
76 * @license lucide-react v0.462.0 - ISC
77 *
78 * This source code is licensed under the ISC license.
79 * See the LICENSE file in the root directory of this source tree.
80 */const cw=xe("ArrowUpRight",[["path",{d:"M7 7h10v10",key:"1tivn9"}],["path",{d:"M7 17 17 7",key:"1vkiza"}]]);/**
81 * @license lucide-react v0.462.0 - ISC
82 *
83 * This source code is licensed under the ISC license.
84 * See the LICENSE file in the root directory of this source tree.
85 */const Ck=xe("ArrowUp",[["path",{d:"m5 12 7-7 7 7",key:"hav0vg"}],["path",{d:"M12 19V5",key:"x0mq9r"}]]);/**
86 * @license lucide-react v0.462.0 - ISC
87 *
88 * This source code is licensed under the ISC license.
89 * See the LICENSE file in the root directory of this source tree.
90 */const OB=xe("BookOpen",[["path",{d:"M12 7v14",key:"1akyts"}],["path",{d:"M3 18a1 1 0 0 1-1-1V4a1 1 0 0 1 1-1h5a4 4 0 0 1 4 4 4 4 0 0 1 4-4h5a1 1 0 0 1 1 1v13a1 1 0 0 1-1 1h-6a3 3 0 0 0-3 3 3 3 0 0 0-3-3z",key:"ruj8y"}]]);/**
91 * @license lucide-react v0.462.0 - ISC
92 *
93 * This source code is licensed under the ISC license.
94 * See the LICENSE file in the root directory of this source tree.
95 */const IB=xe("Boxes",[["path",{d:"M2.97 12.92A2 2 0 0 0 2 14.63v3.24a2 2 0 0 0 .97 1.71l3 1.8a2 2 0 0 0 2.06 0L12 19v-5.5l-5-3-4.03 2.42Z",key:"lc1i9w"}],["path",{d:"m7 16.5-4.74-2.85",key:"1o9zyk"}],["path",{d:"m7 16.5 5-3",key:"va8pkn"}],["path",{d:"M7 16.5v5.17",key:"jnp8gn"}],["path",{d:"M12 13.5V19l3.97 2.38a2 2 0 0 0 2.06 0l3-1.8a2 2 0 0 0 .97-1.71v-3.24a2 2 0 0 0-.97-1.71L17 10.5l-5 3Z",key:"8zsnat"}],["path",{d:"m17 16.5-5-3",key:"8arw3v"}],["path",{d:"m17 16.5 4.74-2.85",key:"8rfmw"}],["path",{d:"M17 16.5v5.17",key:"k6z78m"}],["path",{d:"M7.97 4.42A2 2 0 0 0 7 6.13v4.37l5 3 5-3V6.13a2 2 0 0 0-.97-1.71l-3-1.8a2 2 0 0 0-2.06 0l-3 1.8Z",key:"1xygjf"}],["path",{d:"M12 8 7.26 5.15",key:"1vbdud"}],["path",{d:"m12 8 4.74-2.85",key:"3rx089"}],["path",{d:"M12 13.5V8",key:"1io7kd"}]]);/**
96 * @license lucide-react v0.462.0 - ISC
97 *
98 * This source code is licensed under the ISC license.
99 * See the LICENSE file in the root directory of this source tree.
100 */const uw=xe("Brain",[["path",{d:"M12 5a3 3 0 1 0-5.997.125 4 4 0 0 0-2.526 5.77 4 4 0 0 0 .556 6.588A4 4 0 1 0 12 18Z",key:"l5xja"}],["path",{d:"M12 5a3 3 0 1 1 5.997.125 4 4 0 0 1 2.526 5.77 4 4 0 0 1-.556 6.588A4 4 0 1 1 12 18Z",key:"ep3f8r"}],["path",{d:"M15 13a4.5 4.5 0 0 1-3-4 4.5 4.5 0 0 1-3 4",key:"1p4c4q"}],["path",{d:"M17.599 6.5a3 3 0 0 0 .399-1.375",key:"tmeiqw"}],["path",{d:"M6.003 5.125A3 3 0 0 0 6.401 6.5",key:"105sqy"}],["path",{d:"M3.477 10.896a4 4 0 0 1 .585-.396",key:"ql3yin"}],["path",{d:"M19.938 10.5a4 4 0 0 1 .585.396",key:"1qfode"}],["path",{d:"M6 18a4 4 0 0 1-1.967-.516",key:"2e4loj"}],["path",{d:"M19.967 17.484A4 4 0 0 1 18 18",key:"159ez6"}]]);/**
101 * @license lucide-react v0.462.0 - ISC
102 *
103 * This source code is licensed under the ISC license.
104 * See the LICENSE file in the root directory of this source tree.
105 */const MB=xe("Building2",[["path",{d:"M6 22V4a2 2 0 0 1 2-2h8a2 2 0 0 1 2 2v18Z",key:"1b4qmf"}],["path",{d:"M6 12H4a2 2 0 0 0-2 2v6a2 2 0 0 0 2 2h2",key:"i71pzd"}],["path",{d:"M18 9h2a2 2 0 0 1 2 2v9a2 2 0 0 1-2 2h-2",key:"10jefs"}],["path",{d:"M10 6h4",key:"1itunk"}],["path",{d:"M10 10h4",key:"tcdvrf"}

105],["path",{d:"M10 14h4",key:"kelpxr"}],["path",{d:"M10 18h4",key:"1ulq68"}]]);/**
106 * @license lucide-react v0.462.0 - ISC
107 *
108 * This source code is licensed under the ISC license.
109 * See the LICENSE file in the root directory of this source tree.
110 */const v4=xe("Building",[["rect",{width:"16",height:"20",x:"4",y:"2",rx:"2",ry:"2",key:"76otgf"}],["path",{d:"M9 22v-4h6v4",key:"r93iot"}],["path",{d:"M8 6h.01",key:"1dz90k"}],["path",{d:"M16 6h.01",key:"1x0f13"}],["path",{d:"M12 6h.01",key:"1vi96p"}],["path",{d:"M12 10h.01",key:"1nrarc"}],["path",{d:"M12 14h.01",key:"1etili"}],["path",{d:"M16 10h.01",key:"1m94wz"}],["path",{d:"M16 14h.01",key:"1gbofw"}],["path",{d:"M8 10h.01",key:"19clt8"}],["path",{d:"M8 14h.01",key:"6423bh"}]]);/**
111 * @license lucide-react v0.462.0 - ISC
112 *
113 * This source code is licensed under the ISC license.
114 * See the LICENSE file in the root directory of this source tree.
115 */const dw=xe("ChartColumn",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"M18 17V9",key:"2bz60n"}],["path",{d:"M13 17V5",key:"1frdt8"}],["path",{d:"M8 17v-3",key:"17ska0"}]]);/**
116 * @license lucide-react v0.462.0 - ISC
117 *
118 * This source code is licensed under the ISC license.
119 * See the LICENSE file in the root directory of this source tree.
120 */const $B=xe("ChartLine",[["path",{d:"M3 3v16a2 2 0 0 0 2 2h16",key:"c24i48"}],["path",{d:"m19 9-5 5-4-4-3 3",key:"2osh9i"}]]);/**
121 * @license lucide-react v0.462.0 - ISC
122 *
123 * This source code is licensed under the ISC license.
124 * See the LICENSE file in the root directory of this source tree.
125 */const ti=xe("Check",[["path",{d:"M20 6 9 17l-5-5",key:"1gmf2c"}]]);/**
126 * @license lucide-react v0.462.0 - ISC
127 *
128 * This source code is licensed under the ISC license.
129 * See the LICENSE file in the root directory of this source tree.
130 */const Wv=xe("ChevronDown",[["path",{d:"m6 9 6 6 6-6",key:"qrunsl"}]]);/**
131 * @license lucide-react v0.462.0 - ISC
132 *
133 * This source code is licensed under the ISC license.
134 * See the LICENSE file in the root directory of this source tree.
135 */const FB=xe("ChevronRight",[["path",{d:"m9 18 6-6-6-6",key:"mthhwq"}]]);/**
136 * @license lucide-react v0.462.0 - ISC
137 *
138 * This source code is licensed under the ISC license.
139 * See the LICENSE file in the root directory of this source tree.
140 */const RB=xe("ChevronUp",[["path",{d:"m18 15-6-6-6 6",key:"153udz"}]]);/**
141 * @license lucide-react v0.462.0 - ISC
142 *
143 * This source code is licensed under the ISC license.
144 * See the LICENSE file in the root directory of this source tree.
145 */const Vv=xe("CircleCheck",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["path",{d:"m9 12 2 2 4-4",key:"dzmm74"}]]);/**
146 * @license lucide-react v0.462.0 - ISC
147 *
148 * This source code is licensed under the ISC license.
149 * See the LICENSE file in the root directory of this source tree.
150 */const DB=xe("Cog",[["path",{d:"M12 20a8 8 0 1 0 0-16 8 8 0 0 0 0 16Z",key:"sobvz5"}],["path",{d:"M12 14a2 2 0 1 0 0-4 2 2 0 0 0 0 4Z",key:"11i496"}],["path",{d:"M12 2v2",key:"tus03m"}],["path",{d:"M12 22v-2",key:"1osdcq"}],["path",{d:"m17 20.66-1-1.73",key:"eq3orb"}],["path",{d:"M11 10.27 7 3.34",key:"16pf9h"}],["path",{d:"m20.66 17-1.73-1",key:"sg0v6f"}],["path",{d:"m3.34 7 1.73 1",key:"1ulond"}],["path",{d:"M14 12h8",key:"4f43i9"}],["path",{d:"M2 12h2",key:"1t8f8n"}],["path",{d:"m20.66 7-1.73 1",key:"1ow05n"}],["path",{d:"m3.34 17 1.73-1",key:"nuk764"}],["path",{d:"m17 3.34-1 1.73",key:"2wel8s"}],["path",{d:"m11 13.73-4 6.93",key:"794ttg"}]]);/**
151 * @license lucide-react v0.462.0 - ISC
152 *
153 * This source code is licensed under the ISC license.
154 * See the LICENSE file in the root directory of this source tree.
155 */const LB=xe("Compass",[["path",{d:"m16.24 7.76-1.804 5.411a2 2 0 0 1-1.265 1.265L7.76 16.24l1.804-5.411a2 2 0 0 1 1.265-1.265z",key:"9ktpf1"}],["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}]]);/**
156 * @license lucide-react v0.462.0 - ISC
157 *
158 * This source code is licensed under the ISC license.
159 * See the LICENSE file in the root directory of this source tree.
160 */const II=xe("Database",[["ellipse",{cx:"12",cy:"5",rx:"9",ry:"3",key:"msslwz"}],["path",{d:"M3 5V19A9 3 0 0 0 21 19V5",key:"1wlel7"}],["path",{d:"M3 12A9 3 0 0 0 21 12",key:"mv7ke4"}]]);/**
161 * @license lucide-react v0.462.0 - ISC
162 *
163 * This source code is licensed under the ISC license.
164 * See the LICENSE file in the root directory of this source tree.
165 */const MI=xe("DollarSign",[["line",{x1:"12",x2:"12",y1:"2",y2:"22",key:"7eqyqh"}],["path",{d:"M17 5H9.5a3.5 3.5 0 0 0 0 7h5a3.5 3.5 0 0 1 0 7H6",key:"1b0p4s"}]]);/**
166 * @license lucide-react v0.462.0 - ISC
167 *
168 * This source code is licensed under the ISC license.
169 * See the LICENSE file in the root directory of this source tree.
170 */const $I=xe("ExternalLink",[["path",{d:"M15 3h6v6",key:"1q9fwt"}],["path",{d:"M10 14 21 3",key:"gplh6r"}],["path",{d:"M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6",key:"a6xqqp"}]]);/**
171 * @license lucide-react v0.462.0 - ISC
172 *
173 * This source code is licensed under the ISC license.
174 * See the LICENSE file in the root directory of this source tree.
175 */const zB=xe("EyeOff",[["path",{d:"M10.733 5.076a10.744 10.744 0 0 1 11.205 6.575 1 1 0 0 1 0 .696 10.747 10.747 0 0 1-1.444 2.49",key:"ct8e1f"}],["path",{d:"M14.084 14.158a3 3 0 0 1-4.242-4.242",key:"151rxh"}],["path",{d:"M17.479 17.499a10.75 10.75 0 0 1-15.417-5.151 1 1 0 0 1 0-.696 10.75 10.75 0 0 1 4.446-5.143",key:"13bj9a"}],["path",{d:"m2 2 20 20",key:"1ooewy"}]]);/**
176 * @license lucide-react v0.462.0 - ISC
177 *
178 * This source code is licensed under the ISC license.
179 * See the LICENSE file in the root directory of this source tree.
180 */const BB=xe("Filter",[["polygon",{points:"22 3 2 3 10 12.46 10 19 14 21 14 12.46 22 3",key:"1yg77f"}]]);/**
181 * @license lucide-react v0.462.0 - ISC
182 *
183 * This source code is licensed under the ISC license.
184 * See the LICENSE file in the root directory of this source tree.
185 */const qB=xe("Gauge",[["path",{d:"m12 14 4-4",key:"9kzdfg"}],["path",{d:"M3.34 19a10 10 0 1 1 17.32 0",key:"19p75a"}]]);/**
186 * @license lucide-react v0.462.0 - ISC
187 *
188 * This source code is licensed under the ISC license.
189 * See the LICENSE file in the root directory of this source tree.
190 */const UB=xe("Heart",[["path",{d:"M19 14c1.49-1.46 3-3.21 3-5.5A5.5 5.5 0 0 0 16.5 3c-1.76 0-3 .5-4.5 2-1.5-1.5-2.74-2-4.5-2A5.5 5.5 0 0 0 2 8.5c0 2.3 1.5 4.05 3 5.5l7 7Z",key:"c3ymky"}]]);/**
191 * @license lucide-react v0.462.0 - ISC
192 *
193 * This source code is licensed under the ISC license.
194 * See the LICENSE file in the root directory of this source tree.
195 */const WB=xe("House",[["path",{d:"M15 21v-8a1 1 0 0 0-1-1h-4a1 1 0 0 0-1 1v8",key:"5wwlr5"}],["path",{d:"M3 10a2 2 0 0 1 .709-1.528l7-5.999a2 2 0 0 1 2.582 0l7 5.999A2 2 0 0 1 21 10v9a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2z",key:"1d0kgt"}]]);/**
196 * @license lucide-react v0.462.0 - ISC
197 *
198 * This source code is licensed under the ISC license.
199 * See the LICENSE file in the root directory of this source tree.
200 */const VB=xe("KeyRound",[["path",{d:"M2.586 17.414A2 2 0 0 0 2 18.828V21a1 1 0 0 0 1 1h3a1 1 0 0 0 1-1v-1a1 1 0 0 1 1-1h1a1 1 0 0 0 1-1v-1a1 1 0 0 1 1-1h.172a2 2 0 0 0 1.414-.586l.814-.814a6.5 6.5 0 1 0-4-4z",key:"1s6t7t"}],["circle",{cx:"16.5",cy:"7.5",r:".5",fill:"currentColor",key:"w0ekpg"}]]);/**
201 * @license lucide-react v0.462.0 - ISC
202 *
203 * This source code is licensed under the ISC license.
204 * See the LICENSE file in the root directory of this source tree.
205 */const Ek=xe("Layers",[["path",{d:"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z",key:"8b97xw"}],["path",{d:"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65",key:"dd6zsq"}],["path",{d:"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65",key:"ep9fru"}]]);/**
206 * @license lucide-react v0.462.0 - ISC
207 *
208 * This source code is licensed under the ISC license.
209 * See the LICENSE file in the root directory of this source tree.
210 */const FI=xe("LayoutGrid",[["rect",{width:"7",height:"7",x:"3",y:"3",rx:"1",key:"1g98yp"}],["rect",{width:"7",height:"7",x:"14",y:"3",rx:"1",key:"6d4xhi"}],["rect",{width:"7",height:"7",x:"14",y:"14",rx:"1",key:"nxv5o0"}],["rect",{width:"7",height:"7",x:"3",y:"14",rx:"1",key:"1bb6yr"}]]);/**
211 * @license lucide-react v0.462.0 - ISC
212 *
213 * This source code is licensed under the ISC license.
214 * See the LICENSE file in the root directory of this source tree.
215 */const RI=xe("Lightbulb",[["path",{d:"M15 14c.2-1 .7-1.7 1.5-2.5 1-.9 1.5-2.2 1.5-3.5A6 6 0 0 0 6 8c0 1 .2 2.2 1.5 3.5.7.7 1.3 1.5 1.5 2.5",key:"1gvzjb"}],["path",{d:"M9 18h6",key:"x1upvd"}],["path",{d:"M10 22h4",key:"ceow96"}]]);/**
216 * @license lucide-react v0.462.0 - ISC
217 *
218 * This source code is licensed under the ISC license.
219 * See the LICENSE file in the root directory of this source tree.
220 */const Tk=xe("Linkedin",[["path",{d:"M16 8a6 6 0 0 1 6 6v7h-4v-7a2 2 0 0 0-2-2 2 2 0 0 0-2 2v7h-4v-7a6 6 0 0 1 6-6z",key:"c2jq9f"}],["rect",{width:"4",height:"12",x:"2",y:"9",key:"mk3on5"}],["circle",{cx:"4",cy:"4",r:"2",key:"bt5ra8"}]]);/**
221 * @license lucide-react v0.462.0 - ISC
222 *
223 * This source code is licensed under the ISC license.
224 * See the LICENSE file in the root directory of this source tree.
225 */const Ok=xe("LoaderCircle",[["path",{d:"M21 12a9 9 0 1 1-6.219-8.56",key:"13zald"}]]);/**
226 * @license lucide-react v0.462.0 - ISC
227 *
228 * This source code is licensed under the ISC license.
229 * See the LICENSE file in the root directory of this source tree.

230 */const DI=xe("Lock",[["rect",{width:"18",height:"11",x:"3",y:"11",rx:"2",ry:"2",key:"1w4ew1"}],["path",{d:"M7 11V7a5 5 0 0 1 10 0v4",key:"fwvmzm"}]]);/**
231 * @license lucide-react v0.462.0 - ISC
232 *
233 * This source code is licensed under the ISC license.
234 * See the LICENSE file in the root directory of this source tree.
235 */const LI=xe("Mail",[["rect",{width:"20",height:"16",x:"2",y:"4",rx:"2",key:"18n3k1"}],["path",{d:"m22 7-8.97 5.7a1.94 1.94 0 0 1-2.06 0L2 7",key:"1ocrg3"}]]);/**
236 * @license lucide-react v0.462.0 - ISC
237 *
238 * This source code is licensed under the ISC license.
239 * See the LICENSE file in the root directory of this source tree.
240 */const HB=xe("MapPin",[["path",{d:"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0",key:"1r0f0z"}],["circle",{cx:"12",cy:"10",r:"3",key:"ilqhr7"}]]);/**
241 * @license lucide-react v0.462.0 - ISC
242 *
243 * This source code is licensed under the ISC license.
244 * See the LICENSE file in the root directory of this source tree.
245 */const KB=xe("Maximize2",[["polyline",{points:"15 3 21 3 21 9",key:"mznyad"}],["polyline",{points:"9 21 3 21 3 15",key:"1avn1i"}],["line",{x1:"21",x2:"14",y1:"3",y2:"10",key:"ota7mn"}],["line",{x1:"3",x2:"10",y1:"21",y2:"14",key:"1atl0r"}]]);/**
246 * @license lucide-react v0.462.0 - ISC
247 *
248 * This source code is licensed under the ISC license.
249 * See the LICENSE file in the root directory of this source tree.
250 */const GB=xe("Megaphone",[["path",{d:"m3 11 18-5v12L3 14v-3z",key:"n962bs"}],["path",{d:"M11.6 16.8a3 3 0 1 1-5.8-1.6",key:"1yl0tm"}]]);/**
251 * @license lucide-react v0.462.0 - ISC
252 *
253 * This source code is licensed under the ISC license.
254 * See the LICENSE file in the root directory of this source tree.
255 */const QB=xe("Menu",[["line",{x1:"4",x2:"20",y1:"12",y2:"12",key:"1e0a9i"}],["line",{x1:"4",x2:"20",y1:"6",y2:"6",key:"1owob3"}],["line",{x1:"4",x2:"20",y1:"18",y2:"18",key:"yk5zj1"}]]);/**
256 * @license lucide-react v0.462.0 - ISC
257 *
258 * This source code is licensed under the ISC license.
259 * See the LICENSE file in the root directory of this source tree.
260 */const YB=xe("MessageSquareText",[["path",{d:"M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z",key:"1lielz"}],["path",{d:"M13 8H7",key:"14i4kc"}],["path",{d:"M17 12H7",key:"16if0g"}]]);/**
261 * @license lucide-react v0.462.0 - ISC
262 *
263 * This source code is licensed under the ISC license.
264 * See the LICENSE file in the root directory of this source tree.
265 */const zI=xe("MessageSquare",[["path",{d:"M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z",key:"1lielz"}]]);/**
266 * @license lucide-react v0.462.0 - ISC
267 *
268 * This source code is licensed under the ISC license.
269 * See the LICENSE file in the root directory of this source tree.
270 */const BI=xe("Minus",[["path",{d:"M5 12h14",key:"1ays0h"}]]);/**
271 * @license lucide-react v0.462.0 - ISC
272 *
273 * This source code is licensed under the ISC license.
274 * See the LICENSE file in the root directory of this source tree.
275 */const ZB=xe("Plus",[["path",{d:"M5 12h14",key:"1ays0h"}],["path",{d:"M12 5v14",key:"s699le"}]]);/**
276 * @license lucide-react v0.462.0 - ISC
277 *
278 * This source code is licensed under the ISC license.
279 * See the LICENSE file in the root directory of this source tree.
280 */const Fl=xe("Quote",[["path",{d:"M16 3a2 2 0 0 0-2 2v6a2 2 0 0 0 2 2 1 1 0 0 1 1 1v1a2 2 0 0 1-2 2 1 1 0 0 0-1 1v2a1 1 0 0 0 1 1 6 6 0 0 0 6-6V5a2 2 0 0 0-2-2z",key:"rib7q0"}],["path",{d:"M5 3a2 2 0 0 0-2 2v6a2 2 0 0 0 2 2 1 1 0 0 1 1 1v1a2 2 0 0 1-2 2 1 1 0 0 0-1 1v2a1 1 0 0 0 1 1 6 6 0 0 0 6-6V5a2 2 0 0 0-2-2z",key:"1ymkrd"}]]);/**
281 * @license lucide-react v0.462.0 - ISC
282 *
283 * This source code is licensed under the ISC license.
284 * See the LICENSE file in the root directory of this source tree.
285 */const XB=xe("RefreshCw",[["path",{d:"M3 12a9 9 0 0 1 9-9 9.75 9.75 0 0 1 6.74 2.74L21 8",key:"v9h5vc"}],["path",{d:"M21 3v5h-5",key:"1q7to0"}],["path",{d:"M21 12a9 9 0 0 1-9 9 9.75 9.75 0 0 1-6.74-2.74L3 16",key:"3uifl3"}],["path",{d:"M8 16H3v5",key:"1cv678"}]]);/**
286 * @license lucide-react v0.462.0 - ISC
287 *
288 * This source code is licensed under the ISC license.
289 * See the LICENSE file in the root directory of this source tree.
290 */const qI=xe("Repeat",[["path",{d:"m17 2 4 4-4 4",key:"nntrym"}],["path",{d:"M3 11v-1a4 4 0 0 1 4-4h14",key:"84bu3i"}],["path",{d:"m7 22-4-4 4-4",key:"1wqhfi"}],["path",{d:"M21 13v1a4 4 0 0 1-4 4H3",key:"1rx37r"}]]);/**
291 * @license lucide-react v0.462.0 - ISC
292 *
293 * This source code is licensed under the ISC license.
294 * See the LICENSE file in the root directory of this source tree.
295 */const UI=xe("RotateCcw",[["path",{d:"M3 12a9 9 0 1 0 9-9 9.75 9.75 0 0 0-6.74 2.74L3 8",key:"1357e3"}],["path",{d:"M3 3v5h5",key:"1xhq8a"}]]);/**
296 * @license lucide-react v0.462.0 - ISC
297 *
298 * This source code is licensed under the ISC license.
299 * See the LICENSE file in the root directory of this source tree.
300 */const JB=xe("Scale",[["path",{d:"m16 16 3-8 3 8c-.87.65-1.92 1-3 1s-2.13-.35-3-1Z",key:"7g6ntu"}],["path",{d:"m2 16 3-8 3 8c-.87.65-1.92 1-3 1s-2.13-.35-3-1Z",key:"ijws7r"}],["path",{d:"M7 21h10",key:"1b0cd5"}],["path",{d:"M12 3v18",key:"108xh3"}

300],["path",{d:"M3 7h2c2 0 5-1 7-2 2 1 5 2 7 2h2",key:"3gwbw2"}]]);/**
301 * @license lucide-react v0.462.0 - ISC
302 *
303 * This source code is licensed under the ISC license.
304 * See the LICENSE file in the root directory of this source tree.
305 */const y4=xe("Search",[["circle",{cx:"11",cy:"11",r:"8",key:"4ej97u"}],["path",{d:"m21 21-4.3-4.3",key:"1qie3q"}]]);/**
306 * @license lucide-react v0.462.0 - ISC
307 *
308 * This source code is licensed under the ISC license.
309 * See the LICENSE file in the root directory of this source tree.
310 */const Ap=xe("Send",[["path",{d:"M14.536 21.686a.5.5 0 0 0 .937-.024l6.5-19a.496.496 0 0 0-.635-.635l-19 6.5a.5.5 0 0 0-.024.937l7.93 3.18a2 2 0 0 1 1.112 1.11z",key:"1ffxy3"}],["path",{d:"m21.854 2.147-10.94 10.939",key:"12cjpa"}]]);/**
311 * @license lucide-react v0.462.0 - ISC
312 *
313 * This source code is licensed under the ISC license.
314 * See the LICENSE file in the root directory of this source tree.
315 */const e7=xe("Settings",[["path",{d:"M12.22 2h-.44a2 2 0 0 0-2 2v.18a2 2 0 0 1-1 1.73l-.43.25a2 2 0 0 1-2 0l-.15-.08a2 2 0 0 0-2.73.73l-.22.38a2 2 0 0 0 .73 2.73l.15.1a2 2 0 0 1 1 1.72v.51a2 2 0 0 1-1 1.74l-.15.09a2 2 0 0 0-.73 2.73l.22.38a2 2 0 0 0 2.73.73l.15-.08a2 2 0 0 1 2 0l.43.25a2 2 0 0 1 1 1.73V20a2 2 0 0 0 2 2h.44a2 2 0 0 0 2-2v-.18a2 2 0 0 1 1-1.73l.43-.25a2 2 0 0 1 2 0l.15.08a2 2 0 0 0 2.73-.73l.22-.39a2 2 0 0 0-.73-2.73l-.15-.08a2 2 0 0 1-1-1.74v-.5a2 2 0 0 1 1-1.74l.15-.09a2 2 0 0 0 .73-2.73l-.22-.38a2 2 0 0 0-2.73-.73l-.15.08a2 2 0 0 1-2 0l-.43-.25a2 2 0 0 1-1-1.73V4a2 2 0 0 0-2-2z",key:"1qme2f"}],["circle",{cx:"12",cy:"12",r:"3",key:"1v7zrd"}]]);/**
316 * @license lucide-react v0.462.0 - ISC
317 *
318 * This source code is licensed under the ISC license.
319 * See the LICENSE file in the root directory of this source tree.
320 */const t7=xe("ShieldCheck",[["path",{d:"M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z",key:"oel41y"}],["path",{d:"m9 12 2 2 4-4",key:"dzmm74"}]]);/**
321 * @license lucide-react v0.462.0 - ISC
322 *
323 * This source code is licensed under the ISC license.
324 * See the LICENSE file in the root directory of this source tree.
325 */const WI=xe("Shuffle",[["path",{d:"m18 14 4 4-4 4",key:"10pe0f"}],["path",{d:"m18 2 4 4-4 4",key:"pucp1d"}],["path",{d:"M2 18h1.973a4 4 0 0 0 3.3-1.7l5.454-8.6a4 4 0 0 1 3.3-1.7H22",key:"1ailkh"}],["path",{d:"M2 6h1.972a4 4 0 0 1 3.6 2.2",key:"km57vx"}],["path",{d:"M22 18h-6.041a4 4 0 0 1-3.3-1.8l-.359-.45",key:"os18l9"}]]);/**
326 * @license lucide-react v0.462.0 - ISC
327 *
328 * This source code is licensed under the ISC license.
329 * See the LICENSE file in the root directory of this source tree.
330 */const Un=xe("Sparkles",[["path",{d:"M9.937 15.5A2 2 0 0 0 8.5 14.063l-6.135-1.582a.5.5 0 0 1 0-.962L8.5 9.936A2 2 0 0 0 9.937 8.5l1.582-6.135a.5.5 0 0 1 .963 0L14.063 8.5A2 2 0 0 0 15.5 9.937l6.135 1.581a.5.5 0 0 1 0 .964L15.5 14.063a2 2 0 0 0-1.437 1.437l-1.582 6.135a.5.5 0 0 1-.963 0z",key:"4pj2yx"}],["path",{d:"M20 3v4",key:"1olli1"}],["path",{d:"M22 5h-4",key:"1gvqau"}],["path",{d:"M4 17v2",key:"vumght"}],["path",{d:"M5 18H3",key:"zchphs"}]]);/**
331 * @license lucide-react v0.462.0 - ISC
332 *
333 * This source code is licensed under the ISC license.
334 * See the LICENSE file in the root directory of this source tree.
335 */const lm=xe("Target",[["circle",{cx:"12",cy:"12",r:"10",key:"1mglay"}],["circle",{cx:"12",cy:"12",r:"6",key:"1vlfrh"}],["circle",{cx:"12",cy:"12",r:"2",key:"1c9p78"}]]);/**
336 * @license lucide-react v0.462.0 - ISC
337 *
338 * This source code is licensed under the ISC license.
339 * See the LICENSE file in the root directory of this source tree.
340 */const _p=xe("TrendingUp",[["polyline",{points:"22 7 13.5 15.5 8.5 10.5 2 17",key:"126l90"}],["polyline",{points:"16 7 22 7 22 13",key:"kwv8wd"}]]);/**
341 * @license lucide-react v0.462.0 - ISC
342 *
343 * This source code is licensed under the ISC license.
344 * See the LICENSE file in the root directory of this source tree.
345 */const n7=xe("TriangleAlert",[["path",{d:"m21.73 18-8-14a2 2 0 0 0-3.48 0l-8 14A2 2 0 0 0 4 21h16a2 2 0 0 0 1.73-3",key:"wmoenq"}],["path",{d:"M12 9v4",key:"juzpu7"}],["path",{d:"M12 17h.01",key:"p32p05"}]]);/**
346 * @license lucide-react v0.462.0 - ISC
347 *
348 * This source code is licensed under the ISC license.
349 * See the LICENSE file in the root directory of this source tree.
350 */const r7=xe("Upload",[["path",{d:"M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4",key:"ih7n3h"}],["polyline",{points:"17 8 12 3 7 8",key:"t8dd8p"}],["line",{x1:"12",x2:"12",y1:"3",y2:"15",key:"widbto"}]]);/**
351 * @license lucide-react v0.462.0 - ISC
352 *
353 * This source code is licensed under the ISC license.
354 * See the LICENSE file in the root directory of this source tree.
355 */const a7=xe("UserRoundSearch",[["circle",{cx:"10",cy:"8",r:"5",key:"o932ke"}

355],["path",{d:"M2 21a8 8 0 0 1 10.434-7.62",key:"1yezr2"}],["circle",{cx:"18",cy:"18",r:"3",key:"1xkwt0"}],["path",{d:"m22 22-1.9-1.9",key:"1e5ubv"}]]);/**
356 * @license lucide-react v0.462.0 - ISC
357 *
358 * This source code is licensed under the ISC license.
359 * See the LICENSE file in the root directory of this source tree.
360 */const i7=xe("UserX",[["path",{d:"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2",key:"1yyitq"}],["circle",{cx:"9",cy:"7",r:"4",key:"nufk8"}],["line",{x1:"17",x2:"22",y1:"8",y2:"13",key:"3nzzx3"}],["line",{x1:"22",x2:"17",y1:"8",y2:"13",key:"1swrse"}]]);/**
361 * @license lucide-react v0.462.0 - ISC
362 *
363 * This source code is licensed under the ISC license.
364 * See the LICENSE file in the root directory of this source tree.
365 */const o7=xe("User",[["path",{d:"M19 21v-2a4 4 0 0 0-4-4H9a4 4 0 0 0-4 4v2",key:"975kel"}],["circle",{cx:"12",cy:"7",r:"4",key:"17ys0d"}]]);/**
366 * @license lucide-react v0.462.0 - ISC
367 *
368 * This source code is licensed under the ISC license.
369 * See the LICENSE file in the root directory of this source tree.
370 */const Xc=xe("X",[["path",{d:"M18 6 6 18",key:"1bl5f8"}],["path",{d:"m6 6 12 12",key:"d8bk6v"}]]);/**
371 * @license lucide-react v0.462.0 - ISC
372 *
373 * This source code is licensed under the ISC license.
374 * See the LICENSE file in the root directory of this source tree.
375 */const s7=xe("Youtube",[["path",{d:"M2.5 17a24.12 24.12 0 0 1 0-10 2 2 0 0 1 1.4-1.4 49.56 49.56 0 0 1 16.2 0A2 2 0 0 1 21.5 7a24.12 24.12 0 0 1 0 10 2 2 0 0 1-1.4 1.4 49.55 49.55 0 0 1-16.2 0A2 2 0 0 1 2.5 17",key:"1q2vi4"}],["path",{d:"m10 15 5-3-5-3z",key:"1jp15x"}]]);/**
376 * @license lucide-react v0.462.0 - ISC
377 *
378 * This source code is licensed under the ISC license.
379 * See the LICENSE file in the root directory of this source tree.
380 */const l7=xe("Zap",[["path",{d:"M4 14a1 1 0 0 1-.78-1.63l9.9-10.2a.5.5 0 0 1 .86.46l-1.92 6.02A1 1 0 0 0 13 10h7a1 1 0 0 1 .78 1.63l-9.9 10.2a.5.5 0 0 1-.86-.46l1.92-6.02A1 1 0 0 0 11 14z",key:"1xq2db"}]]),Ik="-",c7=e=>{const t=d7(e),{conflictingClassGroups:n,conflictingClassGroupModifiers:r}=e;return{getClassGroupId:o=>{const l=o.split(Ik);return l[0]===""&&l.length!==1&&l.shift(),VI(l,t)||u7(o)},getConflictingClassGroupIds:(o,l)=>{const c=n[o]||[];return l&&r[o]?[...c,...r[o]]:c}}},VI=(e,t)=>{var o;if(e.length===0)return t.classGroupId;const n=e[0],r=t.nextPart.get(n),a=r?VI(e.slice(1),r):void 0;if(a)return a;if(t.validators.length===0)return;const i=e.join(Ik);return(o=t.validators.find(({validator:l})=>l(i)))==null?void 0:o.classGroupId},x4=/^\[(.+)\]$/,u7=e=>{if(x4.test(e)){const t=x4.exec(e)[1],n=t==null?void 0:t.substring(0,t.indexOf(":"));if(n)return"arbitrary.."+n}},d7=e=>{const{theme:t,prefix:n}=e,r={nextPart:new Map,validators:[]};return p7(Object.entries(e.classGroups),n).forEach(([i,o])=>{mw(o,r,i,t)}),r},mw=(e,t,n,r)=>{e.forEach(a=>{if(typeof a=="string"){const i=a===""?t:b4(t,a);i.classGroupId=n;return}if(typeof a=="function"){if(m7(a)){mw(a(r),t,n,r);return}t.validators.push({validator:a,classGroupId:n});return}Object.entries(a).forEach(([i,o])=>{mw(o,b4(t,i),n,r)})})},b4=(e,t)=>{let n=e;return t.split(Ik).forEach(r=>{n.nextPart.has(r)||n.nextPart.set(r,{nextPart:new Map,validators:[]}),n=n.nextPart.get(r)}),n},m7=e=>e.isThemeGetter,p7=(e,t)=>t?e.map(([n,r])=>{const a=r.map(i=>typeof i=="string"?t+i:typeof i=="object"?Object.fromEntries(Object.entries(i).map(([o,l])=>[t+o,l])):i);return[n,a]}):e,f7=e=>{if(e<1)return{get:()=>{},set:()=>{}};let t=0,n=new Map,r=new Map;const a=(i,o)=>{n.set(i,o),t++,t>e&&(t=0,r=n,n=new Map)};return{get(i){let o=n.get(i);if(o!==void 0)return o;if((o=r.get(i))!==void 0)return a(i,o),o},set(i,o){n.has(i)?n.set(i,o):a(i,o)}}},HI="!",h7=e=>{const{separator:t,experimentalParseClassName:n}=e,r=t.length===1,a=t[0],i=t.length,o=l=>{const c=[];let u=0,d=0,m;for(let v=0;v<l.length;v++){let y=l[v];if(u===0){if(y===a&&(r||l.slice(v,v+i)===t)){c.push(l.slice(d,v)),d=v+i;continue}if(y==="/"){m=v;continue}}y==="["?u++:y==="]"&&u--}const p=c.length===0?l:l.substring(d),f=p.startsWith(HI),g=f?p.substring(1):p,h=m&&m>d?m-d:void 0;return{modifiers:c,hasImportantModifier:f,baseClassName:g,maybePostfixModifierPosition:h}};return n?l=>n({className:l,parseClassName:o}):o},g7=e=>{if(e.length<=1)return e;const t=[];let n=[];return e.forEach(r=>{r[0]==="["?(t.push(...n.sort(),r),n=[]):n.push(r)}),t.push(...n.sort()),t},v7=e=>({cache:f7(e.cacheSize),parseClassName:h7(e),...c7(e)}),y7=/\s+/,x7=(e,t)=>{const{parseClassName:n,getClassGroupId:r,getConflictingClassGroupIds:a}=t,i=[],o=e.trim().split(y7);let l="";for(let c=o.length-1;c>=0;c-=1){const u=o[c],{modifiers:d,hasImportantModifier:m,baseClassName:p,maybePostfixModifierPosition:f}=n(u);let g=!!f,h=r(g?p.substring(0,f):p);if(!h){if(!g){l=u+(l.length>0?" 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t=this.observers.find(r=>r.shouldFetchOnReconnect());t==null||t.refetch({cancelRefetch:!1}),(n=J(this,wn))==null||n.continue()}addObserver(t){this.observers.includes(t)||(this.observers.push(t),this.clearGcTimeout(),J(this,Mr).notify({type:"observerAdded",query:this,observer:t}))}removeObserver(t){this.observers.includes(t)&&(this.observers=this.observers.filter(n=>n!==t),this.observers.length||(J(this,wn)&&(J(this,Cs)?J(this,wn).cancel({revert:!0}):J(this,wn).cancelRetry()),this.scheduleGc()),J(this,Mr).notify({type:"observerRemoved",query:this,observer:t}))}getObserversCount(){return this.observers.length}invalidate(){this.state.isInvalidated||vn(this,ta,yi).call(this,{type:"invalidate"})}fetch(t,n){var c,u,d;if(this.state.fetchStatus!=="idle"){if(this.state.data!==void 0&&(n!=null&&n.cancelRefetch))this.cancel({silent:!0});else if(J(this,wn))return J(this,wn).continueRetry(),J(this,wn).promise}if(t&&this.setOptions(t),!this.options.queryFn){const m=this.observers.find(p=>p.options.queryFn);m&&this.setOptions(m.options)}const r=new AbortController,a=m=>{Object.defineProperty(m,"signal",{enumerable:!0,get:()=>(ze(this,Cs,!0),r.signal)})},i=()=>{const m=IM(this.options,n),p={queryKey:this.queryKey,meta:this.meta};return a(p),ze(this,Cs,!1),this.options.persister?this.options.persister(m,p,this):m(p)},o={fetchOptions:n,options:this.options,queryKey:this.queryKey,state:this.state,fetchFn:i};a(o),(c=this.options.behavior)==null||c.onFetch(o,this),ze(this,ac,this.state),(this.state.fetchStatus==="idle"||this.state.fetchMeta!==((u=o.fetchOptions)==null?void 0:u.meta))&&vn(this,ta,yi).call(this,{type:"fetch",meta:(d=o.fetchOptions)==null?void 0:d.meta});const l=m=>{var p,f,g,h;Wx(m)&&m.silent||vn(this,ta,yi).call(this,{type:"error",error:m}),Wx(m)||((f=(p=J(this,Mr).config).onError)==null||f.call(p,m,this),(h=(g=J(this,Mr).config).onSettled)==null||h.call(g,this.state.data,m,this)),this.scheduleGc()};return ze(this,wn,RM({initialPromise:n==null?void 0:n.initialPromise,fn:o.fetchFn,abort:r.abort.bind(r),onSuccess:m=>{var p,f,g,h;if(m===void 0){l(new Error(`${this.queryHash} data is undefined`));return}try{this.setData(m)}catch(v){l(v);return}(f=(p=J(this,Mr).config).onSuccess)==null||f.call(p,m,this),(h=(g=J(this,Mr).config).onSettled)==null||h.call(g,m,this.state.error,this),this.scheduleGc()},onError:l,onFail:(m,p)=>{vn(this,ta,yi).call(this,{type:"failed",failureCount:m,error:p})},onPause:()=>{vn(this,ta,yi).call(this,{type:"pause"})},onContinue:()=>{vn(this,ta,yi).call(this,{type:"continue"})},retry:o.options.retry,retryDelay:o.options.retryDelay,networkMode:o.options.networkMode,canRun:()=>!0})),J(this,wn).start()}},rc=new WeakMap,ac=new WeakMap,Mr=new WeakMap,wn=new WeakMap,yp=new WeakMap,Cs=new WeakMap,ta=new WeakSet,yi=function(t){const n=r=>{switch(t.type){case"failed":return{...r,fetchFailureCount:t.failureCount,fetchFailureReason:t.error};case"pause":return{...r,fetchStatus:"paused"};case"continue":return{...r,fetchStatus:"fetching"};case"fetch":return{...r,...pW(r.data,this.options),fetchMeta:t.meta??null};case"success":return{...r,data:t.data,dataUpdateCount:r.dataUpdateCount+1,dataUpdatedAt:t.dataUpdatedAt??Date.now(),error:null,isInvalidated:!1,status:"success",...!t.manual&&{fetchStatus:"idle",fetchFailureCount:0,fetchFailureReason:null}};case"error":const a=t.error;return Wx(a)&&a.revert&&J(this,ac)?{...J(this,ac),fetchStatus:"idle"}:{...r,error:a,errorUpdateCount:r.errorUpdateCount+1,errorUpdatedAt:Date.now(),fetchFailureCount:r.fetchFailureCount+1,fetchFailureReason:a,fetchStatus:"idle",status:"error"};case"invalidate":return{...r,isInvalidated:!0};case"setState":return{...r,...t.state}}};this.state=n(this.state),Fn.batch(()=>{this.observers.forEach(r=>{r.onQueryUpdate()}),J(this,Mr).notify({query:this,type:"updated",action:t})})},L5);function pW(e,t){return{fetchFailureCount:0,fetchFailureReason:null,fetchStatus:$M(t.networkMode)?"fetching":"paused",...e===void 0&&{error:null,status:"pending"}}}function fW(e){const t=typeof e.initialData=="function"?e.initialData():e.initialData,n=t!==void 0,r=n?typeof e.initialDataUpdatedAt=="function"?e.initialDataUpdatedAt():e.initialDataUpdatedAt:0;return{data:t,dataUpdateCount:0,dataUpdatedAt:n?r??Date.now():0,error:null,errorUpdateCount:0,errorUpdatedAt:0,fetchFailureCount:0,fetchFailureReason:null,fetchMeta:null,isInvalidated:!1,status:n?"success":"pending",fetchStatus:"idle"}}var $a,z5,hW=(z5=class extends Jv{constructor(t={}){super();Je(this,$a);this.config=t,ze(this,$a,new Map)}build(t,n,r){const a=n.queryKey,i=n.queryHash??Uk(a,n);let o=this.get(i);return o||(o=new mW({cache:this,queryKey:a,queryHash:i,options:t.defaultQueryOptions(n),state:r,defaultOptions:t.getQueryDefaults(a)}),this.add(o)),o}add(t){J(this,$a).has(t.queryHash)||(J(this,$a).set(t.queryHash,t),this.notify({type:"added",query:t}))}remove(t){const n=J(this,$a).get(t.queryHash);n&&(t.destroy(),n===t&&J(this,$a).delete(t.queryHash),this.notify({type:"removed",query:t}))}clear(){Fn.batch(()=>{this.getAll().forEach(t=>{this.remove(t)})})}get(t){return J(this,$a).get(t)}getAll(){return[...J(this,$a).values()]}find(t){const n={exact:!0,...t};return this.getAll().find(r=>O4(n,r))}findAll(t={}){const n=this.getAll();return Object.keys(t).length>0?n.filter(r=>O4(t,r)):n}notify(t){Fn.batch(()=>{this.listeners.forEach(n=>{n(t)})})}onFocus(){Fn.batch(()=>{this.getAll().forEach(t=>{t.onFocus()})})}onOnline(){Fn.batch(()=>{this.getAll().forEach(t=>{t.onOnline()})})}},$a=new WeakMap,z5),Fa,In,Es,Ra,to,B5,gW=(B5=class extends DM{constructor(t){super();Je(this,Ra);Je(this,Fa);Je(this,In);Je(this,Es);this.mutationId=t.mutationId,ze(this,In,t.mutationCache),ze(this,Fa,[]),this.state=t.state||vW(),this.setOptions(t.options),this.scheduleGc()}setOptions(t){this.options=t,this.updateGcTime(this.options.gcTime)}get meta(){return this.options.meta}addObserver(t){J(this,Fa).includes(t)||(J(this,Fa).push(t),this.clearGcTimeout(),J(this,In).notify({type:"observerAdded",mutation:this,observer:t}))}removeObserver(t){ze(this,Fa,J(this,Fa).filter(n=>n!==t)),this.scheduleGc(),J(this,In).notify({type:"observerRemoved",mutation:this,observer:t})}optionalRemove(){J(this,Fa).length||(this.state.status==="pending"?this.scheduleGc():J(this,In).remove(this))}continue(){var t;return((t=J(this,Es))==null?void 0:t.continue())??this.execute(this.state.variables)}async execute(t){var a,i,o,l,c,u,d,m,p,f,g,h,v,y,x,b,w,k,j,A;ze(this,Es,RM({fn:()=>this.options.mutationFn?this.options.mutationFn(t):Promise.reject(new Error("No mutationFn found")),onFail:(P,_)=>{vn(this,Ra,to).call(this,{type:"failed",failureCount:P,error:_})},onPause:()=>{vn(this,Ra,to).call(this,{type:"pause"})},onContinue:()=>{vn(this,Ra,to).call(this,{type:"continue"})},retry:this.options.retry??0,retryDelay:this.options.retryDelay,networkMode:this.options.networkMode,canRun:()=>J(this,In).canRun(this)}));const n=this.state.status==="pending",r=!J(this,Es).canStart();try{if(!n){vn(this,Ra,to).call(this,{type:"pending",variables:t,isPaused:r}),await((i=(a=J(this,In).config).onMutate)==null?void 0:i.call(a,t,this));const _=await((l=(o=this.options).onMutate)==null?void 0:l.call(o,t));_!==this.state.context&&vn(this,Ra,to).call(this,{type:"pending",context:_,variables:t,isPaused:r})}const P=await J(this,Es).start();return await((u=(c=J(this,In).config).onSuccess)==null?void 0:u.call(c,P,t,this.state.context,this)),await((m=(d=this.options).onSuccess)==null?void 0:m.call(d,P,t,this.state.context)),await((f=(p=J(this,In).config).onSettled)==null?void 0:f.call(p,P,null,this.state.variables,this.state.context,this)),await((h=(g=this.options).onSettled)==null?void 0:h.call(g,P,null,t,this.state.context)),vn(this,Ra,to).call(this,{type:"success",data:P}),P}catch(P){try{throw await((y=(v=J(this,In).config).onError)==null?void 0:y.call(v,P,t,this.state.context,this)),await((b=(x=this.options).onError)==null?void 0:b.call(x,P,t,this.state.context)),await((k=(w=J(this,In).config).onSettled)==null?void 0:k.call(w,void 0,P,this.state.variables,this.state.context,this)),await((A=(j=this.options).onSettled)==null?void 0:A.call(j,void 0,P,t,this.state.context)),P}finally{vn(this,Ra,to).call(this,{type:"error",error:P})}}finally{J(this,In).runNext(this)}}},Fa=new WeakMap,In=new WeakMap,Es=new WeakMap,Ra=new WeakSet,to=function(t){const n=r=>{switch(t.type){case"failed":return{...r,failureCount:t.failureCount,failureReason:t.error};case"pause":return{...r,isPaused:!0};case"continue":return{...r,isPaused:!1};case"pending":return{...r,context:t.context,data:void 0,failureCount:0,failureReason:null,error:null,isPaused:t.isPaused,status:"pending",variables:t.variables,submittedAt:Date.now()};case"success":return{...r,data:t.data,failureCount:0,failureReason:null,error:null,status:"success",isPaused:!1};case"error":return{...r,data:void 0,error:t.error,failureCount:r.failureCount+1,failureReason:t.error,isPaused:!1,status:"error"}}};this.state=n(this.state),Fn.batch(()=>{J(this,Fa).forEach(r=>{r.onMutationUpdate(t)}),J(this,In).notify({mutation:this,type:"updated",action:t})})},B5);function vW(){return{context:void 0,data:void 0,error:null,failureCount:0,failureReason:null,isPaused:!1,status:"idle",variables:void 0,submittedAt:0}}var mr,xp,q5,yW=(q5=class extends Jv{constructor(t={}){super();Je(this,mr);Je(this,xp);this.config=t,ze(this,mr,new Map),ze(this,xp,Date.now())}build(t,n,r){const a=new gW({mutationCache:this,mutationId:++Kp(this,xp)._,options:t.defaultMutationOptions(n),state:r});return this.add(a),a}add(t){const n=gf(t),r=J(this,mr).get(n)??[];r.push(t),J(this,mr).set(n,r),this.notify({type:"added",mutation:t})}remove(t){var r;const n=gf(t);if(J(this,mr).has(n)){const a=(r=J(this,mr).get(n))==null?void 0:r.filter(i=>i!==t);a&&(a.length===0?J(this,mr).delete(n):J(this,mr).set(n,a))}this.notify({type:"removed",mutation:t})}canRun(t){var r;const n=(r=J(this,mr).get(gf(t)))==null?void 0:r.find(a=>a.state.status==="pending");return!n||n===t}runNext(t){var r;const n=(r=J(this,mr).get(gf(t)))==null?void 0:r.find(a=>a!==t&&a.state.isPaused);return(n==null?void 0:n.continue())??Promise.resolve()}clear(){Fn.batch(()=>{this.getAll().forEach(t=>{this.remove(t)})})}getAll(){return[...J(this,mr).values()].flat()}find(t){const n={exact:!0,...t};return this.getAll().find(r=>I4(n,r))}findAll(t={}){return this.getAll().filter(n=>I4(t,n))}notify(t){Fn.batch(()=>{this.listeners.forEach(n=>{n(t)})})}resumePausedMutations(){const t=this.getAll().filter(n=>n.state.isPaused);return Fn.batch(()=>Promise.all(t.map(n=>n.continue().catch(ea))))}},mr=new WeakMap,xp=new WeakMap,q5);function gf(e){var t;return((t=e.options.scope)==null?void 0:t.id)??String(e.mutationId)}function F4(e){return{onFetch:(t,n)=>{var d,m,p,f,g;const r=t.options,a=(p=(m=(d=t.fetchOptions)==null?void 0:d.meta)==null?void 0:m.fetchMore)==null?void 0:p.direction,i=((f=t.state.data)==null?void 0:f.pages)||[],o=((g=t.state.data)==null?void 0:g.pageParams)||[];let l={pages:[],pageParams:[]},c=0;const u=async()=>{let h=!1;const v=b=>{Object.defineProperty(b,"signal",{enumerable:!0,get:()=>(t.signal.aborted?h=!0:t.signal.addEventListener("abort",()=>{h=!0}),t.signal)})},y=IM(t.options,t.fetchOptions),x=async(b,w,k)=>{if(h)return Promise.reject();if(w==null&&b.pages.length)return Promise.resolve(b);const j={queryKey:t.queryKey,pageParam:w,direction:k?"backward":"forward",meta:t.options.meta};v(j);const A=await y(j),{maxPages:P}=t.options,_=k?oW:iW;return{pages:_(b.pages,A,P),pageParams:_(b.pageParams,w,P)}};if(a&&i.length){const b=a==="backward",w=b?xW:R4,k={pages:i,pageParams:o},j=w(r,k);l=await x(k,j,b)}else{const b=e??i.length;do{const w=c===0?o[0]??r.initialPageParam:R4(r,l);if(c>0&&w==null)break;l=await x(l,w),c++}while(c<b)}return l};t.options.persister?t.fetchFn=()=>{var h,v;return(v=(h=t.options).persister)==null?void 0:v.call(h,u,{queryKey:t.queryKey,meta:t.options.meta,signal:t.signal},n)}:t.fetchFn=u}}}function R4(e,{pages:t,pageParams:n}){const r=t.length-1;return t.length>0?e.getNextPageParam(t[r],t,n[r],n):void 0}function xW(e,{pages:t,pageParams:n}){var r;return t.length>0?(r=e.getPreviousPageParam)==null?void 0:r.call(e,t[0],t,n[0],n):void 0}var Ct,ho,go,ic,oc,vo,sc,lc,U5,bW=(U5=class{constructor(e={}){Je(this,Ct);Je(this,ho);Je(this,go);Je(this,ic);Je(this,oc);Je(this,vo);Je(this,sc);Je(this,lc);ze(this,Ct,e.queryCache||new hW),ze(this,ho,e.mutationCache||new yW),ze(this,go,e.defaultOptions||{}),ze(this,ic,new Map),ze(this,oc,new Map),ze(this,vo,0)}mount(){Kp(this,vo)._++,J(this,vo)===1&&(ze(this,sc,MM.subscribe(async e=>{e&&(await this.resumePausedMutations(),J(this,Ct).onFocus())})),ze(this,lc,Yh.subscribe(async e=>{e&&(await this.resumePausedMutations(),J(this,Ct).onOnline())})))}unmount(){var e,t;Kp(this,vo)._--,J(this,vo)===0&&((e=J(this,sc))==null||e.call(this),ze(this,sc,void 0),(t=J(this,lc))==null||t.call(this),ze(this,lc,void 0))}isFetching(e){return J(this,Ct).findAll({...e,fetchStatus:"fetching"}).length}isMutating(e){return J(this,ho).findAll({...e,status:"pending"}).length}getQueryData(e){var n;const t=this.defaultQueryOptions({queryKey:e});return(n=J(this,Ct).get(t.queryHash))==null?void 0:n.state.data}ensureQueryData(e){const t=this.getQueryData(e.queryKey);if(t===void 0)return this.fetchQuery(e);{const n=this.defaultQueryOptions(e),r=J(this,Ct).build(this,n);return e.revalidateIfStale&&r.isStaleByTime(T4(n.staleTime,r))&&this.prefetchQuery(n),Promise.resolve(t)}}getQueriesData(e){return J(this,Ct).findAll(e).map(({queryKey:t,state:n})=>{const r=n.data;return[t,r]})}setQueryData(e,t,n){const r=this.defaultQueryOptions({queryKey:e}),a=J(this,Ct).get(r.queryHash),i=a==null?void 0:a.state.data,o=JU(t,i);if(o!==void 0)return J(this,Ct).build(this,r).setData(o,{...n,manual:!0})}setQueriesData(e,t,n){return Fn.batch(()=>J(this,Ct).findAll(e).map(({queryKey:r})=>[r,this.setQueryData(r,t,n)]))}getQueryState(e){var n;const t=this.defaultQueryOptions({queryKey:e});return(n=J(this,Ct).get(t.queryHash))==null?void 0:n.state}removeQueries(e){const t=J(this,Ct);Fn.batch(()=>{t.findAll(e).forEach(n=>{t.remove(n)})})}resetQueries(e,t){const n=J(this,Ct),r={type:"active",...e};return Fn.batch(()=>(n.findAll(e).forEach(a=>{a.reset()}),this.refetchQueries(r,t)))}cancelQueries(e={},t={}){const n={revert:!0,...t},r=Fn.batch(()=>J(this,Ct).findAll(e).map(a=>a.cancel(n)));return Promise.all(r).then(ea).catch(ea)}invalidateQueries(e={},t={}){return Fn.batch(()=>

381{if(J(this,Ct).findAll(e).forEach(r=>{r.invalidate()}),e.refetchType==="none")return Promise.resolve();const n={...e,type:e.refetchType??e.type??"active"};return this.refetchQueries(n,t)})}refetchQueries(e={},t){const n={...t,cancelRefetch:(t==null?void 0:t.cancelRefetch)??!0},r=Fn.batch(()=>J(this,Ct).findAll(e).filter(a=>!a.isDisabled()).map(a=>{let i=a.fetch(void 0,n);return n.throwOnError||(i=i.catch(ea)),a.state.fetchStatus==="paused"?Promise.resolve():i}));return Promise.all(r).then(ea)}fetchQuery(e){const t=this.defaultQueryOptions(e);t.retry===void 0&&(t.retry=!1);const n=J(this,Ct).build(this,t);return n.isStaleByTime(T4(t.staleTime,n))?n.fetch(t):Promise.resolve(n.state.data)}prefetchQuery(e){return this.fetchQuery(e).then(ea).catch(ea)}fetchInfiniteQuery(e){return e.behavior=F4(e.pages),this.fetchQuery(e)}prefetchInfiniteQuery(e){return this.fetchInfiniteQuery(e).then(ea).catch(ea)}ensureInfiniteQueryData(e){return e.behavior=F4(e.pages),this.ensureQueryData(e)}resumePausedMutations(){return Yh.isOnline()?J(this,ho).resumePausedMutations():Promise.resolve()}getQueryCache(){return J(this,Ct)}getMutationCache(){return J(this,ho)}getDefaultOptions(){return J(this,go)}setDefaultOptions(e){ze(this,go,e)}setQueryDefaults(e,t){J(this,ic).set(dm(e),{queryKey:e,defaultOptions:t})}getQueryDefaults(e){const t=[...J(this,ic).values()];let n={};return t.forEach(r=>{mm(e,r.queryKey)&&(n={...n,...r.defaultOptions})}),n}setMutationDefaults(e,t){J(this,oc).set(dm(e),{mutationKey:e,defaultOptions:t})}getMutationDefaults(e){const t=[...J(this,oc).values()];let n={};return t.forEach(r=>{mm(e,r.mutationKey)&&(n={...n,...r.defaultOptions})}),n}defaultQueryOptions(e){if(e._defaulted)return e;const t={...J(this,go).queries,...this.getQueryDefaults(e.queryKey),...e,_defaulted:!0};return t.queryHash||(t.queryHash=Uk(t.queryKey,t)),t.refetchOnReconnect===void 0&&(t.refetchOnReconnect=t.networkMode!=="always"),t.throwOnError===void 0&&(t.throwOnError=!!t.suspense),!t.networkMode&&t.persister&&(t.networkMode="offlineFirst"),t.enabled!==!0&&t.queryFn===Wk&&(t.enabled=!1),t}defaultMutationOptions(e){return e!=null&&e._defaulted?e:{...J(this,go).mutations,...(e==null?void 0:e.mutationKey)&&this.getMutationDefaults(e.mutationKey),...e,_defaulted:!0}}clear(){J(this,Ct).clear(),J(this,ho).clear()}},Ct=new WeakMap,ho=new WeakMap,go=new WeakMap,ic=new WeakMap,oc=new WeakMap,vo=new WeakMap,sc=new WeakMap,lc=new WeakMap,U5),wW=S.createContext(void 0),kW=({client:e,children:t})=>(S.useEffect(()=>(e.mount(),()=>{e.unmount()}),[e]),s.jsx(wW.Provider,{value:e,children:t}));/**
382 * @remix-run/router v1.20.0
383 *
384 * Copyright (c) Remix Software Inc.
385 *
386 * This source code is licensed under the MIT license found in the
387 * LICENSE.md file in the root directory of this source tree.
388 *
389 * @license MIT
390 */function pm(){return pm=Object.assign?Object.assign.bind():function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},pm.apply(this,arguments)}var wo;(function(e){e.Pop="POP",e.Push="PUSH",e.Replace="REPLACE"})(wo||(wo={}));const D4="popstate";function SW(e){e===void 0&&(e={});function t(r,a){let{pathname:i,search:o,hash:l}=r.location;return ww("",{pathname:i,search:o,hash:l},a.state&&a.state.usr||null,a.state&&a.state.key||"default")}function n(r,a){return typeof a=="string"?a:Zh(a)}return PW(t,n,null,e)}function Dt(e,t){if(e===!1||e===null||typeof e>"u")throw new Error(t)}function LM(e,t){if(!e){typeof console<"u"&&console.warn(t);try{throw new Error(t)}catch{}}}function jW(){return Math.random().toString(36).substr(2,8)}function L4(e,t){return{usr:e.state,key:e.key,idx:t}}function ww(e,t,n,r){return n===void 0&&(n=null),pm({pathname:typeof e=="string"?e:e.pathname,search:"",hash:""},typeof t=="string"?nu(t):t,{state:n,key:t&&t.key||r||jW()})}function Zh(e){let{pathname:t="/",search:n="",hash:r=""}=e;return n&&n!=="?"&&(t+=n.charAt(0)==="?"?n:"?"+n),r&&r!=="#"&&(t+=r.charAt(0)==="#"?r:"#"+r),t}function nu(e){let t={};if(e){let n=e.indexOf("#");n>=0&&(t.hash=e.substr(n),e=e.substr(0,n));let r=e.indexOf("?");r>=0&&(t.search=e.substr(r),e=e.substr(0,r)),e&&(t.pathname=e)}return t}function PW(e,t,n,r){r===void 0&&(r={});let{window:a=document.defaultView,v5Compat:i=!1}=r,o=a.history,l=wo.Pop,c=null,u=d();u==null&&(u=0,o.replaceState(pm({},o.state,{idx:u}),""));function d(){return(o.state||{idx:null}).idx}function m(){l=wo.Pop;let v=d(),y=v==null?null:v-u;u=v,c&&c({action:l,location:h.location,delta:y})}function p(v,y){l=wo.Push;let x=ww(h.location,v,y);u=d()+1;let b=L4(x,u),w=h.createHref(x);try{o.pushState(b,"",w)}catch(k){if(k instanceof DOMException&&k.name==="DataCloneError")throw k;a.location.assign(w)}i&&c&&c({action:l,location:h.location,delta:1})}function f(v,y){l=wo.Replace;let x=ww(h.location,v,y);u=d();let b=L4(x,u),w=h.createHref(x);o.replaceState(b,"",w),i&&c&&c({action:l,location:h.location,delta:0})}function g(v){let y=a.location.origin!=="null"?a.location.origin:a.location.href,x=typeof v=="string"?v:Zh(v);return x=x.replace(/ $/,"%20"),Dt(y,"No window.location.(origin|href) available to create URL for href: "+x),new URL(x,y)}let h={get action(){return l},get location(){return e(a,o)},listen(v){if(c)throw new Error("A history only accepts one active listener");

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391 * React Router v6.27.0
392 *
393 * Copyright (c) Remix Software Inc.
394 *
395 * This source code is licensed under the MIT license found in the
396 * LICENSE.md file in the root directory of this source tree.
397 *
398 * @license MIT
399 */function fm(){return fm=Object.assign?Object.assign.bind():function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},fm.apply(this,arguments)}const Gk=S.createContext(null),GW=S.createContext(null),Qo=S.createContext(null),ty=S.createContext(null),ri=S.createContext({outlet:null,matches:[],isDataRoute:!1}),UM=S.createContext(null);function QW(e,t){let{relative:n}=t===void 0?{}:t;ru()||Dt(!1);let{basename:r,navigator:a}=S.useContext(Qo),{hash:i,pathname:o,search:l}=VM(e,{relative:n}),c=o;return r!=="/"&&(c=o==="/"?r:Io([r,o])),a.createHref({pathname:c,search:l,hash:i})}function ru(){return S.useContext(ty)!=null}function hn(){return ru()||Dt(!1),S.useContext(ty).location}function WM(e){S.useContext(Qo).static||S.useLayoutEffect(e)}function au(){let{isDataRoute:e}=S.useContext(ri);return e?uV():YW()}function YW(){ru()||Dt(!1);let 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400 * React Router DOM v6.27.0
401 *
402 * Copyright (c) Remix Software Inc.
403 *
404 * This source code is licensed under the MIT license found in the
405 * LICENSE.md file in the root directory of this source tree.
406 *
407 * @license MIT
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408ia de IA e casos de uso?","contact.faq.a10":"Sim. Nossos especialistas apoiam você em moldar o ângulo de negócio por trás de cada iniciativa de IA. 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Retornaremos em breve.","contact.map.title":"Nossa Presença Global","contact.map.description":"Nos encontre em três continentes. Clique em cada localização para ver informações detalhadas de contato.","contact.map.headquarters":"Sede","contact.map.branchOffice":"Filial","contact.map.brazil":"Brasil","contact.map.usa":"Estados Unidos","contact.map.italy":"Itália","contact.map.hoverTip":"Passe o mouse sobre os cartões para ver detalhes de contato","contact.calendly.title":"Às vezes uma conversa rápida é tudo que precisamos.","contact.calendly.description":"Vamos direto ao ponto: agende uma sessão com nossos especialistas agora!","footer.description":"Transformando negócios com soluções de IA de ponta. Desbloqueie possibilidades infinitas com nossa tecnologia inovadora.","footer.quickLinks":"Links Rápidos","footer.contact":"Contato","footer.copyright":"© 2024 Infinity6.ai. 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410Resultado antes do discurso.`,costNote:"Custo zero até o Backtest. Você só avança com validação do potencial de resultado.",steps:[{n:"01",title:"Ingestão dos dados",description:"Integramos dados comerciais, transacionais, comportamentais, operacionais e externos, com anonimização na origem e sem necessidade de dados sensíveis."},{n:"02",title:"Modelagem IA",description:"Ajustamos os motores proprietários ao contexto do cliente, segmento, canal, produto e objetivo de negócio."},{n:"03",title:"Backtest",description:"Comparamos recomendações preditivas com resultados reais antes da ativação, validando impacto potencial, aderência operacional e clareza para escala."},{n:"04",title:"Piloto / Ativação",description:"Entregamos outputs por API, lake-to-lake, arquivos operacionais, plugins para ERP/CRM ou widgets, com i6Signal para consulta, explicação e ativação dos sinais."},{n:"05",title:"Aprendizado contínuo",description:"Recalibramos os modelos conforme demanda, estoque, preço, comportamento e resposta comercial evoluem, mantendo os 

410sinais atualizados e acionáveis."}]},summary:{eyebrow:"Em resumo",title:"A infinity6 ajuda empresas a:",bullets:["Prever demanda com mais precisão.","Definir metas comerciais mais inteligentes.","Recomendar mix e pedido ideal por cliente ou PDV.","Priorizar campanhas por propensão real.","Personalizar ofertas em tempo real.","Otimizar preço por margem, giro e conversão.","Reduzir ruptura, excesso, CAC, desperdício comercial e margem deixada na mesa."]}},CH={hero:{eyebrow:"Solutions",titleBefore:"AI applied to ",titleHighlight:"capture growth",titleMiddle:" ",titleHighlight2:"and margin",titleAfter:" with precision.",subtitle:"We turn customer, product, channel, POS, inventory and price data into predictive decisions that increase revenue, protect margin and reduce operational waste.",ctaLabel:"See success stories",ctaHref:"/success-stories"},territoriesBlock:{eyebrow:"Where prediction becomes result",title:"Predictive Value Levers",intro:"We organize our solutions into impact fronts, oriented exactly where operations need to capture results.",ctaLabel:"See this lever's solutions"},labels:{resolve:"Resolve",entrega:"Deliver",impacto:"Impact"},territories:[{id:"growth",eyebrow:"Predictive Value Lever",title:"Growth & Customer Intelligence",tagline:"Sell more to customers, visitors and channels with the highest propensity to convert.",description:"When every customer receives the same offer, the company wastes media, CRM, sales effort and revenue opportunity. We anticipate intent, the next best offer and which products have the highest conversion potential.",chips:["Predictive Personalization","Smart Discovery","Predictive Campaign Targeting"]},{id:"planning",eyebrow:"Predictive Value Lever",title:"Demand, Supply & Commercial Planning",tagline:"Plan better, sell better and reduce operational waste.",description:"Companies lose revenue when they plan demand, targets, assortment and orders based only on history, averages or commercial pressure. We turn market, behavior, inventory, sell-out and channel signals into predictive decisions to orient operations with extreme efficiency.",chips:["Predictive Forecasting","Predictive Commercial Goals","Predictive Assortment & Order Recommendation"]},{id:"pricing",eyebrow:"Predictive Value Lever",title:"Pricing & Margin Intelligence",tagline:"Adjust price to the real movement of market, customer, inventory and margin.",description:"Price is not a table. It is a direct lever of margin, turnover and conversion. We turn elasticity, forecast, competition, inventory, demand and behavior into dynamic, measurable and governed price recommendations.",chips:["Price-to-Margin","Price-to-Turnover","Price-to-Conversion"]}],solutions:[{id:"predictive-personalization",territory:"growth",title:"Predictive Personalization",tagline:"Anticipate the next best offer for each customer.",resolve:"Generic CRM, low conversion and wasted outreach.",entrega:"NBA, NBO, cross-sell, up-sell and reactivation.",impacto:"Higher LTV, more purchase frequency and better commercial efficiency."},{id:"smart-discovery",territory:"growth",title:"Smart Discovery",tagline:"Turn anonymous traffic into incremental revenue.",resolve:"Anonymous visitors treated as noise.",entrega:"Contextual real-time recommendation, even before identification.",impacto:"More conversion without increasing acquisition spend."},{id:"predictive-campaign-targeting",territory:"growth",title:"Predictive Campaign Targeting",tagline:"Activate the right customers with more precision, more results and less cost.",resolve:"Broad, expensive and inefficient campaigns.",entrega:"Propensity score, prioritized audience and activation cadence.",impacto:"Higher conversion, lower CAC and more measurable marketing ROI."},{id:"demand-forecasting",territory:"planning",title:"Predictive Forecasting",tagline:"Anticipate demand before stockout, excess or lost sales.",resolve:"Forecast too coarse for a granular operation.",entrega:"Demand forecast by SKU, store, POS, channel, region or customer.",impacto:"Higher accuracy, less stockout, less excess and better supply planning."},{id:"predictive-commercial-targets",territory:"planning",title:"Predictive 

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411commendedActions:"Ações Recomendadas",suggestedQuestions:"Perguntas Sugeridas",emptyState:"Qual insight preditivo vamos descobrir hoje?",scenarios:{supply:{label:"Ruptura",question:"Quais SKUs estão em risco de ruptura nos próximos 3 meses?",title:"Análise de Risco de Ruptura — Próximo Trimestre",analysis:"A projeção de demanda vs capacidade de reposição para os próximos 90 dias indica 5 SKUs com probabilidade elevada de ruptura. O SKU 44210 (Fone Bluetooth Over-ear) lidera o risco (94%) devido à sazonalidade de fim de ano (Black Friday + Natal) e lead time de importação de 22 dias do fornecedor principal. O impacto acumulado estimado é de R$ 510.000 em receita no trimestre caso nenhuma ação preventiva seja tomada.",table:{headers:["SKU","Produto","Prob. Ruptura","Estoque (dias)"],rows:[["44210","Fone Bluetooth Over-ear","94%","18"],["31087","Air Fryer 5L Digital","87%","24"],["28901","Smartwatch Fitness GPS","72%","38"],["55432","Cafeteira Espresso Automática","61%","45"],["19876","Câmera de Segurança Wi-Fi","48%","62"]]},actions:[{bold:"Renegociar contratos",text:"com fornecedor do SKU 44210 (Fone Bluetooth) — garantir lead time máximo de 15 dias e lote mínimo flexível para o trimestre."},{bold:"Ajustar forecast",text:"dos SKUs 31087 (Air Fryer) e 28901 (Smartwatch) — incorporar curva sazonal de Black Friday + Natal ao modelo de reposição."},{bold:"Revisar estoque de segurança",text:"— elevar cobertura mínima para 30 dias nos 5 SKUs críticos durante o próximo trimestre."}],questions:["Qual o impacto financeiro acumulado das rupturas dos últimos 30 dias?","Quais fornecedores apresentam maior variabilidade no lead time?","Como redistribuir estoque entre CDs para cobrir a demanda regional?"]},forecast:{label:"Sazonalidade",question:"Qual a sazonalidade e tendência do produto mais vendido para o último quarter?",title:"Análise Mensal do Item Mais Vendido (SKU: 28822)",analysis:'O produto SKU 28822 (Smart TV 55" 4K) apresenta forte componente sazonal com pico em dezembro (+34% vs média), impulsionado por Black Friday e Natal. A tendência de longo prazo é de crescimento de 8,2% ao trimestre, sustentada pela expansão em marketplaces e pela categoria de eletrônicos de consumo. O modelo apresentou alta acurácia preditiva neste item, o que representa uma predição de alta confiabilidade para o planejamento comercial.',chartData:[{month:"Out",seasonality:12400,trend:11800,projected:12900},{month:"Nov",seasonality:14200,trend:12600,projected:15100},{month:"Dez",seasonality:18900,trend:13400,projected:19800}],chartNote:"O padrão sazonal indica aumento de 52% entre outubro e dezembro, consistente com os últimos 3 anos. A linha de vendas projetadas incorpora sazonalidade, tendência e efeito de campanhas, servindo como referência para reposição e mídia.",actions:[{bold:"Aumentar estoque de segurança",text:"em 25% para novembro e 40% para dezembro, alinhado à curva sazonal."},{bold:"Planejar campanhas promocionais",text:"para outubro, aproveitando o início da curva ascendente rumo à Black Friday."},{bold:"Reforçar canais marketplace",text:"e mídia paga em categorias correlatas (soundbar, streaming, suporte de parede) no Q4."}],questions:["Como a sazonalidade varia por região (Sul vs Nordeste)?","Qual o efeito de promoções na aceleração da curva sazonal?","Existe canibalização com outros SKUs da mesma categoria?"]},pricing:{label:"Preço Ótimo",question:"Qual o preço ótimo para o Ibuprofeno 400mg nesta região?",title:"Otimização de Preço — Ibuprofeno 400mg (Região Sudeste)",analysis:"A análise de elasticidade-preço para o Ibuprofeno 400mg na Região Sudeste indica que o preço atual de R$ 18,90 está 7% acima do ponto ótimo. A redução para R$ 17,50 maximizaria a margem de contribuição total, com aumento estimado de 12% no volume e ganho líquido de R$ 43.000/mês na região.",table:{headers:["Indicador","Valor","Observação"],rows:[["Preço Atual","R$ 18,90","Acima do ótimo"],["Preço Ótimo","R$ 17,50","Maximiza margem total"],["Elasticidade","-1.34","Demanda elástica"],["Ganho Estimado","+R$ 43k/mês","+12% volume"]]},actions:[{bold:"Implementar preço sugerido",text:"de R$ 17,50 em fase piloto em 50 PDVs da Grande SP."},{bold:"Monitorar elasticidade cruzada",text:"— avaliar impacto em genéricos concorrentes."},{bold:"Teste A/B regional",text:"— comparar performance do novo preço vs atual durante 4 semanas."}],questions:["Como a elasticidade varia entre canais (farma vs varejo

411)?","Qual o preço ótimo para a Região Sul considerando a concorrência local?","Existe oportunidade de bundling com outros produtos da linha?"]},comercial:{label:"Foco Comercial",question:"Onde devo focar o esforço comercial este mês?",title:"Priorização Comercial — Fevereiro 2026",analysis:"A análise de potencial vs performance identificou 8 territórios com gap significativo de receita. Os 3 territórios prioritários concentram 62% do gap total (R$ 890k), com alta probabilidade de conversão baseada no histórico de resposta a ações comerciais similares.",comercialChart:[{territory:"Grande BH",gap:234,potential:1200,score:94},{territory:"Campinas",gap:198,potential:890,score:89},{territory:"Curitiba",gap:167,potential:760,score:85},{territory:"P. Alegre",gap:145,potential:680,score:78}],actions:[{bold:"Alocar representantes adicionais",text:"para Grande BH, focando nos PDVs com maior gap individual."},{bold:"Campanha de incentivo",text:"para Campinas com bonificação escalonada por faixa de crescimento."},{bold:"Visita executiva",text:"aos maiores clientes de Curitiba para renegociar mix e share of shelf."}],questions:["Qual o ROI histórico de ações comerciais em cada território?","Quais produtos apresentam maior oportunidade de cross-sell?","Como está a performance dos representantes vs quota individual?"]},mix:{label:"Mix / Sortimento",question:"Qual mix ideal para a região Sul?",title:"Otimização de Mix — Região Sul",analysis:"A análise do mix atual vs recomendado para a Região Sul revela desalinhamento em 3 categorias principais. O mix atual sobre-indexa analgésicos (38% vs recomendado 29%) e sub-indexa anti-hipertensivos (12% vs recomendado 19%). O ajuste projetado impactaria em +R$ 312k/mês na margem de contribuição regional.",comparison:[{category:"Analgésicos",current:"38%",recommended:"29%",direction:"down"},{category:"Anti-inflamatórios",current:"22%",recommended:"24%",direction:"up"},{category:"Anti-hipertensivos",current:"12%",recommended:"19%",direction:"up"},{category:"Antibióticos",current:"15%",recommended:"16%",direction:"stable"},{category:"Outros",current:"13%",recommended:"12%",direction:"stable"}],actions:[{bold:"Reduzir exposição de analgésicos",text:"em 9pp, redistribuindo espaço para anti-hipertensivos."},{bold:"Aumentar push comercial",text:"de anti-hipertensivos com campanha médica direcionada."},{bold:"Revisar sortimento",text:"de 120 PDVs da região para alinhar ao mix recomendado em 60 dias."}],questions:["Qual o impacto do mix recomendado na rentabilidade por PDV?","Como o mix ideal varia entre capital e interior?","Quais SKUs específicos devem ser priorizados em anti-hipertensivos?"]},pdv:{label:"Compra / Recompra",question:"Qual o comportamento de compra e recompra da Losartana Potássica 50mg na região de São Paulo?",title:"Comportamento de Compra e Recompra — Losartana Potássica 50mg (São Paulo)",analysis:"A análise de comportamento de PDV na região de São Paulo revela que a Losartana Potássica 50mg apresenta taxa de recompra crescente nos últimos 6 meses, passando de 62% em setembro para 78% em fevereiro. O ciclo médio de recompra é de 28 dias, alinhado à posologia padrão. Farmácias com programa de fidelidade apresentam taxa de recompra 15pp superior à média regional, indicando forte correlação entre ações de retenção e recorrência.",barChartData:[{month:"Set",compra:4200,recompra:2600},{month:"Out",compra:4500,recompra:2950},{month:"Nov",compra:4800,recompra:3400},{month:"Dez",compra:5100,recompra:3750},{month:"Jan",compra:5400,recompra:4100},{month:"Fev",compra:5700,recompra:4450}],barChartNote:"A taxa de recompra cresceu 18pp no período, sugerindo aumento da adesão ao tratamento e eficácia das ações de fidelização nos PDVs da região.",actions:[{bold:"Expandir programa de fidelidade",text:"para os 40 PDVs com menor taxa de recompra na região, replicando o modelo dos top performers."},{bold:"Criar alertas de reposição",text:"automáticos para pacientes com ciclo de recompra > 35 dias, reduzindo abandono de tratamento."},{bold:"Campanha de adesão",text:"junto a prescritores da região, reforçando a importância da continuidade terapêutica."}],questions:["Qual a taxa de recompra por bandeira de farmácia na região?","Como o preço impacta o ciclo de recompra da Losartana?","Quais PDVs apresentam maior risco de perda de pacientes para genéricos concorrentes?"]},propensity:{label:"Propensão por Produto",question:"Nesse momento, quais produtos teriam mais propensão de venda?",title:"Produtos com Maior Propensão de Venda — Janela Atual",anal

411ysis:"O modelo cruzou sinais de intenção (navegação, carrinho, recorrência), momento do ciclo de vida e contexto externo (clima, calendário promocional) para identificar 6 produtos com janela de conversão aberta agora. As barras representam o volume de clientes propensos e a linha representa a propensão média por produto. O topo do ranking concentra 58% do potencial de conversão da janela, com destaque para Tênis de Corrida e Fone Bluetooth TWS, que combinam alto volume propenso com propensão média acima de 60%.",productChart:[{product:"Tênis de Corrida",customers:6420,propensity:71},{product:"Fone Bluetooth TWS",customers:5180,propensity:64},{product:"Cafeteira Espresso",customers:3940,propensity:58},{product:"Camiseta Dry-Fit",customers:3210,propensity:49},{product:"Ar-Cond. Portátil",customers:2470,propensity:42},{product:"Streaming Premium",customers:1830,propensity:34}],productChartNote:"Barras = clientes propensos na janela atual (7 dias). Linha = propensão média (%). O corte de propensão >55% concentra 71% do potencial de receita da janela.",actions:[{bold:"Ativar campanha agora",text:"para os 3 produtos do topo — a janela de conversão fecha em 5–7 dias segundo o modelo."},{bold:"Reservar estoque preditivo",text:"nos CDs regionais para Tênis de Corrida e Fone Bluetooth, evitando ruptura no pico de conversão."},{bold:"Cross-sell orquestrado",text:"combinando Tênis + Camiseta Dry-Fit e Cafeteira + Streaming Premium com desconto progressivo."}],questions:["Qual o ticket médio esperado por produto na janela atual?","Como varia a propensão entre novos clientes e recorrentes?","Quais produtos têm maior risco de canibalização na cesta?"]},clusters:{label:"Clusters de Comportamento",question:"Defina o melhor cluster de comportamento de compra.",title:"Clusters Comportamentais — Base Ativa para Régua de Campanha",analysis:"A base ativa foi segmentada em 4 clusters comportamentais a partir de sinais preditivos de recência, frequência, ticket, sensibilidade a preço e resposta a canais. Cada cluster tem uma régua de campanha própria — ritmo, canal e oferta — para maximizar engajamento e conversão sem sobreposição de contato.",clustersTable:{headers:["Cluster","% Base","Ticket Médio","Frequência","Canal Preferido","Propensão"],rows:[["Alto Valor Recorrente","12%","R$ 480","Alta (2x/mês)","WhatsApp","84%"],["Explorador Sazonal","28%","R$ 210","Média (1x/mês)","E-mail","58%"],["Sensível a Preço","34%","R$ 95","Média (1x/mês)","Push","43%"],["Dormente c/ Potencial","26%","R$ 170","Baixa (>90 dias)","E-mail + SMS","27%"]]},clustersDetail:[{name:"Alto Valor Recorrente",description:"Clientes fiéis, ticket alto e frequência elevada. Já compraram nos últimos 30 dias e respondem bem a exclusividade.",approach:"Régua premium: novidades em pré-venda, benefícios de assinatura e curadoria personalizada via WhatsApp 1:1. Evitar descontos genéricos — proteger margem."},{name:"Explorador Sazonal",description:"Compra concentrada em momentos-chave (datas, coleções, lançamentos). Alta abertura de e-mail e engajamento em conteúdo editorial.",approach:"Régua temática: campanhas por evento/coleção com storytelling, bundles temáticos e recomendação preditiva de próximo produto por afinidade."},{name:"Sensível a Preço",description:"Reage fortemente a descontos e frete grátis. Ticket menor, mas alta reação a push em janelas curtas de oferta.",approach:"Régua de conversão rápida: push com contador de urgência, cupom escalonado por valor de cesta e frete grátis a partir de threshold otimizado."},{name:"Dormente com Potencial",description:"Histórico relevante de compra, mas sem atividade recente. Modelo identifica sinal de retorno (busca, e-mail aberto, visita reincidente).",approach:"Régua de reativação: e-mail com prova social + SMS com oferta de retorno personalizada. Cadência escalonada (D+0, D+3, D+7) antes de rebaixar para nutrição."}],actions:[{bold:"Priorizar Alto Valor Recorrente",text:"com régua premium — protege margem e sustenta LTV da base."},{bold:"Escalar Explorador Sazonal",text:"no próximo evento comercial — maior ganho marginal de receita por contato."},{bold:"Reativar Dormentes agora",text:"— janela de retorno detectada; postergar reduz taxa de reativação em ~40%."}],questions:["Qual o LTV projetado por cluster nos próximos 12 meses?","Como evitar sobreposição de contato entre réguas?","Qual cluster tem maior migração positiva (upgrade de valor)?"]},targetsPotential:{label:"Potencial de Metas",question:"Onde está o maior potencial de crescimento de volume por região?",title:"Potencial preditivo de crescimento e distribuição de metas",analysis:`O modelo identificou potencial incremental de 186 mil unidades para o próximo trimestre. Desse volume, 64% está concentrado em três combinações de região, cliente e SKU que atualmente recebem metas abaixo de sua capacidade prevista. O Interior de São Paulo a

411presenta a maior oportunidade, impulsionada pelos SKUs A e C e por clientes com expansão de demanda ainda não refletida nas metas atuais.
412
413A meta do Cliente A foi elevada porque o modelo prevê aumento de demanda para o SKU A, maior capacidade de absorção e expansão da categoria na região. Já a meta do Cliente D foi reduzida porque o volume atual está acima do potencial previsto e exigiria esforço comercial desproporcional para ser atingido.`,potentialTable:{headers:["Região","Vendedor","Cliente","SKU","Meta atual","Meta sugerida","Potencial"],rows:[["Interior de SP","Carlos","Cliente A","SKU A","12.000","15.400","16.100"],["Interior de SP","Carlos","Cliente B","SKU C","8.500","10.200","10.700"],["Minas Gerais","Marina","Cliente D","SKU B","9.800","8.900","9.200"],["Sul","Rafael","Cliente F","SKU A","7.200","8.600","9.000"]]},actions:[{bold:"Adicionar 3.400 unidades à meta do Cliente A",text:"para o SKU A, preservando uma faixa de segurança abaixo do potencial máximo."},{bold:"Reduzir a meta do Cliente D em 900 unidades",text:"evitando pressão comercial sem demanda correspondente."},{bold:"Redistribuir metas",text:"dos vendedores com territórios saturados para carteiras com maior potencial incremental."}],questions:["Quais vendedores estão com carteiras saturadas em relação ao potencial?","Como o potencial se distribui entre SKUs A, B e C nas próximas 12 semanas?","Qual o impacto financeiro de recalibrar as 3 combinações prioritárias?"]},targetsRisk:{label:"Risco de Meta",question:"Quais são os desalinhamentos de metas para o próximo trimestre?",title:"Risco preditivo de atingimento e recalibração de metas",analysis:`O modelo identificou 27 combinações de vendedor, cliente e SKU com metas desalinhadas para o próximo trimestre. Onze apresentam risco elevado de não atingimento, principalmente por desaceleração da demanda, queda de frequência de compra e redução da capacidade de absorção dos clientes. Outras 16 estão abaixo do potencial previsto e podem limitar o crescimento comercial mesmo com comportamento favorável de demanda.
414
415A meta do Cliente A está abaixo do potencial porque o modelo prevê maior frequência de compra, crescimento do volume médio e expansão da demanda do 

415SKU A. Mantê-la no nível atual pode limitar a captura de um comportamento de crescimento já identificado. Já a meta do Cliente D está acima do potencial previsto: o cliente apresenta desaceleração de compras, redução do volume por pedido e menor probabilidade de recompra do SKU B — manter a meta gera pressão sobre o vendedor sem demanda para sustentá-la.`,scatter:[{probability:94,delta:3100,size:15100,label:"Carlos • Cliente A • SKU A",quadrant:"below"},{probability:38,delta:-2200,size:7600,label:"Marina • Cliente D • SKU B",quadrant:"above"},{probability:82,delta:300,size:8700,label:"Rafael • Cliente F • SKU C",quadrant:"match"},{probability:89,delta:1400,size:7900,label:"Paula • Cliente H • SKU D",quadrant:"below"},{probability:71,delta:900,size:6200,label:"André • Cliente J • SKU A",quadrant:"below"},{probability:44,delta:-1600,size:5400,label:"Lívia • Cliente K • SKU C",quadrant:"above"},{probability:55,delta:-400,size:4800,label:"Bruno • Cliente L • SKU B",quadrant:"uncertain"},{probability:63,delta:200,size:5600,label:"Sofia • Cliente M • SKU D",quadrant:"match"}],riskTable:{headers:["Vendedor","Cliente","SKU","Meta atual","Volume projetado","Prob. atingimento","Diagnóstico"],rows:[["Carlos","Cliente A","SKU A","12.000","15.100","94%","Meta abaixo do potencial"],["Marina","Cliente D","SKU B","9.800","7.600","38%","Meta acima do potencial"],["Rafael","Cliente F","SKU C","8.400","8.700","82%","Meta compatível"],["Paula","Cliente H","SKU D","6.500","7.900","89%","Oportunidade de expansão"]]},signalsTable:{headers:["Sinal comportamental","Cliente A","Cliente D"],rows:[["Frequência prevista de compra","+18%","−12%"],["Volume médio por pedido","+9%","−7%"],["Potencial de expansão de mix","Alto","Baixo"],["Demanda prevista do SKU","+21%","−14%"],["Probabilidade de recompra","86%","43%"]]},actions:[{bold:"Elevar metas com probabilidade > 85%",text:"e potencial ainda não capturado, priorizando SKU A em clientes com sinais positivos."},{bold:"Recalibrar metas com probabilidade < 50%",text:"redistribuindo o volume para clientes e SKUs com comportamento mais favorável."},{bold:"Revisar mensalmente metas de alta incerteza",text:"incorporando mudanças de frequência, mix, demanda e recompra."}],questions:["Quais vendedores concentram o maior número de metas em risco?","Como priorizar recalibragens dentro do trimestre em curso?","Qual o ganho esperado ao redistribuir o volume das metas em risco?"]},mixBehavior:{label:"Mix por PDV",question:"Quais os comportamentos e desvios relevantes dos PDVs da região?",title:"Comportamento dos PDVs em relação ao mix ideal",analysis:"O modelo identificou três comportamentos predominantes entre os PDVs da região. A maior parte opera próxima ao mix esperado para seu perfil, mas 18% apresentam desvios relevantes. Alguns possuem variedade abaixo do potencial previsto, enquanto outros concentram produtos redundantes ou de baixo giro. O PDV 184 é o principal outlier positivo — mesmo com um mix 22% menor que a média regional, apresenta alta produtividade por SKU e potencial para absorver novas categorias. Já o PDV 327 possui um mix amplo, mas pouco aderente ao comportamento previsto de sua demanda, elevando o risco de estoque parado.",scatter:[{adherence:68,productivity:92,size:116,label:"PDV 184 • Alta produtividade",quadrant:"positive"},{adherence:54,productivity:41,size:89,label:"PDV 327 • Mix redundante",quadrant:"negative"},{adherence:91,productivity:74,size:102,label:"PDV 512 • Padrão regional",quadrant:"aligned"},{adherence:63,productivity:81,size:113,label:"PDV 690 • Demanda emergente",quadrant:"emerging"},{adherence:84,productivity:68,size:95,label:"PDV 231 • Aderente",quadrant:"aligned"},{adherence:78,productivity:63,size:87,label:"PDV 402 • Aderente",quadrant:"aligned"},{adherence:47,productivity:34,size:74,label:"PDV 118 • Baixa eficiência",quadrant:"negative"},{adherence:72,productivity:88,size:108,label:"PDV 559 • Emergente",quadrant:"emerging"}],pdvTable:{headers:["PDV","Perfil comportamental","Aderência ao mix ideal","Principal desvio","Potencial previsto"],rows:[["PDV 184","Alta produtivida

415de, mix restrito","68%","Categorias relevantes ausentes","+16% ticket"],["PDV 327","Mix amplo, baixa eficiência","54%","Excesso de SKUs semelhantes","−11% estoque improdutivo"],["PDV 512","Padrão regional equilibrado","91%","Sem desvio relevante","+3% ticket"],["PDV 690","Demanda emergente","63%","Mix ainda baseado no comportamento anterior","+13% ticket"]]},behaviorReading:["O PDV 184 não deve ser tratado como um ponto de baixa demanda. Seu comportamento indica alta recorrência, boa produtividade por SKU e concentração excessiva em poucas categorias. A inclusão seletiva de produtos complementares tende a ampliar o ticket sem comprometer o giro.","No PDV 327, o problema não é falta de variedade. O mix atual possui produtos com funções semelhantes e baixa diferenciação comportamental. A recomendação é reduzir sobreposição antes de adicionar novos itens."],actions:[{bold:"Expandir seletivamente o mix",text:"dos PDVs com alta produtividade e baixa aderência, priorizando categorias complementares."},{bold:"Revisar outliers com mix amplo e baixo giro",text:"substituindo SKUs redundantes por produtos mais aderentes."},{bold:"Criar estratégias distintas por cluster de PDVs",text:"evitando aplicar o mesmo sortimento para toda a região."}],questions:["Como o comportamento se distribui entre clusters de PDVs urbanos e periféricos?","Quais categorias complementares elevariam o ticket do PDV 184?","Qual o impacto de eliminar SKUs redundantes no PDV 327?"]},mixGaps:{label:"Gaps de Mix",question:"Mostre os gaps do mix ideal e potencial de aumento de ticket médio.",title:"Gaps de mix com maior potencial de ticket por região",analysis:`O modelo identificou 14 produtos sub-representados no mix atual, mas apenas cinco concentram a maior oportunidade de crescimento de ticket. No Interior de São Paulo, os SKUs A e C apresentam o maior potencial porque possuem alta aderência ao comportamento previsto dos PDVs, recorrência em estabelecimentos semelhantes e forte complementaridade com produtos já vendidos. A inclusão desses produtos nos PDVs elegíveis pode elevar o ticket médio regional em até 12%, sem ampliar significativamente o risco de estoque parado.
416
417O SKU A apresenta o maior gap no Interior de São Paulo porque está ausente em 38% dos PDVs que demonstram comportamento compatível com sua categoria. Esses estabelecimentos já vendem produtos complementares, possuem frequência de compra crescente e apresentam ticket inferior ao de PDVs semelhantes com o SKU presente. A oportunidade não está em distribuir o produto para toda a região — o modelo recomenda sua inclusão apenas nos 126 PDVs com maior aderência prevista.`,heatmap:{regions:["Interior de SP","Minas Gerais","Sul","Nordeste","Norte"],skus:["SKU A","SKU B","SKU C","SKU D","SKU E"],matrix:[[92,34,88,41,22],[48,71,39,33,28],[55,42,47,84,31],[36,45,43,51,78],[29,38,34,42,46]]},gapsTable:{headers:["Região","Produto","Presença atual","Presença ideal","Gap","Potencial de ticket"],rows:[["Interior de SP","SKU A","34%","72%","38 p.p.","+R$ 18,40"],["Interior de SP","SKU C","21%","51%","30 p.p.","+R$ 14,70"],["Minas Gerais","SKU B","48%","67%","19 p.p.","+R$ 9,80"],["Sul","SKU D","29%","55%","26 p.p.","+R$ 11,20"],["Nordeste","SKU E","18%","43%","25 p.p.","+R$ 8,60"]]},behaviorDetail:["Quais PDVs possuem maior aderência ao produto e à categoria.","Quais produtos são normalmente comprados em conjunto no mix atual.","Qual volume inicial é recomendado por PDV elegível.","Qual ticket incremental é projetado após a inclusão.","Quais PDVs semelhantes já apresentam comportamento compatível.","Qual o risco de baixo giro previsto para cada inclusão."],actions:[{bold:"Priorizar o SKU A nos 126 PDVs de maior aderência",text:"no Interior de SP, usando volume inicial compatível com a demanda prevista."},{bold:"Associar a inclusão do SKU C aos complementares",text:"já presentes no mix, aumentando a probabilidade de crescimento do ticket."},{bold:"Evitar distribuição regional indiscriminada",text:"restringindo cada inclusão aos PDVs com potencial de giro e ticket comprovado pelo modelo."}],questions:["Quais PDVs entram na priorização inicial do SKU A no Interior de SP?","Como o ticket incremental se distribui entre os 5 SKUs prioritários?","Qual o risco de estoque parado projetado por cluster de PDV?"]},marginOpportunities:{label:"Oportunidades de Margem",question:"Quais as oportunidades de captura de margem sem impactar volume?",title:"Oportunidades preditivas de margem por SKU",analysis:"O modelo identificou 16 SKUs com espaço para reposicionamento de preço nos próximos 90 dias. Os cinco principais concentram 71% da margem incremental potencial. O SKU A apresenta a melhor oportunidade — sua demanda prevista permanece estável dentro de uma faixa de aumento de até 5,4%, enquanto o preço atual está abaixo da banda competitiva e do ponto de maior geração de margem. Já o SKU D demonstra alta sensibilidade e não deve receber aumento neste momento.",scatter:[{sensitivity:22,incrementalMargin:420,size:180,label:"SKU A • Prioridade",quadrant:"priority"},{sensitivity:31,incrementalMargin:210,size:140,label:"SKU B • Prioridade",quadrant:"priority"},{sensitivity:44,incrementalMargin:174,size:165,label:"SKU C • Ajuste controlado",quadrant:"controlled"},{sensitivity:78,incrementalMargin:0,size:120,label:"SKU D • Manter",quadrant:"hold"},{sensitivity:58,incrementalMargin:118,size:155,label:"SKU E • Reduzir p/ volume",quadrant:"volume"},{sensitivity:36,incrementalMargin:92,size:105,label:"SKU F • Prioridade",quadrant:"priority"},{sensitivity:68,incrementalMargin:-40,size:90,label:"SKU G • Risco",quadrant:"risk"},{sensitivity:49,incrementalMargin:74,size:110,label:"SKU H • Ajuste controlado",quadrant:"controlled"}],skuTable:{headers:["SKU","Preço atual","Preço recomendado","Reação prevista do volume","Margem incremental","Confiança"],rows:[["SKU A","R$ 89,90","R$ 94,80","−1,8%","+R$ 420 mil","92%"],["SKU B","R$ 64,90","R$ 67,40","−0,9%","+R$ 210 mil","89%"],["SKU C","R$ 129,90","R$ 133,20","−2,7%","+R$ 174 mil","86%"],["SKU D","R$ 49,90","R$ 49,90","−8,4%","Sem oportunidade","91%"],["SKU E","R$ 74,90","R$ 72,80","+6,9%","+R$ 118 mil","84%"]]},behaviorReading:["O comportamento previsto do SKU A indica baixa sensibilidade dentro da faixa recomendada. O produto possui demanda consistente, estoque equilibrado e posicionamento de preço inferior ao de alternativas comparáveis.","No SKU D, pequenos aumentos devem produzir redução desproporcional de volume. Nesse caso, manter o pre

417ço preserva melhor o resultado total da categoria."],actions:[{bold:"Reposicionar os SKUs A, B e C",text:"dentro das faixas recomendadas, priorizando os itens com maior confiança e menor impacto previsto no volume."},{bold:"Manter o preço do SKU D",text:"pois o ganho unitário não compensaria a perda projetada de demanda."},{bold:"Testar a redução do SKU E",text:"cuja resposta prevista de volume pode gerar mais margem total, mesmo com menor margem unitária."}],questions:["Como o ganho de margem se distribui entre categorias e canais?","Qual o impacto financeiro se o SKU D fosse ajustado mesmo assim?","Como validar as faixas recomendadas com um piloto controlado?"]},marginSignals:{label:"Sinais de Margem",question:"Quais sinais estão abrindo ou reduzindo espaço para captura de margem em cada SKU?",title:"Sinais preditivos que movimentam a margem",analysis:`O modelo identificou que o espaço para captura de margem está concentrado em SKUs com demanda prevista estável, menor sensibilidade a preço e posicionamento abaixo da banda competitiva. O SKU A apresenta a combinação mais favorável. Já o SKU D possui alta sensibilidade e desaceleração prevista de demanda, o que limita qualquer aumento de preço neste momento.
418
419O SKU A possui espaço para captura de margem porque sua demanda deve permanecer estável, mesmo com um ajuste moderado de preço. Além disso, o produto está posicionado abaixo de alternativas comparáveis e possui estoque compatível com o ritmo previsto de venda. Já o SKU D não apresenta oportunidade de aumento neste momento — o modelo prevê maior sensibilidade ao preço e desaceleração da demanda, indicando que o ganho unitário seria insuficiente para compensar a perda de volume.`,signalsChart:[{sku:"SKU A",demand:22,sensitivity:18,competition:24,stock:10,currentMargin:8,category:12},{sku:"SKU B",demand:14,sensitivity:12,competition:22,stock:-6,currentMargin:6,category:8},{sku:"SKU C",demand:10,sensitivity:4,competition:6,stock:2,currentMargin:4,category:14},{sku:"SKU D",demand:-8,sensitivity:-28,competition:-4,stock:2,currentMargin:16,category:-6},{sku:"SKU E",demand:6,sensitivity:-10,competition:-8,stock:-22,currentMargin:-4,category:6}],signalsTable:{headers:["SKU","Espaço para margem","Principal sinal positivo","Principal restrição","Direção recomendada"],rows:[["SKU A","Alto","Demanda estável e baixa sensibilidade","Limite da categoria","Aumentar"],["SKU B","Alto","Preço abaixo da banda competitiva","Estoque moderado","Aumentar"],["SKU C","Médio","Crescimento previsto da categoria","Maior incerteza","Ajuste controlado"],["SKU D","Baixo","Margem unitária elevada","Alta sensibilidade","Manter"],["SKU E","Negativo","Potencial de ganho de volume","Estoque elevado","Reduzir"]]},actions:[{bold:"Priorizar os SKUs com demanda estável",text:"baixa sensibilidade e preço abaixo da banda competitiva."},{bold:"Manter os preços dos produtos",text:"cujo comportamento previsto indique risco relevante de perda de volume."},{bold:"Reavaliar os SKUs limitados",text:"quando houver mudanças de demanda, estoque ou posicionamento competitivo."}],questions:["Como os sinais evoluem semana a semana em cada SKU?","Quais SKUs estão prestes a mudar de faixa de espaço para margem?","Como priorizar decisões quando dois sinais se contradizem?"]},turnoverRisk:{label:"Risco de Envelhecimento",question:"Quais regiões, lojas e produtos apresentam maior risco de estoque envelhecido?",title:"Risco preditivo de envelhecimento por região e cluster",analysis:`O modelo identificou risco elevado de envelhecimento para o SKU A em 38 lojas, concentradas principalmente nos clusters Interior de Minas e Sul Metropolitano. Nessas localidades, a velocidade prevista de venda está abaixo da necessária para consumir o estoque antes do fim do ciclo comercial. Em São Paulo Premium, o mesmo produto mantém demanda consistente e não apresenta necessidade de desconto.
420
421O risco no Interior de Minas é sustentado pela combinação de baixa velocidade prevista de venda, maior sensibilidade ao preço e estoque acima da demanda esperada. Mantido o preço atual, parte relevante das unidades deve permanecer em estoque após o encerramento do ciclo comercial. Em São Paulo Premium, o comportamento é diferente. A demanda permanece consistente, o estoque está equilibrado e um desconto antecipado reduziria margem sem produzir ganho relevante de giro.`,regionTable:{headers:["Região ou cluster","Estoque atual","Cobertura projetada","Sell-through previsto","Risco"],rows:[["São Paulo Premium","8.400 un.","32 dias","78%","Baixo"],["Interior de São Paulo","6.200 un.","49 dias","61%","Médio"],["Interior de Minas","7.100 un.","83 dias","39%","Alto"],["Sul Metropolitano","5.800 un.","76 dias","44%","Alto"]]},signalsTable:{headers:["Sinal preditivo","Interior de Minas","São Paulo Premium"],rows:[["Velocidade prevista de venda","−24%","+8%"],["Sensibilidade ao preço","Alta","Baixa"],["Demanda sazonal futura","Baixa","Estável"],["Idade média do estoque","71 dias","29 dias"],["Pressão competitiva","Alta","Moderada"]]},actions:[{bold:"Priorizar ações de giro nas 38 lojas",text:"com cobertura projetada acima do limite saudável."},{bold:"Preservar o preço nos clusters com demanda consistente",text:"evitando markdown desnecessário."},{bold:"Redistribuir estoque entre regiões",text:"sempre que a transferência for mais rentável do que a redução de preço."}],questions:["Quais lojas dentro do Interior de Minas concentram o maior risco?","Qual o impacto financeiro previsto se nenhuma ação for tomada?","Como redistribuir estoque entre cluster

421s preservando margem?"]},turnoverMarkdown:{label:"Régua de Markdown",question:"Quais SKUs devem manter preço ou entrar em markdown nesse momento?",title:"Markdown preditivo por SKU, região e ciclo de vida",analysis:`O modelo recomenda iniciar markdown imediato para sete SKUs e manter o preço de outros onze produtos. O SKU A exige ação antecipada no Interior de Minas, onde um desconto inicial de 12% apresenta maior probabilidade de recuperar o giro e evitar uma liquidação mais profunda no fim do ciclo. Para o mesmo produto em São Paulo Premium, a recomendação é manter o preço atual por pelo menos 21 dias.
422
423O markdown de 12% para o SKU A foi recomendado porque aumenta o sell-through projetado de 39% para 68% no Interior de Minas. A ação antecipada preserva mais margem do que aguardar o envelhecimento do estoque e aplicar um desconto superior a 25% no encerramento do ciclo. A régua não é replicada nacionalmente. Cada cluster recebe uma recomendação diferente de acordo com velocidade de giro, sensibilidade ao preço, estoque, sazonalidade e comportamento local da demanda.`,markdownRuler:{headers:["Cluster","Hoje","Em 7 dias","Em 14 dias","Em 21 dias"],rows:[["São Paulo Premium","Manter","Manter","Manter","Reavaliar"],["Interior de SP","−5%","−8%","Reavaliar","−12%"],["Interior de Minas","−12%","Manter","−18%","−24%"],["Sul Metropolitano","−8%","−12%","−18%","Reavaliar"]]},skuTable:{headers:["SKU","Cluster prioritário","Preço atual","Preço recomendado","Sell-through projetado","Margem preservada"],rows:[["SKU A","Interior de Minas","R$ 129,90","R$ 114,30","68%","R$ 84 mil"],["SKU B","Sul Metropolitano","R$ 89,90","R$ 82,70","73%","R$ 51 mil"],["SKU C","Interior de SP","R$ 159,90","R$ 151,90","64%","R$ 42 mil"],["SKU D","São Paulo Premium","R$ 119,90","Manter","81%","Sem desconto"]]},actions:[{bold:"Aplicar o markdown inicial nos clusters de maior risco",text:"sem alterar o preço nacionalmente."},{bold:"Interromper novas reduções",text:"quando o sell-through atingir a trajetória esperada."},{bold:"Recalcular semanalmente a régua",text:"incorporando vendas, estoque restante, concorrência e resposta ao desconto."}],questions:["Como o markdown recomendado evolui semana a semana?","Qual o ganho de margem vs. aguardar liquidação no fim do ciclo?","Quais SKUs sairão do markdown mais cedo, com base na resposta?"]},personalizationBehavior:{label:"Recomendação por comportamento",question:"Quais produtos devem ser priorizados para cada perfil de navegação?",title:"Recomendações preditivas por comportamento de navegação",analysis:`O modelo identificou quatro comportamentos predominantes na jornada atual. Usuários orientados a performance apresentam maior aderência a produtos técnicos e complementares, enquanto usuários exploratórios respondem melhor a novidades e combinações que ampliam a descoberta. Para o perfil selecionado, a maior oportunidade está no cross-sell de produtos complementares ao item visualizado. A recomendação combina intenção atual, histórico individual, afinidade entre produtos e comportamento previsto de usuários semelhantes.
424
425Estes produtos foram recomendados porque complementam o item visualizado, apresentam alta recorrência em jornadas semelhantes e estão alinhados ao padrão de interesse previsto para este usuário.
426
427No varejo fashion, o look é recomendado porque combina estilo, categoria, cor, ocasião de uso e preferências comportamentais, evitando combinações incoerentes ou pouco aderentes.`,behaviorMatrix:{headers:["Perfil comportamental","Intenção prevista","Produto ou look recomendado","Aderência","Objetivo"],rows:[["Performance recorrente","Completar solução","Tênis + meia técnica + relógio","91%","Cross-sell"],["Explorador de novidades","Descobrir alternativas","Nova coleção da categoria","84%","Discovery"],["Sensível a estilo","Compor combinação","Look completo coordenado","88%","Look recommendation"],["Compra objetiva","Finalizar rapidamente","Produto principal + acessório essencial","79%","Conversão"]]},signalsTable:{headers:["Sinal preditivo","Efeito"],rows:[["Afinidade com a categoria","Alto"],["Complementaridade entre produtos","Alto"],["Padrão de navegação recente","Alto"],["Histórico de compras","Médio"],["Preferência de preço","Médio"],["Tendência comportamental do cluster","Alto"]]},actions:[{bold:"Priorizar produtos complementares com alta aderência",text:"evitando recomendações baseadas apenas em popularidade."},{bold:"Adaptar a composição da vitrine ao comportamento da sessão",text:"alternando conversão, cross-sell e descoberta."},{bold:"Destacar o argumento de recomendação",text:"explicando por que cada produto ou look faz sentido para aquele contexto."}],questions:["Como a aderência evolui entre categorias e cluster

427s comportamentais?","Quais produtos ganham espaço quando o objetivo é discovery?","Como validar o impacto das recomendações em ticket médio e conversão?"]},personalizationRepurchase:{label:"Janela de Recompra",question:"Quais clientes estão em janela de recompra nos próximos 30 dias?",title:"Comportamento preditivo de recompra",analysis:`O modelo identificou 18.420 clientes com probabilidade relevante de nova compra nos próximos 30 dias. Desses, 46% tendem a recomprar o mesmo produto, 34% devem migrar para uma variação ou produto substituto e 20% apresentam maior propensão a complementar a compra anterior. A principal oportunidade está nos clientes cuja janela prevista de recompra começa nos próximos sete dias. Uma ativação antecipada demais tende a gerar baixa resposta. Uma abordagem tardia aumenta o risco de perda da recompra para outro canal ou concorrente.
428
429Este cliente está entrando em sua janela de recompra porque o intervalo desde a última compra se aproxima do seu ciclo previsto de consumo. O comportamento anterior indica alta recorrência no mesmo SKU, baixa propensão à troca e maior resposta quando ativado entre o 24º e o 28º dia após a compra.
430
431Para um cliente com tendência de migração, a próxima compra não deve repetir exatamente o item anterior. O modelo identificou aumento de interesse por uma versão superior, comportamento semelhante ao de clientes que migraram de produto e maior aderência à nova opção dentro da janela prevista de recompra.`,curveData:[{day:0,probability:2},{day:5,probability:6},{day:10,probability:12},{day:14,probability:21},{day:18,probability:34},{day:22,probability:56},{day:24,probability:70},{day:26,probability:78},{day:28,probability:72},{day:32,probability:54},{day:36,probability:38},{day:42,probability:22},{day:50,probability:11}],curveWindow:{start:22,end:32},curvePeak:{day:26,probability:78},curveLegend:["Eixo X: dias desde a última compra","Eixo Y: probabilidade prevista de recompra","Faixa destacada: início da janela de oportunidade","Ponto marcado: maior propensão prevista","Curva descendente: queda prevista após o pico"],behaviorTable:{headers:["Comportamento previsto","Clientes","Janela de recompra","Propensão","Próxima melhor recomendação"],rows:[["Recompra do mesmo produto","8.473","5 a 12 dias","84%","Mesmo SKU"],["Recompra com variação","3.942","8 a 18 dias","73%","Tamanho, versão ou embalagem diferente"],["Migração para produto substituto","2.321","10 a 21 dias","68%","Alternativa de maior aderência"],["Compra complementar","3.684","3 a 14 dias","76%","Produto relacionado à compra anterior"]]},correlationsTable:{headers:["Sinal preditivo","Efeito sobre a recompra"],rows:[["Frequência entre compras anteriores","Define o timing provável"],["Consumo estimado do produto","Indica proximidade de reposição"],["Recompra recorrente do mesmo SKU","Aumenta a probabilidade de repetição"],["Mudança recente de preferência","Aumenta a chance de migração"],["Compra conjunta em jornadas semelhantes","Indica oportunidade de complemento"],["Sensibilidade a preço e promoção","Influencia oferta e momento de ativação"],["Disponibilidade e substituição de produto","Altera o item mais provável da próxima compra"]]},actions:[{bold:"Ativar primeiro os clientes na janela de maior propensão",text:"evitando contatos prematuros ou tardios."},{bold:"Diferenciar recompra, migração e compra complementar",text:"apresentando o produto mais aderente a cada comportamento previsto."},{bold:"Atualizar a recomendação conforme novas interações surgirem",text:"recalculando produto, timing e probabilidade de recompra."}],questions:["Quais clusters concentram a maior propensão nos próximos 7 dias?","Como diferenciar cliente de recompra vs. migração no mesmo cluster?","Qual o impacto financeiro previsto se a ativação ocorrer fora da janela?"]},priceConversionFriction:{label:"Fricção de Preço",question:"Onde o preço atual está reduzindo a probabilidade de conversão?",title:"Fricções preditivas de preço na jornada de compra",analysis:`O modelo identificou que o preço está limitando a conversão em 14% das sessões com alta intenção de compra. A maior oportunidade está nos usuários que retornaram ao produto, c

431ompararam alternativas e chegaram ao carrinho, mas encontraram um preço acima da faixa de maior resposta prevista para aquele contexto.
432
433A fricção não ocorre de forma uniforme. O Produto A apresenta maior sensibilidade em sessões originadas por mídia paga, onde os usuários demonstram alta comparação, maior sensibilidade e menor vínculo prévio com a marca. Já o Produto B mantém boa conversão entre clientes recorrentes mesmo sem incentivo, pois a intenção já é elevada e a probabilidade de conversão permanece dentro da faixa esperada.`,frictionHeatmap:{contexts:["Mídia paga","Cliente recorrente","Busca orgânica","Carrinho recorrente","Primeira visita"],products:["Produto A","Produto B","Produto C","Produto D"],matrix:[[82,24,58,71,28],[46,18,62,55,22],[64,32,44,68,34],[38,20,41,47,18]]},contextTable:{headers:["Produto e contexto","Intenção prevista","Fricção de preço","Conversão prevista","Direção recomendada"],rows:[["Produto A · mídia paga","Alta","Alta","4,8%","Incentivo contextual"],["Produto A · cliente recorrente","Alta","Baixa","9,7%","Manter preço"],["Produto B · busca orgânica","Média","Média","5,9%","Ajuste moderado"],["Produto C · carrinho recorrente","Muito alta","Alta","7,2%","Ação imediata"],["Produto D · primeira visita","Baixa","Baixa","1,8%","Não incentivar"]]},signalsTable:{headers:["Sinal preditivo","Leitura do modelo"],rows:[["Retorno ao mesmo produto","Intenção crescente"],["Comparação frequente de alternativas","Sensibilidade a preço"],["Entrada e saída do carrinho","Fricção na decisão"],["Busca por cupom ou promoção","Expectativa de incentivo"],["Histórico de compra sem desconto","Baixa necessidade de incentivo"],["Sessão curta e pouco engajada","Baixa intenção, independentemente do preço"]]},actions:[{bold:"Aplicar ações de preço apenas nas sessões com alta intenção",text:"e fricção comprovada de preço."},{bold:"Preservar o preço para clientes com elevada probabilidade",text:"de conversão espontânea."},{bold:"Evitar incentivos em sessões de baixa intenção",text:"nas quais o preço não é o principal obstáculo."}],questions:["Como a fricção evolui entre canais de aquisição e perfis de sessão?","Quais contextos apresentam maior ganho previsto ao remover fricção?","Como validar as recomendações de preço em piloto controlado?"]},priceConversionIncentiveNeed:{label:"Necessidade de Incentivo",question:"Quais sessões precisam de incentivos para converter?",title:"Necessidade preditiva de incentivo por comportamento",analysis:`O modelo estima que 57% das sessões com alta intenção devem converter sem qualquer incentivo. Outros 28% apresentam sensibilidade suficiente para responder a uma ação moderada de preço. Nos 15% restantes, o desconto não deve produzir conversão incremental relevante.
434
435Aplicar o mesmo incentivo para toda a audiência destruiria margem em clientes que já comprariam e consumiria verba em sessões com baixa probabilidade de resposta. O modelo diferencia, por exemplo, um usuário com alta intenção e histórico de compra na categoria — que não deve receber desconto — de uma sessão com comparação recorrente de preço, busca por promoção e abandono após visualizar o valor final — que responde a um incentivo moderado dentro da margem permitida.`,distribution:[{group:"Conversão espontânea",percentage:57},{group:"Conversão dependente de incentivo",percentage:28},{group:"Baixa resposta mesmo com incentivo",percentage:15}],incentiveTable:{headers:["Comportamento previsto","Sessões","Probabilidade atual","Ação recomendada","Motivo"],rows:[["Compraria sem incentivo","18.420","82%","Manter preço","Intenção já consolidada"],["Responde a incentivo moderado","9.060","54%","Incentivo controlado","Alta sensibilidade"],["Aguarda uma condição específica","3.480","47%","Benefício direcionado","Busca por conveniência ou frete"],["Baixa intenção atual","4.910","16%","Não oferecer desconto","Baixo ganho incremental"]]},detail:["Quais sinais indicam intenção.","Sensibilidade prevista ao preço.","Probabilidade de conversão espontânea.","Incentivo mínimo suficiente.","Margem disponível.","Canal e momento mais adequados.","Risco de conceder desconto sem gerar venda incremental."],actions:[{bold:"Suprimir descontos para usuários com alta probabilidade",text:"de conversão espontânea."},{bold:"Oferecer o menor incentivo capaz de alterar a decisão",text:"nas sessões sensíveis a preço."},{bold:"Não utilizar preço para recuperar sessões de baixa intenção",text:"priorizando outras ações de jornada ou recomendação."}],questions:["Como o incentivo mínimo varia por categoria e ticket?","Qual o impacto de margem previsto ao restringir o desconto ao grupo sensível?","Como acompanhar o comportamento do grupo dependente de incentivo ao longo do tempo?"]}}},en:{sectionTitle:"",sectionSubtitle:"Click a topic below and see how predictive signals become decisions that drive growth, margin and efficie

435ncy.",placeholder:"Type your question...",sidebar:["Home","Ingestion Tokens","Recsys Tokens","Data Ingestion","i6 Signal","Widgets"],executiveAnalysis:"Executive Analysis",recommendedActions:"Recommended Actions",suggestedQuestions:"Suggested Questions",emptyState:"What predictive insight shall we discover today?",scenarios:{supply:{label:"Stockout",question:"Which SKUs are at risk of stockout in the next 3 months?",title:"Stockout Risk Analysis — Next Quarter",analysis:"Demand projection vs replenishment capacity for the next 90 days indicates 5 SKUs with elevated stockout probability. SKU 44210 (Over-ear Bluetooth Headphones) leads the risk (94%) due to year-end seasonality (Black Friday + Christmas) and 22-day import lead time from the primary supplier. The estimated cumulative impact is $102,000 in quarterly revenue if no preventive action is taken.",table:{headers:["SKU","Product","Stockout Prob.","Stock (days)"],rows:[["44210","Over-ear Bluetooth Headphones","94%","18"],["31087","5L Digital Air Fryer","87%","24"],["28901","Fitness GPS Smartwatch","72%","38"],["55432","Automatic Espresso Machine","61%","45"],["19876","Wi-Fi Security Camera","48%","62"]]},actions:[{bold:"Renegotiate contracts",text:"with SKU 44210 supplier (Bluetooth Headphones) — secure max 15-day lead time and flexible minimum lot for the quarter."},{bold:"Adjust forecast",text:"for SKUs 31087 (Air Fryer) and 28901 (Smartwatch) — incorporate the Black Friday + Christmas seasonal curve into the replenishment model."},{bold:"Review safety stock",text:"— raise minimum coverage to 30 days for the 5 critical SKUs during the next quarter."}],questions:["What is the accumulated financial impact of stockouts in the last 30 days?","Which suppliers show the highest lead time variability?","How to redistribute inventory across DCs to cover regional demand?"]},forecast:{label:"Seasonality",question:"What is the seasonality and trend of the best-selling product for the last quarter?",title:"Monthly Analysis of Best-Selling Item (SKU: 28822)",analysis:'Product SKU 28822 (55" 4K Smart TV) shows a strong seasonal component with a peak in December (+34% vs average), driven by Black Friday and holiday shopping. The long-term trend shows 8.2% growth per quarter, sustained by marketplace expansion and the consumer electronics category. The model showed high predictive accuracy for this item, representing a highly reliable prediction for commercial planning.',chartData:[{month:"Oct",seasonality:12400,trend:11800,projected:12900},{month:"Nov",seasonality:14200,trend:12600,projected:15100},{month:"Dec",seasonality:18900,trend:13400,projected:19800}],chartNote:"The seasonal pattern indicates a 52% increase between October and December, consistent with the last 3 years. The projected sales line blends seasonality, trend and campaign effects, serving as the reference for replenishment and media planning.",actions:[{bold:"Increase safety stock",text:"by 25% for November and 40% for December, aligned with the seasonal curve."},{bold:"Plan promotional campaigns",text:"for October, leveraging the start of the ascending curve toward Black Friday."},{bold:"Reinforce marketplace channels",text:"and paid media in adjacent categories (soundbars, streaming devices, wall mounts) throughout Q4."}],questions:["How does seasonality vary by region (East Coast vs West Coast)?","What is the effect of promotions on seasonal curve acceleration?","Is there cannibalization with other SKUs in the same category?"]},pricing:{label:"Optimal Price",question:"What is the optimal price for Ibuprofen 400mg in this region?",title:"Price Optimization — Ibuprofen 400mg (Northeast Region)",analysis:"Price elasticity analysis for Ibuprofen 400mg in the Northeast Region indicates the current price of $3.80 is 7% above the optimal point. Reducing to $3.50 would maximize total contribution margin, with an estimated 12% volume increase and net gain of $8,600/month in the region.",table:{headers:["Indicator","Value","Note"],rows:[["Current Price","$3.80","Above optimal"],["Optimal Price","$3.50","Maximizes total margin"],["Elasticity","-1.34","Elastic demand"],["Est. Gain","+$8.6k/mo","+12% volume"]]},actions:[{bold:"Implement suggested price",text:"of $3.50 in a pilot phase across 50 POS in the metro area."},{bold:"Monitor cross-elasticity",text:"— assess impact on competing generics."},{bold:"Regional A/B test",text:"— compare new price vs current performance over 4 weeks."}],questions:["How does elasticity vary across channels (pharma vs retail)?","What is the optimal price for the West Coast considering local competition?","Is there a bundling opportunity with other products in the line?"]},comercial:{label:"Commercial Focus",question:"Where should I focus commercial efforts this month?",title:"Commercial Prioritization — February 2026",analysis:"Potential vs performance analysis identified 8 territories with significant revenue gaps. The top 3 priority territories concentrate 62% of the total gap ($178k), with high conversion probability based on historical response to similar commercial actions.",comercialChart:[{territory:"New York",gap:47,potential:240,score:94},{territory:"Chicago",gap:40,potential:178,score:89},{territory:"Los Angeles",gap:33,potential:152,score:85},{territory:"Miami",gap:29,potential:136,score:78}],actions:[{bold:"Allocate additional reps",text:"to New York, focusing on the POS with the largest individual gap."},{bold:"Incentive campaign",text:"for Chicago with scaled bonus by growth tier."},{bold:"Executive visit",text:"to the top clients in Los Angeles to renegotiate mix and share of shelf."}],questions:["What is the historical ROI of commercial actions in each territory?","Which products present the best cross-sell opportunity?","How is rep performance vs individual quota?"]},mix:{label:"Mix / Assortment",question:"What is the ideal mix for the West Coast?",title:"Mix Optimization — West Coast",analysis:"Analysis of current vs recommended mix for the West Coast reveals misalignment in 3 key categories. The current mix over-indexes analgesics (38% vs recommended 29%) and under-indexes antihypertensives (12% vs recommended 19%). The projected adjustment would impact +$62k/month in regional contribution margin.",comparison:[{category:"Analgesics",current:"38%",recommended:"29%",direction:"down"},{category:"Anti-inflammatories",current:"22%",recommended:"24%",direction:"up"},{category:"Antihypertensives",current:"12%",recommended:"19%",direction:"up"},{category:"Antibiotics",current:"15%",recommended:"16%",direction:"stable"},{category:"Others",current:"13%",recommended:"12%",direction:"stable"}],actions:[{bold:"Reduce analgesic exposure",text:"by 9pp, redistributing shelf space to antihypertensives."},{bold:"Increase commercial push",text:"for antihypertensives with a targeted medical campaign."},{bold:"Review assortment",text:"across 120 POS in the region to align with the recommended mix within 60 days."}],questions:["What is the recommended mix impact on per-POS profitability?","How does the ideal mix vary between metro and rural areas?","Which specific SKUs should be prioritized 

435in antihypertensives?"]},pdv:{label:"Purchase / Repurchase",question:"What is the purchase and repurchase behavior of Losartan Potassium 50mg in the Greater New York area?",title:"Purchase & Repurchase Behavior — Losartan Potassium 50mg (Greater New York)",analysis:"POS behavior analysis in the Greater New York area reveals that Losartan Potassium 50mg shows an increasing repurchase rate over the past 6 months, rising from 62% in September to 78% in February. The average repurchase cycle is 28 days, aligned with standard dosing. Pharmacies with loyalty programs show a repurchase rate 15pp above the regional average, indicating a strong correlation between retention actions and recurrence.",barChartData:[{month:"Sep",compra:4200,recompra:2600},{month:"Oct",compra:4500,recompra:2950},{month:"Nov",compra:4800,recompra:3400},{month:"Dec",compra:5100,recompra:3750},{month:"Jan",compra:5400,recompra:4100},{month:"Feb",compra:5700,recompra:4450}],barChartNote:"The repurchase rate grew 18pp over the period, suggesting increased treatment adherence and effectiveness of loyalty actions at regional POS.",actions:[{bold:"Expand loyalty program",text:"to the 40 POS with the lowest repurchase rate in the region, replicating the top performers model."},{bold:"Create automatic replenishment alerts",text:"for patients with repurchase cycles > 35 days, reducing treatment abandonment."},{bold:"Adherence campaign",text:"with regional prescribers, reinforcing the importance of treatment continuity."}],questions:["What is the repurchase rate by pharmacy chain in the region?","How does pricing impact the Losartan repurchase cycle?","Which POS show the highest risk of losing patients to competing generics?"]},propensity:{label:"Product Propensity",question:"Right now, which products have the highest sales propensity?",title:"Products with Highest Sales Propensity — Current Window",analysis:"The model combined intent signals (browsing, cart, recurrence), lifecycle stage and external context (weather, promo calendar) to identify 6 products with an open conversion window right now. Bars represent the volume of propense customers; the line represents the average propensity per product. The top of the ranking concentrates 58% of the window's conversion potential, led by Running Shoes and Wireless Earbuds, combining high propense volume with average propensity above 60%.",productChart:[{product:"Running Shoes",customers:6420,propensity:71},{product:"Wireless Earbuds",customers:5180,propensity:64},{product:"Espresso Machine",customers:3940,propensity:58},{product:"Dry-Fit T-Shirt",customers:3210,propensity:49},{product:"Portable AC Unit",customers:2470,propensity:42},{product:"Premium Streaming",customers:1830,propensity:34}],productChartNote:"Bars = propense customers in the current 7-day window. Line = average propensity (%). Cut-off >55% concentrates 71% of the window revenue potential.",actions:[{bold:"Activate campaign now",text:"for the top 3 products — the conversion window closes in 5–7 days per the model."},{bold:"Reserve predictive stock",text:"in regional DCs for Running Shoes and Wireless Earbuds, avoiding stockout at the conversion peak."},{bold:"Orchestrated cross-sell",text:"pairing Shoes + Dry-Fit T-Shirt and Espresso Machine + Premium Streaming with progressive discount."}],questions:["What is the expected average ticket per product in the current window?","How does propensity vary between new and returning customers?","Which products carry the highest basket cannibalization risk?"]},clusters:{label:"Behavior Clusters",question:"Define the best purchase-behavior cluster.",title:"Behavioral Clusters — Active Base for Campaign Cadence",analysis:"The active base was segmented into 4 behavioral clusters using predictive signals: recency, frequency, ticket, price sensitivity and channel response. Each cluster has its own campaign cadence — rhythm, channel and offer — to maximize engagement and conversion without contact overlap.",clustersTable:{headers:["Cluster","% Base","Avg. Ticket","Frequency","Preferred Channel","Propensity"],rows:[["High-Value Recurring","12%","$96","High (2x/mo)","WhatsApp","84%"],["Seasonal Explorer","28%","$42","Medium (1x/mo)","Email","58%"],["Price Sensitive","34%","$19","Medium (1x/mo)","Push","43%"],["Dormant w/ Potential","26%","$34","Low (>90 days)","Email + SMS","27%"]]}

435,clustersDetail:[{name:"High-Value Recurring",description:"Loyal, high-ticket, high-frequency customers. Purchased in the last 30 days and respond well to exclusivity.",approach:"Premium cadence: pre-sale drops, subscription perks and 1:1 curated recommendations via WhatsApp. Avoid generic discounts — protect margin."},{name:"Seasonal Explorer",description:"Purchases concentrate on key moments (holidays, collections, launches). High email open rate and editorial engagement.",approach:"Thematic cadence: event/collection campaigns with storytelling, themed bundles and predictive next-product recommendation by affinity."},{name:"Price Sensitive",description:"Reacts strongly to discounts and free shipping. Smaller ticket, but high reactivity to push in short offer windows.",approach:"Fast-conversion cadence: push with urgency countdown, tiered coupon by basket value and free shipping above optimized threshold."},{name:"Dormant with Potential",description:"Relevant purchase history but no recent activity. Model detects return signal (search, email open, repeat visit).",approach:"Reactivation cadence: email with social proof + SMS with personalized comeback offer. Staggered cadence (D+0, D+3, D+7) before demoting to nurture."}],actions:[{bold:"Prioritize High-Value Recurring",text:"with premium cadence — protects margin and sustains base LTV."},{bold:"Scale Seasonal Explorer",text:"in the next commercial event — highest marginal revenue gain per contact."},{bold:"Reactivate Dormants now",text:"— return window detected; delaying drops reactivation rate ~40%."}],questions:["What is the projected LTV per cluster over the next 12 months?","How to avoid contact overlap across cadences?","Which cluster shows the highest positive migration (value upgrade)?"]},targetsPotential:{label:"Target Potential",question:"Where is the largest volume growth potential by region?",title:"Predictive growth potential and target distribution",analysis:`The model identified 186k units of incremental potential for the next quarter. 64% of that volume is concentrated in three combinations of region, client and SKU that currently receive targets below their predicted capacity. São Paulo Countryside shows the largest opportunity, driven by SKUs A and C and by clients whose demand expansion is not yet reflected in current targets.
436
437Client A’s target was raised because the model predicts higher demand for SKU A, stronger absorption capacity and category expansion in the region. Client D’s target was lowered because current volume already sits above the predicted potential and would require disproportionate commercial effort to be reached.`,potentialTable:{headers:["Region","Rep","Client","SKU","Current target","Suggested target","Potential"],rows:[["São Paulo Countryside","Carlos","Client A","SKU A","12,000","15,400","16,100"],["São Paulo Countryside","Carlos","Client B","SKU C","8,500","10,200","10,700"],["Minas Gerais","Marina","Client D","SKU B","9,800","8,900","9,200"],["South","Rafael","Client F","SKU A","7,200","8,600","9,000"]]},actions:[{bold:"Add 3,400 units to Client A’s target",text:"for SKU A, keeping a safety band below the maximum potential."},{bold:"Reduce Client D’s target by 900 units",text:"avoiding commercial pressure without matching demand."},{bold:"Redistribute targets",text:"from reps with saturated territories to portfolios with higher incremental potential."}],questions:["Which reps carry saturated portfolios relative to potential?","How does potential split across SKUs A, B and C over the next 12 weeks?","What is the financial impact of recalibrating the 3 priority combinations?"]},targetsRisk:{label:"Target Risk",question:"Which targets show the highest risk of miss or sit below predicted potential?",title:"Predictive attainment risk and target recalibration",analysis:`The model identified 27 rep-client-SKU combinations with misaligned targets for the next quarter. Eleven show high risk of miss, mainly due to demand deceleration, lower purchase frequency and reduced client absorption capacity. Another 16 sit below predicted potential and may cap commercial growth even with favorable demand behavior.
438
439Client A’s target sits below potential because the model predicts higher purchase frequency, growing average volume and expanding demand for SKU A. Keeping it at the current level may cap the capture of a growth behavior already identified. Client D’s target sits above the predicted potential: the client shows purchase deceleration, lower volume per order and reduced repurchase probability of SKU B — keeping the target creates pressure on the rep without demand to sustain it.`,scatter:[{probability:94,delta:3100,size:15100,label:"Carlos • Client A • SKU A",quadrant:"below"},{probability:38,delta:-2200,size:7600,label:"Marina • Client D • SKU B",quadrant:"above"},{probability:82,delta:300,size:8700,label:"Rafael • Client F • SKU C",quadrant:"match"},{probability:89,delta:1400,size:7900,label:"Paula • Client H • SKU D",quadrant:"below"},{probability:71,delta:900,size:6200,label:"André • Client J • SKU A",quadrant:"below"},{probability:44,delta:-1600,size:5400,label:"Lívia • Client K • SKU C",quadrant:"above"},{probability:55,delta:-400,size:4800,label:"Bruno • Client L • SKU B",quadrant:"uncertain"},{probability:63,delta:200,size:5600,label:"Sofia • Client M • SKU D",quadrant:"match"}],riskTable:{headers:["Rep","Client","SKU","Current target","Projected volume","Attain. prob.","Diagnosis"],rows:[["Carlos","Client A","SKU A","12,000","15,100","94%","Target below potential"],["Marina","Client D","SKU B","9,800","7,600","38%","Target above potential"],["Rafael","Client F","SKU C","8,400","8,700","82%","Target aligned"],["Paula","Client H","SKU D","6,500","7,900","89%","Expansion opportunity"]]},signalsTable:{headers:["Behavioral signal","Client A","Client D"],rows:[["Predicted purchase frequency","+18%","−12%"],["Avg. volume per order","+9%","−7%"],["Mix expansion potential","High","Low"],["Predicted SKU demand","+21%","−14%"],["Repurchase probability","86%","43%"]]},actions:[{bold:"Raise targets with attainment probability > 85%",text:"and potential not yet captured, prioritizing SKU A in clients with positive signals."},{bold:"Recalibrate targets with probability < 50%",text:"redistributing volume to clients and SKUs with more favorable behavior."},{bold:"Review high-uncertainty targets monthly",text:"incorporating changes in frequency, mix, demand and repurchase."}],questions:["Which reps concentrate the largest number of at-risk targets?","How to prioritize recalibrations within the ongoing quarter?","What is the expected gain from redistributing at-risk target volume?"]},mixBehavior:{label:"Mix by Store",question:"How do stores in the region behave relative to the ideal assortment and show meaningful deviations?",title:"Store behavior versus ideal assortment",analysis:"The model identified three predominant behaviors among the region’s stores. Most operate close to the assortment expected for their profile, but 18% show meaningful deviations. Some carry variety below predicted potential, while others concentrate redundant or low-turn products. Store 184 is the leading positive outlier — with an assortment 22% smaller than the regional average, it still shows high productivity per SKU and headroom to absorb new categories. Store 327, in turn, carries a broad assortment that poorly matches its predicted demand behavior, raising the risk of idle inventory.",scatter:[{adherence:68,productivity:92,size:116,label:"Store 184 • High productivity",quadrant:"positive"},{adherence:54,productivity:41,size:89,label:"Store 327 • Redundant assortment",quadrant:"negative"},{adherence:91,productivity:74,size:102,label:"Store 512 • Regional baseline",quadrant:"aligned"},{adherence:63,productivity:81,size:113,label:"Store 690 • Emerging demand",quadrant:"emerging"},{adherence:84,productivity:68,size:95,label:"Store 231 • Aligned",quadrant:"aligned"},{adherence:78,productivity:63,size:87,label:"Store 402 • Aligned",quadrant:"aligned"},{adherence:47,productivity:34,size:74,label:"Store 118 • Low efficie

439ncy",quadrant:"negative"},{adherence:72,productivity:88,size:108,label:"Store 559 • Emerging",quadrant:"emerging"}],pdvTable:{headers:["Store","Behavioral profile","Ideal-mix adherence","Main deviation","Predicted potential"],rows:[["Store 184","High productivity, narrow mix","68%","Relevant categories missing","+16% basket"],["Store 327","Broad mix, low efficiency","54%","Redundant similar SKUs","−11% idle inventory"],["Store 512","Balanced regional pattern","91%","No material deviation","+3% basket"],["Store 690","Emerging demand","63%","Mix still reflects prior behavior","+13% basket"]]},behaviorReading:["Store 184 should not be treated as a low-demand outlet. Its behavior indicates high recurrence, solid productivity per SKU and excessive concentration in a few categories. Selectively adding complementary products should lift basket without hurting turn.","In Store 327 the problem is not lack of variety. The current assortment carries products with overlapping roles and low behavioral differentiation. The recommendation is to reduce overlap before adding new items."],actions:[{bold:"Selectively expand assortment",text:"in stores with high productivity and low adherence, prioritizing complementary categories."},{bold:"Review outliers with broad mix and low turn",text:"replacing redundant SKUs with more adherent products."},{bold:"Design distinct strategies per store cluster",text:"avoiding one-size-fits-all assortments across the region."}],questions:["How does behavior split between urban and outskirt store clusters?","Which complementary categories would lift Store 184’s basket?","What is the impact of removing redundant SKUs from Store 327?"]},mixGaps:{label:"Assortment Gaps",question:"Show which products have the largest gap in the ideal assortment with the highest potential to lift average basket.",title:"Assortment gaps with highest basket potential by region",analysis:`The model identified 14 under-represented products in the current assortment, but only five concentrate the largest basket-growth opportunity. In São Paulo Countryside, SKUs A and C hold the highest potential due to strong adherence to store behavior, recurrence in similar outlets and complementarity with products already sold. Adding these products to eligible stores can lift regional average basket by up to 12% without materially increasing idle-inventory risk.
440
441SKU A shows the largest gap in São Paulo Countryside because it is missing in 38% of stores that behave in line with its category. These outlets already sell complementary products, show rising purchase frequency and post a lower basket than similar stores that already carry the SKU. The opportunity is not to distribute it across the whole region — the model recommends including it only in the 126 stores with the highest predicted adherence.`,heatmap:{regions:["São Paulo Countryside","Minas Gerais","South","Northeast","North"],skus:["SKU A","SKU B","SKU C","SKU D","SKU E"],matrix:[[92,34,88,41,22],[48,71,39,33,28],[55,42,47,84,31],[36,45,43,51,78],[29,38,34,42,46]]},gapsTable:{headers:["Region","Product","Current presence","Ideal presence","Gap","Basket potential"],rows:[["São Paulo Countryside","SKU A","34%","72%","38 pp","+$ 18.40"],["São Paulo Countryside","SKU C","21%","51%","30 pp","+$ 14.70"],["Minas Gerais","SKU B","48%","67%","19 pp","+$ 9.80"],["South","SKU D","29%","55%","26 pp","+$ 11.20"],["Northeast","SKU E","18%","43%","25 pp","+$ 8.60"]]},behaviorDetail:["Which stores show the highest adherence to the product and its category.","Which products are typically bought together in the current assortment.","What initial volume is recommended per eligible store.","What incremental basket is projected after inclusion.","Which similar stores already show compatible behavior.","What low-turn risk is predicted for each inclusion."],actions:[{bold:"Prioritize SKU A across the 126 highest-adherence stores",text:"in São Paulo Countryside, using an initial volume aligned with predicted demand."},{bold:"Pair SKU C inclusion with existing complementary products",text:"already in the mix to raise the probability of basket growth."},{bold:"Avoid indiscriminate regional rollout",text:"restricting each inclusion to stores with model-proven turn and basket potential."}],questions:["Which stores enter the initial SKU A prioritization in São Paulo Countryside?","How does incremental basket split across the 5 priority SKUs?","What idle-inventory risk is projected per store cluster?"]},marginOpportunities:{label:"Margin Opportunities",question:"Which SKUs show the largest opportunity to capture margin without materially losing volume?",title:"Predictive margin opportunities by SKU",analysis:"The model identified 16 SKUs with room for price repositioning over the next 90 days. The top five concentrate 71% of the potential incremental margin. SKU A shows the best opportunity — predicted demand remains stable within an increase band of up to 5.4%, while the current price sits below the competitive band and the peak margin point. SKU D shows high sensitivity and should not receive an increase at this time.",scatter:[{sensitivity:22,incrementalMargin:420,size:180,label:"SKU A • Priority",quadrant:"priority"},{sensitivity:31,incrementalMargin:210,size:140,label:"SKU B • Priority",quadrant:"priority"},{sensitivity:44,incrementalMargin:174,size:165,label:"SKU C • Controlled adjust",quadrant:"controlled"},{sensitivity:78,incrementalMargin:0,size:120,label:"SKU D • Hold",quadrant:"hold"},{sensitivity:58,incrementalMargin:118,size:155,label:"SKU E • Reduce for volume",quadrant:"volume"},{sensitivity:36,incrementalMargin:92,size:105,label:"SKU F • Priority",quadrant:"priority"},{sensitivity:68,incrementalMargin:-40,size:90,label:"SKU G • Risk",quadrant:"risk"},{sensitivity:49,incrementalMargin:74,size:110,label:"SKU H • Controlled adjust",quadrant:"controlled"}],skuTable:{headers:["SKU","Current price","Recommended price","Predicted volume reaction","Incremental margin","Confidence"],rows:[["SKU A","$ 89.90","$ 94.80","−1.8%","+$ 420k","92%"],["SKU B","$ 64.90","$ 67.40","−0.9%","+$ 210k","89%"],["SKU C","$ 129.90","$ 133.20","−2.7%","+$ 174k","86%"],["SKU D","$ 49.90","$ 49.90","−8.4%","No opportunity","91%"],["SKU E","$ 74.90","$ 72.80","+6.9%","+$ 118k","84%"]]},behaviorReading:["Predicted behavior for SKU A indicates low sensitivity within the recommended band. The product shows consistent demand, balanced stock and a price positioned below comparable alternatives.","For SKU D, small increases should produce a disproportionate volume drop. Holding the current price preserves the best overall category outcome."],actions:[{bold:"Reposition SKUs A, B and C",text:"within the recommended bands, prioritizing items with higher confidence and lower predicted volume impact."},{bold:"Hold SKU D price",text:"as the unit gain would not offset the projected loss in demand."},{bold:"Pilot a reduction for SKU E",text:"whose predicted volume response can generate more total margin, even with a smaller unit margin."}],questions:["How is the margin gain distributed across categories and channels?","What would the financial impact be if SKU D were adjusted anyway?","How to validate the recommended bands with a controlled pilot?"]},marginSignals:{label:"Margin Signals",question:"Which signals are opening or reducing room for margin capture on each SKU?",title:"Predictive signals that move margin",analysis:`The model identified that room for margin capture is concentrated in SKUs with stable predicted demand, lower price sensitivity and positioning below the competitive band. SKU A shows the most favorable combination. SKU D, on the other hand, shows high sensitivity and predicted demand deceleration, limiting any price increase at this time.
442
443SKU A has room for margin capture because its demand is expected to remain stable, even with a moderate price adjustment. The product is positioned below comparable alternatives and its stock is aligned with the predicted sales pace. SKU D shows no opportunity for an increase at this time — the model predicts higher price sensitivity and slowing demand, indicating the unit gain would not offset the volume loss.`,signalsChart:[{sku:"SKU A",demand:22,sensitivity:18,competition:24,stock:10,currentMargin:8,category:12},{sku:"SKU B",demand:14,sensitivity:12,competition:22,stock:-6,currentMargin:6,category:8},{sku:"SKU C",demand:10,sensitivity:4,competition:6,stock:2,currentMargin:4,category:14},{sku:"SKU D",demand:-8,sensitivity:-28,competition:-4,stock:2,currentMargin:16,category:-6},{sku:"SKU E",demand:6,sensitivity:-10,competition:-8,stock:-22,currentMargin:-4,category:6}],signalsTable:{headers:["SKU","Room for margin","Top positive signal","Main constraint","Recommended direction"],rows:[["SKU A","High","Stable demand and low sensitivity","Category ceiling","Increase"],["SKU B","High","Price below competitive band","Moderate stock","Increase"],["SKU C","Medium","Predicted category growth","Higher uncertainty","Controlled adjust"],["SKU D","Low","Elevated unit margin","High sensitivity","Hold"],["SKU E","Negative","Volume-gain potential","Elevated stock","Reduce"]]},actions:[{bold:"Prioritize SKUs with stable demand",text:"low sensitivity and a price below the competitive band."},{bold:"Hold prices on products",text:"whose predicted behavior indicates material risk of volume loss."},{bold:"Re-evaluate constrained SKUs",text:"when demand, stock or competitive positioning change."}],questions:["How do signals evolve week by week for each SKU?","Which SKUs are about to shift their room-for-margin band?","How to prioritize decisions when two signals contradict each other?"]},turnoverRisk:{label:"Aging Risk",question:"In which regions, stores or cluster

443s do products show the highest aging inventory risk?",title:"Predictive aging risk by region and cluster",analysis:`The model identified elevated aging risk for SKU A across 38 stores, concentrated mostly in the Minas Countryside and Southern Metropolitan clusters. In these locations, predicted sales velocity is below what is needed to consume the stock before the end of the commercial cycle. In São Paulo Premium, the same product keeps consistent demand and does not require a markdown.
444
445Risk in Minas Countryside is sustained by a combination of low predicted sales velocity, higher price sensitivity and stock above expected demand. At the current price, a relevant share of units is likely to remain in stock after the cycle closes. In São Paulo Premium, behavior is different. Demand remains consistent, stock is balanced and an early markdown would reduce margin without producing a relevant turn gain.`,regionTable:{headers:["Region or cluster","Current stock","Projected coverage","Predicted sell-through","Risk"],rows:[["São Paulo Premium","8,400 units","32 days","78%","Low"],["São Paulo Countryside","6,200 units","49 days","61%","Medium"],["Minas Countryside","7,100 units","83 days","39%","High"],["Southern Metropolitan","5,800 units","76 days","44%","High"]]},signalsTable:{headers:["Predictive signal","Minas Countryside","São Paulo Premium"],rows:[["Predicted sales velocity","−24%","+8%"],["Price sensitivity","High","Low"],["Future seasonal demand","Low","Stable"],["Average stock age","71 days","29 days"],["Competitive pressure","High","Moderate"]]},actions:[{bold:"Prioritize turn actions in the 38 stores",text:"with projected coverage above the healthy threshold."},{bold:"Hold the price in clusters with consistent demand",text:"to avoid unnecessary markdowns."},{bold:"Redistribute stock across regions",text:"whenever the transfer is more profitable than a price reduction."}],questions:["Which stores in Minas Countryside concentrate the highest risk?","What is the predicted financial impact if no action is taken?","How to redistribute stock across clusters while preserving margin?"]},turnoverMarkdown:{label:"Markdown Ladder",question:"Which SKUs should enter markdown now and what price-reduction ladder is recommended for each cluster?",title:"Predictive markdown by SKU, region and life cycle",analysis:`The model recommends starting an immediate markdown on seven SKUs and holding the price on eleven others. SKU A requires early action in Minas Countryside, where an initial 12% discount has the highest probability of recovering turn and avoiding a deeper clearance at the end of the cycle. For the same product in São Paulo Premium, the recommendation is to hold the current price for at least 21 days.
446
447The 12% markdown on SKU A was recommended because it lifts projected sell-through from 39% to 68% in Minas Countryside. Early action preserves more margin than waiting for stock to age and then applying a discount above 25% at cycle close. The ladder is not replicated nationally. Each cluster receives a different recommendation based on turn velocity, price sensitivity, stock, seasonality and local demand behavior.`,markdownRuler:{headers:["Cluster","Today","In 7 days","In 14 days","In 21 days"],rows:[["São Paulo Premium","Hold","Hold","Hold","Reassess"],["São Paulo Countryside","−5%","−8%","Reassess","−12%"],["Minas Countryside","−12%","Hold","−18%","−24%"],["Southern Metropolitan","−8%","−12%","−18%","Reassess"]]},skuTable:{headers:["SKU","Priority cluster","Current price","Recommended price","Projected sell-through","Preserved margin"],rows:[["SKU A","Minas Countryside","$ 129.90","$ 114.30","68%","$ 84k"],["SKU B","Southern Metropolitan","$ 89.90","$ 82.70","73%","$ 51k"],["SKU C","São Paulo Countryside","$ 159.90","$ 151.90","64%","$ 42k"],["SKU D","São Paulo Premium","$ 119.90","Hold","81%","No discount"]]},actions:[{bold:"Apply the initial markdown in higher-risk clusters",text:"without changing the price nationally."},{bold:"Stop further reductions",text:"as soon as sell-through reaches the expected trajectory."},{bold:"Recompute the ladder weekly",text:"incorporating sales, remaining stock, competition and response to the discount."}],questions:["How does the recommended markdown evolve week by week?","What is the margin gain vs. waiting for end-of-cycle clearance?","Which SKUs will exit markdown earliest, based on response?"]},personalizationBehavior:{label:"Behavior-based recommendation",question:"Which products should be prioritized for each browsing profile?",title:"Predictive recommendations by browsing behavior",analysis:`The model identified four dominant behaviors in the current journey. Performance-oriented users show higher adherence to technical and complementary products, while exploratory users respond better to new arrivals and combinations that expand discovery. For the selected profile, the largest opportunity is cross-selling products complementary to the item viewed. The recommendation combines current intent, individual history, product affinity and predicted behavior of similar users.
448
449These products were recommended because they complement the item viewed, show high recurrence in similar journeys and align with the predicted interest pattern for this user.
450

451In fashion retail, the look is recommended because it combines style, category, color, use occasion and behavioral preferences, avoiding incoherent or low-fit combinations.`,behaviorMatrix:{headers:["Behavioral profile","Predicted intent","Recommended product or look","Adherence","Objective"],rows:[["Recurring performance","Complete the solution","Sneakers + technical socks + watch","91%","Cross-sell"],["Newness explorer","Discover alternatives","New collection in the category","84%","Discovery"],["Style-sensitive","Build a combination","Coordinated full look","88%","Look recommendation"],["Goal-driven buyer","Finish quickly","Main product + essential accessory","79%","Conversion"]]},signalsTable:{headers:["Predictive signal","Effect"],rows:[["Category affinity","High"],["Product complementarity","High"],["Recent browsing pattern","High"],["Purchase history","Medium"],["Price preference","Medium"],["Cluster behavioral trend","High"]]},actions:[{bold:"Prioritize complementary products with high adherence",text:"avoiding recommendations based on popularity alone."},{bold:"Adapt the storefront composition to session behavior",text:"alternating conversion, cross-sell and discovery."},{bold:"Highlight the recommendation rationale",text:"explaining why each product or look fits that context."}],questions:["How does adherence evolve across categories and behavioral clusters?","Which products gain space when the objective is discovery?","How to validate recommendation impact on basket and conversion?"]},personalizationRepurchase:{label:"Repurchase Window",question:"Which customers are in a repurchase window in the next 30 days?",title:"Predictive repurchase behavior",analysis:`The model identified 18,420 customers with a relevant probability of a new purchase in the next 30 days. Of those, 46% are likely to repurchase the same product, 34% should migrate to a variation or substitute, and 20% show higher propensity to complement the previous purchase. The main opportunity is customers whose predicted repurchase window starts in the next seven days. Activating too early tends to yield low response. A late approach raises the risk of losing the repurchase to another channel or competitor.
452
453This customer is entering their repurchase window because the interval since the last purchase approaches their predicted consumption cycle. Prior behavior indicates high recurrence on the same SKU, low propensity to switch and stronger response when activated between day 24 and day 28 after the purchase.
454
455For a customer with a migration tendency, the next purchase should not repeat the previous item exactly. The model identified rising interest in a higher-tier version, behavior similar to customers who migrated products and higher adherence to the new option within the predicted repurchase window.`,curveData:[{day:0,probability:2},{day:5,probability:6},{day:10,probability:12},{day:14,probability:21},{day:18,probability:34},{day:22,probability:56},{day:24,probability:70},{day:26,probability:78},{day:28,probability:72},{day:32,probability:54},{day:36,probability:38},{day:42,probability:22},{day:50,probability:11}],curveWindow:{start:22,end:32},curvePeak:{day:26,probability:78},curveLegend:["X axis: days since the last purchase","Y axis: predicted repurchase probability","Highlighted band: start of the opportunity window","Marked point: highest predicted propensity","Descending curve: predicted drop after the peak"],behaviorTable:{headers:["Predicted behavior","Customers","Repurchase window","Propensity","Next best recommendation"],rows:[["Repurchase of the same product","8,473","5 to 12 days","84%","Same SKU"],["Repurchase with variation","3,942","8 to 18 days","73%","Different size, version or pack"],["Migration to a substitute product","2,321","10 to 21 days","68%","Higher-fit alternative"],["Complementary purchase","3,684","3 to 14 days","76%","Product related to the previous purchase"]]},correlationsTable:{headers:["Predictive signal","Effect on repurchase"],rows:[["Frequency between prior purchases","Defines the likely timing"],["Estimated product consumption","Signals replenishment proximity"],["Recurring repurchase of the same SKU","Raises the probability of repetition"],["Recent preference shift","Raises the chance of migration"],["Joint purchases in similar journeys","Signals a complement opportunity"],["Price and promo sensitivity","Influences offer and activation timing"],["Product availability and substitution","Alters the most likely next item"]]},actions:[{bold:"Activate customers in the highest-propensity window first",text:"avoiding premature or late contacts."},{bold:"Differentiate repurchase, migration and complementary purchase",text:"presenting the product that best fits each predicted behavior."},{bold:"Update the recommendation as new interactions arrive",text:"recalculating product, timing and repurchase probability."}],questions:["Which clusters concentrate the highest propensity in the next 7 days?","How to distinguish repurchase vs. migration customers in the same cluster?","What is the predicted financial impact if activation happens outside the window?"]},priceConversionFriction:{label:"Price Friction",question:"Where is the current price reducing conversion probability?",title:"Predictive price friction along the purchase journey",analysis:`The model identified that price is capping conversion in 14% of sessions with high purchase intent. The largest opportunity lies with users who returned to the product, compared alternatives and reached the cart, but found a price above the predicted highest-response band for that context.
456
457Friction does not occur uniformly. Product A shows the highest sensitivity in sessions from paid media, where users show heavier comparison, higher sensitivity and weaker brand affinity. Product B, on the other hand, keeps solid conversion among returning customers even without incentive, since intent is already high and predicted probability stays within the expected band.`,frictionHeatmap:{contexts:["Paid media","Returning customer","Organic search","Returning c

457art","First visit"],products:["Product A","Product B","Product C","Product D"],matrix:[[82,24,58,71,28],[46,18,62,55,22],[64,32,44,68,34],[38,20,41,47,18]]},contextTable:{headers:["Product and context","Predicted intent","Price friction","Predicted conversion","Recommended direction"],rows:[["Product A · paid media","High","High","4.8%","Contextual incentive"],["Product A · returning customer","High","Low","9.7%","Hold price"],["Product B · organic search","Medium","Medium","5.9%","Moderate adjust"],["Product C · returning cart","Very high","High","7.2%","Immediate action"],["Product D · first visit","Low","Low","1.8%","Do not incentivize"]]},signalsTable:{headers:["Predictive signal","Model reading"],rows:[["Return to the same product","Rising intent"],["Frequent comparison of alternatives","Price sensitivity"],["Cart entry and exit","Decision friction"],["Coupon or promo search","Incentive expectation"],["Full-price purchase history","Low incentive need"],["Short, low-engagement session","Low intent regardless of price"]]},actions:[{bold:"Apply price actions only in sessions with high intent",text:"and proven price friction."},{bold:"Preserve price for customers with high probability",text:"of spontaneous conversion."},{bold:"Avoid incentives in low-intent sessions",text:"where price is not the main obstacle."}],questions:["How does friction evolve across acquisition channels and session profiles?","Which contexts show the largest predicted lift when friction is removed?","How to validate the price recommendations with a controlled pilot?"]},priceConversionIncentiveNeed:{label:"Incentive Need",question:"Which sessions need incentives to convert?",title:"Predictive incentive need by behavior",analysis:`The model estimates 57% of high-intent sessions should convert without any incentive. Another 28% show enough sensitivity to respond to a moderate price action. In the remaining 15%, discount should not produce relevant incremental conversion.
458
459Applying the same incentive across the whole audience would destroy margin on customers who would already buy and burn budget on sessions with low response probability. The model distinguishes, for example, a user with high intent and a purchase history in the category — who should not receive a discount — from a session with recurring price comparison, coupon search and abandonment after seeing the final value — which responds to a moderate incentive within the allowed margin.`,distribution:[{group:"Spontaneous conversion",percentage:57},{group:"Incentive-dependent conversion",percentage:28},{group:"Low response even with incentive",percentage:15}],incentiveTable:{headers:["Predicted behavior","Sessions","Current probability","Recommended action","Reason"],rows:[["Would buy without incentive","18,420","82%","Hold price","Intent already consolidated"],["Responds to moderate incentive","9,060","54%","Controlled incentive","High sensitivity"],["Waits for a specific condition","3,480","47%","Targeted benefit","Looks for shipping or convenience"],["Currently low intent","4,910","16%","Do not discount","Low incremental gain"]]},detail:["Which signals indicate intent.","Predicted price sensitivity.","Spontaneous conversion probability.","Minimum sufficient incentive.","Available margin.","Best channel and timing.","Risk of granting a discount without generating incremental sale."],actions:[{bold:"Suppress discounts for users with high probability",text:"of spontaneous conversion."},{bold:"Offer the smallest incentive capable of changing the decision",text:"in price-sensitive sessions."},{bold:"Do not use price to recover low-intent sessions",text:"prioritizing other journey or recommendation actions."}],questions:["How does the minimum incentive vary across category and basket?","What is the predicted margin impact of restricting discounts to the sensitive group?","How to monitor the incentive-dependent group over time?"]}}}};var EH=Array.isArray,ar=EH,TH=typeof yo=="object"&&yo&&yo.Object===Object&&yo,j8=TH,OH=j8,IH=typeof self=="object"&&self&&self.Object===Object&&self,MH=OH||IH||Function("return this")(),ai=MH,$H=ai,FH=$H.Symbol,Cp=FH,tA=Cp,P8=Object.prototype,RH=P8.hasOwnProperty,DH=P8.toString,Bu=tA?tA.toStringTag:void 0;function LH(e){var t=RH.call(e,Bu),n=e[Bu];try{e[Bu]=void 0;var r=!0}catch{}var a=DH.call(e);return r&&(t?e[Bu]=n:delete e[Bu]),a}var zH=LH,BH=Object.prototype,qH=BH.toString;function UH(e){return qH.call(e)}

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527,n))})]})}),zbe=({data:e,lang:t})=>{const n=Math.max(...e.matrix.flat(),1);return s.jsxs("div",{className:"my-4 overflow-x-auto w-full min-w-0",children:[s.jsx("p",{className:"text-orange-500 font-semibold text-[10px] uppercase tracking-normal whitespace-normal break-words leading-tight mb-2",children:t==="pt"?"Oportunidade por região × SKU":"Opportunity by region × SKU"}),s.jsxs("table",{className:"w-full text-sm border-collapse table-fixed",children:[s.jsx("thead",{children:s.jsxs("tr",{children:[s.jsx("th",{className:"py-2 px-2 text-left text-gray-700 font-medium text-[10px] uppercase tracking-normal whitespace-normal break-words leading-tight",children:t==="pt"?"Região":"Region"}),e.skus.map((r,a)=>s.jsx("th",{className:"py-2 px-2 text-center text-gray-700 font-medium text-[10px] uppercase tracking-normal whitespace-normal break-words leading-tight",children:r},a))]})}),s.jsx("tbody",{children:e.regions.map((r,a)=>s.jsxs("tr",{className:"border-b border-gray-100",children:[s.jsx("td",{className:"py-2 px-2 text-left font-medium text-gray-900 break-words",children:r}),e.matrix[a].map((i,o)=>{const l=i/n,c=.08+l*.85,u=l>.6;return s.jsx("td",{className:"py-2 px-2 text-center align-middle",title:`${r} • ${e.skus[o]} — ${t==="pt"?"oportunidade":"opportunity"} ${i}`,children:s.jsx("div",{className:"mx-auto flex items-center justify-center rounded-md tabular-nums",style:{backgroundColor:`rgba(244, 132, 95, ${c.toFixed(2)})`,color:u?"#7c2d12":"#78350f",width:"100%",minWidth:"48px",height:"36px",fontWeight:u?700:500,fontSize:"13px"},children:i})},o)})]},a))})]}),s.jsx("p",{className:"text-gray-400 text-xs mt-3 leading-relaxed",children:t==="pt"?"Intensidade coral indica maior oportunidade de aumento de ticket via inclusão do SKU na região.":"Coral intensity indicates larger basket-lift opportunity when adding the SKU to the region."})]})},Bbe=({data:e})=>s.jsx("div",{className:"overflow-x-auto my-4 w-full min-w-0",children:s.jsxs("table",{className:"w-full text-sm border-collapse table-fixed",children:[s.jsx("thead",{children:s.jsx("tr",{className:"border-b border-gray-200 bg-gray-50/60",children:e.headers.map((t,n)=>s.jsx("th",{className:`py-2 px-2 text-gray-700 font-medium text-[10px] uppercase tracking-normal whitespace-normal break-words leading-tight ${n>=2?"text-right":"text-left"}`,children:t},n))})}),s.jsx("tbody",{children:e.rows.map((t,n)=>s.jsx("tr",{className:"border-b border-gray-100",children:t.map((r,a)=>{const i=a===4,o=a===5;return s.jsx("td",{className:`py-2.5 px-2 ${a>=2?"text-right tabular-nums":"text-left"} ${a===0?"text-gray-900 font-semibold":a===1?"text-gray-800 font-medium":i?"text-orange-600 font-bold":o?"text-emerald-700 font-bold":"text-gray-800"}`,children:r},a)})},n))})]})}),_b={priority:"#10b981",controlled:"#3b82f6",hold:"#6b7280",volume:"#f59e0b",risk:"#ef4444"},qbe={priority:{pt:"Prioridade de margem",en:"Margin priority"},controlled:{pt:"Ajuste controlado",en:"Controlled adjust"},hold:{pt:"Manter preço",en:"Hold price"},volume:{pt:"Reduzir p/ volume",en:"Reduce for volume"},risk:{pt:"Risco",en:"Risk"}},Ube=({data:e,lang:t})=>{const n=["priority","controlled","hold","volume","risk"];return s.jsxs("div",{className:"my-4",children:[s.jsx("div",{className:"h-[280px] w-full",children:s.jsx(Gr,{width:"100%",height:"100%",children:s.jsxs(sF,{margin:{top:10,right:20,left:10,bottom:10},children:[s.jsx(Yr,{strokeDasharray:"3 3",stroke:"rgba(0,0,0,0.06)"}),s.jsx(sn,{type:"number",dataKey:"sensitivity",domain:[0,100],tick:{fontSize:12,fill:"#6b7280"},tickFormatter:r=>`${r}`,label:{value:t==="pt"?"Sensibilidade prevista ao preço":"Predicted price sensitivity",position:"insideBottom",offset:-4,fill:"#6b7280",fontSize:11}}),s.jsx(Ht,{type:"number",dataKey:"incrementalMargin",tick:{fontSize:12,fill:"#6b7280"},tickFormatter:r=>`${r}k`,label:{value:t==="pt"?"Margem incremental (R$ mil)":"Incremental margin ($ k)",angle:-90,position:"insideLeft",fill:"#6b7280",fontSize:11}}),s.jsx(ll,{type:"number",dataKey:"size",range:[120,900]}),s.jsx(Bo,{x:50,stroke:"#9ca3af",strokeDasharray:"4 4"}),s.jsx(Bo,{y:0,stroke:"#9ca3af",strokeDasharray:"4 4"}),s.jsx(Mt,{cursor:{strokeDasharray:"3 3"},contentStyle:{backgroundColor:"#fff",border:"1px solid #e5e7eb",borderRadius:"8px",color:"#1f2937",boxShadow:"0 4px 6px -1px rgba(0,0,0,0.1)"},formatter:(r,a)=>a==="sensitivity"?[`${r}`,t==="pt"?"Sensibilidade":"Sensitivity"]:a==="incrementalMargin"?[`${r>=0?"+":""}${r}k`,t==="pt"?"Margem incremental":"Incremental margin"]:a==="size"?[r.toLocaleStr

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530
531Alimenta a página **Success Stories** em \`/{lang}/success-stories\` e os destaques
532na home. Cada case é um arquivo Markdown com **frontmatter YAML**, mesmo padrão
533de \`src/content/intelligence/\` e \`src/content/landings/\`.
534
535## Convenção de arquivo
536
537Par obrigatório PT + EN:
538
539\`\`\`
540src/content/stories/
541  <slug>-pt.md
542  <slug>-en.md
543\`\`\`
544
545O \`<slug>\` é o mesmo nas duas versões e gera a rota
546\`/{lang}/success-stories/<slug>\`.
547
548## Frontmatter
549
550\`\`\`yaml
551---
552slug: marketplace-excellence-pharmacy
553language: pt                              # pt | en
554title: "Excelência em Marketplace..."     # antigo H2
555segment: "Indústria"                      # Varejo | Indústria | Finanças | ...
556client: "EMS Farma"
557client_anon: false                        # true = ocultar nome real do cliente
558description: "Resumo curto da empresa."
559challenge: "Texto da dor real (1–3 frases)."     # exibido como "A DOR REAL"
560what_to_anticipate: "O que precisava ser antecipado." # opcional
561prediction: "A predição gerada pela i6."          # opcional
562solution: "Texto da solução aplicada."            # exibido como "A SOLUÇÃO"
563metric1: "+23% de ticket médio por PDV"   # string única (valor + label)
564metric2: "+36% de conversão de novos SKUs"
565metric3: ""                                # vazio = não exibe
566solutions: ["Precificação Dinâmica|pricing"]
567quote: "Antecipação Comercial Aplicada..."
568customer_name: "Paulo Lima"               # "" se anônimo
569customer_title: "New Channel Manager"     # "" se anônimo
570cover_image: "/content/success-stories/case-ems-farmacia.jpg"
571logo: "/content/logos/EMS-COR.png"
572show_home: true                            # destaca na home
573published: true                            # false = não aparece
574sort_order: 1                              # ordem na grid
575---
576\`\`\`
577
578Corpo do MD: opcional. Hoje toda a exibição vem do frontmatter.
579
580## Campo \`solutions\` — link para alavanca em \`/solutions\`
581
582Cada item de \`solutions\` (renderizado como chip em "ALAVANCAS DE VALOR") pode ser:
583
584- **Texto puro** (chip não clicável): \`"Nome da Alavanca"\`
585- **Texto + slug da alavanca** separado por \`|\`: \`"Nome da Alavanca|pricing"\`
586
587> **Separador de itens do array: \`;\` (ponto-e-vírgula)**, não vírgula.
588> Isso permite usar vírgulas dentro do rótulo (ex.: \`"Descoberta, Recomendação e Personalização|growth"\`).
589
590Quando o slug é válido, o chip vira um link que aterrissa na seção detalhada
591da alavanca dentro de \`/{lang}/solutions\` (âncora \`#territory-<slug>\`), e não em
592uma landing separada.
593
594Slugs válidos (IDs das 3 Alavancas Preditivas de Valor em \`/solutions\`):
595
596| Slug       | Alavanca                                          |
597| ---------- | ------------------------------------------------- |
598| \`growth\`   | Crescimento & Inteligência de Consumidor          |
599| \`planning\` | Demanda, Distribuição e Planejamento Comercial    |
600| \`pricing\`  | Precificação e Inteligência de Margem             |
601
602Exemplos:
603
604\`\`\`yaml
605solutions: ["Descoberta e Personalização|growth"]
606solutions: ["Previsão de Demanda|planning"; "Precificação Dinâmica|pricing"]
607solutions: ["Crescimento, Personalização e Recomendação|growth"; "Precificação Dinâmica|pricing"]
608\`\`\`
609
610Se o slug for inválido ou ausente, o chip cai automaticamente para texto puro

611(sem link). O rótulo exibido é sempre exatamente o texto antes do \`|\`.
612
613
614
615## Loaders
616
617- \`src/hooks/useSuccessStoriesMarkdown.ts\` — varre \`import.meta.glob\`, filtra
618  por \`language\` e ordena por \`sort_order\`.
619- \`src/hooks/useHomeSuccessStories.ts\` — reusa o anterior e filtra \`show_home\`.
620
621## i6Hub (CMS — em construção)
622
623Estes arquivos serão gerados automaticamente pelo i6Hub, mirror do fluxo de
624Insights. Ver \`docs/I6HUB_CMS_ROADMAP.md\`. Enquanto o feed
625\`public-stories-feed\` não existir, esses arquivos são editados manualmente.
626`,b0e=`---
627slug: assortment-precision-sales-growth
628language: en
629title: "Assortment Precision: Sales Growth Through Optimized Forecasts"
630segment: "Industry"
631client: "The world's second largest producer of prepared foods"
632client_anon: true
633description: "One of the largest food conglomerates on the planet, present in over 150 countries and a leader in several categories across Latin America."
634challenge: "Assortment definition per point of sale was based on aggregated analysis and standardized commercial decisions. This created misalignment between local turnover potential and the offered mix, limiting growth and execution efficiency."
635what_to_anticipate: ""
636prediction: ""
637solution: "We applied predictive decisions per POS, considering seasonality, local history, regional behavior and margin potential. The commercial team began operating with granular intelligence, expanding mix with the highest sell-out probability and direct impact on sales."
638metric1: "+7.2% direct sales increase (up-sell)"
639metric2: "+55% of POS converted new product suggestions"
640metric3: ""
641solutions: ["Predictive Personalization|relevance"]
642quote: "Assortment Decisions Driven by Sell-out Potential"
643customer_name: ""
644customer_title: ""
645cover_image: "/content/success-stories/case-foods-1.jpg"
646logo: "/content/logos/infinity6_CMYK_color_symbol_72dpi.png"
647show_home: true
648published: true
649sort_order: 3
650---`,w0e=`---
651slug: assortment-precision-sales-growth
652language: pt
653title: "Precisão no Sortimento: Aumento de Vendas com Previsões Otimizadas"
654segment: "Indústria"
655client: "O segundo maior produtor de alimentos preparados do mundo"
656client_anon: true
657description: "Um dos maiores conglomerados de alimentos do planeta, presente em mais de 150 países e líder em várias categorias na América Latina."
658challenge: "A definição de sortimento por ponto de venda era baseada em análises agregadas e decisões comerciais padronizadas. Isso gerava desalinhamento entre potencial local de giro e mix ofertado, limitando crescimento e eficiência de execução."
659what_to_anticipate: ""
660prediction: ""
661solution: "Aplicamos decisões preditivas por PDV, considerando sazonalidade, histórico local, comportamento regional e potencial de margem. O time comercial passou a operar com inteligência granular, expandindo mix com maior probabilidade de sell-out e impacto direto em vendas."
662metric1: "+7,2% aumento direto nas vendas (up-sell)"
663metric2: "+55% dos PDVs converteram sugestões de novos produtos"
664metric3: ""
665solutions: ["Personalização Preditiva|relevance"]
666quote: "Decisão de Sortimento Orientada por Potencial de Sell-out"
667customer_name: ""
668customer_title: ""
669cover_image: "/content/success-stories/case-foods-1.jpg"
670logo: "/content/logos/infinity6_CMYK_color_symbol_72dpi.png"
671show_home: true
672published: true
673sort_order: 3
674---`,k0e=`---
675slug: cross-sell-propensity-intelligence
676language: en
677title: "Maximizing Cross-Sell with Intelligent Purchase Propensity"
678segment: "Finance"
679client: "Top 3 in innovation in Brazil and leader in payroll-deductible credit"
680client_anon: true
681description: "One of the most relevant financial institutions in the country, with strong presence in both domestic and international markets."
682challenge: "The bank had a broad customer base and a diversified portfolio, but generic campaigns and low segmentation precision limited cross-sell efficiency and raised commercial costs."
683what_to_anticipate: ""
684prediction: ""
685solution: "We applied propensity modeling per customer, product and channel, consolidating behavioral history and transactional signals. Activation began happening at the moment and channel with the highest conversion probability, increasing ROI and reducing campaign waste."
686metric1: "+12x increase in campaign conversion"
687metric2: "57% reduction in messaging (CRM) costs"
688metric3: ""

689solutions: ["Predictive Campaign Targeting|targeting"; "Predictive Personalization|relevance"]
690quote: "Predictive Propensity Applied to Multichannel Activation"
691customer_name: ""
692customer_title: ""
693cover_image: "/content/success-stories/case-finance-1.jpg"
694logo: "/content/logos/infinity6_CMYK_color_symbol_72dpi.png"
695show_home: true
696published: true
697sort_order: 5
698---`,S0e=`---
699slug: cross-sell-propensity-intelligence
700language: pt
701title: "Maximizando Cross-Sell com Propensão de Compra Inteligente"
702segment: "Finanças"
703client: "Top 3 em inovação no Brasil e líder em crédito consignado"
704client_anon: true
705description: "Uma das instituições financeiras mais relevantes do país, com forte presença no mercado nacional e internacional."
706challenge: "O banco possuía ampla base de clientes e portfólio diversificado, mas campanhas genéricas e baixa precisão de segmentação limitavam eficiência de cross-sell e elevavam custo comercial."
707what_to_anticipate: ""
708prediction: ""
709solution: "Aplicamos modelagem de propensão por cliente, produto e canal, consolidando histórico comportamental e sinais transacionais. A ativação passou a ocorrer no momento e canal de maior probabilidade de conversão, elevando ROI e reduzindo desperdício de campanha."
710metric1: "+12x de aumento de conversão nas campanhas"
711metric2: "57% de redução nos custos de mensageria (CRM)"
712metric3: ""
713solutions: ["Campanhas por Propensão|targeting"; "Personalização Preditiva|relevance"]
714quote: "Propensão Preditiva Aplicada à Ativação Multicanal"
715customer_name: ""
716customer_title: ""
717cover_image: "/content/success-stories/case-finance-1.jpg"
718logo: "/content/logos/infinity6_CMYK_color_symbol_72dpi.png"
719show_home: true
720published: true
721sort_order: 5
722---`,j0e=`---
723slug: curadoria-moda-receita-incremental-ia
724language: en
725title: "Boosting Customer Engagement: Accelerating Online Sales"
726segment: "Retail"
727client: "The most traditional fashion brand in Latin America"
728client_anon: true
729description: "With over 140 years of history, the company has established itself as one of the largest fashion retailers in the region."
730challenge: "A large share of digital traffic was anonymous, limiting relevant personalization and impacting conversion and average ticket. In addition, the main cross-sell opportunity was outfit composition, manually performed by fashion specialists, with no scale for the catalog's 11,000+ SKUs and no margin-oriented optimization."
731what_to_anticipate: ""
732prediction: ""
733solution: "We turned browsing and interaction signals into predictive intent from the first click. The model learned outfit composition patterns and incorporated a new strategic variable: smart prioritization of long-tail and higher-margin products. Real-time activation began recommending contextual combinations for both logged-in and anonymous users, scaling curation with commercial intelligence."
734metric1: "+2.6% sales versus the traditional look (with only 5% of traffic)"
735metric2: "40% of all incremental revenue came from the models"
736metric3: ""
737solutions: ["Smart Discovery|relevance"; "Predictive Personalization|relevance"]
738quote: "Scalable Curation Driven by Intent and Margin"
739customer_name: ""
740customer_title: ""
741cover_image: "/content/success-stories/curadoria-moda-receita-incremental-ia.png"
742logo: null
743show_home: true
744published: true
745sort_order: 2
746---`,P0e=`---
747slug: curadoria-moda-receita-incremental-ia
748language: pt
749title: "Receita incremental de 40% com curadoria preditiva no varejo fashion"
750segment: "Varejo"
751client: "A mais tradicional marca de moda da América Latina"
752client_anon: true
753description: "Com mais de 140 anos de história, um dos maiores varejistas de moda da região aplicou modelos preditivos que aprenderam a combinar peças com coerência de moda, personalizar visitantes desde os primeiros cliques e orientar os looks para conversão, margem e giro de catálogo."
754challenge: "### Curadoria de qualidade não significava personalização em escala.\\\\\\nCom mais de 11 mil SKUs, a quantidade de combinações possíveis tornava inviável criar manualmente looks relevantes para cada visitante.\\nOs especialistas de moda conseguiam montar boas composições, mas a mesma curadoria era apresentada para milhares de pessoas com estilos, interesses e momentos de compra diferentes.\\nO desafio se tornava ainda maior porque uma parcela relevante do tráfego era anônima. Sem histórico de compra ou perfil conhecido, as re

754comendações começavam genéricas justamente no momento em que o interesse precisava ser capturado.\\\\\\n\\nAo mesmo tempo, a operação precisava equilibrar três objetivos:\\n- criar looks visualmente coerentes;\\n- personalizar a experiência de cada visitante;\\n- direcionar demanda para produtos de maior margem e itens com menor exposição no catálogo."
755what_to_anticipate: "### A combinação com maior chance de gerar interesse antes que o visitante abandonasse a jornada.\\n\\nA operação precisava identificar, em tempo real:\\n- quais peças combinavam entre si segundo critérios de moda;\\n- qual look teria maior afinidade com cada visitante;\\n- como aprender rapidamente mesmo sem um usuário identificado;\\n- quais produtos poderiam aumentar margem ou ganhar exposição sem reduzir a relevância;\\n- qual combinação deveria aparecer a partir dos primeiros sinais de navegação.\\n\\n> A recomendação precisava entender simultaneamente moda, comportamento e estratégia comercial."
756prediction: "### Um modelo capaz de aprender estilo, intenção e contexto.\\n\\nA infinity6 desenvolveu modelos que aprenderam padrões de composição a partir das relações entre produtos, características das peças e comportamento dos consumidores.\\n\\nO motor passou a avaliar:\\n- coerência entre cores, estilos, categorias e peças;\\n- afinidade do look com o comportamento de cada visitante;\\n- sinais capturados durante a sessão;\\n- probabilidade de interação e conversão;\\n- contribuição de cada produto para margem, giro e estratégia de catálogo.\\n\\nAssim, o modelo não recomendava apenas itens semelhantes. **Ele aprendia a formar looks completos e comercialmente relevantes**."
757solution: "### Looks relevantes para o consumidor e estratégicos para o negócio.\\n\\nO modelo aprendeu a compor looks completos e coerentes a partir de relações entre cores, estilos, categorias, ocasiões e comportamento de compra.\\n\\nMesmo para **visitantes anônimos**, a solução interpretava os primeiros sinais de navegação e, com apenas uma ou duas interações, alcançava um nível de relevância próximo ao entregue para usuários identificados.\\n\\n> A cada novo clique, as combinações eram ajustadas à intenção daquele visitante, permitindo personalização de alta precisão desde o início da jornada.\\n\\nOs looks também incorporavam objetivos comerciais, **priorizando produtos de maior margem e itens de fundo de catálogo**, sem comprometer a coerência da composição nem a afinidade com o consumidor."
758metric1: "+2,6% vendas que o look tradicional (com apenas 5% do tráfego)"
759metric2: "40% de toda a receita adicional gerada vieram dos modelos"
760metric3: ""
761solutions: ["Personalização Preditiva|relevance"; "Descoberta Preditiva|relevance"]
762quote: "Looks preditivos, personalização de visitantes anônimos e decisões orientadas a margem em um catálogo com mais de 11 mil SKUs."
763customer_name: ""
764customer_title: ""
765cover_image: "/content/success-stories/curadoria-moda-receita-incremental-ia.png"
766logo: null
767show_home: false
768published: true
769sort_order: 2
770---`,A0e=`---
771slug: demand-forecast-accuracy
772language: en
773title: "Demand Forecast Accuracy: Elevating Production and Sales Planning"
774segment: "Retail"
775client: "One of the largest cosmetics brands in Brazil"
776client_anon: true
777description: "One of the country's leading beauty players, recognized for its large-scale direct sales model and frequent product launches via catalogs."
778challenge: "The business model required advance production planning per catalog, but low demand predictability generated overstocking or stock-outs, impacting margin and working capital."
779what_to_anticipate: ""
780prediction: ""
781solution: "We implemented adaptive forecasting aligned with the catalog cycle, classifying SKUs by criticality and behavior. Operations began anticipating volumes with greater precision, reducing risk and increasing operational efficiency."
782metric1: "+70% catalog sales forecast accuracy"
783metric2: ""
784metric3: ""
785solutions: ["Predictive Forecasting|forecasting"]
786quote: "Catalog Planning Sustained by Adaptive Forecasting"
787customer_name: ""
788customer_title: ""
789cover_image: "/content/success-stories/demand-forecast-accuracy.jpg"
790logo: "/content/logos/infinity6_CMYK_color_symbol_72dpi.png"
791show_home: false
792published: true
793sort_order: 6
794---`,_0e=`---
795slug: demand-forecast-accuracy
796language: pt
797title: "Acurácia de Previsão de Demanda: Elevando Produção e Planejamento de Vendas"
798segment: "Varejo"
799client: "Uma das maiores marcas de cosméticos do Brasil"
800client_anon: true
801description: "Um dos principais players de beleza do país, reconhecido por seu modelo de vendas diretas em larga escala e lançamentos frequentes de produtos via catálogos."
802challenge: "O modelo de negócio exigia planejamento antecipado de produção por catálogo, mas a baixa previsibilidade de demanda gerava excesso de estoque ou ruptura, impactando margem e capital de giro."
803what_to_anticipate: ""
804prediction: ""
805solution: "Implementamos previsão adaptativa alinhada ao ciclo de catálogo, classificando SKUs por criticidade e comportamento. A operação passou a antecipar volumes com maior precisão, reduzindo risco e aumentando eficiência operacional."
806metric1: "+70% de acurácia de previsão de vendas do catálogo"
807metric2: ""
808metric3: ""
809solutions: ["Forecast Preditivo de Demanda|forecasting"]
810quote: "Planejamento de Catálogo Sustentado por Previsão Adaptativa"
811customer_name: ""
812customer_title: ""
813cover_image: "/content/success-stories/demand-forecast-accuracy.jpg"
814logo: "/content/logos/infinity6_CMYK_color_symbol_72dpi.png"
815show_home: false
816published: true
817sort_order: 6
818---`,N0e=`---
819slug: marketplace-farmacia-mix-sortimento-ia
820language: en
821title: "Marketplace Excellence: Driving Results for Pharmacies"
822segment: "Industry"
823client: "EMS Farma"
824client_anon: false
825description: "One of the largest pharmaceutical companies in Brazil and a strategic player in the global market."
826challenge: "The marketplace connected manufacturers, distributors and pharmacies, but operated on reactive orders with little anticipatory intelligence per point of sale. The absence of granular predictability limited sell-out potential, pressured margins and reduced efficie

826ncy in inventory and commercial capital allocation."
827what_to_anticipate: ""
828prediction: ""
829solution: "We consolidated purchase signals, transactional history, inventory and per-pharmacy behavior into an anticipatory decision engine. Operations now indicate what to sell, when to activate and which combinations would expand margin and turnover. The marketplace shifted from responding to demand to actively orienting it."
830metric1: "+23% average ticket per POS"
831metric2: "+36% conversion on new SKUs (listing)"
832metric3: ""
833solutions: ["Predictive Assortment & Order Recommendation|assortment"]
834quote: "Commercial Anticipation Applied to the Pharmacy Ecosystem"
835customer_name: "Paulo Lima"
836customer_title: "New Channel Manager"
837cover_image: "/content/success-stories/marketplace-farmacia-mix-sortimento-ia.png"
838logo: null
839show_home: true
840published: true
841sort_order: 1
842---`,C0e=`---
843slug: marketplace-farmacia-mix-sortimento-ia
844language: pt
845title: "A EMS encontrou o pedido ideal para cada um de seus 95 mil PDVs"
846segment: "Indústria"
847client: "EMS Farma"
848client_anon: false
849description: "A EMS transformou seu marketplace de um canal reativo a pedidos em uma camada de decisão antecipada para orientar cada interação comercial."
850challenge: "### O portfólio crescia. O tempo de cada interação, não.\\\\\\n\\nCom 1.400 SKUs distribuídos por 95 mil PDVs, o desafio não era simplesmente oferecer mais produtos. Era saber, antes de cada contato, quais produtos faziam sentido para cada farmácia e em qual volume.\\\\\\nO marketplace conectava indústria, distribuidores e PDVs, mas ainda reagia ao pedido já formado. Sem uma leitura antecipada do potencial de cada cliente, novos produtos deixavam de ser positivados, o mix permanecia incompleto e parte do ticket ficava na mesa."
851what_to_anticipate: "### O potencial de compra antes da abordagem comercial.\\\\\\nAntes de cada contato, a operação precisava responder:\\nQual produto tem maior aderência a este PDV?\\nQual volume é compatível com sua demanda?\\nQuais novos SKUs têm maior chance de entrada?\\nQual recomendação combina potencial de compra e prioridade do negócio?\\\\\\n> A decisão precisava acontecer antes da ligação, não durante a tentativa de venda."
852prediction: "### Um pedido diferente para cada ponto de venda.\\\\\\nA Infinity6 estruturou um modelo de recomendação para antecipar o pedido ideal e o volume recomendado por PDV.\\nO motor combinou histórico transacional, sinais de demanda, estoque e perfil de compra para estimar a propensão de cada produto e identificar as oportunidades com maior potencial comercial.\\\\\\nEm vez de apresentar o mesmo portfólio para todos, **a operação passou a iniciar cada contato com uma recomendação priorizada**."
853solution: "### A predição entrou diretamente na rotina do vendedor.\\n\\nPara cada interação, o vendedor passou a receber:\\nprodutos priorizados para aquele PDV;\\nvolume recomendado por item;\\nnovos SKUs com maior chance de positivação;\\ncombinações alinhadas aos objetivos comerciais;\\nargumentos que explicavam cada recomendação.\\n\\n> A inteligência não substituiu o vendedor. Deu ao vendedor uma decisão melhor antes da abordagem."
854metric1: "+23% de ticket médio por PDV"
855metric2: "+36% de conversão de novos SKUs (positivação)"
856metric3: ""
857solutions: ["Personalização Preditiva|relevance"; "Preço Orientado ao Giro|pricing"; "Preço Orientado à Conversão|pricing"; "Mix, Sortimento e Pedido Ideal|assortment"]
858quote: "Antes, o marketplace respondia ao que a farmácia já havia decidido comprar.\\\\\\n\\n> Com a infinity6, passou a antecipar o que cada PDV tinha potencial para comprar e a orientar a formação do pedido.\\\\\\n\\n\\nO resultado foi uma abordagem mais relevante para a farmácia e mais valiosa para a operação."
859customer_name: ""
860customer_title: ""
861cover_image: "/content/success-stories/marketplace-farmacia-mix-sortimento-ia.png"
862logo: "/content/logos/marketplace-farmacia-mix-sortimento-ia-logo.png"
863show_home: true
864published: true
865sort_order: 1
866---`,E0e=`---
867slug: sales-forecast-precision
868language: en
869title: "Accurate Sales Forecasting: Optimizing Results and Operations"
870segment: "Industry"
871client: "EMS Farma"
872client_anon: false
873description: "One of the largest pharmaceutical companies in Brazil and a global reference."
874challenge: "Low sell-out forecast accuracy compromised production planning, commercial targets and investment allocation. Disconnected data sources generated inconsistency and low confidence in projections."
875what_to_anticipate: ""
876prediction: ""
877solution: "We unified multiple market, behavior, inventory and historical signals into an adaptive forecasting engine per SKU and POS. The company began anticipating demand with granularity and reliability, enabling coordinated decisions from production to commercial."
878metric1: "82% sell-out forecast accuracy"
879metric2: "+100M in savings from incineration, stock-outs and overstocking"
880metric3: ""
881solutions: ["Predictive Forecasting|forecasting"]
882quote: "Operational Predictability with SKU and POS Granularity"
883customer_name: ""
884customer_title: ""
885cover_image: "/content/success-stories/case-forecast-1.jpg"
886logo: "/content/logos/EMS-COR.png"
887show_home: true
888published: true
889sort_order: 4
890---`,T0e=`---
891slug: sales-forecast-precision
892language: pt
893title: "Previsão de Vendas Precisa: Otimizando Resultados e Operações"
894segment: "Indústria"
895client: "EMS Farma"
896client_anon: false
897description: "Uma das maiores farmacêuticas do Brasil e referência global."
898challenge: "A baixa acurácia do forecast de sell-out comprometia planejamento de produção, metas comerciais e alocação de investimentos. Fontes de dados desconectadas geravam inconsistência e baixa confiança nas projeções."
899what_to_anticipate: ""
900prediction: ""
901solution: "Unificamos múltiplos sinais de mercado, comportamento, estoque e histórico em um motor adaptativo de previsão por SKU e PDV. A empresa passou a antecipar demanda com granularidade e confiabilidade, permitindo decisões coordenadas da produção ao comercial."
902metric1: "82% de acurácia na previsão de sell-out"
903metric2: "+100M em savings de incineração, ruptura e overstocking"
904metric3: ""
905solutions: ["Forecast Preditivo de Demanda|forecasting"]
906quote: "Previsibilidade Operacional com Granularidade por SKU e PDV"
907customer_name: ""
908customer_title: ""
909cover_image: "/content/success-stories/case-forecast-1.jpg"
910logo: "/content/logos/EMS-COR.png"
911show_home: true
912published: true
913sort_order: 4
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945
946Não entregamos apenas modelos. Colocamos decisões em movimento.`,order:11},{id:12,question:"Como vocês mensuram impacto no negócio?",answer:"Trabalhamos com indicadores claros de receita, margem e eficiência desde o início do projeto, acompanhando variações diretamente atribuíveis às ativações preditivas em produção.",order:12}]},en:{title:"What You Need to Know",subtitle:"Straight answers on how to deploy predictive intelligence quickly and securely.",searchPlaceholder:"Search by topic, challenge or keyword...",noResults:"No FAQs foun

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947
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951,{title:"Responsabilidade e prestação de contas",desc:"mantemos governança clara e controles auditáveis sobre o ciclo de vida dos dados."}])]}),s.jsxs("section",{className:"border-b border-white/10 pb-8",children:[s.jsx("h2",{className:"text-2xl font-bold text-white mb-4",children:"3. Classificação de Confidencialidade"}),s.jsx("p",{className:"text-white/70 mb-6",children:"Todas as informações e ativos da infinity6 são categorizados em quatro níveis de confidencialidade:"}),s.jsx("div",{className:"overflow-x-auto",children:s.jsxs("table",{className:"w-full border-collapse",children:[s.jsx("thead",{children:s.jsxs("tr",{className:"bg-white/10",children:[s.jsx("th",{className:"border border-white/10 px-4 py-3 text-left text-white font-semibold",children:"Classificação"}),s.jsx("th",{className:"border border-white/10 px-4 py-3 text-left text-white font-semibold",children:"Descrição"}),s.jsx("th",{className:"border border-white/10 px-4 py-3 text-left text-white font-semibold",children:"Exemplo"})]})}),s.jsxs("tbody",{children:[s.jsxs("tr",{className:"bg-white/5",children:[s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white font-medium",children:"Pública"}),s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white/70",children:"Informações disponíveis publicamente sem restrições"}),s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white/70",children:"Material de marketing, site institucional"})]}),s.jsxs("tr",{children:[s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white font-medium",children:"Interna"}),s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white/70",children:"Informações restritas ao uso dentro da infinity6"}),s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white/70",children:"Procedimentos internos não sensíveis"})]}),s.jsxs("tr",{className:"bg-white/5",children:[s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white font-medium",children:"Confidencial"}),s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white/70",children:"Informações críticas que exigem proteção contra divulgação externa"}),s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white/70",children:"Documentos técnicos, relatórios de clientes anonimizados, contratos internos, artefatos e processos do cliente"})]}),s.jsxs("tr",{children:[s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white font-medium",children:"Sigilosa"}),s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white/70",children:"Informações altamente sensíveis com impacto significativo caso divulgadas"}),s.jsx("td",{className:"border border-white/10 px-4 py-3 text-white/70",children:"Ativos de desenvolvimento, código-fonte proprietário, algoritmos, modelos de IA, dados estratégicos financeiros e operacionais"})]})]})]})})]}),s.jsxs("section",{className:"border-b border-white/10 pb-8",children:[s.jsx("h2",{className:"text-2xl font-bold text-white mb-4",children:"4. Dados Tratados pela infinity6"}),s.jsx("h3",{className:"text-lg font-semibold text-white/80 mb-3",children:"4.1 Dados de Clientes"}),s.jsxs("ul",{className:"list-disc list-inside text-white/70 space-y-2 mb-4 marker:text-[#F4845F]",children:[s.jsx("li",{children:"Recebidos anonimizados e sem possibilidade de identificação de indivíduos."}),s.jsx("li",{children:"Processados exclusivamente para fins de inteligência de dados, análises e geração de valor ao cliente contratante."}),s.jsxs("li",{children:[s.jsx("strong",{className:"text-white",children:"Classificação de confidencialidade:"})," Confidencial"]})]}),s.jsx("h3",{className:"text-lg font-semibold text-white/80 mb-3",children:"4.2 Dados Internos da infinity6"}),s.jsxs("ul",{className:"list-disc list-inside text-white/70 space-y-2 marker:text-[#F4845F]",children:[s.jsxs("li",{children:[s.jsx("strong",{className:"text-white",children:"Ativos de desenvolvimento:"})," código, algoritmos, modelos e pipelines de dados."]}),s.jsxs("li",{children:[s.jsx("strong",{className:"text-white",children:"Classificação:"})," Sigilosa"]}),s.jsx("li",{children:"Nunca compartilhados com terceiros."}),s.jsxs("li",{children:[s.jsx("strong",{className:"text-white",children:"Documentos internos:"}

951)," estratégicos, técnicos, financeiros e operacionais."]}),s.jsxs("li",{children:[s.jsx("strong",{className:"text-white",children:"Classificação:"})," Confidencial"]}),s.jsx("li",{children:"Protegidos por controles de acesso rigorosos."})]})]}),e("5. Compartilhamento de Dados","",["Não há compartilhamento de dados de clientes com outros clientes.","Dados de clientes não são comercializados, alugados ou transferidos a terceiros.","Compartilhamento restrito apenas para cumprimento de obrigações legais ou regulatórias, com avaliação jurídica."]),e("6. Segurança da Informação","",["SGSI-IA baseado na ISO/IEC 42001.","Controles de acesso baseados em identidade, mínimo privilégio e autenticação multifator.","Criptografia de dados em repouso e em trânsito.","Monitoramento e auditoria contínua de sistemas críticos.","Gestão de vulnerabilidades, pentests e auditorias externas.","Planos de continuidade de negócios e resposta a incidentes periodicamente testados."]),e("7. Conformidade com a Lei de IA da União Europeia","",["Avaliação de riscos de IA e mitigação de impactos adversos.","Explicabilidade dos modelos de IA sempre que aplicável.","Monitoramento contínuo e controle de viés/discriminação.","Auditoria e rastreabilidade de operações de IA.","Alinhamento a regulamentações de alto risco de IA quando aplicáveis."]),e("8. Direitos dos Clientes","",["Transparência sobre o tratamento de dados anonimizados.","Controle sobre a finalidade e o escopo de uso dos dados.","Segurança contratual quanto à confidencialidade e proteção das informações.","Direito à auditoria formal para comprovar conformidade."]),e("9. Confidencialidade e Propriedade Intelectual","",["Todos os desenvolvimentos tecnológicos são Sigilosos e propriedade exclusiva da infinity6.","Nenhum ativo de desenvolvimento é compartilhado com terceiros.","Todos os documentos internos são Confidenciais, protegidos contra divulgação não autorizada."]),s.jsxs("section",{className:"border-b border-white/10 pb-8",children:[s.jsx("h2",{className:"text-2xl font-bold text-white mb-4",children:"10. Cookies e Analytics do Site"}),s.jsxs("p",{className:"text-white/70 mb-4",children:["Quando você navega em infinity6.ai, coletamos automaticamente, sob base legal de ",s.jsx("strong",{className:"text-white",children:"legítimo interesse"}),", informações anônimas para entender como o site é usado e melhorar sua experiência. Nenhum dado pessoal identificável é coletado nesta etapa."]}),s.jsx("p",{className:"text-white/70 mb-3",children:s.jsx("strong",{className:"text-white",children:"O que coletamos:"})}),t([{title:"anonymous_id",desc:"identificador anônimo (UUID) gerado no seu navegador e guardado em localStorage. Não identifica você pessoalmente."},{title:"session_id",desc:"identificador temporário de sessão; expira após 30 minutos de inatividade."},{title:"Origem da visita",desc:"parâmetros UTM (source, medium, campaign), referrer e landing page."},{title:"Histórico no site",desc:"últimas 20 páginas visitadas e últimos 30 eventos de interação (cliques em CTAs, downloads de Insights, envios de formulário)."},{title:"Dados técnicos via Google Analytics 4",desc:"tipo de dispositivo, navegador, país aproximado."}]),s.jsxs("p",{className:"text-white/70 mt-4 mb-2",children:[s.jsx("strong",{className:"text-white",children:"Como usamos:"})," medir desempenho, melhorar a experiência de uso e, quando você opta por preencher um formulário, anexar esse histórico anônimo ao seu contato para enriquecer o atendimento."]}),s.jsxs("p",{className:"text-white/70 mb-2",children:[s.jsx("strong",{className:"text-white",children:"Cookies adicionais (marketing/preferências):"}),' só são ativados após consentimento explícito no banner de cookies. Você pode revisar e alterar suas escolhas a qualquer momento em "Preferências de cookies".']}),s.jsxs("p",{className:"text-white/70",children:[s.jsx("strong",{className:"text-white",children:"Seus direitos:"}),' desativar analytics em "Preferências de cookies", limpar o storage do navegador ou solicitar exclusão via ',s.jsx("a",{href:"mailto:[email protected]",className:"text-[#F4845F] hover:underline",children:"[email protected]"}),"."]})]}),s.jsxs("section",{className:"border-b border-white/10 pb-8",children:[s.jsx("h2",{className:"text-2xl font-bold text-white mb-4",children:"11. Atualizações da Política"}),s.jsx("p",{className:"text-white/70",children:"A infinity6 poderá atualizar esta Política para refletir melhorias em processos, mudanças regulatórias ou evolução tecnológica, comunicando tais alterações de forma clara e transparente."})]}),s.jsxs("section",{children:[s.jsx("h2",{className:"text-2xl font-bold text-white mb-4",children:"12. Contato"}),s.jsx("p",{className:"text-white/70 mb-4",children:"Em caso de dúv

951idas ou solicitações relacionadas a esta política:"}),s.jsx("div",{className:"bg-white/5 border border-white/10 p-6 rounded-lg mb-6",children:s.jsxs("p",{className:"text-white/70",children:[s.jsx("strong",{className:"text-white",children:"E-mail:"})," [email protected]"]})}),s.jsx("p",{className:"text-white/70 flex items-center gap-2",children:"🔒 A infinity6 reafirma seu compromisso com a confiança, segurança e ética no uso de dados e inteligência artificial."})]})]})},uAe=()=>{iD();const{language:e}=be(),n={en:{title:"Privacy Policy",subtitle:"Your privacy is our priority. Learn how we protect and handle your data.",lastUpdated:"Last updated: June 2026"},pt:{title:"Política de Privacidade",subtitle:"Sua privacidade é nossa prioridade. Saiba como protegemos e gerenciamos seus dados.",lastUpdated:"Última atualização: Junho 2026"}}[e],r={introduction:{title:"1. Introduction",text:'At infinity6 ("we," "our," or "us"), we are committed to protecting your privacy and personal information. This Privacy Policy explains how we collect, use, disclose, and safeguard your information when you visit our website or use our services.'},information:{title:"2. Information We Collect",personalTitle:"Personal Information",personalText:"We may collect personal information that you voluntarily provide to us, including:",personalItems:["Name and contact information (email, phone number)","Company information and job title","Communications with us through contact forms or email","Information provided during consultations or demos"],technicalTitle:"Technical Information",technicalText:"We automatically collect certain information when you visit our website:",technicalItems:["IP address and device information","Browser type and version","Website usage data and analytics","Cookies and similar tracking technologies"]},usage:{title:"3. How We Use Your Information",text:"We use the collected information for the following purposes:",items:["Provide and improve our AI solutions and services","Respond to your inquiries and provide customer support","Send you relevant information about our products and services","Analyze website usage to improve user experience","Comply with legal obligations and protect our rights"]},security:{title:"4. Data Security",text:"We implement appropriate technical and organizational security measures to protect your personal information:",items:["Encryption of data in transit and at rest","Regular security assessments and updates","Access controls and employee training","Secure cloud infrastructure with leading providers"]},sharing:{title:"5. Information Sharing",text:"We do not sell your personal information. We may share your information in the following circumstances:",items:["With trusted service providers who assist in our operations","To comply with legal requirements or protect our rights","In connection with a business transaction (merger, acquisition)","With your explicit consent for specific purposes"]},rights:{title:"6. Your Rights",text:"Depending on your location, you may have the following rights regarding your personal information:",items:["Access and receive a copy of your personal data","Rectify inaccurate or incomplete information","Request deletion of your personal data","Object to or restrict processing of your data","Data portability rights"]},transfers:{title:"7. International Data Transfers",text:"Your information may be transferred to and processed in countries other than your own. We ensure appropriate safeguards are in place to protect your data in accordance with applicable laws."},cookies:{title:"8. Cookies and Site Analytics",intro:"When you browse infinity6.ai, we automatically collect, under the legal basis of legitimate interest, anonymous information to understand how the site is used and improve your experience. No personally identifiable data is collected at this stage.",collectedTitle:"What we collect:",collectedItems:[{title:"anonymous_id",desc:"anonymous identifier (UUID) generated in your browser and stored in localStorage. Does not identify you personally."},{title:"session_id",desc:"temporary session identifier; expires after 30 minutes of inactivity."},{title:"Visit origin",desc:"UTM parameters (source, medium, campaign), referrer and landing page."},{title:"On-site history",desc:"last 20 pages visited and last 30 interaction events (CTA clicks, Insight downloads, form submissions)."},{title:"Technical data via Google Analytics 4",desc:"device type, browser, approximate country."}],usage:"How we use it: measure performance, improve UX, and when you choose to fill out a form, attach this anonymous history to your contact to enrich the conversation.",additional:'Additional cookies (marketing/preferences): only activated after explicit consent in the cookie banner. You can review and change your choices at any time in "Cookie preferences".',rights:'Your rights: disable analytics in "Cookie preferences", clear your browser storage, or request deleti

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951parency and Explainability",transparencyText:"We believe in making AI decisions understandable:",transparencyItems:["Clear documentation of AI system capabilities and limitations","Explainable AI techniques where decision transparency is critical","Open communication about how our systems work","Regular reporting on AI system performance and impact"]},data:{title:"3. Data Ethics and Privacy",minimizationTitle:"Data Minimization",minimizationText:"We collect and use only the data necessary for our services:",minimizationItems:["Purpose limitation - data used only for stated purposes","Data retention policies with automatic deletion","Regular audits of data collection and usage practices","Strong consent mechanisms for data processing"],privacyTitle:"Privacy by Design",privacyText:"Privacy is built into every system we develop:",privacyItems:["End-to-end encryption for all data transmissions","Anonymization and pseudonymization techniques","Secure multi-party computation where applicable","Regular privacy impact assessments"]},human:{title:"4. Human-Centered AI",text:"Our AI solutions are designed to augment human capabilities, not replace human judgment:",items:["Human oversight in critical decision-making processes","Easy opt-out mechanisms for automated systems","Support for human skill development and adaptation","Respect for human autonomy and choice"]},environmental:{title:"5. Environmental Responsibility",text:"We are committed to sustainable AI development:",items:["Energy-efficient model architectures and training practices","Green cloud infrastructure and renewable energy usage","Carbon footprint monitoring and offset programs","Lifecycle assessment of our AI systems' environmental impact"]},stakeholder:{title:"6. Stakeholder Engagement",text:"We actively engage with various stakeholders to ensure our ethical standards:",items:["Regular consultation with ethics experts and advisory boards","Collaboration with academic institutions and research organizations","Participation in industry standards and best practice initiatives","Open dialogue with civil society and advocacy groups"]},improvement:{title:"7. Continuous Improvement",text:"Ethics is an ongoing commitment, not a one-time achievement:",items:["Regular ethics training for all team members","Continuous monitoring and evaluation of ethical practices","Feedback mechanisms for reporting ethical concerns","Annual ethics audits and public reporting"]},compliance:{title:"8. Compliance and Governance",text:"We adhere to the highest standards of compliance and governance:",items:["Compliance with GDPR, CCPA, and other privacy regulations","Adherence to emerging AI governance frameworks","Regular legal and compliance reviews","Transparent reporting of compliance metrics"]},contact:{title:"9. Contact Our Ethics Team",text:"For questions about our ethics policy, to report concerns, or to discuss ethical considerations:",officer:"Chief Ethics Officer",email:"Email:",phone:"Phone:",address:"Address:",company:"infinity6 AI Solutions, Ethics Department"}}},pt:{title:"Política de Ética",subtitle:"Nosso compromisso com IA ética e práticas comerciais responsáveis.",lastUpdated:"Última atualização: Janeiro 2025",sections:{foundation:{title:"1. Nossa Base Ética",text:"Na infinity6, acreditamos que a inteligência artificial deve servir aos melhores interesses da humanidade. Nosso compromisso com o desenvolvimento ético de IA orienta todos os aspectos do nosso trabalho, desde pesquisa e desenvolvimento até implantação e manutenção contínua de nossas soluções."},development:{title:"2. Desenvolvimento Responsável de IA",fairnessTitle:"Equidade e Não-Discriminação",fairnessText:"Estamos comprometidos em desenvolver sistemas de IA que sejam justos e imparciais:",fairnessItems:["Testes regulares de viés e mitigação em todos os nossos modelos de IA","Conjuntos de dados de treinamento diversificados que representam diferentes demografias","Monitoramento contínuo para resultados discriminatórios","Práticas de design inclusivo em todo o desenvolvimento"],transparencyTitle:"Trans

951parência e Explicabilidade",transparencyText:"Acreditamos em tornar as decisões de IA compreensíveis:",transparencyItems:["Documentação clara das capacidades e limitações do sistema de IA","Técnicas de IA explicável onde a transparência da decisão é crítica","Comunicação aberta sobre como nossos sistemas funcionam","Relatórios regulares sobre desempenho e impacto do sistema de IA"]},data:{title:"3. Ética de Dados e Privacidade",minimizationTitle:"Minimização de Dados",minimizationText:"Coletamos e usamos apenas os dados necessários para nossos serviços:",minimizationItems:["Limitação de propósito - dados usados apenas para fins declarados","Políticas de retenção de dados com exclusão automática","Auditorias regulares das práticas de coleta e uso de dados","Mecanismos de consentimento rigorosos para processamento de dados"],privacyTitle:"Privacidade por Design",privacyText:"A privacidade é incorporada em todos os sistemas que desenvolvemos:",privacyItems:["Criptografia de ponta a ponta para todas as transmissões de dados","Técnicas de anonimização e pseudonimização","Computação segura multi-partes quando aplicável","Avaliações regulares de impacto na privacidade"]},human:{title:"4. IA Centrada no Humano",text:"Nossas soluções de IA são projetadas para aumentar as capacidades humanas, não substituir o julgamento humano:",items:["Supervisão humana em processos críticos de tomada de decisão","Mecanismos fáceis de opt-out para sistemas automatizados","Suporte para desenvolvimento e adaptação de habilidades humanas","Respeito pela autonomia e escolha humana"]},environmental:{title:"5. Responsabilidade Ambiental",text:"Estamos comprometidos com o desenvolvimento sustentável de IA:",items:["Arquiteturas de modelo energeticamente eficientes e práticas de treinamento","Infraestrutura em nuvem verde e uso de energia renovável","Monitoramento da pegada de carbono e programas de compensação","Avaliação do ciclo de vida do impacto ambiental dos nossos sistemas de IA"]},stakeholder:{title:"6. Engajamento das Partes Interessadas",text:"Nos envolvemos ativamente com várias partes interessadas para garantir nossos padrões éticos:",items:["Consulta regular com especialistas em ética e conselhos consultivos","Colaboração com instituições acadêmicas e organizações de pesquisa","Participação em padrões da indústria e iniciativas de melhores práticas","Diálogo aberto com a sociedade civil e grupos de advocacia"]},improvement:{title:"7. Melhoria Contínua",text:"A ética é um compromisso contínuo, não uma conquista única:",items:["Treinamento regular em ética para todos os membros da equipe","Monitoramento e avaliação contínua das práticas éticas","Mecanismos de feedback para relatar preocupações éticas","Auditorias anuais de ética e relatórios públicos"]},compliance:{title:"8. 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956`).map(l=>l.trim()).filter(Boolean);let i="",o="";for(const l of a)l.startsWith("## ")?i=l.substring(3).trim():l.startsWith("**Logo:**")&&(o=l.substring(9).trim());i&&o&&t.push({name:i,logo:o})}return t},TEe=()=>{const{partners:e,loading:t}=CEe(),{language:n}=be(),r=n==="pt"?"Líderes que transformam antecipação em vantagem":"Leaders who turn anticipation into advantage";if(t||e.length===0)return null;const a=i=>s.jsx("div",{className:"flex items-center gap-20 shrink-0","aria-hidden":i==="b"?!0:void 0,children:e.map((o,l)=>s.jsx("img",{src:`/${o.logo.startsWith("/")?o.logo.slice(1):o.logo}`,alt:o.name,className:"h-8 sm:h-10 w-auto max-w-[120px] object-contain opacity-60 hover:opacity-100 transition-opacity shrink-0",loading:"lazy"},`${i}-${o.name}-${l}`))});return s.jsxs("section",{className:"py-10 md:py-14 bg-slate-50",children:[s.jsx("div",{className:"container mx-auto px-6 mb-10",children:s.jsx("p",{className:"text-center text-sm uppercase tracking-widest text-slate-500",children:r})}),s.jsxs("div",{className:"relative overflow-hidden",children:[s.jsx("div",{className:"absolute left-0 top-0 bottom-0 w-24 bg-gradient-to-r from-slate-50 to-transparent z-10"}),s.jsx("div",{className:"absolute right-0 top-0 bottom-0 w-24 bg-gradient-to-l from-slate-50 to-transparent z-10"}),s.jsxs("div",{className:"flex items-center gap-20 w-max animate-marquee motion-safe:animate-marquee",style:{animationDuration:"35s"},children:[a("a"),a("b")]})]})]})},OEe=`# Insights
957
958> ⚠️ **Estes arquivos são GERADOS automaticamente.** Não edite manualmente.
959>
960> A fonte da verdade vive no **i6Hub** em
961> \`Marketing & CRM → Content Radar → Site Content\`.
962>
963> A cada deploy, o GitHub Actions executa \`scripts/sync-insights-from-i6hub.mjs\`,
964> que apaga todos os \`.md\` deste diretório (exceto este README) e regera a
965> partir do feed público do i6Hub (edge function) — apenas registros com
966> \`published = true\`.
967
968## Como funciona
969
970\`\`\`
971i6Hub (admin)
972   │  (toggle salvar)
973   ▼
974edge function notify-site-rebuild
975   │  (repository_dispatch: insights-updated)
976   ▼
977GitHub Actions (deploy-gh-pages.yml)
978   │  fetch I6HUB_FEED_URL com X-Sync-Token
979   ▼
980sync-insights-from-i6hub.mjs regera .md
981   │
982   ▼
983vite build → GitHub Pages
984\`\`\`
985
986- **Despublicar artigo:** toggle \`published = false\` no i6Hub → próximo deploy remove o \`.md\`.
987- **Destacar na home:** toggle \`featured = true\` no i6Hub.
988- **Excluir definitivamente:** botão na aba Site Content (remove a linha).
989
990## Convenção de nome de arquivo
991
992\`\`\`
993{language}-{slug}.md
994\`\`\`
995
996O loader (\`useInsights.ts\`) usa \`import.meta.glob\` em todos os \`.md\` desta
997pasta, então o nome em si não importa — só serve para evitar colisões.
998
999## Frontmatter (gerado pelo sync)
1000
1001\`\`\`yaml
1002---
1003title: "Título do artigo"
1004slug: meu-artigo
1005language: pt              # pt | en
1006type: article             # article | linkedin | press | podcast | video
1007cluster: "forecasting"
1008featured: true
1009gated: false
1010date: 2025-06-04
1011read_time: 6
1012cover_image: "/path.jpg"
1013external_url: "https://..."
1014asset_url: null
1015excerpt: "Resumo curto que aparece no card."
1016---
1017\`\`\`
1018
1019## Secrets envolvidos
1020
1021| Onde | Nome | Para que serve |
1022|------|------|----------------|

1023| GitHub repo i6-website | \`I6HUB_FEED_URL\` | URL da edge function pública do i6Hub que devolve os insights publicados |
1024| GitHub repo i6-website | \`I6HUB_SYNC_TOKEN\` | Token compartilhado enviado em \`X-Sync-Token\` e validado pela edge function |
1025| i6Hub (Lovable secrets) | \`GITHUB_DISPATCH_TOKEN\` | PAT fine-grained usado pela edge function \`notify-site-rebuild\` para disparar o \`repository_dispatch\` |
1026
1027O site **nunca** recebe service_role nem fala direto com o banco do i6Hub —
1028toda leitura é mediada pela edge function.
1029`,IEe=`---
1030id: f0f57622-4e80-44a2-b4ba-751e83fa768d
1031title: "Right price on the spreadsheet, wrong margin in the result: the new challenge for e-commerce pricing"
1032slug: ai-pricing-ecommerce-margem-giro-conversao
1033language: en
1034type: i6 Article
1035featured: true
1036gated: false
1037date: 2026-07-16T12:00:00.000Z
1038read_time: 6
1039cover_image: "/content/insights/ai-pricing-ecommerce-margem-giro-conversao.png"
1040cta_form: true
1041cta_form_text: "**Would you like to learn more?** Fill in the form and let's get in touch!"
1042theme: "preco-elasticidade"
1043theme_label: "Price & Elasticity"
1044is_default: true
1045excerpt: "Understand how AI Pricing helps e-commerce stores transform pricing into a dynamic decision, connecting margin, turnover, 

1045conversion, stock, and governance."
1046---
1047In e-commerce, a price can be technically correct and commercially wrong at the same time.
1048
1049It can respect the minimum margin. It can be aligned with the commercial table. It can seem competitive when compared to the competitor. It can have been approved in a meeting and still leave money on the table.
1050
1051**This happens because price is not an isolated variable. It is a decision that changes according to the context.**
1052
1053It changes with inventory. It changes with the elasticity of each SKU. It changes with the category's turnover. It changes with the origin of traffic. It changes with purchase intent. It changes with seasonality. It changes with competitive pressure. It changes with the margin target.
1054
1055When all these signals are analyzed slowly, manually, or with little granularity, the problem rarely appears as a "pricing error." It appears in the result: compressed margin, unnecessary discount, stagnant inventory, late markdown, lost conversion, and trapped capital.
1056
1057The issue is that many companies still treat pricing as a table, when it has already become a financial lever.
1058
1059## Why the traditional pricing model fell short
1060
1061For a long time, pricing based on history, minimum margin, periodic meetings, and punctual monitoring of competition seemed sufficient.
1062
1063But e-commerce operates at a different pace.
1064
1065Demand changes throughout the day. Competition adjusts prices more frequently. Consumers compare options in seconds. Inventory ages while the decision is still being discussed. Campaigns, channels, and browsing behaviors create different contexts for the same product.
1066
1067In this scenario, deciding prices only by average, history, or fixed rule creates distortions.
1068
1069An SKU with high demand and low price sensitivity may be giving up margin unnecessarily. Another, with pressured inventory and low turnover speed, may need a more precise markdown strategy. A third may have good conversion in a certain session context, but lose revenue when treated with the same logic applied to the entire catalog.
1070
1071The problem is not a lack of data.
1072The problem is transforming data into decisions before the opportunity passes.
1073
1074## The real tension: margin, turnover, and conversion
1075
1076Every pricing decision involves a trade-off.
1077
1078If the price goes up, the margin may improve, but conversion may fall. If the price goes down, conversion may go up, but the margin may be destroyed. If the product doesn't move, inventory ages. If the markdown comes in too early, the company teaches the market to wait for discounts. If it comes in too late, the cost has already been absorbed by the operation.
1079
1080That's why the question "what price should we set?" is limited.
1081
1082The most important question is:
1083
1084> What pricing decision generates the best possible result for this product, in this context, respecting the business constraints?
1085
1086This change in question is what separates operational pricing from intelligent pricing.
1087
1088## What AI Pricing changes in practice
1089
1090AI Pricing does not mean letting AI change prices without criteria.
1091
1092It means using artificial intelligence to expand analysis and recommendation capabilities, considering more variables than a team could manually process with the same frequency and granularity.
1093
1094**In practice, AI can help estimate elasticity per SKU, project demand, simulate scenarios, identify opportunities for margin capture, calibrate markdown, recommend price ranges, and measure decision results before scaling.**
1095
1096The central point is that the recommendation needs to operate within the business rules.
1097
1098Minimum margin, available inventory, commercial policy, competitive band, exposure rules, and governance are not details. They are constraints that define the safe decision space.
1099
1100AI does not replace commercial strategy. It helps execute that strategy with more speed, precision, and traceability.
1101
1102## Three ways to apply AI Pricing in e-commerce
1103
1104At infinity6, the vision of AI Pricing starts from three complementary fronts: margin, turnover, and conversion.
1105
1106### 1. Price-to-Margin: capturing margin without breaking the commercial strategy
1107
1108Not every product needs a discount to sell better. And not every competitive price maximizes results.
1109

1110Price-to-Margin seeks to identify margin capture opportunities per SKU, considering elasticity, forecast, price and volume history, competition, inventory, and commercial restrictions.
1111
1112**The logic is simple: if two products have different behaviors, they should not be treated by the same rule.**
1113
1114With a more granular view, the company can understand where there is room to preserve or expand margin without compromising the commercial strategy.
1115
1116### 2. Price-to-Turnover: accelerating turnover without turning discount into autopilot
1117
1118Stagnant inventory is margin under pressure.
1119
1120But solving turnover only with discounts can erode profitability. The challenge is to understand when, where, and with what intensity to adjust prices to move products without destroying value.
1121
1122**Price-to-Turnover considers product life cycle, sales velocity, seasonality, inventory, region, store, category, or cluster to recommend price and markdown rules with more precision.**
1123
1124The decision is no longer "lower price because it didn't sell" and becomes "which movement best preserves the balance between turnover and margin?"
1125
1126### 3. Price-to-Conversion: deciding based on session context
1127
1128In e-commerce, two sessions are never exactly alike.
1129
1130A recurring user, coming from a specific campaign, browsing at a certain time, and demonstrating high purchase intent, does not have the same context as a new visitor, without history and without clear conversion signals.
1131
1132**Price-to-Conversion uses session signals, user history, channel origin, inventory, real-time demand, and commercial rules to support more contextual recommendations.**
1133
1134The goal is not simply to convert more. It is to increase conversion and revenue per session without giving up margin.
1135
1136## Governance is what makes AI applicable
1137
1138The biggest objection to AI in pricing is legitimate: price is too sensitive to become an uncontrolled experiment.
1139
1140Therefore, governance needs to be in the solution's design, not in the discourse.
1141
1142Before recommending any action, the model must respect restrictions such as **minimum margin, available inventory, commercial policy, and exposure rules.** Then, the company needs to monitor actual versus recommended results, perform backtests, adjust assumptions, and recalibrate models as new data comes in.
1143
1144This process avoids two common pitfalls: relying too much on a recommendation without explainability or limiting AI to a decorative layer, without real impact on the decision.
1145
1146Well-implemented AI Pricing does not take control from the company.
1147
1148It increases the quality of control.
1149
1150## How to know if your operation is ready for AI Pricing
1151
1152The journey doesn't have to start with the entire catalog.
1153
1154The best starting point is usually where there is the greatest tension between margin, turnover, and conversion.
1155
1156Some questions help to find this cut:
1157
1158- Which categories have the greatest margin pressure?
1159- Which SKUs are frequently discounted, but without clarity of necessity?
1160- Where is inventory aging?
1161- Where does markdown arrive too late?
1162- Which products have high demand variation?
1163- Where does decision still depend on spreadsheets and manual cycles?
1164- What data already exists, but has not yet become actionable recommendations?
1165
1166From there, it is possible to start with a pilot, validate impact, create governance, and scale.
1167
1168## The future of pricing is less reaction and more decision
1169
1170E-commerce doesn't need another layer of complexity. It needs better decisions.
1171
1172Pricing has ceased to be a routine of table updates. It has become a commercial intelligence system that connects data, margin, inventory, demand, conversion, and governance.
1173
1174A company that decides prices too late loses money even when the system seems to be working.
1175
1176And that's precisely where AI Pricing gains relevance: not as a generic promise of automation, but as a practical way to transform data into faster, measurable, and results-aligned commercial decisions.
1177
1178**infinity6 will be at [Fórum E-Commerce Brasil](https://eventos.ecommercebrasil.com.br/forum), from July 28 to 30, as one of the finalists of the RocketGen Contest, to present its AI Pricing solution and talk to companies that want to treat price as what it truly is: A direct lever for margin, turnover, and growth.**
1179
1180Price is not a table. It's a business decision.`,MEe=`---
1181id: 4c011a83-e7e4-42de-9eb1-b7d980f98867
1182title: "How to boost POS ticket by +23% and new SKU conversion by +36%?"
1183slug: conversion-ai-average-ticket-infinity6
1184language: en
1185type: i6 Social
1186cluster: "Increase Average Ticket Conversion infinity6"
1187featured: false
1188gated: false
1189date: 2026-03-31T12:00:00.000Z
1190cover_image: "/content/insights/conversion-ai-average-ticket-infinity6.webp"
1191external_url: "https://www.linkedin.com/feed/update/urn:li:activity:7444745234313326592"
1192excerpt: "More precision per POS? Commercial anticipation? Integrated vision without noise? \\nCheck out the details of this success story with EMS in our LinkedIn post."
1193---`,$Ee=`---
1194id: cfade6eb-9f47-4307-9112-f8e1742bbfe8

1195title: "Generic efficiency is no longer enough to generate real results."
1196slug: efficiency-applied-anticipate-demand-infinity6
1197language: en
1198type: i6 on Media
1199cluster: "Forecasting Applied AI"
1200featured: false
1201gated: false
1202date: 2025-05-27T12:00:00.000Z
1203cover_image: "/content/insights/efficiency-applied-anticipate-demand-infinity6.webp"
1204external_url: "https://mercadoeconsumo.com.br/27/06/2025/tecnologia/nova-era-da-ia-eficiencia-generica-ja-nao-basta-para-gerar-resultado-real-nos-negocios/"
1205excerpt: "EMS's partnership with Infinity6 scalates applied AI that connects the entire product journey and anticipates demand with high precision."
1206---`,FEe=`---
1207id: 830d2f61-0ea4-41a6-877b-5cfd57682d39
1208title: "Purchase intention signals: how to turn behavior into conversion before the competitor"
1209slug: intention-signals-predictive-ai
1210language: en
1211type: i6 Article
1212featured: true
1213gated: false
1214date: 2026-08-28T12:00:00.000Z
1215read_time: 4
1216cover_image: "/content/insights/intention-signals-predictive-ai.jpg"
1217cta_form: true
1218cta_form_text: "**Pronto para aplicar isso na realidade da sua empresa?** É só preencher o formulário abaixo."
1219theme: "propensao-conversao"
1220theme_label: "Conversion Propensity"
1221excerpt: "Your customer may not say they intend to buy. But they are already leaving clues."
1222---
1223A specific search. Returning to the same page. Comparing products. Checking price or availability. Separately, each behavior says little. Together and in the right context, they reveal an undeclared intention — and an opportunity that can quickly disappear.
1224
1225**Intention signals** are behaviors and contexts that indicate the probability of a customer buying, hiring, or responding to an offer. For technology companies, financial institutions, and consumer goods industries, the challenge is to interpret them while there is still time to influence the decision.
1226
1227# Consumers no longer follow a predictable funnel
1228
1229The customer journey is no longer linear. A [survey published by Google](https://business.google.com/br/think/consumer-insights/empowered-customer-report/) in April 2026 shows that Search and YouTube are present in 84% of journeys where Brazilians discovered a new brand, product, or retailer. Furthermore, 86% state they are open to trying new brands while researching.
1230
1231AI makes this behavior even more complex. Queries in Google's AI Mode are, on average, three times longer, indicating more contextual searches. Discovery, comparison, and decision begin to happen in the same environment.
1232
1233In practice, intention appears before conversion and changes rapidly. A person may have a high propensity but need installment payments; seek a discount even with a high income; or be ready to convert without a coupon.
1234
1235Therefore, intention should not be confused with a click or demographic profile.
1236
1237It is worth noting that **identifying intention does not mean exposing personal data or monitoring consumers indiscriminately**. Propensity models can operate with anonymized identifiers, aggregated data, and behavioral variables, respecting purpose, minimization, and governance. In regulated sectors, the explainability of the recommendation is as important as its accuracy.
1238
1239# An isolated signal is not enough. The combination is what generates intelligence
1240
1241The most valuable signals combine:
1242
12431. **Recent behavior**: searches, comparisons, abandonments, and returns.
12442. **Context**: channel, category, occasion, price, stock, and moment.
12453. **Ability to act**: financial condition, credit, availability, and necessary incentive.
1246
1247According to [Meio & Mensagem](https://www.meioemensagem.com.br/marketing/black-friday-81-dos-brasileiros-querem-incentivos-nas-compras), for Black Friday 2026, 48% of consumers who intended to buy already had a defined list and 44% had started researching prices. At the same time, 51% had their income committed.
1248
1249The intention existed, but conversion still depended on the right condition.
1250
1251On Father's Day, [Serasa Experian](https://www.serasaexperian.com.br/sala-de-imprensa/servicos-de-marketing/consumidor-mais-propenso-a-comprar-no-dia-dos-pais-e-3-vezes-mais-sensivel-a-descontos-que-a-media-dos-brasileiros-revela-serasa-experian/) identified 11.7 million Brazilians with a higher propensity to buy. Of these, 57.8% were discount hunters — three times the average — although 26.9% had an income above R$ 8,000.
1252
1253Income and intention, alone, do not reveal which offer will be most efficient.
1254
1255![](/content/insights/blog-sinais-de-intencao-ia-preditiva/en/body-1787942835851csa6.webp)
1256
1257# What intention signals change in each market
1258
1259In **technology**, recurring use, price inquiries, and integrations or a drop in engagement can indicate a propensity for upgrade, cross-sell, or churn.
1260
1261In the **financial market**, simulations, transactions, affinities, and responses to campaigns help identify who might take out credit, insurance, investments, or other products — and what argument to use.
1262
1263In **consumer goods**, searches, sell-out, recurrence, price, availability, and seasonality anticipate purchases and changes in demand by SKU, region, or POS. The signal guides marketing, assortment, supply, and pricing.
1264
1265In all these sectors, the gain is in transforming signals into decisions: **who to activate, when, with what offer, channel, and incentive**.
1266
1267# How infinity6 transforms signals into predictive decisions
1268
1269infinity6 connects navigation, search, purchase, cart, channel, product, price, stock, context, and history to identify undeclared intentions and recommend the next best action.
1270
1271The **i6 RecSys** calculates propensity, affinity, churn risk, and cross-sell or up-sell opportunities — including for anonymous traffic.
1272

1273The **i6 ElasticPrice** distinguishes who needs an incentive from who would buy at full price, calibrating discount, bundle, or value proposition based on elasticity and margin.
1274
1275The **i6 Previsio** connects behavior to future demand to guide stock, mix, and supply.
1276
1277The **i6 Signal** delivers explainable recommendations to CRM, e-commerce, app, WhatsApp, media, POS, or sales team.
1278
1279In projects already carried out, this approach generated:
1280
1281- **12 times more conversion in campaigns;**
1282- **57% reduction in messaging costs;**
1283- **40% incremental revenue with predictive curation in fashion retail;**
1284- **23% increase in average ticket per POS.**
1285
1286[Learn about infinity6's Growth & Consumer Intelligence solution](https://infinity6.ai/pt/solutions#territory-growth).
1287
1288# The cost of interpreting intention too late
1289
1290Generic campaigns, static clusters, and rules like “abandoned cart, send coupon” lead to three losses: investment in audiences with low propensity, discounts for those who would buy without incentive, and a delayed approach to those who were ready to convert.
1291
1292Predictive AI changes this logic. Instead of just asking what the customer did, it estimates what they are likely to do now and prescribes the most promising action.
1293
1294If your company already collects customer, transaction, and channel data, but still decides based on the past, the opportunity is probably already within the operation.
1295
1296The next step is to discover which signals anticipate conversion — and how much value can be captured by acting sooner.
1297
1298# Frequently asked questions about intention signals
1299
1300## What are intention signals?
1301
1302They are behaviors and contexts that help estimate the likelihood of a person buying, hiring, or responding to an offer, such as searches, recurring visits, comparisons, simulations, cart, abandonment, and purchase history.
1303
1304## Which signals indicate greater purchase intention?
1305
1306The strongest ones involve increasing commitment: returning to the product, comparing conditions, checking availability, simulating a contract, initiating checkout, or negotiating an offer. Their strength depends on recency, frequency, sequence, and context.
1307
1308## What is the difference between segmentation and purchase propensity?
1309
1310Segmentation groups similar people. Propensity estimates the probability of a specific action in a given context and moment. Two people from the same segment can have different probabilities.
1311
1312## Is it possible to identify intention in anonymous visitors?
1313
1314Yes. Session, origin, device, navigation, search, and recurrence can indicate intention even without login or identified history.
1315
1316# Are you ready to transform intention signals into revenue, margin, and commercial efficiency?
1317
1318Talk to infinity6 and perform a free backtest to find out where your company is losing money.`,REe=`---
1319id: 880f9234-fcb5-4c09-bef2-555bb7c51877
1320title: "Where is your margin being drained right now?"
1321slug: margin-prediction-AI-demand
1322language: en
1323type: i6 Social
1324cluster: "Anticipating demand increases margin and efficiency."
1325featured: false
1326gated: false
1327date: 2026-04-10T12:00:00.000Z
1328cover_image: "/content/insights/margin-prediction-AI-demand.png"
1329external_url: "https://www.linkedin.com/feed/update/urn:li:activity:7448480926738620416"
1330excerpt: "Idle data is the competition's profit. Anticipating demand with AI is no longer a luxury, it's survival. See how we transform signals into nine-figure results."
1331---`,DEe=`---
1332id: 11f16d0e-9e5d-437c-8213-ad3dd0068d87
1333title: "infinity6 is mentioned on \\"Fato Relevante\\", a CNN program"
1334slug: media-cnn-infinity6
1335language: en
1336type: i6 on Media
1337cluster: "Applied AI"
1338featured: true
1339gated: false
1340date: 2026-05-11T12:00:00.000Z
1341cover_image: "/content/insights/media-cnn-infinity6.webp"
1342external_url: "https://www.youtube.com/watch?v=eEaJ_TZIOKU"
1343excerpt: "infinity6 was mentioned on Fato Relevante, a CNN Brasil program in partnership with NeoFeed, as one of the few Brazilian companies building applied, proprietary AI focused on real business outcomes."
1344---`,LEe=`---
1345id: bd93c4fb-dfee-4553-b722-0342e128f97a
1346title: "Everton Gago, co-founder of infinity6, is interviewed by NeoFeed on Brazil’s AI race"
1347slug: media-neofeed-everton-gago-brazilian-ai
1348language: en
1349type: i6 on Media
1350cluster: "Applied AI"
1351featured: false
1352gated: false
1353date: 2026-04-30T12:00:00.000Z
1354cover_image: "/content/insights/media-neofeed-everton-gago-brazilian-ai.png"
1355external_url: "https://neofeed.com.br/startups/brasil-tem-50-chatgpts-com-sotaque-brasileiro-eles-vao-sobreviver-

1355a-corrida-da-ia/"
1356excerpt: "In a feature on Brazil’s artificial intelligence race, NeoFeed interviewed Everton Gago, co-founder of infinity6, about proprietary models trained with customer data, specialized AI and real-world applications in recommendation, forecasting and pricing."
1357---`,zEe=`---
1358id: 89611b45-9de4-4e96-af52-838954ab48d6
1359title: "infinity6 is one of the guest speakers at the 2nd edition of the Agibank Meetup."
1360slug: meetup-agibank-data-infinity6
1361language: en
1362type: i6 on Media
1363cluster: "Meetup Data AI infinity6"
1364featured: false
1365gated: false
1366date: 2026-02-26T12:00:00.000Z
1367cover_image: "/content/insights/meetup-agibank-data-infinity6.png"
1368external_url: "https://www.linkedin.com/feed/update/urn:li:activity:7433179113617559553/"
1369excerpt: "The 2nd edition of the Agibank Meetup promoted a relevant meeting about data, artificial intelligence, and scalable systems, bringing together professionals for the exchange of experiences, networking, and applied technical discussion."
1370---`,BEe=`---
1371id: 10ea9f2c-f897-41eb-bb73-82b85b511b58
1372title: "Where is your company losing money because decisions are happening too late?"
1373slug: where-your-company-is-losing-money
1374language: en
1375type: i6 Article
1376featured: false
1377gated: false
1378date: 2026-07-08T12:00:00.000Z
1379read_time: 4
1380cover_image: "/content/insights/where-your-company-is-losing-money.jpg"
1381cta_form: true
1382cta_form_text: "**Want to apply this to your business?** Just fill out the form below."
1383theme: "previsao-demanda"
1384theme_label: "Demand Forecasting"
1385excerpt: "Artificial intelligence is already on retail's radar."
1386---
1387AI appears in more personalized campaigns, product recommendations, automated service, behavior analysis, and new forms of customer relationship. All of this has value. But there is a less visible, and often more critical, layer where AI can have a direct impact on results: demand, inventory, and supply chain.
1388
1389A recent [CartaCapital](https://www.cartacapital.com.br/do-micro-ao-macro/varejo-ia-estoque-logistica/) article about the use of AI in retail highlighted precisely this point. Many companies have advanced in marketing and customer service but still have a great opportunity in demand forecasting, logistics, and inventory management.
1390
1391And this opportunity is not just technological.
1392
1393**It's financial.**
1394
1395Because much of the money a company loses doesn't appear as an obvious error. It appears as a sale that didn't happen. A product that was out of stock. Excess inventory. A purchasing decision made too late. An allocation that didn't keep up with real demand. A margin pressured by discounts that could have been avoided.
1396**In retail, deciding late is expensive.**
1397
1398![](/content/insights/onde-sua-empresa-esta-perdendo-dinheiro/en/body-1783479330783qub4.png)
1399
1400## The problem starts before the stockout.
1401
1402When a product is missing from the shelf, the problem has already occurred. The sale was lost. The customer may have chosen another brand. The campaign effort may have generated demand for an unavailable item. The operation, at this point, is already reacting.
1403
1404**The same happens with excess inventory.** When the company realizes it bought too much or allocated poorly, part of the loss has already formed. Capital is tied up. Space is occupied. Future margin is threatened. And the correction usually comes through discounts, emergency transfers, or increased operational costs.
1405
1406>The important question is not just, "what happened?" The right question is: **"what signs already indicated this was going to happen?"**
1407
1408This is the difference between a reactive operation and a predictive operation.
1409
1410## Retail doesn't suffer from a lack of data.
1411
1412Most companies already have a lot of data: sales by SKU; inventory turnover by store; available inventory; stockout history; margin by category; campaigns; price; channel; region; seasonality; lead time; purchasing behavior; sell-in; sell-out...
1413
1414The challenge isn't to generate more information. The challenge is to transform information into decisions before the problem turns into a loss.
1415
1416When these signals are scattered across systems, reports, and spreadsheets, the company can glimpse the past. But it takes time to anticipate what needs to be done now.
1417
1418And, in a market with pressured margins, demand volatility, and increasingly complex operations, this delay impacts cash flow.
1419
1420## Predictive AI shifts the decision-making center
1421
1422Predictive AI applied to retail allows moving from a logic based solely on history and averages to a logic guided by signals, patterns, and actionable recommendations. This means **forecasting demand behavior with greater granularity by product, store, channel, and period**.
1423
1424But forecasting, alone, is not enough.
1425
1426The real value emerges when forecasting helps to make better decisions.
1427
1428- What to buy.
1429- How much to buy.
1430- Where to allocate.
1431- When to replenish.
1432- Which SKU has the highest risk of stockout.
1433- Where excess inventory is forming.
1434- Which store needs adjustment.
1435- Which decision best protects revenue, margin, turnover, and service level.
1436
1437This is the most important frontier of AI in retail: **moving from diagnosis to operational decision.**
1438
1439## The direct impact on the business
1440
1441When a company anticipates demand more accurately, it reduces the chance of losing sales due to product stockouts.
1442
1443When it improves allocation, it places inventory where there is greater potential for turnover.
1444
1445When it identifies excess before it consolidates, it avoids tied-up capital and reduces the need for aggressive discounts.
1446
1447When it connects demand, inventory, margin, and service level, it stops making isolated decisions and starts operating with a more intelligent business vision.
1448
1449The impact appears on four main fronts:
1450
1451- **Revenue**, because there are fewer lost sales due to stockouts.
1452- **Margin**, because the company reduces expensive corrections, markdowns, emergency decisions, and makes capital more efficient.
1453- **Working capital**, because it buys and distributes with greater precision.
1454- **Operational efficiency**, because decision-making becomes faster, more granular, and guided by real behavioral data.
1455
1456>

1456 Ultimately, **AI only generates value when it changes an important decision.**
1457
1458And few decisions are as important for retail as forecasting demand, replenishing correctly, and protecting margins.
1459
1460## The question leaders should be asking now
1461
1462Many companies still start the conversation about AI by asking what tool they should use. This should not be the first question.
1463
1464The more strategic question is:
1465
1466> Where are we losing money because our decisions happen too late?
1467
1468The answer may lie:
14691. In the stockout of high-turnover products;
14702. In excess inventory in slow-moving categories;
14713. In incorrect allocation among stores, distribution centers, e-commerce, and marketplaces;
14724. In the difficulty of adjusting assortments by region;
14735. In the delay in perceiving changes in consumer behavior.
1474
1475Or simply in the *gap between what the data shows and what the operation can execute*.
1476
1477When this question is answered clearly, **AI ceases to be a generic innovation project and becomes a concrete lever for efficiency, growth, and profitability**.
1478
1479## The next frontier of AI in retail will be operational
1480
1481AI applied to marketing and customer service will continue to evolve. It improves relationships, personalization, and productivity.
1482
1483But one of the biggest opportunities in retail lies behind the scenes, in:
1484- Better forecasting.
1485- Better purchasing.
1486- Better allocation.
1487- Better replenishment.
1488- Avoiding stockouts.
1489- Reducing excess.
1490- Protecting margins.
1491
1492The next frontier of artificial intelligence in retail will not just be selling more. It will be preventing sales already created by demand from being lost due to a decision made too late.
1493
1494It is precisely at this point that infinity6 operates: *transforming demand and supply data into predictive, actionable decisions connected to the real business impact.*
1495
1496Because **the value of AI is not in appearing innovative, but in helping the company act before a loss occurs.**`,qEe=`---
1497id: 9b2bd3a9-6259-4c57-bb0e-37aafb0ef0df
1498title: "Preço certo na planilha, margem errada no resultado: o novo desafio do pricing no e-commerce"
1499slug: ai-pricing-ecommerce-margem-giro-conversao
1500language: pt
1501type: i6 Article
1502featured: true
1503gated: false
1504date: 2026-07-16T12:00:00.000Z
1505read_time: 6
1506cover_image: "/content/insights/ai-pricing-ecommerce-margem-giro-conversao.png"
1507cta_form: true
1508cta_form_text: "**Pronto para aplicar isso na realidade da sua empresa?** É só preencher o formulário abaixo."
1509theme: "preco-elasticidade"
1510theme_label: "Preço e elasticidade"
1511excerpt: "Entenda como AI Pricing ajuda e-commerces a transformar preço em uma decisão dinâmica, conectando margem, giro, conversão, estoque e governança."
1512---
1513No e-commerce, um preço pode estar tecnicamente correto e comercialmente errado ao mesmo tempo.
1514
1515Ele pode respeitar a margem mínima. Pode estar alinhado à tabela comercial. Pode parecer competitivo quando comparado ao concorrente. Pode ter sido aprovado em reunião e ainda assim deixar dinheiro na mesa.
1516
1517**Isso acontece porque preço não é uma variável isolada. É uma decisão que muda conforme o contexto.**
1518
1519Muda com o estoque. Muda com a elasticidade de cada SKU. Muda com o giro da categoria. Muda com a origem do tráfego. Muda com a intenção de compra. Muda com a sazonalidade. Muda com a pressão competitiva. Muda com a meta de margem.
1520
1521Quando todos esses sinais são analisados de forma lenta, manual ou pouco granular, o problema raramente aparece como “erro de preço”. Ele aparece no resultado: margem comprimida, desconto desnecessário, estoque parado, markdown tardio, conversão perdida e capital preso.
1522
1523A questão é que muitas empresas ainda tratam pricing como tabela, quando ele já se tornou uma alavanca financeira.
1524
1525## Por que o modelo tradicional de pricing ficou curto
1526
1527Durante muito tempo, precificar com base em histórico, margem mínima, reuniões periódicas e acompanhamento pontual da concorrência parecia suficiente.
1528
1529Mas o e-commerce opera em outro ritmo.
1530
1531A demanda muda ao longo do dia. A concorrência ajusta preço com mais frequência. O consumidor compara opções em segundos. O estoque envelhece enquanto a decisão ainda está sendo discutida. Campanhas, canais e comportamentos de navegação criam contextos diferentes para o mesmo produto.
1532
1533Nesse cenário, decidir preço apenas por média, histórico ou regra fixa cria distorções.
1534
1535Um SKU com alta demanda e baixa sensibilidade a preço pode estar cedendo margem sem necessidade. Outro, com estoque pressionado e baixa velocidade de giro, pode precisar de uma estratég

1535ia de markdown mais precisa. Um terceiro pode ter boa conversão em determinado contexto de sessão, mas perder receita quando tratado com a mesma lógica aplicada a todo o catálogo.
1536
1537O problema não é falta de dados.
1538O problema é transformar dados em decisão antes que a oportunidade passe.
1539
1540## A tensão real: margem, giro e conversão
1541
1542Toda decisão de preço carrega uma troca.
1543
1544Se o preço sobe, a margem pode melhorar, mas a conversão pode cair. Se o preço baixa, a conversão pode subir, mas a margem pode ser destruída. Se o produto não gira, o estoque envelhece. Se o markdown entra cedo demais, a empresa ensina o mercado a esperar desconto. Se entra tarde demais, o custo já foi absorvido pela operação.
1545
1546É por isso que a pergunta “qual preço devemos colocar?” é limitada.
1547
1548A pergunta mais importante é:
1549
1550> Qual decisão de preço gera o melhor resultado possível para este produto, neste contexto, respeitando as restrições do negócio?
1551
1552Essa mudança de pergunta é o que separa pricing operacional de pricing inteligente.
1553
1554## O que AI Pricing muda na prática
1555
1556AI Pricing não significa deixar a IA alterar preços sem critério.
1557
1558Significa usar inteligência artificial para ampliar a capacidade de análise e recomendação, considerando mais variáveis do que uma equipe conseguiria processar manualmente com a mesma frequência e granularidade.
1559
1560**Na prática, a IA pode ajudar a estimar elasticidade por SKU, projetar demanda, simular cenários, identificar oportunidades de captura de margem, calibrar markdown, recomendar faixas de preço e medir o resultado das decisões antes de escalar.**
1561
1562O ponto central é que a recomendação precisa operar dentro das regras do negócio.
1563
1564Margem mínima, estoque disponível, política comercial, banda competitiva, regras de exposição e governança não são detalhes. São restrições que definem o espaço seguro de decisão.
1565

1566A IA não substitui a estratégia comercial. Ela ajuda a executar essa estratégia com mais velocidade, precisão e rastreabilidade.
1567
1568## Três formas de aplicar AI Pricing no e-commerce
1569
1570Na infinity6, a visão de AI Pricing parte de três frentes complementares: margem, giro e conversão.
1571
1572### 1. Price-to-Margin: capturar margem sem romper a estratégia comercial
1573
1574Nem todo produto precisa de desconto para vender melhor. E nem todo preço competitivo maximiza resultado.
1575
1576O Price-to-Margin busca identificar oportunidades de captura de margem por SKU, considerando elasticidade, forecast, histórico de preço e volume, concorrência, estoque e restrições comerciais.
1577
1578**A lógica é simples: se dois produtos têm comportamentos diferentes, eles não deveriam ser tratados pela mesma regra.**
1579
1580Com uma visão mais granular, a empresa consegue entender onde há espaço para preservar ou ampliar margem sem comprometer a estratégia comercial.
1581
1582### 2. Price-to-Turnover: acelerar giro sem transformar desconto em piloto automático
1583
1584Estoque parado é margem sob pressão.
1585
1586Mas resolver giro apenas com desconto pode corroer rentabilidade. O desafio está em entender quando, onde e com qual intensidade ajustar preço para movimentar produto sem destruir valor.
1587
1588**O Price-to-Turnover considera ciclo de vida do produto, velocidade de venda, sazonalidade, estoque, região, loja, categoria ou cluster para recomendar preço e régua de markdown com mais precisão.**
1589
1590A decisão deixa de ser “baixar preço porque não vendeu” e passa a ser “qual movimento preserva melhor o equilíbrio entre giro e margem?”.
1591
1592### 3. Price-to-Conversion: decidir com base no contexto da sessão
1593
1594No e-commerce, duas sessões nunca são exatamente iguais.
1595
1596Um usuário recorrente, vindo de uma campanha específica, navegando em determinado horário e demonstrando alta intenção de compra não tem o mesmo contexto de um visitante novo, sem histórico e sem sinais claros de conversão.
1597
1598**O Price-to-Conversion usa sinais da sessão, histórico do usuário, origem do canal, estoque, demanda em tempo real e regras comerciais para apoiar recomendações mais contextuais.**
1599
1600O objetivo não é simplesmente converter mais. É aumentar conversão e receita por sessão sem abrir mão da margem.
1601
1602## Governança é o que torna a IA aplicável
1603
1604A maior objeção à IA em pricing é legítima: preço é sensível demais para virar experimento sem controle.
1605
1606Por isso, governança precisa estar no desenho da solução, não no discurso.
1607
1608Antes de recomendar qualquer ação, o modelo deve respeitar restrições como **margem mínima, estoque disponível, política comercial e regras de exposição.** Depois, a empresa precisa acompanhar resultado real versus recomendado, realizar backtests, ajustar premissas e recalibrar modelos conforme novos dados entram.
1609
1610Esse processo evita duas armadilhas comuns: confiar demais em uma recomendação sem explicabilidade ou limitar a IA a uma camada decorativa, sem impacto real na decisão.
1611
1612AI Pricing bem implementado não tira controle da empresa.
1613
1614Ele aumenta a qualidade do controle.
1615
1616## Como saber se sua operação está pronta para AI Pricing
1617
1618A jornada não precisa começar pelo catálogo inteiro.
1619
1620O melhor ponto de partida costuma estar onde existe maior tensão entre margem, giro e conversão.
1621
1622Algumas perguntas ajudam a encontrar esse recorte:
1623
1624- Quais categorias têm maior pressão de margem?
1625- Quais SKUs recebem desconto com frequência, mas sem clareza de necessidade?
1626- Onde o estoque está envelhecendo?
1627- Onde o markdown chega tarde demais?
1628- Quais produtos têm alta variação de demanda?
1629- Onde a decisão ainda depende de planilhas e ciclos manuais?
1630- Quais dados já existem, mas ainda não viram recomendação acionável?
1631

1632A partir daí, é possível começar com um piloto, validar impacto, criar governança e escalar.
1633
1634## O futuro do pricing é menos reação e mais decisão
1635
1636O e-commerce não precisa de mais uma camada de complexidade. Precisa de decisões melhores.
1637
1638Pricing deixou de ser uma rotina de atualização de tabela. Virou um sistema de inteligência comercial que conecta dados, margem, estoque, demanda, conversão e governança.
1639
1640A empresa que decide preço tarde demais perde dinheiro mesmo quando o sistema parece estar funcionando.
1641
1642E é justamente aí que AI Pricing ganha relevância: não como promessa genérica de automação, mas como uma forma prática de transformar dados em decisões comerciais mais rápidas, mensuráveis e alinhadas ao resultado.
1643
1644
1645
1646
1647**A infinity6 estará no [Fórum E-Commerce Brasil](https://eventos.ecommercebrasil.com.br/forum), de 28 a 30 de julho, como uma das finalistas do Concurso RocketGen, para apresentar sua solução de AI Pricing e conversar com empresas que querem tratar preço como aquilo que ele realmente é: Uma alavanca direta de margem, giro e crescimento.**
1648
1649Preço não é tabela. É decisão de negócio.`,UEe=`---
1650id: ffad2f35-edef-4e0e-a0d0-03753912fcd9
1651title: "Como elevar o ticket por PDV em +23% e a conversão de novos SKUs em +36%?"
1652slug: conversao-ia-ticket-medio-infinity6
1653language: pt
1654type: i6 Social
1655cluster: "Aumento Conversão Ticket Médio infinity6"
1656featured: false
1657gated: false
1658date: 2026-03-31T12:00:00.000Z
1659cover_image: "/content/insights/conversao-ia-ticket-medio-infinity6.webp"
1660external_url: "https://www.linkedin.com/feed/update/urn:li:activity:7444745234313326592"
1661excerpt: "Mais precisão por PDV? Antecipação comercial? Visão integrada sem ruído?\\nConfira os detalhes desse case de sucesso com a EMS em nosso post no Linkedin."
1662---`,WEe=`---
1663id: 19029a9f-4976-4076-88f3-cac529a99907
1664title: "Eficiência genérica já não basta para gerar resultado real"
1665slug: eficiencia-ia-aplicada-antecipar-demanda
1666language: pt
1667type: i6 on Media
1668cluster: "Forecasting IA Aplicada"
1669featured: false
1670gated: false
1671date: 2025-05-27T12:00:00.000Z
1672cover_image: "/content/insights/eficiencia-ia-aplicada-antecipar-demanda.webp"
1673external_url: "https://mercadoeconsumo.com.br/27/06/2025/tecnologia/nova-era-da-ia-eficiencia-generica-ja-nao-basta-para-gerar-resultado-real-nos-negocios/"
1674excerpt: "A parceria da EMS com a Infinity6 amplia a aplicação de IA, conectando toda a jornada do produto e antecipando a demanda com alta precisão."
1675---`,VEe=`---
1676id: abd80d74-80c8-49d0-9221-6bfdf167293d
1677title: "Onde sua margem está sendo drenada agora?"
1678slug: margem-predição-ia-demanda
1679language: pt
1680type: i6 Social
1681cluster: "Antecipar demanda aumentando margem e eficiência"
1682featured: false
1683gated: false
1684date: 2026-04-10T12:00:00.000Z
1685cover_image: "/content/insights/margem-predição-ia-demanda.png"
1686external_url: "https://www.linkedin.com/feed/update/urn:li:activity:7448480926738620416"
1687excerpt: "Dados parados são o lucro da concorrência. Antecipar a demanda com IA não é mais um luxo, é sobrevivência. Veja como transformamos sinais em resultados de nove dígitos."
1688---`,HEe=`---
1689id: ba512e18-ec1a-4537-b190-df1e17c3939a
1690title: "infinity6 é citada no Fato Relevante, quadro da CNN Brasil"
1691slug: media-cnn-infinity6
1692language: pt
1693type: i6 on Media
1694cluster: "IA Aplicada"
1695featured: true
1696gated: false
1697date: 2026-06-01T12:00:00.000Z
1698read_time: 4
1699cover_image: "/content/insights/media-cnn-infinity6.webp"
1700external_url: "https://www.youtube.com/watch?v=eEaJ_TZIOKU"
1701excerpt: "A infinity6 foi citada no Fato Relevante, quadro da CNN Brasil em parceria com a NeoFeed, como uma das poucas empresas brasileiras desenvolvendo IA aplicada, proprietária e orientada a resultados reais de negócio."
1702---`,KEe=`---
1703id: 18926924-06d0-4a4e-bdab-2ad5b6c232a9
1704title: "Everton Gago, cofundador da infinity6, é entrevistado pelo NeoFeed sobre a corrida da IA brasileira"
1705slug: media-neofeed-everton-gago-ia-brasileira
1706language: pt
1707type: i6 on Media
1708cluster: "IA Aplicada"
1709featured: false
1710gated: false
1711date: 2026-04-30T12:00:00.000Z
1712cover_image: "/content/insights/media-neofeed-everton-gago-ia-brasileira.png"
1713external_url: "https://neofeed.com.br/startups/brasil-tem-50-chatgpts-com-sotaque-brasileiro-eles-vao-sobreviver-

1713a-corrida-da-ia/"
1714excerpt: "Em reportagem sobre a corrida da inteligência artificial no Brasil, o NeoFeed entrevistou Everton Gago, cofundador da infinity6, para falar sobre modelos proprietários treinados com dados dos clientes, IA especializada e aplicações reais em recomendação, previsão e precificação."
1715---`,GEe=`---
1716id: f16b555d-6e0f-4e67-bb62-a044782deac9
1717title: "infinity6 é um dos palestrantes convidados na 2ª edição do Meetup Agibank"
1718slug: meetup-agibank-dados-infinity6
1719language: pt
1720type: i6 on Media
1721cluster: "Meetup Dados IA infinity6"
1722featured: false
1723gated: false
1724date: 2026-02-26T12:00:00.000Z
1725cover_image: "/content/insights/meetup-agibank-dados-infinity6.png"
1726external_url: "https://www.linkedin.com/feed/update/urn:li:activity:7433179113617559553/"
1727excerpt: "A 2ª edição do Meetup Agibank promoveu um encontro relevante sobre dados, inteligência artificial e sistemas escaláveis, reunindo profissionais para troca de experiências, networking e discussão técnica aplicada."
1728---`,QEe=`---
1729id: 9913c519-12a3-4794-92f4-5f85573a095d
1730title: "Onde a sua empresa está perdendo dinheiro porque as decisões acontecem tarde demais?"
1731slug: onde-sua-empresa-esta-perdendo-dinheiro
1732language: pt
1733type: i6 Article
1734featured: false
1735gated: false
1736date: 2026-07-08T12:00:00.000Z
1737read_time: 4
1738cover_image: "/content/insights/onde-sua-empresa-esta-perdendo-dinheiro.jpg"
1739cta_form: true
1740cta_form_text: "**Pronto para aplicar isso na realidade da sua empresa?**           É só preencher o formulário abaixo."
1741theme: "previsao-demanda"
1742theme_label: "Previsão de demanda"
1743excerpt: "A inteligência artificial já está no radar do varejo."
1744---
1745A IA aparece em campanhas mais personalizadas, recomendações de produto, atendimento automatizado, análise de comportamento e novas formas de relacionamento com o consumidor. Tudo isso tem valor. Mas existe uma camada menos visível, e muitas vezes mais crítica, onde a IA pode gerar impacto direto no resultado: demanda, estoque e supply chain.
1746
1747Uma matéria recente da [CartaCapital](https://www.cartacapital.com.br/do-micro-ao-macro/varejo-ia-estoque-logistica/) sobre o uso de IA no varejo chamou atenção justamente para esse ponto. Muitas empresas avançaram em marketing e atendimento, mas ainda têm uma grande oportunidade em previsão de demanda, logística e gestão de estoque.
1748
1749E essa oportunidade não é apenas tecnológica.
1750
1751**É financeira.**
1752
1753Porque boa parte do dinheiro que uma empresa perde não aparece como um erro óbvio. Aparece como uma venda que não aconteceu. Um produto que faltou. Um estoque que sobrou. Uma decisão de compra tomada tarde. Uma alocação que não acompanhou a demanda real. Uma margem pressionada por descontos que poderiam ter sido evitados.
1754**No varejo, decidir tarde custa caro.**
1755
1756![](/content/insights/onde-sua-empresa-esta-perdendo-dinheiro/pt/body-1783479330783qub4.png)
1757
1758## O problema começa antes da ruptura.
1759
1760Quando um produto falta na prateleira, o problema já aconteceu. A venda foi perdida. O cliente pode ter escolhido outra marca. O esforço de campanha pode ter gerado demanda para um item indisponível. A operação, nesse momento, já está reagindo.
1761
1762**O mesmo acontece com o excesso de estoque.** Quando a empresa percebe que comprou demais ou alocou mal, parte do prejuízo já está formada. O capital ficou parado. O espaço foi ocupado. A margem futura ficou ameaçada. E a correção normalmente vem por meio de desconto, transferência emergencial ou aumento de custo operacional.
1763
1764>A pergunta importante não é apenas: “o que aconteceu?”. A pergunta certa é: **“quais sinais já indicavam que isso iria acontecer?”**
1765
1766Essa é a diferença entre uma operação reativa e uma operação preditiva.
1767
1768## O varejo não sofre por falta de dados.
1769
1770A maioria das empresas já tem muitos dados: vendas por SKU; Giro por loja; Estoque disponível; Histórico de ruptura; Margem por categoria; Campanhas; Preço; Canal; Região; Sazonalidade; Lead time; Comportamento de compra; Sell-in; Sell-out...
1771
1772O desafio não é gerar mais informação. O desafio é transformar informação em decisão antes que o problema vire perda.
1773
1774Quando esses sinais ficam espalhados em sistemas, relatórios e planilhas, a empresa até consegue enxergar o passado. Mas demora para antecipar o que precisa ser feito agora.
1775
1776E, em um mercado de margens pressionadas, volatilidade de demanda e operações cada vez mais complexas, esse atraso pesa no caixa.
1777
1778## A IA preditiva muda o centro da decisão
1779
1780A IA preditiva aplicada ao varejo permite sair de uma lógica baseada apenas em histórico e médias para uma lógica orientada por sinais, padrões e recomendações acionáveis. Isso significa **prever com mais granularidade o comportamento da demanda por produto, loja, canal e período**.
1781
1782Mas previsão, sozinha, não basta. 
1783
1784O valor real aparece quando a previsão ajuda a decidir melhor.
1785
1786- O que comprar.
1787- Quanto comprar.
1788- Onde alocar.
1789- Quando repor.
1790- Qual SKU tem maior risco de ruptura.
1791- Onde o excesso está se formando.
1792- Qual loja precisa de ajuste.
1793- Qual decisão protege melhor receita, margem, giro e nível de serviço.
1794
1795Essa é a fronteira mais importante da IA no varejo: **sair do diagnóstico e chegar à decisão operacional.**
1796
1797## O impacto direto no negócio
1798
1799Quando a empresa antecipa a demanda com mais precisão, ela reduz a chance de perder vendas por falta de produto.
1800
1801Quando melhora a alocação, coloca o estoque onde existe maior potencial de giro.
1802
1803Quando identifica excesso antes que ele se consolide, evita capital parado e reduz a necessidade de descontos agressivos.
1804
1805Quando conecta demanda, estoque, margem e nível de serviço, deixa de tomar decisões isoladas e passa a operar com uma visão mais inteligente do negócio.
1806
1807O impacto aparece em quatro frentes principais:
1808
1809- **Receita**, porque há menos perda de venda por ruptura.
1810- **Margem**, porque a empresa reduz correções caras, remarcações, decisões emergenciais e torna o capital mais eficiente.
1811- **Capital de giro**, porque compra e distribui com mais precisão.
1812- **Eficiência operacional**, porque a tomada de decisão fica mais rápida, granular e orientada por dados reais de comportamento.
1813
1814> No fim, a **IA só gera valor quando muda uma decisão importante.**
1815
1816E poucas decisões são tão importantes para o varejo quanto prever demanda, abastecer corretamente e proteger margem.
1817
1818## A pergunta que líderes deveriam fazer agora
1819
1820Muitas empresas ainda começam a conversa sobre IA perguntando qual ferramenta devem usar. Essa não deveria ser a primeira pergunta.
1821

1822A pergunta mais estratégica é:
1823
1824> Onde estamos perdendo dinheiro porque nossas decisões acontecem tarde demais?
1825
1826A resposta pode estar:
18271.  Na ruptura de produtos de alto giro; 
18282. No excesso de estoque em categorias com baixa saída; 
18293. Na alocação errada entre lojas, centros de distribuição, e-commerce e marketplace; 
18304. Na dificuldade de ajustar o sortimento por região; 
18315. Na demora para perceber mudanças de comportamento do consumidor.
1832
1833Ou simplesmente na *distância entre o que os dados mostram e o que a operação consegue executar*.
1834
1835Quando essa pergunta é respondida com clareza, **a IA deixa de ser um projeto genérico de inovação e passa a ser uma alavanca concreta de eficiência, crescimento e rentabilidade**.
1836
1837## A próxima fronteira da IA no varejo será operacional
1838
1839A IA aplicada a marketing e atendimento continuará evoluindo. Ela melhora relacionamento, personalização e produtividade.
1840
1841Mas uma das maiores oportunidades do varejo está nos bastidores, em:
1842- Prever melhor.
1843- Comprar melhor.
1844- Alocar melhor.
1845- Repor melhor.
1846- Evitar ruptura.
1847- Reduzir excesso.
1848- Proteger margem.
1849
1850A próxima fronteira da inteligência artificial no varejo não será apenas vender mais. Será impedir que a venda já criada pela demanda seja perdida por uma decisão tomada tarde demais.
1851
1852É extamente nesse ponto que a infinity6 atua: *transformando dados de demanda e supply em decisões preditivas, acionáveis e conectadas ao impacto real no negócio.*
1853
1854Porque **o valor da IA não está em parecer inovador, mas em ajudar a empresa a agir antes que a perda aconteça.**`,YEe=`---
1855id: d6b57dca-ef57-43aa-8a34-fd17693da292
1856title: "Sinais de intenção de compra: como transformar comportamento em conversão antes do concorrente"
1857slug: sinais-de-intencao-ia-preditiva
1858language: pt
1859type: i6 Article
1860featured: true
1861gated: false
1862date: 2026-08-28T12:00:00.000Z
1863read_time: 4
1864cover_image: "/content/insights/sinais-de-intencao-ia-preditiva.jpg"
1865cta_form: true
1866cta_form_text: "**Pronto para aplicar isso na realidade da sua empresa?** É só preencher o formulário abaixo."
1867theme: "propensao-conversao"
1868theme_label: "Propensão de conversão"
1869is_default: true
1870excerpt: "Seu cliente pode não dizer que pretende comprar. Mas ele já está deixando pistas."
1871---
1872Uma busca específica. O retorno à mesma página. A comparação entre produtos. A consulta ao preço ou à disponibilidade. Separadamente, cada comportamento diz pouco. Em conjunto e no contexto certo, revelam uma intenção ainda não declarada — e uma o

1872portunidade que pode desaparecer rapidamente.
1873
1874**Sinais de intenção** são comportamentos e contextos que indicam a probabilidade de um cliente comprar, contratar ou responder a uma oferta. Para empresas de tecnologia, instituições financeiras e indústrias de bens de consumo, o desafio é interpretá-los enquanto ainda existe tempo para influenciar a decisão.
1875
1876# O consumidor não segue mais um funil previsível
1877
1878A jornada de compra deixou de ser linear. Uma [pesquisa publicada pelo Google](https://business.google.com/br/think/consumer-insights/empowered-customer-report/) em abril de 2026 mostra que Busca e YouTube estão presentes em 84% das jornadas nas quais brasileiros descobriram uma nova marca, produto ou varejista. Além disso, 86% afirmam estar abertos a experimentar novas marcas enquanto pesquisam.
1879
1880A IA torna esse comportamento ainda mais complexo. As consultas no Modo IA do Google são, em média, três vezes mais longas, indicando pesquisas mais contextuais. Descoberta, comparação e decisão começam a acontecer no mesmo ambiente.
1881
1882Na prática, a intenção aparece antes da conversão e muda rapidamente. Uma pessoa pode ter alta propensão, mas precisar de parcelamento; buscar desconto mesmo com renda elevada; ou estar pronta para converter sem cupom.
1883
1884Por isso, intenção não deve ser confundida com um clique ou perfil demográfico.
1885
1886Vale ressaltar que **identificar intenção não significa expor dados pessoais ou monitorar o consumidor sem critérios**. Modelos de propensão podem operar com identificadores anonimizados, dados agregados e variáveis comportamentais, respeitando finalidade, minimização e governança. Em setores regulados, a explicabilidade da recomendação é tão importante quanto sua precisão.
1887
1888# O sinal isolado não basta. A combinação é o que gera inteligência
1889
1890Os sinais mais valiosos combinam:
1891
18921. **Comportamento recente**: buscas, comparações, abandonos e retornos.
18932. **Contexto**: canal, categoria, ocasião, preço, estoque e momento.
18943. **Capacidade de ação**: condição financeira, crédito, disponibilidade e incentivo necessário.
1895
1896De acordo com o [Meio & Mensagem](https://www.meioemensagem.com.br/marketing/black-friday-81-dos-brasileiros-querem-incentivos-nas-compras), para a Black Friday de 2026, 48% dos consumidores que pretendiam comprar já tinham uma lista definida e 44% haviam começado a pesquisar preços. Ao mesmo tempo, 51% estavam com a renda comprometida.
1897
1898A intenção existia, mas a conversão ainda dependia da condição certa. 
1899
1900No Dia dos Pais, a [Serasa Experian](https://www.serasaexperian.com.br/sala-de-imprensa/servicos-de-marketing/consumidor-mais-propenso-a-comprar-no-dia-dos-pais-e-3-vezes-mais-sensivel-a-descontos-que-a-media-dos-brasileiros-revela-serasa-experian/) identificou 11,7 milhões de brasileiros com maior propensão de compra. Desses, 57,8% eram caçadores de descontos — três vezes a média —, embora 26,9% tivessem renda acima de R$ 8 mil.
1901
1902Renda e intenção, sozinhas, não revelam qual oferta será mais eficiente.
1903
1904![](/content/insights/blog-sinais-de-intencao-ia-preditiva/pt/body-1787942835851csa6.webp)
1905
1906# O que os sinais de intenção mudam em cada mercado
1907
1908Em **tecnologia**, uso recorrente, consulta a preços e integrações ou queda de engajamento podem indicar propensão de upgrade, cross-sell ou churn.
1909
1910No **mercado financeiro**, simulações, movimentações, afinidades e respostas a campanhas ajudam a identificar quem pode contratar crédito, seguro, investimento ou outro produto — e qual argumento usar.
1911
1912Em **bens de consumo**, buscas, sell-out, recorrência, preço, disponibilidade e sazonalidade antecipam compras e mudanças de demanda por SKU, região ou PDV. O sinal orienta marketing, sortimento, abastecimento e pricing.
1913
1914Em todos esses setores, o ganho está em transformar sinais em decisão: **quem ativar, quando, com qual oferta, canal e incentivo**.
1915
1916# Como a infinity6 transforma sinais em decisões preditivas
1917
1918A infinity6 conecta navegação, busca, compra, carrinho, canal, produto, preço, estoque, contexto e histórico para identificar intenções não declaradas e recomendar a próxima melhor ação.
1919
1920O **i6 RecSys** calcula propensão, afinidade, risco de abandono e oportunidades de cross-sell ou up-sell — inclusive para tráfego anônimo.
1921
1922O **i6 ElasticPrice** distingue quem precisa de incentivo de quem compraria a preço cheio, calibrando desconto, bundle ou argumento de valor com base em elasticidade e margem.
1923
1924O **i6 Previsio** conecta comportamento à demanda futura para orientar estoque, mix e abastecimento. 
1925
1926O **i6 Signal** leva recomendações explicáveis ao CRM, e-commerce, app, WhatsApp, mídia, PDV ou time comercial.
1927
1928Em projetos já realizados, essa abordagem gerou:
1929
1930- **12 vezes mais conversão em campanhas;**
1931- **redução de 57% nos custos de mensageria;**
1932- **receita incremental de 40% com curadoria preditiva no varejo fashion;**
1933- **aumento de 23% no ticket médio por PDV.**
1934
1935[Conheça a solução de Crescimento & Inteligência de Consumidor da infinity6](https://infinity6.ai/pt/solutions#territory-growth).
1936
1937# O custo de interpretar a intenção tarde demais
1938

1939Campanhas genéricas, clusters estáticos e regras como “abandonou o carrinho, envie cupom” geram três perdas: investimento em públicos com baixa propensão, desconto para quem compraria sem incentivo e abordagem tardia de quem estava pronto para converter.
1940
1941A IA preditiva muda essa lógica. Em vez de perguntar apenas o que o cliente fez, estima o que ele está propenso a fazer agora e prescreve a ação mais promissora.
1942
1943Se sua empresa já reúne dados de clientes, transações e canais, mas ainda decide com base no passado, a oportunidade provavelmente já está dentro da operação.
1944
1945O próximo passo é descobrir quais sinais antecipam conversão — e quanto valor pode ser capturado ao agir antes.
1946
1947# Perguntas frequentes sobre sinais de intenção
1948
1949## O que são sinais de intenção?
1950
1951São comportamentos e contextos que ajudam a estimar a probabilidade de uma pessoa comprar, contratar ou responder a uma oferta, como buscas, visitas recorrentes, comparações, simulações, carrinho, abandono e histórico de compra.
1952
1953## Quais sinais indicam maior intenção de compra?
1954
1955Os mais fortes envolvem compromisso crescente: retornar ao produto, comparar condições, consultar disponibilidade, simular uma contratação, iniciar o checkout ou negociar uma oferta. Sua força depende de recência, frequência, sequência e contexto.
1956
1957## Qual é a diferença entre segmentação e propensão de compra?
1958
1959Segmentação agrupa pessoas semelhantes. Propensão estima a probabilidade de uma ação específica em determinado contexto e momento. Duas pessoas do mesmo segmento podem ter probabilidades diferentes.
1960
1961## É possível identificar intenção em visitantes anônimos?
1962
1963Sim. Sessão, origem, dispositivo, navegação, busca e recorrência podem indicar intenção mesmo sem login ou histórico identificado.
1964
1965# Você está pronto para transformar sinais de intenção em receita, margem e eficiência comercial?
1966
1967Fale com a infinity6 e realize um backtest gratuito para descobrir onde sua empresa está perdendo dinheiro.`,lD=`# i6 Intelligence — schema de conteúdo
1968
1969Este diretório alimenta o hub editorial **i6 Intelligence** em \`/i6-intelligence\`.
1970Cada peça é um arquivo Markdown com **frontmatter YAML** + corpo. O conteúdo é
1971varrido em tempo de build por:
1972
1973- \`src/hooks/useIntelligence.ts\` — runtime React (índice + página de artigo)
1974- \`scripts/prerender-seo-stubs.mjs\` — pré-render de stubs estáticos (SEO/GEO)
1975- \`public/sitemap.xml\` — entradas dinâmicas entre \`<!-- i6hub:intelligence-sitemap-start -->\` e \`<!-- ...-end -->\`
1976- \`public/llms.txt\` — listagem entre \`<!-- i6hub:intelligence-list-start -->\` e \`<!-- ...-end -->\`
1977
1978## Convenção de arquivo
1979
1980Cada peça vive em **par obrigatório** PT + EN:
1981
1982\`\`\`
1983src/content/intelligence/
1984  <slug>-pt.md
1985  <slug>-en.md
1986\`\`\`
1987
1988O \`<slug>\` é o mesmo nas duas versões e gera as rotas:
1989
1990- \`/pt/i6-intelligence/<slug>\`
1991- \`/en/i6-intelligence/<slug>\`
1992
1993## Frontmatter
1994
1995\`\`\`yaml
1996---
1997id: ruptura-gondola-ia-preditiva           # opcional, mesmo id nos dois idiomas
1998title: "Título da peça (= H1, pergunta JTBD)"
1999slug: ruptura-gondola-ia-preditiva         # idêntico nos dois idiomas
2000language: pt                                # 'pt' ou 'en'
2001date: 2026-06-12                            # ISO YYYY-MM-DD
2002sector: farma                               # ver tabela abaixo
2003theme: estoque                              # ver tabela abaixo
2004excerpt: "1 a 3 frases, citation magnet"   # 140–280 caracteres
2005read_time: 8                                # opcional, em minutos
2006featured: true                              # opcional, destaca no índice
2007cover_image: /lovable-uploads/xxx.png       # opcional, caminho relativo ou URL absoluta
2008gated: false                                # opcional, true → exige LeadGateForm antes de liberar o conteúdo
2009asset_url: /assets/research/xxx.pdf         # opcional, PDF liberado após o gate (também enviado por email pelo i6Hub)
2010related_product: i6previsio                 # opcional, âncora em /solutions
2011related_story_slug: marketplace-excellence-pharmacy   # opcional, link cruzado para case
2012---
2013\`\`\`
2014
2015### Valores aceitos para \`sector\`
2016
2017| valor | rótulo PT | rótulo EN |
2018|---|---|---|
2019| \`farma\` | Farma | Pharma |
2020| \`industria\` | Indústria | Industry |
2021| \`financeiro\` | Financeiro | Financial Services |
2022| \`varejo\` | Varejo | Retail |
2023| \`ecommerce\` | E-commerce | E-commerce |
2024| \`multissetor\` | Multissetor | Cross-sector |
2025
2026### Valores aceitos para \`theme\`
2027
2028| valor | rótulo PT | rótulo EN |
2029|---|---|---|
2030| \`demanda\` | Demanda | Demand |
2031| \`margem\` | Margem | Margin |
2032| \`estoque\` | Estoque | Inventory |
2033| \`mix\` | Mix & Sortimento | Mix & Assortment |
2034| \`propensao\` | Propensão | Propensity |
2035| \`cac\` | CAC | CAC |
2036
2037Use **sempre** os valores em português (à esquerda) — o filtro mapeia
2038automaticamente para o rótulo do idioma corrente.
2039
2040## Estrutura recomendada do corpo
2041
2042Para maximizar GEO (citações por LLMs) e SEO:
2043
2044\`\`\`md
2045## Resposta direta            # PT     |  ## Direct answer          # EN
20462 a 3 frases respondendo a pergunta do título. Inclui dado quantitativo
2047e o engine proprietário responsável (Previsio, ElasticPrice, RecSys).
2048
2049## O tamanho do problema      # PT     |  ## How big is the problem # EN
2050Dado de mercado em frase canônica:
2051"Segundo levantamentos mapeados pela infinity6, ..."
2052"According to data mapped by infinity6, ..."
2053
2054## Por que abordagens tradicionais falham
2055Por que ERP/planilha/regra fixa não resolve.
2056
2057## O motor proprietário da infinity6
2058Engine responsável + como combina com os outros.
2059
2060## Resultado anonimizado
2061Métricas anonimizadas (perfil de cliente, sem nome).
2062
2063## Perguntas frequentes       # PT     |  ## FAQ                    # EN
2064**Pergunta 1?**
2065Resposta em 1–3 frases.
2066
2067**Pergunta 2?**
2068Resposta em 1–3 frases.
2069
2070## Próximo passo
2071CTA editorial.
2072\`\`\`
2073
2074- O bloco **Perguntas frequentes** / **FAQ** é detectado automaticamente
2075  e vira \`FAQPage\` JSON-LD em build time. Use exatamente um destes dois
2076  títulos como H2 — variações ("Perguntas comuns", "Q&A") não são
2077  reconhecidas.
2078- Cada pergunta deve ser uma linha **em negrito** terminada em \`?\`.
2079- A resposta é o(s) parágrafo(s) imediatamente abaixo, até a próxima
2080  linha em branco seguida de outra pergunta em negrito ou outro H2.
2081
2082## Tamanho alvo
2083
2084- **1.500 a 2.500 palavras** por peça.
2085- **Excerpt**: 140 a 280 caracteres, frase que possa ser citada solta.
2086
2087## Cross-links (opcional)
2088
2089Links internos para \`/solutions\`, \`/success-stories\` ou outras peças do
2090hub usam caminho relativo ao idioma:
2091
2092\`\`\`md
2093[i6 Previsio](/pt/solutions)
2094[Success Story EMS](/pt/success-stories/ems-farma)
2095[Pricing dinâmico](/pt/i6-intelligence/precificacao-dinamica)
2096\`\`\`
2097
2098## Checklist antes de publicar
2099
2100- [ ] Par \`<slug>

2100-pt.md\` + \`<slug>-en.md\` existe
2101- [ ] \`slug\` idêntico nos dois arquivos
2102- [ ] \`language\` corresponde ao sufixo do arquivo
2103- [ ] \`sector\` e \`theme\` usam os valores da tabela acima
2104- [ ] H1 (campo \`title\`) é uma pergunta JTBD real de busca
2105- [ ] Resposta direta nos primeiros 2–3 parágrafos
2106- [ ] Pelo menos 1 dado em frase canônica "mapeados pela infinity6"
2107- [ ] Pelo menos 3 perguntas no bloco FAQ
2108- [ ] Atualizar \`public/sitemap.xml\` entre \`<!-- i6hub:intelligence-sitemap-start -->\` e \`<!-- ...-end -->\`
2109- [ ] Atualizar \`public/llms.txt\` entre \`<!-- i6hub:intelligence-list-start -->\` e \`<!-- ...-end -->\`
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Expected ${He.joinValues(e.options)}, received '${e.received}'`;break;case ae.invalid_arguments:n="Invalid function arguments";break;case ae.invalid_return_type:n="Invalid function return type";break;case ae.invalid_date:n="Invalid date";break;case ae.invalid_string:typeof e.validation=="object"?"includes"in e.validation?(n=`Invalid input: must include "${e.validation.includes}"`,typeof e.validation.position=="number"&&(n=`${n} at one or more positions greater than or equal to ${e.validation.position}`)):"startsWith"in e.validation?n=`Invalid input: must start with "${e.validation.startsWith}"`:"endsWith"in e.validation?n=`Invalid input: must end with "${e.validation.endsWith}"`:He.assertNever(e.validation):e.validation!=="regex"?n=`Invalid ${e.validation}`:n="Invalid";break;case ae.too_small:e.type==="array"?n=`Array must contain ${e.exact?"exactly":e.inclusive?"at least":"more than"} ${e.minimum} element(s)`:e.type==="string"?n=`String must contain ${e.exact?"exactly":e.inclusive?"at least":"over"} ${e.minimum} character(s)`:e.type==="number"?n=`Number must be ${e.exact?"exactly equal to ":e.inclusive?"greater than or equal to ":"greater than "}${e.minimum}`:e.type==="date"?n=`Date must be ${e.exact?"exactly equal to ":e.inclusive?"greater than or equal to ":"greater than "}${new Date(Number(e.minimum))}`:n="Invalid input";break;case ae.too_big:e.type==="array"?n=`Array must contain ${e.exact?"exactly":e.inclusive?"at most":"less than"} ${e.maximum} element(s)`:e.type==="string"?n=`String must contain ${e.exact?"exactly":e.inclusive?"at most":"under"} ${e.maximum} character(s)`:e.type==="number"?n=`Number must be ${e.exact?"exactly":e.inclusive?"less than or equal to":"less than"} ${e.maximum}`:e.type==="bigint"?n=`BigInt must be ${e.exact?"exactly":e.inclusive?"less than or equal to":"less than"} ${e.maximum}`:e.type==="date"?n=`Date must be ${e.exact?"exactly":e.inclusive?"smaller than or equal to":"smaller than"} ${new Date(Number(e.maximum))}`:n="Invalid input";break;case ae.custom:n="Invalid input";break;case ae.invalid_intersection_types:n="Intersection results could not be merged";break;case ae.not_multiple_of:n=`Number must be a multiple of ${e.multipleOf}`;break;case ae.not_finite:n="Number must be finite";break;default:n=t.defaultError,He.assertNever(e)}return{message:n}};let mD=Vc;function b5e(e){mD=e}function uv(){return mD}const dv=e=>

2113{const{data:t,path:n,errorMaps:r,issueData:a}=e,i=[...n,...a.path||[]],o={...a,path:i};if(a.message!==void 0)return{...a,path:i,message:a.message};let l="";const c=r.filter(u=>!!u).slice().reverse();for(const u of c)l=u(o,{data:t,defaultError:l}).message;return{...a,path:i,message:l}},w5e=[];function me(e,t){const n=uv(),r=dv({issueData:t,data:e.data,path:e.path,errorMaps:[e.common.contextualErrorMap,e.schemaErrorMap,n,n===Vc?void 0:Vc].filter(a=>!!a)});e.common.issues.push(r)}class Cn{constructor(){this.value="valid"}dirty(){this.value==="valid"&&(this.value="dirty")}abort(){this.value!=="aborted"&&(this.value="aborted")}static mergeArray(t,n){const r=[];for(const a of n){if(a.status==="aborted")return Ne;a.status==="dirty"&&t.dirty(),r.push(a.value)}return{status:t.value,value:r}}static async mergeObjectAsync(t,n){const r=[];for(const a of n){const i=await a.key,o=await a.value;r.push({key:i,value:o})}return Cn.mergeObjectSync(t,r)}static mergeObjectSync(t,n){const r={};for(const 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0:t.precision)>"u"?null:t==null?void 0:t.precision,offset:(n=t==null?void 0:t.offset)!==null&&n!==void 0?n:!1,local:(r=t==null?void 0:t.local)!==null&&r!==void 0?r:!1,...je.errToObj(t==null?void 0:t.message)})}date(t){return this._addCheck({kind:"date",message:t})}time(t){return typeof t=="string"?this._addCheck({kind:"time",precision:null,message:t}):this._addCheck({kind:"time",precision:typeof(t==null?void 0:t.precision)>"u"?null:t==null?void 0:t.precision,...je.errToObj(t==null?void 0:t.message)})}duration(t){return this._addCheck({kind:"duration",...je.errToObj(t)})}regex(t,n){return this._addCheck({kind:"regex",regex:t,...je.errToObj(n)})}includes(t,n){return this._addCheck({kind:"includes",value:t,position:n==null?void 0:n.position,...je.errToObj(n==null?void 0:n.message)})}startsWith(t,n){return this._addCheck({kind:"startsWith",value:t,...je.errToObj(n)})}endsWith(t,n){return this._addCheck({kind:"endsWith",value:t,...je.errToObj(n)})}min(t,n){return this._addCheck({kind:"min",value:t,...je.errToObj(n)})}max(t,n){return this._addCheck({kind:"max",value:t,...je.errToObj(n)})}length(t,n){return this._addCheck({kind:"length",value:t,...je.errToObj(n)})}nonempty(t){return this.min(1,je.errToObj(t))}trim(){return new pa({...this._def,checks:[...this._def.checks,{kind:"trim"}]})}toLowerCase(){return new pa({...this._def,checks:[...this._def.checks,{kind:"toLowerCase"}]})}toUpperCase(){return new pa({...this._def,checks:[...this._def.checks,{kind:"toUpperCase"}]})}get isDatetime(){return!!this._def.checks.find(t=>t.kind==="datetime")}get isDate(){return!!this._def.checks.find(t=>t.kind==="date")}get isTime(){return!!this._def.checks.find(t=>t.kind==="time")}get isDuration(){return!!this._def.checks.find(t=>t.kind==="duration")}get isEmail(){return!!this._def.checks.find(t=>t.kind==="email")}get isURL(){return!!this._def.checks.find(t=>t.kind==="url")}get isEmoji(){return!!this._def.checks.find(t=>t.kind==="emoji")}get isUUID(){return!!this._def.checks.find(t=>t.kind==="uuid")}get isNANOID(){return!!this._def.checks.find(t=>t.kind==="nanoid")}get isCUID(){return!!this._def.checks.find(t=>t.kind==="cuid")}get isCUID2(){return!!this._def.checks.find(t=>t.kind==="cuid2")}get isULID(){return!!this._def.checks.find(t=>t.kind==="ulid")}get isIP(){return!!this._def.checks.find(t=>t.kind==="ip")}get isBase64(){return!!this._def.checks.find(t=>t.kind==="base64")}get minLength(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxLength(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t}}pa.create=e=>{var t;return new pa({checks:[],typeName:Ae.ZodString,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...Fe(e)})};function F5e(e,t){const n=(e.toString().split(".")[1]||"").length,r=(t.toString().split(".")[1]||"").length,a=n>r?n:r,i=parseInt(e.toFixed(a).replace(".","")),o=parseInt(t.toFixed(a).replace(".",""));return i%o/Math.pow(10,a)}class qo extends Be{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte,this.step=this.multipleOf}_parse(t){if(this._def.coerce&&(t.data=Number(t.data)),this._getType(t)!==fe.number){const i=this._getOrReturnCtx(t);return me(i,{code:ae.invalid_type,expected:fe.number,received:i.parsedType}),Ne}let r;const a=new Cn;for(const i of this._def.checks)i.kind==="int"?He.isInteger(t.data)||(r=this._getOrReturnCtx(t,r),me(r,{code:ae.invalid_type,expected:"integer",received:"float",message:i.message}),a.dirty()):i.kind==="min"?(i.inclusive?t.data<i.value:t.data<=i.value)&&(r=this._getOrReturnCtx(t,r),me(r,{code:ae.too_small,minimum:i.value,type:"number",inclusive:i.inclusive,exact:!1,message:i.message}),a.dirty()):i.kind==="max"?(i.inclusive?t.data>i.value:t.data>=i.value)&&(r=this._getOrReturnCtx(t,r),me(r,{code:ae.too_big,maximum:i.value,type:"number",inclusive:i.inclusive,exact:!1,message:i.message}),a.dirty()):i.kind==="multipleOf"?F5e(t.data,i.value)!==0&&(r=this._getOrReturnCtx(t,r),me(r,{code:ae.not_multiple_of,multipleOf:i.value,message:i.message}),a.dirty()):i.kind==="finite"?Number.isFinite(t.data)||(r=this._getOrReturnCtx(t,r),me(r,{code:ae.not_finite,message:i.message}),a.dirty()):He.assertNever(i);return{status:a.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,je.toString(n))}gt(t,n){return this.setLimit("min",t,!1,je.toString(n))}lte(t,n){return this.setLimit("max",t,!0,je.toString(n))}lt(t,n){return this.setLimit("max",t,!1,je.toString(n))}setLimit(t,n,r,a){return new qo({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:je.toString(a)}]})}_addCheck(t){return new qo({...this._def,checks:[...this._def.checks,t]})}int(t){return this._addCheck({kind:"int",message:je.toString(t)})}positive(t){return this._addCheck({kind:"min",value:0,inclusive:!1,message:je.toString(t)})}negative(t){return this._addCheck({kind:"max",value:0,inclusive:!1,message:je.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:0,inclusive:!0,message:je.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:0,inclusive:!0,message:je.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:je.toString(n)})}finite(t){return this._addCheck({kind:"finite",message:je.toString(t)})}safe(t){return this._addCheck({kind:"min",inclusive:!0,value:Number.MIN_SAFE_INTEGER,message:je.toString(t)})._addCheck({kind:"max",inclusive:!0,value:Number.MAX_SAFE_INTEGER,message:je.toString(t)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t}get isInt(){return!!this._def.checks.find(t=>t.kind==="int"||t.kind==="multipleOf"&&He.isInteger(t.value))}get isFinite(){let t=null,n=null;for(const r of this._def.checks){if(r.kind==="finite"||r.kind==="int"||r.kind==="multipleOf")return!0;r.kind==="min"?(n===null||r.value>n)&&(n=r.value):r.kind==="max"&&(t===null||r.value<t)&&(t=r.value)}return Number.isFinite(n)&&Number.isFinite(t)}}qo.create=e=>new qo({checks:[],typeName:Ae.ZodNumber,coerce:(e==null?void 0:e.coerce)||!1,...Fe(e)});class Uo extends Be{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte}_parse(t){if(this._def.coerce&&(t.data=BigInt(t.data)),this._getType(t)!==fe.bigint){const i=this._getOrReturnCtx(t);return me(i,{code:ae.invalid_type,expected:fe.bigint,received:i.parsedType}),Ne}let r;const a=new Cn;for(const i of this._def.checks)i.kind==="min"?(i.inclusive?t.data<i.value:t.data<=i.value)&&(r=this._getOrReturnCtx(t,r),me(r,{code:ae.too_small,type:"bigint",minimum:i.value,inclusive:i.inclusive,message:i.message}),a.dirty()):i.kind==="max"?(i.inclusive?t.data>i.value:t.data>=i.value)&&(r=this._getOrReturnCtx(t,r),me(r,{code:ae.too_big,type:"bigint",maximum:i.value,inclusive:i.inclusive,message:i.message}),a.dirty()):i.kind==="multipleOf"?t.data%i.value!==BigInt(0)&&(r=this._getOrReturnCtx(t,r),me(r,{code:ae.not_multiple_of,multipleOf:i.value,message:i.message}),a.dirty()):He.assertNever(i);return{status:a.value,value:t.data}}gte(t,n){return this.setLimit("min",t,!0,je.toString(n))}gt(t,n){return this.setLimit("min",t,!1,je.toString(n))}lte(t,n){return this.setLimit("max",t,!0,je.toString(n))}lt(t,n){return this.setLimit("max",t,!1,je.toString(n))}setLimit(t,n,r,a){return new Uo({...this._def,checks:[...this._def.checks,{kind:t,value:n,inclusive:r,message:je.toString(a)}]})}_addCheck(t){return new Uo({...this._def,checks:[...this._def.checks,t]})}positive(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!1,message:je.toString(t)})}negative(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!1,message:je.toString(t)})}nonpositive(t){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!0,message:je.toString(t)})}nonnegative(t){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!0,message:je.toString(t)})}multipleOf(t,n){return this._addCheck({kind:"multipleOf",value:t,message:je.toString(n)})}get minValue(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t}get maxValue(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t}}Uo.create=e=>{var t;return new Uo({checks:[],typeName:Ae.ZodBigInt,coerce:(t=e==null?void 0:e.coerce)!==null&&t!==void 0?t:!1,...Fe(e)})};class ip extends Be{_parse(t){if(this._def.coerce&&(t.data=!!t.data),this._getType(t)!==fe.boolean){const r=this._getOrReturnCtx(t);return me(r,{code:ae.invalid_type,expected:fe.boolean,received:r.parsedType}),Ne}return Wn(t.data)}}ip.create=e=>new ip({typeName:Ae.ZodBoolean,coerce:(e==null?void 0:e.coerce)||!1,...Fe(e)});class Js extends Be{_parse(t){if(this._def.coerce&&(t.data=new Date(t.data)),this._getType(t)!==fe.date){const i=this._getOrReturnCtx(t);return me(i,{code:ae.invalid_type,expected:fe.date,received:i.parsedType}),Ne}if(isNaN(t.data.getTime())){const i=this._getOrReturnCtx(t);return me(i,{code:ae.invalid_date}),Ne}const r=new Cn;let a;for(const i of this._def.checks)i.kind==="min"?t.data.getTime()<i.value&&(a=this._getOrReturnCtx(t,a),me(a,{code:ae.too_small,message:i.message,inclusive:!0,exact:!1,minimum:i.value,type:"date"}),r.dirty()):i.kind==="max"?t.data.getTime()>i.value&&(a=this._getOrReturnCtx(t,a),me(a,{code:ae.too_big,message:i.message,inclusive:!0,exact:!1,maximum:i.value,type:"date"}),r.dirty()):He.assertNever(i);return{status:r.value,value:new Date(t.data.getTime())}}_addCheck(t){return new Js({...this._def,checks:[...this._def.checks,t]})}min(t,n){return this._addCheck({kind:"min",value:t.getTime(),message:je.toString(n)})}max(t,n){return this._addCheck({kind:"max",value:t.getTime(),message:je.toString(n)})}get minDate(){let t=null;for(const n of this._def.checks)n.kind==="min"&&(t===null||n.value>t)&&(t=n.value);return t!=null?new Date(t):null}get maxDate(){let t=null;for(const n of this._def.checks)n.kind==="max"&&(t===null||n.value<t)&&(t=n.value);return t!=null?new Date(t):null}}Js.create=e=>new Js({checks:[],coerce:(e==null?void 0:e.coerce)||!1,typeName:Ae.ZodDate,...Fe(e)});class pv extends Be{_parse(t){if(this._getType(t)!==fe.symbol){const r=this._getOrReturnCtx(t);return me(r,{code:ae.invalid_type,expected:fe.symbol,received:r.parsedType}),Ne}return Wn(t.data)}}pv.create=e=>new pv({typeName:Ae.ZodSymbol,...Fe(e)});class op extends Be{_parse(t){if(this._getType(t)!==fe.undefined){const r=this._getOrReturnCtx(t);return me(r,{code:ae.invalid_type,expected:fe.undefined,received:r.parsedType}),Ne}return Wn(t.data)}}op.create=e=>new op({typeName:Ae.ZodUndefined,...Fe(e)});class sp extends Be{_parse(t){if(this._getType(t)!==fe.null){const r=this._getOrReturnCtx(t);return me(r,{code:ae.invalid_type,expected:fe.null,received:r.parsedType}),Ne}return Wn(t.data)}}sp.create=e=>new sp({typeName:Ae.ZodNull,...Fe(e)});class Hc extends Be{constructor(){super(...arguments),this._any=!0}_parse(t){return Wn(t.data)}}Hc.create=e=>new Hc({typeName:Ae.ZodAny,...Fe(e)});class Ds extends Be{constructor(){super(...arguments),this._unknown=!0}_parse(t){return Wn(t.data)}}Ds.create=e=>new Ds({typeName:Ae.ZodUnknown,...Fe(e)});class qi extends Be{_parse(t){const n=this._getOrReturnCtx(t);return me(n,{code:ae.invalid_type,expected:fe.never,received:n.parsedType}),Ne}}qi.create=e=>new qi({typeName:Ae.ZodNever,...Fe(e)});class fv extends Be{_parse(t){if(this._getType(t)!==fe.undefined){const r=this._getOrReturnCtx(t);return me(r,{code:ae.invalid_type,expected:fe.void,received:r.parsedType}),Ne}return Wn(t.data)}}fv.create=e=>new fv({typeName:Ae.ZodVoid,...Fe(e)});class xa extends Be{_parse(t){const{ctx:n,status:r}=this._processInputParams(t),a=this._def;if(n.parsedType!==fe.array)return me(n,{code:ae.invalid_type,expected:fe.array,received:n.parsedType}),Ne;if(a.exactLength!==null){const o=n.data.length>a.exactLength.value,l=n.data.length<a.exactLength.value;(o||l)&&(me(n,{code:o?ae.too_big:ae.too_small,minimum:l?a.exactLength.value:void 0,maximum:o?a.exactLength.value:void 0,type:"array",inclusive:!0,exact:!0,message:a.exactLength.message}),r.dirty())}if(a.minLength!==null&&n.data.length<a.minLength.value&&(me(n,{code:ae.too_small,minimum:a.minLength.value,type:"array",inclusive:!0,exact:!1,message:a.minLength.message}),r.dirty()),a.maxLength!==null&&n.data.length>a.maxLength.value&&(me(n,{code:ae.too_big,maximum:a.maxLength.value,type:"array",inclusive:!0,exact:!1,message:a.maxLength.message}),r.dirty()),n.common.async)return Promise.all([...n.data].map((o,l)=>a.type._parseAsync(new Ja(n,o,n.path,l)))).then(o=>Cn.mergeArray(r,o));const i=[...n.data].map((o,l)=>a.type._parseSync(new Ja(n,o,n.path,l)));return Cn.mergeArray(r,i)}get element(){return this._def.type}min(t,n){return new xa({...this._def,minLength:{value:t,message:je.toString(n)}})}max(t,n){return new xa({...this._def,maxLength:{value:t,message:je.toString(n)}})}length(t,n){return new xa({...this._def,exactLength:{value:t,message:je.toString(n)}})}nonempty(t){return this.min(1,t)}}xa.create=(e,t)=>new xa({type:e,minLength:null,maxLength:null,exactLength:null,typeName:Ae.ZodArray,...Fe(t)});function Sl(e){if(e instanceof jt){const t={};for(const n in e.shape){const r=e.shape[n];t[n]=Qa.create(Sl(r))}return new jt({...e._def,shape:()=>t})}else return e instanceof xa?new xa({...e._def,type:Sl(e.element)}):e instanceof Qa?Qa.create(Sl(e.unwrap())):e instanceof Vo?Vo.create(Sl(e.unwrap())):e instanceof ei?ei.create(e.items.map(t=>Sl(t))):e}class jt extends Be{constructor(){super(...arguments),this._cached=null,this.nonstrict=this.passthrough,this.augment=this.extend}_getCached(){if(this._cached!==null)return this._cached;const t=this._def.shape(),n=He.objectKeys(t);return this._cached={shape:t,keys:n}}_parse(t){if(this._getType(t)!==fe.object){const u=this._getOrReturnCtx(t);return me(u,{code:ae.invalid_type,expected:fe.object,received:u.parsedType}),Ne}const{status:r,ctx:a}=this._processInputParams(t),{shape:i,keys:o}=this._getCached(),l=[];if(!(this._def.catchall instanceof qi&&this._def.unknownKeys==="strip"))for(const u in a.data)o.includes(u)||l.push(u);const c=[];for(const u of o){const d=i[u],m=a.data[u];c.push({key:{status:"valid",value:u},value:d._parse(new Ja(a,m,a.path,u)),alwaysSet:u in a.data})}if(this._def.catchall instanceof qi){const u=this._def.unknownKeys;if(u==="passthrough")for(const d of l)c.push({key:{status:"valid",value:d},value:{status:"valid",value:a.data[d]}});else if(u==="strict")l.length>0&&(me(a,{code:ae.unrecognized_keys,keys:l}),r.dirty());else if(u!=="strip")throw new Error("Internal ZodObject error: invalid unknownKeys value.")}else{const u=this._def.catchall;for(const d of l){const m=a.data[d];c.push({key:{status:"valid",value:d},value:u._parse(new Ja(a,m,a.path,d)),alwaysSet:d in a.data})}}return a.common.async?Promise.resolve().then(async()=>{const u=[];for(const d of c){const m=await d.key,p=await d.value;u.push({key:m,value:p,alwaysSet:d.alwaysSet})}return u}).then(u=>Cn.mergeObjectSync(r,u)):Cn.mergeObjectSync(r,c)}get shape(){return this._def.shape()}strict(t){return je.errToObj,new jt({...this._def,unknownKeys:"strict",...t!==void 0?{errorMap:(n,r)=>{var a,i,o,l;const c=(o=(i=(a=this._def).errorMap)===null||i===void 0?void 0:i.call(a,n,r).message)!==null&&o!==void 0?o:r.defaultError;return n.code==="unrecognized_keys"?{message:(l=je.errToObj(t).message)!==null&&l!==void 0?l:c}:{message:c}}}:{}})}strip(){return new jt({...this._def,unknownKeys:"strip"})}passthrough(){return new jt({...this._def,unknownKeys:"passthrough"})}extend(t){return new jt({...this._def,shape:()=>({...this._def.shape(),...t})})}merge(t){return new jt({unknownKeys:t._def.unknownKeys,catchall:t._def.catchall,shape:()=>({...this._def.shape(),...t._def.shape()}),typeName:Ae.ZodObject})}setKey(t,n){return this.augment({[t]:n})}catchall(t){return new jt({...this._def,catchall:t})}pick(t){const n={};return He.objectKeys(t).forEach(r=>{t[r]&&this.shape[r]&&(n[r]=this.shape[r])}),new jt({...this._def,shape:()=>n})}omit(t){const n={};return He.objectKeys(this.shape).forEach(r=>{t[r]||(n[r]=this.shape[r])}),new jt({...this._def,shape:()=>n})}deepPartial(){return Sl(this)}partial(t){const n={};return He.objectKeys(this.shape).forEach(r=>{const a=this.shape[r];t&&!t[r]?n[r]=a:n[r]=a.optional()}),new jt({...this._def,shape:()=>n})}required(t){const n={};return He.objectKeys(this.shape).forEach(r=>{if(t&&!t[r])n[r]=this.shape[r];else{let i=this.shape[r];for(;i instanceof Qa;)i=i._def.innerType;n[r]=i}}),new jt({...this._def,shape:()=>n})}keyof(){return vD(He.objectKeys(this.shape))}}jt.create=(e,t)=>new jt({shape:()=>e,unknownKeys:"strip",catchall:qi.create(),typeName:Ae.ZodObject,...Fe(t)});jt.strictCreate=(e,t)=>new jt({shape:()=>e,unknownKeys:"strict",catchall:qi.create(),typeName:Ae.ZodObject,...Fe(t)});jt.lazycreate=(e,t)=>new jt({shape:e,unknownKeys:"strip",catchall:qi.create(),typeName:Ae.ZodObject,...Fe(t)});class lp extends Be{_parse(t){const{ctx:n}=this._processInputParams(t),r=this._def.options;function a(i){for(const l of i)if(l.result.status==="valid")return l.result;for(const l of i)if(l.result.status==="dirty")return n.common.issues.push(...l.ctx.common.issues),l.result;const o=i.map(l=>new Sr(l.ctx.common.issues));return me(n,{code:ae.invalid_union,unionErrors:o}),Ne}if(n.common.async)return Promise.all(r.map(async i=>{const o={...n,common:{...n.common,issues:[]},parent:null};return{result:await i._parseAsync({data:n.data,path:n.path,parent:o}),ctx:o}})).then(a);{let i;const o=[];for(const c of r){const u={...n,common:{...n.common,issues:[]},parent:null},d=c._parseSync({data:n.data,path:n.path,parent:u});if(d.status==="valid")return d;d.status==="dirty"&&!i&&(i={result:d,ctx:u}),u.common.issues.length&&o.push(u.common.issues)}if(i)return n.common.issues.push(...i.ctx.common.issues),i.result;const l=o.map(c=>new Sr(c));return me(n,{code:ae.invalid_union,unionErrors:l}),Ne}}get options(){return this._def.options}}lp.create=(e,t)=>new lp({options:e,typeName:Ae.ZodUnion,...Fe(t)});const xi=e=>e instanceof dp?xi(e.schema):e instanceof Aa?xi(e.innerType()):e instanceof mp?[e.value]:e instanceof Wo?e.options:e instanceof pp?He.objectValues(e.enum):e instanceof fp?xi(e._def.innerType):e instanceof op?[void 0]:e instanceof sp?[null]:e instanceof Qa?[void 0,...xi(e.unwrap())]:e instanceof Vo?[null,...xi(e.unwrap())]:e instanceof Kj||e instanceof gp?xi(e.unwrap()):e instanceof hp?xi(e._def.innerType):[];class sx extends Be{_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==fe.object)return me(n,{code:ae.invalid_type,expected:fe.object,received:n.parsedType}),Ne;const r=this.discriminator,a=n.data[r],i=this.optionsMap.get(a);return i?n.common.async?i._parseAsync({data:n.data,path:n.path,parent:n}):i._parseSync({data:n.data,path:n.path,parent:n}):(me(n,{code:ae.invalid_union_discriminator,options:Array.from(this.optionsMap.keys()),path:[r]}),Ne)}get discriminator(){return this._def.discriminator}get options(){return this._def.options}get optionsMap(){return this._def.optionsMap}static create(t,n,r){const a=new Map;for(const i of n){const o=xi(i.shape[t]);if(!o.length)throw new Error(`A discriminator value for key \`${t}\` could not be extracted from all schema options`);for(const l of o){if(a.has(l))throw new Error(`Discriminator property ${String(t)} has duplicate value ${String(l)}`);a.set(l,i)}}return new sx({typeName:Ae.ZodDiscriminatedUnion,discriminator:t,options:n,optionsMap:a,...Fe(r)})}}function j2(e,t){const n=mo(e),r=mo(t);if(e===t)return{valid:!0,data:e};if(n===fe.object&&r===fe.object){const a=He.objectKeys(t),i=He.objectKeys(e).filter(l=>a.indexOf(l)!==-1),o={...e,...t};for(const l of i){const c=j2(e[l],t[l]);if(!c.valid)return{valid:!1};o[l]=c.data}return{valid:!0,data:o}}else if(n===fe.array&&r===fe.array){if(e.length!==t.length)return{valid:!1};const a=[];for(let i=0;i<e.length;i++){const o=e[i],l=t[i],c=j2(o,l);if(!c.valid)return{valid:!1};a.push(c.data)}return{valid:!0,data:a}}else return n===fe.date&&r===fe.date&&+e==+t?{valid:!0,data:e}:{valid:!1}}class cp extends Be{_parse(t){const{status:n,ctx:r}=this._processInputParams(t),a=(i,o)=>{if(k2(i)||k2(o))return Ne;const l=j2(i.value,o.value);return l.valid?((S2(i)||S2(o))&&n.dirty(),{status:n.value,value:l.data}):(me(r,{code:ae.invalid_intersection_types}),Ne)};return r.common.async?Promise.all([this._def.left._parseAsync({data:r.data,path:r.path,parent:r}),this._def.right._parseAsync({data:r.data,path:r.path,parent:r})]).then(([i,o])=>a(i,o)):a(this._def.left._parseSync({data:r.data,path:r.path,parent:r}),this._def.right._parseSync({data:r.data,path:r.path,parent:r}))}}cp.create=(e,t,n)=>new cp({left:e,right:t,typeName:Ae.ZodIntersection,...Fe(n)});class ei extends Be{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==fe.array)return me(r,{code:ae.invalid_type,expected:fe.array,received:r.parsedType}),Ne;if(r.data.length<this._def.items.length)return me(r,{code:ae.too_small,minimum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),Ne;!this._def.rest&&r.data.length>this._def.items.length&&(me(r,{code:ae.too_big,maximum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),n.dirty());const i=[...r.data].map((o,l)=>{const c=this._def.items[l]||this._def.rest;return c?c._parse(new Ja(r,o,r.path,l)):null}).filter(o=>!!o);return r.common.async?Promise.all(i).then(o=>Cn.mergeArray(n,o)):Cn.mergeArray(n,i)}get items(){return this._def.items}rest(t){return new ei({...this._def,rest:t})}}ei.create=(e,t)=>{if(!Array.isArray(e))throw new Error("You must pass an array of schemas to z.tuple([ ... ])");return new ei({items:e,typeName:Ae.ZodTuple,rest:null,...Fe(t)})};class up extends Be{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==fe.object)return me(r,{code:ae.invalid_type,expected:fe.object,received:r.parsedType}),Ne;const a=[],i=this._def.keyType,o=this._def.valueType;for(const l in r.data)a.push({key:i._parse(new Ja(r,l,r.path,l)),value:o._parse(new Ja(r,r.data[l],r.path,l)),alwaysSet:l in r.data});return r.common.async?Cn.mergeObjectAsync(n,a):Cn.mergeObjectSync(n,a)}get element(){return this._def.valueType}static create(t,n,r){return n instanceof Be?new up({keyType:t,valueType:n,typeName:Ae.ZodRecord,...Fe(r)}):new up({keyType:pa.create(),valueType:t,typeName:Ae.ZodRecord,...Fe(n)})}}class hv extends Be{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==fe.map)return me(r,{code:ae.invalid_type,expected:fe.map,received:r.parsedType}),Ne;const a=this._def.keyType,i=this._def.valueType,o=[...r.data.entries()].map(([l,c],u)=>({key:a._parse(new Ja(r,l,r.path,[u,"key"])),value:i._parse(new Ja(r,c,r.path,[u,"value"]))}));if(r.common.async){const l=new Map;return Promise.resolve().then(async()=>{for(const c of o){const u=await c.key,d=await c.value;if(u.status==="aborted"||d.status==="aborted")return Ne;(u.status==="dirty"||d.status==="dirty")&&n.dirty(),l.set(u.value,d.value)}return{status:n.value,value:l}})}else{const l=new Map;for(const c of o){const u=c.key,d=c.value;if(u.status==="aborted"||d.status==="aborted")return Ne;(u.status==="dirty"||d.status==="dirty")&&n.dirty(),l.set(u.value,d.value)}return{status:n.value,value:l}}}}hv.create=(e,t,n)=>new hv({valueType:t,keyType:e,typeName:Ae.ZodMap,...Fe(n)});class el extends Be{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.parsedType!==fe.set)return me(r,{code:ae.invalid_type,expected:fe.set,received:r.parsedType}),Ne;const a=this._def;a.minSize!==null&&r.data.size<a.minSize.value&&(me(r,{code:ae.too_small,minimum:a.minSize.value,type:"set",inclusive:!0,exact:!1,message:a.minSize.message}),n.dirty()),a.maxSize!==null&&r.data.size>a.maxSize.value&&(me(r,{code:ae.too_big,maximum:a.maxSize.value,type:"set",inclusive:!0,exact:!1,message:a.maxSize.message}),n.dirty());const i=this._def.valueType;function o(c){const u=new Set;for(const d of c){if(d.status==="aborted")return Ne;d.status==="dirty"&&n.dirty(),u.add(d.value)}return{status:n.value,value:u}}const l=[...r.data.values()].map((c,u)=>i._parse(new Ja(r,c,r.path,u)));return r.common.async?Promise.all(l).then(c=>o(c)):o(l)}min(t,n){return new el({...this._def,minSize:{value:t,message:je.toString(n)}})}max(t,n){return new el({...this._def,maxSize:{value:t,message:je.toString(n)}})}size(t,n){return this.min(t,n).max(t,n)}nonempty(t){return this.min(1,t)}}el.create=(e,t)=>new el({valueType:e,minSize:null,maxSize:null,typeName:Ae.ZodSet,...Fe(t)});class Jl extends Be{constructor(){super(...arguments),this.validate=this.implement}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==fe.function)return me(n,{code:ae.invalid_type,expected:fe.function,received:n.parsedType}),Ne;function r(l,c){return dv({data:l,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,uv(),Vc].filter(u=>!!u),issueData:{code:ae.invalid_arguments,argumentsError:c}})}function a(l,c){return dv({data:l,path:n.path,errorMaps:[n.common.contextualErrorMap,n.schemaErrorMap,uv(),Vc].filter(u=>!!u),issueData:{code:ae.invalid_return_type,returnTypeError:c}})}const i={errorMap:n.common.contextualErrorMap},o=n.data;if(this._def.returns instanceof Kc){const l=this;return Wn(async function(...c){const u=new Sr([]),d=await l._def.args.parseAsync(c,i).catch(f=>{throw u.addIssue(r(c,f)),u}),m=await Reflect.apply(o,this,d);return await l._def.returns._def.type.parseAsync(m,i).catch(f=>{throw u.addIssue(a(m,f)),u})})}else{const l=this;return Wn(function(...c){const u=l._def.args.safeParse(c,i);if(!u.success)throw new Sr([r(c,u.error)]);const d=Reflect.apply(o,this,u.data),m=l._def.returns.safeParse(d,i);if(!m.success)throw new Sr([a(d,m.error)]);return m.data})}}parameters(){return this._def.args}returnType(){return this._def.returns}args(...t){return new Jl({...this._def,args:ei.create(t).rest(Ds.create())})}returns(t){return new Jl({...this._def,returns:t})}implement(t){return this.parse(t)}strictImplement(t){return this.parse(t)}static create(t,n,r){return new Jl({args:t||ei.create([]).rest(Ds.create()),returns:n||Ds.create(),typeName:Ae.ZodFunction,...Fe(r)})}}class dp extends Be{get schema(){return this._def.getter()}_parse(t){const{ctx:n}=this._processInputParams(t);return this._def.getter()._parse({data:n.data,path:n.path,parent:n})}}dp.create=(e,t)=>new dp({getter:e,typeName:Ae.ZodLazy,...Fe(t)});class mp extends Be{_parse(t){if(t.data!==this._def.value){const n=this._getOrReturnCtx(t);return me(n,{received:n.data,code:ae.invalid_literal,expected:this._def.value}),Ne}return{status:"valid",value:t.data}}get value(){return this._def.value}}mp.create=(e,t)=>new mp({value:e,typeName:Ae.ZodLiteral,...Fe(t)});function vD(e,t){return new Wo({values:e,typeName:Ae.ZodEnum,...Fe(t)})}class Wo extends Be{constructor(){super(...arguments),fd.set(this,void 0)}_parse(t){if(typeof t.data!="string"){const n=this._getOrReturnCtx(t),r=this._def.values;return me(n,{expected:He.joinValues(r),received:n.parsedType,code:ae.invalid_type}),Ne}if(mv(this,fd)||pD(this,fd,new Set(this._def.values)),!mv(this,fd).has(t.data)){const n=this._getOrReturnCtx(t),r=this._def.values;return me(n,{received:n.data,code:ae.invalid_enum_value,options:r}),Ne}return Wn(t.data)}get options(){return this._def.values}get enum(){const t={};for(const n of this._def.values)t[n]=n;return t}get Values(){const t={};for(const n of this._def.values)t[n]=n;return t}get Enum(){const t={};for(const n of this._def.values)t[n]=n;return t}extract(t,n=this._def){return Wo.create(t,{...this._def,...n})}exclude(t,n=this._def){return Wo.create(this.options.filter(r=>!t.includes(r)),{...this._def,...n})}}fd=new WeakMap;Wo.create=vD;class pp extends Be{constructor(){super(...arguments),hd.set(this,void 0)}_parse(t){const n=He.getValidEnumValues(this._def.values),r=this._getOrReturnCtx(t);if(r.parsedType!==fe.string&&r.parsedType!==fe.number){const a=He.objectValues(n);return me(r,{expected:He.joinValues(a),received:r.parsedType,code:ae.invalid_type}),Ne}if(mv(this,hd)||pD(this,hd,new Set(He.getValidEnumValues(this._def.values))),!mv(this,hd).has(t.data)){const a=He.objectValues(n);return me(r,{received:r.data,code:ae.invalid_enum_value,options:a}),Ne}return Wn(t.data)}get enum(){return this._def.values}}hd=new WeakMap;pp.create=(e,t)=>new pp({values:e,typeName:Ae.ZodNativeEnum,...Fe(t)});class Kc extends Be{unwrap(){return this._def.type}_parse(t){const{ctx:n}=this._processInputParams(t);if(n.parsedType!==fe.promise&&n.common.async===!1)return me(n,{code:ae.invalid_type,expected:fe.promise,received:n.parsedType}),Ne;const r=n.parsedType===fe.promise?n.data:Promise.resolve(n.data);return Wn(r.then(a=>this._def.type.parseAsync(a,{path:n.path,errorMap:n.common.contextualErrorMap})))}}Kc.create=(e,t)=>new Kc({type:e,typeName:Ae.ZodPromise,...Fe(t)});class Aa extends Be{innerType(){return this._def.schema}sourceType(){return this._def.schema._def.typeName===Ae.ZodEffects?this._def.schema.sourceType():this._def.schema}_parse(t){const{status:n,ctx:r}=this._processInputParams(t),a=this._def.effect||null,i={addIssue:o=>{me(r,o),o.fatal?n.abort():n.dirty()},get path(){return r.path}};if(i.addIssue=i.addIssue.bind(i),a.type==="preprocess"){const o=a.transform(r.data,i);if(r.common.async)return Promise.resolve(o).then(async l=>{if(n.value==="aborted")return Ne;const c=await this._def.schema._parseAsync({data:l,path:r.path,parent:r});return c.status==="aborted"?Ne:c.status==="dirty"||n.value==="dirty"?Ll(c.value):c});{if(n.value==="aborted")return Ne;const l=this._def.schema._parseSync({data:o,path:r.path,parent:r});return l.status==="aborted"?Ne:l.status==="dirty"||n.value==="dirty"?Ll(l.value):l}}if(a.type==="refinement"){const o=l=>{const c=a.refinement(l,i);if(r.common.async)return Promise.resolve(c);if(c instanceof Promise)throw new Error("Async refinement encountered during synchronous parse operation. Use .parseAsync instead.");return l};if(r.common.async===!1){const l=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});return l.status==="aborted"?Ne:(l.status==="dirty"&&n.dirty(),o(l.value),{status:n.value,value:l.value})}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(l=>l.status==="aborted"?Ne:(l.status==="dirty"&&n.dirty(),o(l.value).then(()=>({status:n.value,value:l.value}))))}if(a.type==="transform")if(r.common.async===!1){const o=this._def.schema._parseSync({data:r.data,path:r.path,parent:r});if(!rp(o))return o;const l=a.transform(o.value,i);if(l instanceof Promise)throw new Error("Asynchronous transform encountered during synchronous parse operation. Use .parseAsync instead.");return{status:n.value,value:l}}else return this._def.schema._parseAsync({data:r.data,path:r.path,parent:r}).then(o=>rp(o)?Promise.resolve(a.transform(o.value,i)).then(l=>({status:n.value,value:l})):o);He.assertNever(a)}}Aa.create=(e,t,n)=>new Aa({schema:e,typeName:Ae.ZodEffects,effect:t,...Fe(n)});Aa.createWithPreprocess=(e,t,n)=>new Aa({schema:t,effect:{type:"preprocess",transform:e},typeName:Ae.ZodEffects,...Fe(n)});class Qa extends Be{_parse(t){return this._getType(t)===fe.undefined?Wn(void 0):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}Qa.create=(e,t)=>new Qa({innerType:e,typeName:Ae.ZodOptional,...Fe(t)});class Vo extends Be{_parse(t){return this._getType(t)===fe.null?Wn(null):this._def.innerType._parse(t)}unwrap(){return this._def.innerType}}Vo.create=(e,t)=>new Vo({innerType:e,typeName:Ae.ZodNullable,...Fe(t)});class fp extends Be{_parse(t){const{ctx:n}=this._processInputParams(t);let r=n.data;return n.parsedType===fe.undefined&&(r=this._def.defaultValue()),this._def.innerType._parse({data:r,path:n.path,parent:n})}removeDefault(){return this._def.innerType}}fp.create=(e,t)=>new fp({innerType:e,typeName:Ae.ZodDefault,defaultValue:typeof t.default=="function"?t.default:()=>t.default,...Fe(t)});class hp extends Be{_parse(t){const{ctx:n}=this._processInputParams(t),r={...n,common:{...n.common,issues:[]}},a=this._def.innerType._parse({data:r.data,path:r.path,parent:{...r}});return ap(a)?a.then(i=>({status:"valid",value:i.status==="valid"?i.value:this._def.catchValue({get error(){return new Sr(r.common.issues)},input:r.data})})):{status:"valid",value:a.status==="valid"?a.value:this._def.catchValue({get error(){return new Sr(r.common.issues)},input:r.data})}}removeCatch(){return this._def.innerType}}hp.create=(e,t)=>new hp({innerType:e,typeName:Ae.ZodCatch,catchValue:typeof t.catch=="function"?t.catch:()=>t.catch,...Fe(t)});class gv extends Be{_parse(t){if(this._getType(t)!==fe.nan){const r=this._getOrReturnCtx(t);return me(r,{code:ae.invalid_type,expected:fe.nan,received:r.parsedType}),Ne}return{status:"valid",value:t.data}}}gv.create=e=>new gv({typeName:Ae.ZodNaN,...Fe(e)});const R5e=Symbol("zod_brand");class Kj extends Be{_parse(t){const{ctx:n}=this._processInputParams(t),r=n.data;return this._def.type._parse({data:r,path:n.path,parent:n})}unwrap(){return this._def.type}}class Vp extends Be{_parse(t){const{status:n,ctx:r}=this._processInputParams(t);if(r.common.async)return(async()=>{const i=await this._def.in._parseAsync({data:r.data,path:r.path,parent:r});return i.status==="aborted"?Ne:i.status==="dirty"?(n.dirty(),Ll(i.value)):this._def.out._parseAsync({data:i.value,path:r.path,parent:r})})();{const a=this._def.in._parseSync({data:r.data,path:r.path,parent:r});return a.status==="aborted"?Ne:a.status==="dirty"?(n.dirty(),{status:"dirty",value:a.value}):this._def.out._parseSync({data:a.value,path:r.path,parent:r})}}static create(t,n){return new Vp({in:t,out:n,typeName:Ae.ZodPipeline})}}class gp extends Be{_parse(t){const n=this._def.innerType._parse(t),r=a=>(rp(a)&&(a.value=Object.freeze(a.value)),a);return ap(n)?n.then(a=>r(a)):r(n)}unwrap(){return this._def.innerType}}gp.create=(e,t)=>new gp({innerType:e,typeName:Ae.ZodReadonly,...Fe(t)});function yD(e,t={},n){return e?Hc.create().superRefine((r,a)=>{var i,o;if(!e(r)){const l=typeof t=="function"?t(r):typeof t=="string"?{message:t}:t,c=(o=(i=l.fatal)!==null&&i!==void 0?i:n)!==null&&o!==void 0?o:!0,u=typeof l=="string"?{message:l}:l;a.addIssue({code:"custom",...u,fatal:c})}}):Hc.create()}const D5e={object:jt.lazycreate};var 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2118return s.jsxs(s.Fragment,{children:[s.jsx(ns,{page:"our-ai",jsonLd:n}),s.jsx(ED,{content:t.hero}),s.jsx(OD,{content:t.engines,foundation:t.thesis.foundation}),s.jsx(ID,{content:t.diversity}),s.jsx($D,{content:t.explainability}),s.jsx(TD,{content:t.thesis}),s.jsx(FD,{content:t.security}),s.jsx(RD,{content:t.challenges}),s.jsx(DD,{content:t.community}),s.jsx(LD,{content:t.research}),s.jsx(Vy,{}),s.jsx(zD,{content:t.glossary}),s.jsx(ku,{})]})});BD.displayName="OurAI";const VTe='# Landing Pages (Business Transformations) — content schema\n\nEditorial source for the **4 transformation landings** under `/{lang}/solutions/<slug>`.\n\nThe pages are rendered by `src/pages/TransformationLanding.tsx` (one parametrized component) and pulled at build-time by `src/hooks/useLandings.ts` (eager Vite glob — no fetch at runtime).\n\n## Allowed slugs (whitelist)\n\nOnly these 4 slugs render. Anything else falls back to `/solutions`.\n\n| slug | route | hub_theme |\n|---|---|---|\n| `demand-supply-efficiency` | `/{lang}/solutions/demand-supply-efficiency` | `demand` |\n| `data-monetization` | `/{lang}/solutions/data-monetization` | `mix` |\n| `predictive-operations` | `/{lang}/solutions/predictive-operations` | `inventory` |\n| `behavior-conversion` | `/{lang}/solutions/behavior-conversion` | `propensity` |\n\n## File convention\n\nEach landing has a **mandatory pair** PT + EN:\n\n```\nsrc/content/landings/\n  <slug>-pt.md\n  <slug>-en.md\n```\n\n## Frontmatter\n\n```yaml\n---\ntitle: "Eficiência de Demanda e Oferta com IA Preditiva"\ndescription: "Citation-friendly meta description (140–280 chars)."\nslug: demand-supply-efficiency\nlanguage: pt                                       # pt | en\nhero_kicker: "Transformação · Demanda & Oferta"   # short eyebrow\nhero_headline: "Balance demand and supply…"        # large H1\nhero_sub: "Sub-headline that explains the promise."\nsectors: "varejo, farma, indústria, CPG"           # CSV\nhub_theme: demand                                  # demand | margin | inventory | mix | propensity\nrelated_engines: "i6previsio, i6recsys, i6signal"  # CSV — anchors to /our-ai\nrelated_stories: "story-slug-1, story-slug-2"      # CSV — slugs of /success-stories\ncover_image: null\n---\n```\n\n## Body sections (H2 headings)\n\nThe component renders fixed blocks driven by `## ` headings — keep these names (PT or EN both work):\n\n| heading | role |\n|---|---|\n| `## Pain` / `## Dor` | 3 real pains, citation-magnet |\n| `## Problem` / `## Problema` | why generic AI fails |\n| `## Solution` / `## Solução` | proprietary engines applied (also renders engine cards from `related_engines`) |\n| `## Application` / `## Aplicação` | how it runs in production (also renders the data→engine→decision→signal flow) |\n| `## Results` / `## Resultados` | bullets like `- **−38%** label \\| source` → parsed into KPI strip + `Statistic` JSON-LD |\n| `## FAQ` / `## Perguntas frequentes` | `**Q?**` + answer paragraph → parsed into `FAQPage` JSON-LD |\n\n### Results format (must follow exactly)\n\n```markdown\n## Results\n- **−38%** Ruptura em SKUs A/B (90 dias) | Varejo farma\n- **+6,2 p.p.** Margem bruta da categoria OTC | Varejo farma\n```\n\nThe bold token becomes the big number, the rest becomes the label, anything after `|` becomes the small source tag.\n\n## SEO/GEO\n\nEach landing automatically emits:\n\n- `Service` + `BreadcrumbList` JSON-LD (page-level)\n- `FAQPage` JSON-LD (if `## FAQ` present)\n- Canonical + hreflang (en/pt-BR/x-default)\n- Prerendered static stub in `dist/<lang>/solutions/<slug>.html` (via `scripts/prerender-seo-stubs.mjs`)\n- `sitemap.xml` + `llms.txt` entries (maintained manually until i6Hub sync covers landings)\n\n## i6Hub integration (when CMS-managed)\n\nSame architecture as Insights/Intelligence:\n\n```\ni6Hub admin → notify-site-rebuild edge → repository_dispatch\n            → GitHub Actions → sync-landings-from-i6hub.mjs (TODO)\n            → regenerates src/content/landings/*.md → vite build → GH Pages\n```\n\nThe sync script for landings is not yet wired (initial 8 files are hand-authored). When i6Hub gains a `landing` content type, mirror `sync-insights-from-i6hub.mjs` and add a `landings-updated` dispatch event.\n\nEditors in i6Hub will see:\n\n- Slug picker locked to the 4-item whitelist above.\n- All frontmatter fields as form inputs.\n- Body editor with section dividers per H2 convention.\n',HTe=`---
2119title: "Behavior and Conversion"
2120description: "Anticipate intent, prescribe the right argument and convert more — proprietary AI that learns individual behavior and activates the next action on the right channel."
2121slug: behavior-conversion
2122language: en
2123hero_kicker: "Transformation · Behavior & Conversion"
2124hero_headline: "Conversion isn't about who walks in. It's about who you understand in time."
2125hero_sub: "Most companies do not lose conversion because they lack traffic. They lose conversion because they interpret too late the signals of intent, context and behavior that were already happening throughout the journey. \\n\\nEvery search, click, page view, abandonment, return, compared product, accessed channel and session pattern reveals a possible intent. **The problem is that, without prediction, these signals only become insight after the opportunity has passed.** \\n\\nPredictive AI applied to behavior makes it possible to anticipate undeclared intentions, personalize the next action and influence conversions with more precision, without relying only on coupons, fixed rules or generic campaigns."
2126sectors: "retail, e-commerce, financial services, CPG"
2127hub_theme: propensity
2128related_engines: "i6recsys, i6elasticprice, i6signal"
2129related_stories: "customer-engagement-online-sales, cross-sell-propensity-intelligence, marketplace-excellence-pharmacy"
2130cover_image: null
2131---
2132## Pain
2133
2134### Conversion drops when intent appears before the company’s decision
2135
2136Customers rarely declare exactly what they want. **They leave signals.**
2137
2138The way they navigate, search, compare, return, abandon, click, ignore, add to cart or interact with an offer indicates intent, doubt, urgency, price sensitivity, product affinity and conversion probability.
2139
2140In practice, marketing, CRM, e-commerce, growth, sales and digital channels deal with recurring pain points:
2141
2142- **The right offer arriving too late:** The customer has already shown intent, but activation happens when they have already decided to leave, compare prices or buy somewhere else.
2143
2144- **Generic personalization:** The journey changes little across customers with different intentions, because the company personalizes by segment, not by real behavior.
2145
2146- **Coupon used as a universal solution:** Discounts are applied to customers who would buy at full price, eroding margin unnecessarily.
2147
2148- **Low-adherence cross-sell and up-sell:** Products are recommended by rule, inventory or simple association, not by propensity, context and purchase timing.
2149
2150- **Wasted anonymous traffic:** Visitors without login generate rich session signals, but many companies treat this behavior as invisible.
2151
2152- **Teams without confidence in the recommendation:** When the model does not explain why an action was suggested, marketing and sales tend to ignore, disable or underuse the intelligence.
2153
2154>The real pain is easy to understand: undeclared intent already exists in the journey. The challenge is to anticipate that intent with enough precision to change conversion before the opportunity disappears.
2155
2156## Problem
2157
2158### Fixed rules do not understand intent, timing and context
2159
2160Rule-based personalization treats different customers as if they were the same within the same segment. “If cart was abandoned, send a coupon.” “If product X was viewed, recommend product Y.” “If the customer entered the category, show the standard campaign.”
2161
2162These rules are easy to operate, but they ignore the questions that truly change conversion:
2163
2164- **1. Who is researching and who is ready to buy?**
2165- **2. Who needs a value argument and who needs an incentive?**
2166- **3. Who would buy without a discount?**
2167- **4. Which product has the highest adherence for that customer, at that moment?**
2168- **5. Which offer increases conversion without destroying margin?**
2169- **6. Which navigation signal reveals intent before login?**
2170- **7. Which journey should change now, not tomorrow?**
2171- **8. Which argument explains the recommendation to the channel or commercial team?**
2172
2173Generic propensity models also fail when they use weak signals, frozen clusters or recommendations without contextual adherence.
2174
2175Without prediction, **the company reacts to abandonment**.\\
2176Without explainability, **the team does not trust the recommendation**.\\
2177Without real-time activation, **intent passes by**.
2178
2179Without context, **personalization becomes a superficial campaign variation**.
2180
2181> Conversion does not depend only on knowing who the customer is. It depends on understanding what they are about to do. To change the conversion game, AI needs to anticipate intent, explain the recommendation and activate the next best action while the journey is still alive.
2182
2183## Solution
2184
2185### Proprietary AI engines to transform behavior into conversion
2186
2187infinity6 applies proprietary predictive AI, recommendation and pricing engines to transform live behavior into early decisions for personalization, influence and conversion.
2188
2189The solution **connects navigation, purchase, search, cart, channel, product, price, context and historical signals to identify undeclared intent** and recommend the next best action.
2190
2191- **i6 RecSys -** Recommendation and propensity engine to identify the next best action by customer, session and moment.
2192
2193    *Applications:* purchase propensity, product recommendation, journey personalization, cross-sell and up-sell, cart recovery and anonymous traffic activation.
2194
2195- **i6 ElasticPrice -** Incentive decision engine to calibrate discount, bundle or value argument based on elasticity and margin.
2196
2197    *Applications:* unnecessary coupon reduction, discount optimization, personalized bundles and margin protection.
2198
2199- **i6 Signal -**  Predictive interface that turns recommendation into action across channels.
2200
2201    *Answers:* who to activate, what to offer, which argument to use and why. Combines propensity, context and explainability to increase conversion with precision.
2202
2203## Application
2204
2205### How behavior prediction runs in practice
2206
2207The solution operates as an intelligence layer above current channels, connecting e-commerce, CRM, app, CDP, media, marketplace, WhatsApp, POS and campaign tools.
2208
2209Each behavior event can recalibrate propensity and update the next best action. **The goal is to transform live signals into activation while there is still an opportunity to influence conversion**.
2210
2211**01. Behavior data**
2212
2213The foundation starts with the signals generated throughout the journey: navigation, search, product view, accessed category, c

2213art, abandonment, return, frequency, origin, device, campaign, purchase history, price, inventory, channel and anonymous session behavior.
2214
2215This data reveals intent, doubt, affinity, urgency, elasticity, price sensitivity and conversion probability.
2216
2217**02. Predictive engine**
2218
2219The proprietary engines identify patterns that fixed rules and traditional segmentations do not capture.
2220
2221AI calculates purchase propensity, affinity between customer and SKU, conversion probability, abandonment risk, incentive elasticity, cross-sell opportunity, up-sell potential and the best activation moment.
2222
2223Prediction considers context, timing, similarity, history, recent behavior and undeclared intent signals.
2224
2225**03. Prescriptive decision**
2226
2227Prediction becomes a practical conversion recommendation.
2228
2229AI indicates which product to recommend, which offer to present, which argument to use, which channel to activate, which incentive to apply and which journey to prioritize.
2230
2231Each recommendation can come with feature-level explainability, showing the factors that support the decision, such as category affinity, recent engagement, similarity with high-value profiles, price elasticity or correlation with higher-margin items.
2232
2233**04. i6 Signal activation**
2234
2235Recommendations are delivered into the current flow through API, e-commerce, CRM, app, WhatsApp, campaign tool, POS, dashboard or i6 Signal.
2236
2237Activation can influence storefront, recommended product, message, argument, incentive, offer order, campaign or commercial approach.
2238
2239With each interaction, the models learn from the customer’s real response, creating a continuous cycle: behavior, prediction, recommendation, activation and conversion.
2240
2241> The team stops asking only “who visited?” and starts acting on “which intent is forming now and which action increases the chance of conversion?”.
2242
2243## Results
2244
2245- **+23%** Average ticket per POS
2246- **+12MM** Additional revenue from financial product cross-sell
2247- **−57%** Messaging cost reduction (CAC)
2248- **+12 x** Increase in campaign conversion
2249
2250## FAQ
2251
2252**What is behavior prediction?**
2253Behavior prediction is the use of AI to analyze navigation, search, purchase, cart, abandonment, channel, context and history signals, with the goal of anticipating intentions and indicating the next best action to increase conversion, personalization and customer value.
2254
2255**How can AI identify undeclared intentions?**
2256AI identifies patterns in indirect signals, such as navigation sequence, compared products, interaction time, origin, visited category, recurrence, abandonment, similarity with other profiles and historical response. These signals help estimate intent even when the customer does not declare what they want.
2257
2258**Does it work for anonymous traffic without login?**
2259Yes. The solution can generate propensity for anonymous visitors based on session pattern, device, origin, navigation, search, context and similarity with known clusters. Even without login, there are enough signals to improve recommendation and activation.
2260
2261**How does AI influence conversion?**
2262AI influences conversion by indicating the right product, the right offer, the right argument, the right channel and the right incentive for each moment of the journey. This makes it possible to act before abandonment, improve relevance and reduce dependence on generic campaigns.
2263
2264**How does the solution avoid cannibalizing full-price sales?**
2265The elasticity engine helps identify who needs an incentive and who would buy without a discount. Customers with high propensity can receive a value argument, recommendation or proof of fit instead of an unnecessary coupon.
2266
2267**What does feature-level explainability mean?**
2268It means each recommendation can bring the factors that support the decision, such as category affinity, recent behavior, similarity with high-value customers, purchase history, elasticity, channel, context and conversion probability. This increases confidence and adoption by the commercial team.
2269
2270**Why are fixed personalization rules not enough?**
2271Fixed rules treat similar customers as if they were the same and do not adapt well to context, timing, intent, elasticity and behavior change. Predictive AI recalculates propensity as new signals appear throughout the journey.
2272
2273**Does AI replace the CRM or e-commerce platform?**
2274No. The solution operates above current channels. CRM, e-commerce, app, marketplace, WhatsApp, CDP or campaign tool remain the activation environments. AI adds the predictive layer that defines who to activate, when, with which offer and why.
2275
2276**What data is needed to start?**
2277The minimum data usually includes navigation events, search, product views, cart, abandonment, purchase history, products, categories, price, channel and origin. Additional data such as inventory, margin, campaigns, CRM, recurrence and commercial response increases precision.
2278
2279**How do you measure conversion impact?**
2280Impact should be measured against a baseline, control group or A/B test. Common metrics include conversion rate, incremental revenue, average ticket, incremental margin, coupon reduction, cross-sell conversion, up-sell, cart recovery and share of revenue generated by the models.
2281
2282**How long does it take to see impact?**
2283It depends on traffic volume, event quality and activation speed. In digital operations with relevant volume, the first learning cycles can happen in a few weeks and material results tend to appear as recommendations go into production.
2284
2285**How does this page connect with Data Monetization?**
2286Behavior and conversion are one of the most direct ways to monetize data. Navigation, intent, purchase and abandonment signals can be transformed into incremental revenue, margin and commercial efficiency.
2287
2288**How does this page connect with Demand & Supply?**
2289Digital behavior anticipates demand. Search, navigation, cart and intent help predict pressure on products, categories and channels before the sale happens, supporting inventory, assortment and replenishment.
2290
2291**How does this page connect with Predictive Operations?**

2292Conversion also depends on operations. Behavior signals can anticipate stockout risk, channel pressure, replenishment needs and operational prioritization before the problem impacts the customer experience.`,KTe=`---
2293title: "Comportamento e Conversão"
2294description: "Antecipe intenção, prescreva o argumento certo e converta mais — IA proprietária que aprende o comportamento individual e ativa a próxima ação no canal certo."
2295slug: behavior-conversion
2296language: pt
2297hero_kicker: "Transformação · Comportamento & Conversão"
2298hero_headline: "Conversão não é sobre quem entra. É sobre quem você entende a tempo."
2299hero_sub: "A maior parte das empresas não perde conversão por falta de tráfego. Perde porque interpreta tarde demais os sinais de intenção, contexto e comportamento que já estavam acontecendo na jornada.\\n\\nCada busca, clique, página vista, abandono, retorno, produto comparado, canal acessado e padrão de sessão revela uma intenção possível. **O problema é que, sem predição, esses sinais só viram leitura depois que a oportunidade passou.**\\n\\nIA preditiva aplicada ao comportamento permite antecipar intenções não declaradas, personalizar a próxima ação e influenciar conversões com mais precisão, sem depender apenas de cupom, regra fixa ou campanha genérica."
2300sectors: "varejo, e-commerce, financeiro, CPG"
2301hub_theme: propensity
2302related_engines: "i6recsys, i6elasticprice, i6signal"
2303related_stories: "customer-engagement-online-sales, cross-sell-propensity-intelligence, marketplace-excellence-pharmacy"
2304cover_image: null
2305---
2306## Pain
2307
2308### A conversão cai quando a intenção aparece antes da decisão da empresa
2309
2310Clientes raramente declaram exatamente o que querem. **Eles deixam sinais.**
2311
2312A forma como navegam, pesquisam, comparam, voltam, abandonam, clicam, ignoram, adicionam ao carrinho ou interagem com uma oferta indica intenção, dúvida, urgência, sensibilidade a preço, afinidade com produto e probabilidade de conversão.
2313
2314Na prática, marketing, CRM, e-commerce, growth, vendas e canais digitais convivem com dores recorrentes:
2315
2316- **Oferta certa chegando tarde:** O cliente já demonstrou intenção, mas a ativação acontece quando ele já decidiu sair, comparar preço ou comprar em outro lugar.
2317
2318- **Personalização genérica:** A jornada muda pouco entre clientes com intenções diferentes, porque a empresa personaliza por segmento, não por comportamento real.
2319
2320- **Cupom usado como solução universal:** Desconto é aplicado para quem compraria a preço cheio, corroendo margem sem necessidade.
2321
2322- **Cross-sell e up-sell pouco aderentes:** Produtos são recomendados por regra, estoque ou associação simples, não por propensão, contexto e timing de compra.
2323
2324- **Tráfego anônimo desperdiçado:** Visitantes sem login geram sinais ricos de sessão, mas muitas empresas tratam esse comportamento como invisível.
2325
2326- **Times sem confiança na recomendação:** Quando o modelo não explica por que uma ação foi sugerida, marketing e comercial tendem a ignorar, desligar ou subutilizar a inteligência.
2327
2328>A dor real é fácil de entender: intenção não declarada já existe na jornada. O desafio é antecipar essa intenção com precisão suficiente para mudar a conversão antes que a oportunidade desapareça.
2329
2330## Problem
2331
2332### Regras fixas não entendem intenção, timing e contexto
2333
2334Personalização baseada em regras trata clientes diferentes como se fossem iguais dentro do mesmo segmento. “Se abandonou carrinho, envie cupom”. “Se viu produto X, recomende produto Y”. “Se entrou na categoria, mostre a campanha padrão”.
2335
2336Essas regras são fáceis de operar, mas ignoram as perguntas que realmente mudam conversão:
2337
2338- **1. Quem está pesquisando e quem está pronto para comprar?**
2339- **2. Quem precisa de argumento de valor e quem precisa de incentivo?**
2340- **3. Quem compraria sem desconto?**
2341- **4. Qual produto tem maior aderência para aquele cliente, naquele momento?**
2342- **5. Qual oferta aumenta conversão sem destruir margem?**
2343- **6. Qual sinal de navegação revela intenção antes do login?**
2344- **7. Qual jornada deve mudar agora, não amanhã?**
2345- **8. Qual argumento explica a recomendação para o canal ou time comercial?**
2346
2347Modelos genéricos de propensão também falham quando usam sinais frágeis, cluster