1import{e as P,b as q,o as n,g as o,z as w,h as t,F as r,r as l,t as s,n as M,j as p,i as u,w as h,k as G,A as O}from"./1BEqaQKX.js";import{d as D}from"./DEJ_tTaM.js";const E={class:"isolate"},L={class:"bg-white pb-16 sm:pb-20"},z={class:"mx-auto max-w-7xl px-6 lg:px-8"},H={class:"-mx-6 border-y border-gray-100 lg:hidden"},W={class:"text-xl font-medium text-gray-900"},F={class:"mt-4 divide-y divide-gray-100"},N={class:"text-[11px] uppercase tracking-widest text-gray-400"},$={class:"text-sm leading-relaxed text-gray-700 break-words"},j={class:"hidden overflow-x-auto lg:block"},U={class:"min-w-[1120px] divide-y divide-gray-100 text-sm"},Y={class:"bg-gray-50"},Q={class:"divide-y divide-gray-100 bg-white"},R={class:"px-4 py-4 font-medium text-gray-900 whitespace-nowrap"},V={class:"px-4 py-4 text-gray-700"},J={class:"px-4 py-4 text-gray-700"},K={class:"px-4 py-4 text-gray-700"},X={class:"px-4 py-4 text-gray-700"},Z={class:"px-4 py-4 text-gray-700"},tt={class:"px-4 py-4 text-gray-700"},et={class:"bg-white pb-16 sm:pb-20"},at={class:"mx-auto max-w-7xl px-6 lg:px-8"},st={class:"grid grid-cols-1 border-t border-gray-100 md:grid-cols-2 lg:grid-cols-3"},nt={class:"text-[11px] uppercase tracking-widest text-bruin-400 mb-2"},ot={class:"text-lg font-medium text-gray-900"},it={class:"mt-3 text-sm leading-relaxed text-gray-600"},rt={class:"bg-gray-50 py-16 sm:py-24"},lt={class:"mx-auto max-w-7xl px-6 lg:px-8"},ct={class:"grid grid-cols-1 md:grid-cols-2 gap-6"},dt={class:"flex items-baseline justify-between gap-4 mb-3"},pt={class:"text-xl font-medium text-gray-900"},gt={class:"text-[11px] uppercase tracking-widest text-bruin-400"},mt={class:"text-base leading-relaxed text-gray-700 mb-4"},ut={class:"grid grid-cols-1 sm:grid-cols-2 gap-4 text-sm border-t border-gray-100 pt-4"},ht={class:"text-gray-700"},yt={class:"text-gray-700"},xt={class:"bg-white py-16 sm:py-24"},bt={class:"mx-auto max-w-7xl px-6 lg:px-8"},wt={class:"grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4"},ft={class:"text-[11px] uppercase tracking-widest text-bruin-400 mb-2"},vt={class:"text-lg font-medium text-gray-900 mb-3"},kt={class:"text-sm leading-relaxed text-gray-600"},_t={class:"bg-gray-50 py-16 sm:py-24"},It={class:"mx-auto max-w-7xl px-6 lg:px-8"},At={class:"grid grid-cols-1 md:grid-cols-2 gap-4"},Bt={class:"text-lg font-medium text-gray-900 mb-3"},Ct={class:"text-base font-medium text-gray-900"},St={class:"mt-2 text-sm text-gray-600"},Tt={class:"bg-white py-16 sm:py-20 border-t border-gray-100"},Pt={class:"mx-auto max-w-7xl px-6 lg:px-8"},qt={class:"grid grid-cols-1 gap-4 sm:grid-cols-2 lg:grid-cols-4"},Mt={class:"text-sm font-medium text-gray-900"},Gt={class:"mt-2 text-sm leading-relaxed text-gray-600"},Ot={class:"bg-white py-16 sm:py-24"},Dt={class:"mx-auto max-w-4xl px-6 lg:px-8"},Et={class:"divide-y divide-gray-100 border-t border-gray-100"},Lt=["open"],zt={class:"flex cursor-pointer list-none items-start justify-between gap-6 py-5 text-left"},Ht={class:"text-base sm:text-lg font-medium text-gray-900 group-hover:text-bruin-400 transition-colors"},Wt={class:"pb-5 pr-12 text-base leading-relaxed text-gray-600"},Ft={class:"bg-white pb-16 sm:pb-20"},Nt={class:"mx-auto max-w-4xl px-6 lg:px-8"},$t={class:"border-t border-gray-100 pt-8"},jt={class:"mt-5 grid grid-cols-1 gap-3 text-sm sm:grid-cols-2"},Ut=["href"],Yt={class:"bg-gray-50 py-16 sm:py-24 border-t border-gray-100"},Qt={class:"mx-auto max-w-3xl px-6 lg:px-8 text-center"},Rt={class:"mt-8 flex flex-wrap gap-4 justify-center"},i="https://getbruin.com/comparisons/sana-ai-vs-bruin/",y="Sana AI vs Bruin - Best AI Work Platforms Compared (2026) | Bruin",g="Compare Sana AI, Bruin, Glean, Microsoft 365 Copilot, ChatGPT Enterprise, Dust, and Hebbia for enterprise AI work, agentic analytics, semantic layers, actions, and data activation.",Vt="2026-06-09",Jt="2026-06-16",te={__name:"sana-ai-vs-bruin",setup(Kt){const f=["Tool","Primary layer","Context","Actions","Data-native fit","Best for","Watch-out"],v=[{label:"Primary layer",key:"primaryLayer"},{label:"Context",key:"context"},{label:"Actions",key:"actions"},{label:"Data-native fit",key:"dataNative"},{label:"Best for",key:"bestFor"},{label:"Watch-out",key:"watchOut"}],m=[{tool:"Bruin",primaryLayer:"Data-native agents and pipeline suite",context:"Semantic layer, ingestion, SQL and Python transforms, quality checks, lineage, catalog, asset tiers, meta-keys, and warehouse context.",actions:"Answer questions, run analyses, trigger actions, activate cohorts, monitor tests, optimize SEO, and write back into operational workflows.",dataNative:"Very high",bestFor:"Teams that need accurate metrics, governed data access, pipeline context, and repeatable activation.",watchOut:"Most powerful when connected to real data assets and workflows."},{tool:"Sana AI",primaryLayer:"Horizontal AI work platform",context:"Company apps, documents, meetings, dashboards, and connected work tools.",actions:"No-code agents that automate, create, analyze, act, and find across tools.",dataNative:"Medium",bestFor:"Broad enterprise AI adoption across knowledge work, meetings, documents, dashboards, and apps.",watchOut:"Still needs a data-native layer when metric accuracy, lineage, and pipeline context matter."},{tool:"Glean",primaryLayer:"Enterprise search and work AI",context:"Enterprise graph, app connectors, hybrid search, permissions, and company context.",actions:"Agents with orchestration, governance, connectors, actions, APIs, and MCP gateway.",dataNative:"Medium",bestFor:"Companies where search, permissions-aware retrieval, and knowledge graph context are the foundation.",watchOut:"Usually complements a data platform rather than replacing semantic models and pipelines."},{tool:"Microsoft 365 Copilot",primaryLayer:"Microsoft workspace AI",context:"Microsoft 365 apps, Teams, SharePoint, business connectors, and Work IQ.",actions:"Ready-to-go agents, Agent Store, and custom agents through Copilot Studio.",dataNative:"Low to medium",bestFor:"Microsoft-standardized companies that want AI inside Teams, Outlook, Office, and SharePoint.",watchOut:"Best inside the Microsoft ecosystem. Data teams still need pipeline, semantic, and governance systems."},{tool:"ChatGPT Enterprise",primaryLayer:"General AI workspace",context:"Company data connectors, built-in apps, custom GPTs, files, and user-prov
1ided context.",actions:"ChatGPT agent, deep research, Codex, custom assistants, and app-based actions.",dataNative:"Medium",bestFor:"Flexible general AI across research, writing, coding, analysis, agents, and custom GPTs.",watchOut:"Production analytics still need external semantic definitions, lineage, checks, and repeatable activation paths."},{tool:"Dust",primaryLayer:"Collaborative agent operations",context:"Shared knowledge, tools, conversations, notifications, skills, and semantic company context.",actions:"Team-built agents for support, sales, marketing, engineering, data, and internal operations.",dataNative:"Medium",bestFor:"AI operators building reusable agents and shared workflows across teams.",watchOut:"Data reliability depends on how well your metric layer and warehouse context are represented."},{tool:"Hebbia",primaryLayer:"Institutional research and analysis",context:"Large document sets, multi-modal inputs, citations, and workflow-specific analytical context.",actions:"Multi-step research workflows with traceable agent work and institutional controls.",dataNative:"Low to medium",bestFor:"Finance, legal, consulting, diligence, and strategy teams analyzing large document sets.",watchOut:"Not primarily an open-source ingestion, transformation, quality, semantic layer, and activation stack."}],k=[{useCase:"Best for governed data answers and activation",pick:"Bruin",why:"Bruin has the pipeline, semantic, catalog, lineage, quality, and activation context that data-native agents need."},{useCase:"Best for broad workplace AI adoption",pick:"Sana AI",why:"Sana is strongest when teams want one AI layer across knowledge, meetings, docs, dashboards, apps, and no-code agents."},{useCase:"Best for enterprise search and permissions-aware retrieval",pick:"Glean",why:"Glean is built around enterprise context, app connectors, search, governance, and agent orchestration."},{useCase:"Best for Microsoft-standardized companies",pick:"Microsoft 365 Copilot",why:"Copilot fits companies that already live in Teams, Outlook, SharePoint, Office, Microsoft 365 Copilot Chat, and Copilot Studio."},{useCase:"Best for a flexible general AI workspace",pick:"ChatGPT Enterprise",why:"ChatGPT Enterprise is a broad AI workspace for research, writing, coding, analysis, connectors, custom GPTs, and agents."},{useCase:"Best for shared agents or document-heavy research",pick:"Dust or Hebbia",why:"Dust fits teams building reusable agents. Hebbia fits finance, legal, consulting, and strategy work over large document sets."}],_=[{name:"Bruin",tag:"Data-native suite",summary:"Bruin combines open-source ingestion and pipeline tooling with Bruin Cloud, semantic context, governance, and agents that can answer questions, perform actions, and activate data. It is not just a data analyst tool: agents can support workflows like SEO optimization, A/B testing cohorts, lifecycle segments, anomaly follow-ups, and campaign-ready audience lists.",win:"You need accurate data answers and action workflows grounded in semantic definitions, lineage, checks, and pipeline state.",watch:"If the project is only knowledge search across documents, a horizontal work AI platform may be enough."},{name:"Sana AI",tag:"AI work platform",summary:"Sana is a polished AI platform for real work: search, meetings, documents, dashboards, agents, and business app actions. It is a strong choice when the AI initiative spans many departments and the main goal is productivity across knowledge work.",win:"You want a broad AI layer for employees, documents, meetings, and no-code agents across business apps.",watch:"It does not replace governed data pipelines, semantic metric definitions, lineage, and warehouse-native activation."},{name:"Glean",tag:"Enterprise search",summary:"Glean is strongest when company knowledge, permissions-aware search, connected apps, and an enterprise graph are the core problem. It gives teams a strong base for AI over workplace context and governed agent rollout.",win:"You need AI grounded in company knowledge across Slack, Teams, docs, tickets, GitHub, and enterprise apps.",watch:"Search context is not the same as data pipeline context, quality checks, or semantic metrics."},{name:"Microsoft 365 Copilot",tag:"Microsoft-native",summary:"Microsoft 365 Copilot is the natural default for companies already standardized on Teams, Outlook, SharePoint, Office, Microsoft security, and Copilot Studio. It brings AI into the tools many employees already use every day.",win:"Your company lives in Microsoft 365 and wants AI inside existing Microsoft workflows and admin controls.",watch:"Warehouse analytics and activation still need a data stack that handles modeling, orchestration, and governance."},{name:"ChatGPT Enterprise",tag:"General AI workspace",summary:"ChatGPT Enterprise is a flexible AI workspace for research, writing, code, data analysis, custom GPTs, agents, and app-connected work. It is useful across many functions, especially when teams need a general frontier model interface.",win:"You want broad AI capability for many teams without committing to one narrow workflow.",watch:"Reliable production analytics still depend on semantic definitions, access rules, lineage, and checks outside the chat surface."},{name:"Dust",tag:"Agent workspace",summary:"Dust is a collaborative workspace for building and using shared agents, with tools, knowledge, skills, notifications, and workflows across teams. It fits teams with AI o
1perators who want to ship useful internal agents quickly.",win:"You have people building shared agents for support, sales, marketing, engineering, data, and operations.",watch:"It is not primarily a pipeline authoring, semantic modeling, data quality, and governed activation suite."},{name:"Hebbia",tag:"Institutional analysis",summary:"Hebbia is strong for multi-step analysis over large document sets, especially in finance, legal, consulting, diligence, and strategy. It emphasizes traceability and workflow-specific analysis for regulated or document-heavy work.",win:"The work is deep research over large document collections where transparency and citations matter.",watch:"It is less focused on day-to-day warehouse analytics, open-source ELT, transformations, and data activation."}],I=[{label:"Semantic layer",title:"Context-specific data answers",copy:"Bruin has a built-in semantic layer so agents can map business language to the right metrics, dimensions, joins, filters, and warehouse assets."},{label:"Activation",title:"Agents can do work after the answer",copy:"Bruin agents can perform data activation tasks such as building A/B testing cohorts, surfacing SEO optimization opportunities, triggering follow-ups, and preparing audience segments."},{label:"Pipeline context",title:"The agent sees how data is made",copy:"When Bruin owns more of the stack, agents can use lineage, freshness, checks, catalog metadata, asset tiers, and pipeline state to decide how much to trust an answer."},{label:"Suite",title:"Use all of Bruin or only the layers you need",copy:"Bruin includes CLI, ingestr, Cloud, agents, dashboards, lineage, catalog, quality, governance, and semantic context. Teams can adopt one layer or the full stack."},{label:"Interop",title:"Works with dbt, Airflow, and existing stacks",copy:"You can use Bruin for ingestion and agentic analytics while keeping dbt and Airflow. Using the end-to-end Bruin stack makes agent context stronger, but adoption is not all-or-nothing."},{label:"Governance",title:"Enterprise controls for data-native agents",copy:"Bruin Cloud adds SSO, RBAC, audit logs, private connectivity, VPC or on-prem patterns, catalog, lineage, cost insights, and data quality controls around agent workflows."}],A=[{situation:"want one broad AI work platform for company knowledge, meetings, documents, and business app actions",pick:"Sana AI",why:"Sana is built for horizontal AI adoption across many employee workflows."},{situation:"need search and answers grounded in company documents, permissions, and enterprise app context",pick:"Glean",why:"Glean is strongest when search, connectors, permissions, and the enterprise graph are the center."},{situation:"already run most employee work inside Teams, Outlook, SharePoint, Office, and Microsoft admin controls",pick:"Microsoft 365 Copilot",why:"Copilot is the default AI layer for Microsoft-standardized organizations."},{situation:"want the most flexible general AI surface for research, writing, coding, analysis, agents, and custom GPTs",pick:"ChatGPT Enterprise",why:"ChatGPT Enterprise gives teams a broad frontier-model workspace across many functions."},{situation:"have AI operators building shared agents, or analysts doing institutional research over large document sets",pick:"Dust or Hebbia",why:"Dust fits internal agent builders. Hebbia fits document-heavy, traceable institutional analysis."},{situation:"need agents that understand governed data, semantic metrics, pipelines, lineage, quality checks, and activation workflows",pick:"Bruin",why:"Bruin is data-native and can be used across ingestion, transformation, governance, semantic context, analysis, and activation."}],x=[{q:"What is the main difference between Sana AI and Bruin?",a:"Sana AI is a horizontal AI work platform for company knowledge, meetings, documents, dashboards, apps, and no-code agents. Bruin is a data-native suite for governed analytics, ingestion, transformations, semantic context, lineage, quality checks, and activation workflows."},{q:"Is Bruin just an AI data analyst?",a:"No. Bruin is a suite of products: open-source CLI, ingestr, Bruin Cloud, agents, dashboards, semantic layer, catalog, lineage, quality checks, and governance. The AI analyst is one part of the system."},{q:"Can Bruin agents perform actions, or only answer questions?",a:"Bruin agents can answer questions and perform actions. Examples include data activation tasks such as SEO optimization opportunities, A/B testing cohorts, lifecycle segmentation, anomaly follow-ups, and operational write-backs."},{q:"Can we use Bruin with dbt and Airflow?",a:"Yes. Bruin is interoperable layer by layer. You can use Bruin for ingestion and agentic analytics while continuing to use dbt for transformations and Airflow for orchestration. Using more of the Bruin stack gives agents stronger context, but it is not required."},{q:"Why does Bruin need a semantic layer?",a:"The semantic layer helps agents map business questions to the right metrics, dimensions, joins, filters, and warehouse assets. That makes answers more context-specific and reduces mistakes caused by raw schema guessing."},{q:"When should we choose Sana AI instead of Bruin?",a:"Choose Sana when the primary goal is broad AI productivity across knowledge search, meetings, documents, summaries, dashboards, and app actions. Choose Bruin when the work depen
1ds on governed company data, semantic definitions, pipelines, lineage, and data activation."},{q:"Which tools should be on the shortlist for AI work platforms?",a:"For horizontal AI work, shortlist Sana AI, Glean, Microsoft 365 Copilot, ChatGPT Enterprise, Dust, and Hebbia depending on your workflow. For data-native agents and activation, shortlist Bruin."},{q:"Is Bruin a Sana AI alternative?",a:"Bruin can be a Sana AI alternative when the use case is data-native: governed metrics, semantic analytics, pipeline-aware agents, and activation. Sana is still the better comparison point for broad employee productivity across knowledge work."}],B=[{title:"AI data analyst tools compared",href:"/comparisons/ai-data-analyst-tools/",copy:"Bruin, ThoughtSpot, Hex, Dot, Julius AI, Querio, Power BI Copilot, and ChatGPT or Claude."},{title:"Claude vs Bruin",href:"/comparisons/claude-vs-bruin/",copy:"How general AI assistants compare with data-native agentic analytics."},{title:"Power BI Copilot vs Bruin",href:"/comparisons/power-bi-copilot-vs-bruin/",copy:"Microsoft-native analytics AI compared with Bruin pipelines, governance, and agents."},{title:"Dot AI vs Bruin",href:"/comparisons/dot-ai-vs-bruin/",copy:"Slack-first AI analytics compared with Bruin as an interoperable data suite."}],C=[{label:"Sana AI product page",href:"https://sanalabs.com/products/sana/"},{label:"Glean Work AI platform",href:"https://www.glean.com/"},{label:"Microsoft 365 Copilot",href:"https://www.microsoft.com/en-us/microsoft-365-copilot"},{label:"OpenAI ChatGPT Enterprise release notes",href:"https://help.openai.com/en/articles/10128477-chatgpt-enterprise-edu-release-notes"},{label:"Dust AI workspace",href:"https://dust.tt/"},{label:"Hebbia product page",href:"https://www.hebbia.com/product"},{label:"Bruin open-source CLI",href:"https://github.com/bruin-data/bruin"},{label:"ingestr open-source ingestion",href:"https://github.com/bruin-data/ingestr"}],S=m.map(c=>({"@type":"SoftwareApplication",name:c.tool,applicationCategory:"BusinessApplication",description:c.bestFor}));return D({}),P({title:y,description:g,ogTitle:y,ogDescription:g,ogUrl:i,ogType:"article",twitterCard:"summary_large_image",twitterTitle:"Sana AI vs Bruin - Best AI Work Platforms Compared",twitterDescription:g,keywords:"sana ai vs bruin, sana ai alternatives, best ai work platform, ai work platform comparison, enterprise ai platform comparison, glean vs bruin, chatgpt enterprise vs bruin, microsoft copilot vs bruin, dust ai vs bruin, hebbia vs bruin, agentic analytics, data activation, semantic layer",robots:"index, follow"}),q({link:[{rel:"canonical",href:i}],script:[{type:"application/ld+json",innerHTML:JSON.stringify({"@context":"https://schema.org","@graph":[{"@type":"WebPage","@id":`${i}#webpage`,url:i,name:y,description:g,isPartOf:{"@type":"WebSite",name:"Bruin",url:"https://getbruin.com/"},breadcrumb:{"@id":`${i}#breadcrumb`},mainEntity:{"@id":`${i}#article`}},{"@type":"Article","@id":`${i}#article`,headline:"Sana AI vs Bruin: Best AI Work Platforms Compared",description:g,mainEntityOfPage:{"@id":`${i}#webpage`},author:{"@type":"Organization",name:"Bruin Data"},publisher:{"@type":"Organization",name:"Bruin Data",logo:{"@type":"ImageObject",url:"https://getbruin.com/bruin-logo-sm128.png"}},datePublished:Vt,dateModified:Jt,about:[{"@type":"Thing",name:"AI work platforms"},{"@type":"Thing",name:"Agentic analytics"},{"@type":"Thing",name:"Semantic layer"},{"@type":"Thing",name:"Data activation"}],mentions:S},{"@type":"BreadcrumbList","@id":`${i}#breadcrumb`,itemListElement:[{"@type":"ListItem",position:1,name:"Home",item:"https://getbruin.com/"},{"@type":"ListItem",position:2,name:"Comparisons",item:"https://getbruin.com/comparisons/"},{"@type":"ListItem",position:3,name:"Sana AI vs Bruin",item:i}]},{"@type":"ItemList","@id":`${i}#tools`,name:"AI work platforms compared",itemListElement:m.map((c,a)=>({"@type":"ListItem",position:a+1,name:c.tool,description:c.bestFor}))},{"@type":"FAQPage","@id":`${i}#faq`,mainEntity:x.map(c=>
1({"@type":"Question",name:c.q,acceptedAnswer:{"@type":"Answer",text:c.a}}))}]})}]}),(c,a)=>{const T=G,b=O;return n(),o("main",E,[a[18]||(a[18]=w('<div class="relative isolate bg-gray-50 border-b border-gray-100"><div class="mx-auto max-w-7xl px-6 pb-16 pt-10 sm:pb-16 sm:pt-32 lg:px-8"><div class="max-w-4xl"><p class="text-xs uppercase tracking-wide text-bruin-400 mb-4">Multi-tool comparison · 2026</p><h1 class="text-4xl font-light tracking-tight text-gray-900 sm:text-6xl leading-tight"> Sana AI vs Bruin: <br><span class="font-serif italic text-bruin-400">AI work platforms compared</span></h1><p class="mt-6 text-lg font-light leading-relaxed text-gray-600 max-w-3xl"> A practical comparison of Sana AI, Bruin, Glean, Microsoft 365 Copilot, ChatGPT Enterprise, Dust, and Hebbia. Sana is built for broad workplace AI. Bruin is built for data-native agents that understand pipelines, semantic context, governed metrics, and activation workflows. </p></div></div></div><div class="bg-white py-12 sm:py-16"><div class="mx-auto max-w-4xl px-6 lg:px-8"><div class="rounded-sm border border-gray-100 bg-gray-50 p-8"><p class="text-xs uppercase tracking-wide text-bruin-400 mb-3">TL;DR</p><p class="text-lg leading-relaxed text-gray-800"> Pick <strong class="text-gray-900">Sana AI</strong> when you want a broad AI work platform for knowledge search, meetings, documents, no-code agents, and business app actions. Pick <strong class="text-gray-900">Bruin</strong> when the work depends on company data: ingestion, transformations, quality checks, lineage, catalog, semantic definitions, governed access, and agents that can answer questions, take actions, and activate data into workflows like SEO optimization or A/B testing cohorts. </p></div></div></div><div class="bg-white pb-12 sm:pb-16"><div class="mx-auto max-w-7xl px-6 lg:px-8"><div class="border-y border-gray-100 py-10"><div class="grid grid-cols-1 gap-8 lg:grid-cols-[minmax(0,0.9fr)_minmax(0,1.1fr)] lg:gap-12"><div><h2 class="text-sm uppercase tracking-wide text-gray-500">Sana AI vs Bruin: short answer</h2><p class="mt-3 text-3xl font-light text-gray-900 sm:text-[32px]"> They solve different parts of the enterprise AI stack. </p></div><div class="space-y-5 text-base leading-relaxed text-gray-700"><p> Sana AI is a horizontal AI work platform for company knowledge, meetings, documents, app-connected workflows, and no-code agents. It is a strong fit when the question is: "How do we give many teams one AI layer for everyday work?" </p><p> Bruin is a data-native agentic analytics suite. It is a stronger fit when the question is: "How do we make AI understand our metrics, pipelines, data quality, lineage, access rules, semantic layer, and downstream activation workflows?" </p></div></div></div></div></div>',3)),t("div",L,[t("div",z,[a[0]||(a[0]=t("div",{class:"mb-10"},[t("h2",{class:"text-sm uppercase tracking-wide text-gray-500"},"At a glance"),t("p",{class:"mt-2 text-3xl font-light text-gray-900 sm:text-[32px]"},"Six horizontal AI work platforms, plus Bruin for data-native agents")],-1)),t("div",H,[(n(),o(r,null,l(m,e=>t("article",{key:`mobile-${e.tool}`,class:"border-b border-gray-100 px-6 py-6 last:border-b-0"},[t("h3",W,s(e.tool),1),t("dl",F,[(n(),o(r,null,l(v,d=>t("div",{key:`${e.tool}-${d.key}`,class:"grid grid-cols-1 gap-1 py-3 sm:grid-cols-[10rem_minmax(0,1fr)] sm:gap-4"},[t("dt",N,s(d.label),1),t("dd",$,s(e[d.key]),1)])),64))])])),64))]),t("div",j,[t("table",U,[t("thead",Y,[t("tr",null,[(n(),o(r,null,l(f,e=>t("th",{key:e,class:"px-4 py-3 text-left font-medium text-gray-500 whitespace-nowrap"},s(e),1)),64))])]),t("tbody",Q,[(n(),o(r,null,l(m,e=>t("tr",{key:e.tool},[t("td",R,s(e.tool),1),t("td",V,s(e.primaryLayer),1),t("td",J,s(e.context),1),t("td",K,s(e.actions),1),t("td",X,s(e.dataNative),1),t("td",Z,s(e.bestFor),1),t("td",tt,s(e.watchOut),1)])),64))])])])])]),t("div",et,[t("div",at,[a[1]||(a[1]=t("div",{class:"mb-10"},[t("h2",{class:"text-sm uppercase tracking-wide text-gray-500"},"Best AI work platform by use case"),t("p",{class:"mt-2 text-3xl font-light text-gray-900 sm:text-[32px]"},"The shortlist depends on what the agent needs to know")],-1)),t("div",st,[(n(),o(r,null,l(k,
1(e,d)=>t("div",{key:e.useCase,class:M(["border-b border-gray-100 py-6 md:px-6 lg:px-8",d%2!==0?"md:border-l md:border-l-gray-100 lg:border-l-0":"",d%3!==0?"lg:border-l lg:border-l-gray-100":""])},[t("p",nt,s(e.pick),1),t("h3",ot,s(e.useCase),1),t("p",it,s(e.why),1)],2)),64))])])]),t("div",rt,[t("div",lt,[a[4]||(a[4]=t("div",{class:"mb-10"},[t("h2",{class:"text-sm uppercase tracking-wide text-gray-500"},"One-paragraph reads"),t("p",{class:"mt-2 text-3xl font-light text-gray-900 sm:text-[32px]"},[p("What each platform actually "),t("span",{class:"font-serif italic text-bruin-400"},"does")])],-1)),t("div",ct,[(n(),o(r,null,l(_,e=>t("article",{key:e.name,class:"bg-white p-6 border border-gray-100 hover:border-bruin-400 transition-colors"},[t("div",dt,[t("h3",pt,s(e.name),1),t("span",gt,s(e.tag),1)]),t("p",mt,s(e.summary),1),t("div",ut,[t("div",null,[a[2]||(a[2]=t("p",{class:"text-[11px] uppercase tracking-widest text-gray-400 mb-1"},"Win condition",-1)),t("p",ht,s(e.win),1)]),t("div",null,[a[3]||(a[3]=t("p",{class:"text-[11px] uppercase tracking-widest text-gray-400 mb-1"},"Watch-out",-1)),t("p",yt,s(e.watch),1)])])])),64))])])]),t("div",xt,[t("div",bt,[a[5]||(a[5]=t("div",{class:"mb-10"},[t("h2",{class:"text-sm uppercase tracking-wide text-gray-500"},"Why Bruin is different"),t("p",{class:"mt-2 text-3xl font-light text-gray-900 sm:text-[32px]"},"Bruin is a suite, not only an AI data analyst")],-1)),t("div",wt,[(n(),o(r,null,l(I,e=>t("div",{key:e.title,class:"border border-gray-100 p-6 hover:border-bruin-400 transition-colors"}
1,[t("p",ft,s(e.label),1),t("h3",vt,s(e.title),1),t("p",kt,s(e.copy),1)])),64))])])]),t("div",_t,[t("div",It,[a[8]||(a[8]=t("div",{class:"mb-10"},[t("h2",{class:"text-sm uppercase tracking-wide text-gray-500"},"Pick by situation"),t("p",{class:"mt-2 text-3xl font-light text-gray-900 sm:text-[32px]"},[p("Decision matrix - which one fits "),t("span",{class:"font-serif italic text-bruin-400"},"you"),p("?")])],-1)),t("div",At,[(n(),o(r,null,l(A,e=>t("div",{key:e.situation,class:"border border-gray-100 bg-white p-6 hover:border-bruin-400 transition-colors"},[a[6]||(a[6]=t("p",{class:"text-sm text-gray-500 mb-2"},"If you...",-1)),t("p",Bt,s(e.situation),1),a[7]||(a[7]=t("p",{class:"text-[11px] uppercase tracking-widest text-bruin-400 mb-1"},"Pick",-1)),t("p",Ct,s(e.pick),1),t("p",St,s(e.why),1)])),64))])])]),a[19]||(a[19]=w('<div class="bg-gray-900 py-16 sm:py-24"><div class="mx-auto max-w-7xl px-6 lg:px-8"><div class="mx-auto max-w-3xl text-center"><h2 class="text-sm uppercase tracking-wide text-gray-400 mb-4">Our honest take</h2><p class="text-2xl sm:text-3xl font-light text-white leading-relaxed mb-8"> "Sana AI and the other horizontal platforms are good choices for broad knowledge work. Bruin wins when the agent has to understand how data is produced, which metrics are trusted, which cohorts should be activated, and what action should happen next." </p><p class="text-gray-400 leading-relaxed max-w-2xl mx-auto"> Bruin can be used layer by layer. Use Bruin for ingestion and agentic analytics while keeping dbt and Airflow, or use the end-to-end Bruin stack. The more context Bruin owns - semantic layer, lineage, checks, catalog, and pipeline state - the stronger the agent becomes. </p></div></div></div>',1)),t("div",Tt,[t("div",Pt,[a[9]||(a[9]=t("div",{class:"mb-10"},[t("h2",{class:"text-sm uppercase tracking-wide text-gray-500"},"Related Bruin comparisons"),t("p",{class:"mt-2 text-3xl font-light text-gray-900 sm:text-[32px]"},"Useful next reads for data teams")],-1)),t("div",qt,[(n(),o(r,null,l(B,e=>u(T,{key:e.href,to:e.href,class:"border border-gray-100 p-5 transition-colors hover:border-bruin-400"},{default:h(()=>
1[t("p",Mt,s(e.title),1),t("p",Gt,s(e.copy),1)]),_:2},1032,["to"])),64))])])]),t("div",Ot,[t("div",Dt,[a[11]||(a[11]=t("div",{class:"mb-10"},[t("h2",{class:"text-sm uppercase tracking-wide text-gray-500"},"FAQ"),t("p",{class:"mt-2 text-3xl font-light text-gray-900 sm:text-[32px]"},"What buyers ask most")],-1)),t("ul",Et,[(n(),o(r,null,l(x,(e,d)=>t("li",{key:e.q},[t("details",{class:"group",open:d===0},[t("summary",zt,[t("span",Ht,s(e.q),1),a[10]||(a[10]=t("span",{"aria-hidden":"true",class:"mt-1 shrink-0 rounded-full border border-gray-200 p-1 text-gray-400 transition-all group-hover:border-bruin-400 group-hover:text-bruin-400 group-open:rotate-45"},[t("svg",{class:"h-3.5 w-3.5",fill:"none",stroke:"currentColor","stroke-width":"2.5",viewBox:"0 0 24 24"},[t("path",{"stroke-linecap":"round",d:"M12 5v14M5 12h14"})])],-1))]),t("div",Wt,s(e.a),1)],8,Lt)])),64))])])]),t("div",Ft,[t("div",Nt,[t("div",$t,[a[12]||(a[12]=t("h2",{class:"text-sm uppercase tracking-wide text-gray-500"},"Sources checked",-1)),a[13]||(a[13]=t("p",{class:"mt-3 text-sm leading-relaxed text-gray-600"}," Product positioning changes quickly, so this page is based on each vendor's current public product pages and docs, plus Bruin's product information. ",-1)),t("ul",jt,[(n(),o(r,null,l(C,e=>t("li",{key:e.href},[t("a",{href:e.href,target:"_blank",rel:"noopener noreferrer",class:"text-gray-700 underline decoration-gray-300 underline-offset-4 transition-colors hover:text-bruin-400 hover:decoration-bruin-400"},s(e.label),9,Ut)])),64))])])])]),t("div",Yt,[t("div",Qt,[a[16]||(a[16]=t("h2",{class:"text-3xl font-light text-gray-900 sm:text-[32px]"},"See Bruin against your real data",-1)),a[17]||(a[17]=t("p",{class:"mt-4 text-gray-600 leading-relaxed"}," The fairest way to compare is to try it with your metrics, pipelines, and activation workflows. Bruin's open-source tools are available on GitHub, and Bruin Cloud adds governance, orchestration, semantic context, and agents. ",-1)),t("div",Rt,[u(b,{href:"https://github.com/bruin-data/bruin",external:"",variant:"primary",size:"lg"},{default:h(()=>[...a[14]||(a[14]=[p("Explore on GitHub",-1)])]),_:1}),u(b,{to:"/book-a-demo/",variant:"secondary",size:"lg"},{default:h(()=>[...a[15]||(a[15]=[p("Book a demo",-1)])]),_:1})])])])])}}};export{te as default};
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.