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d="M54.8169 11.0106C54.8169 12.1443 56.3227 12.6257 57.5337 12.8586H57.5492C58.729 13.1226 62.1289 13.9612 62.1289 16.7877C62.1289 19.0239 60.0176 20.5458 57.2853 20.5458C55.3602 20.5458 53.3731 19.7538 52.1466 18.5425L53.5439 17.1448C54.5219 17.999 55.8415 18.5891 57.2697 18.5891C58.698 18.5891 59.8778 17.9058 59.8778 16.8808C59.8778 15.6385 58.1857 15.2347 56.9127 14.9551C54.615 14.4271 52.5658 13.2779 52.5658 11.0883C52.5658 8.89858 54.5064 7.45432 57.2697 7.45432H57.3318C58.8688 7.45432 60.7472 8.01338 61.8495 9.36447L60.5299 10.6845C59.8313 9.95459 58.5427 9.41105 57.2232 9.41105C55.826 9.41105 54.8169 10.0633 54.8169 11.0106Z"></path></svg><figcaption class="sr-only">AKOS</figcaption></figure></a><div class="hidden xl:block shrink-0"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><p class="ui-text ui-text--variant-overline font-display tracking-wide uppercase ui-text--color-default text-white text-xs font-normal whitespace-nowrap">An AI And Data Studio</p></div></div></div><div class="flex-1"></div><ul class="hidden lg:flex items-center gap-8 shrink-0 mr-2"><li><a class="text-base font-display font-semibold text-white/80 hover:text-white transition-colors whitespace-nowrap" href="/palantir-partner">Palantir</a></li><li><a class="text-base font-display font-semibold text-white/80 hover:text-white transition-colors whitespace-nowrap" href="/industries">Industries</a></li><li><a class="text-base font-display font-semibold text-white/80 hover:text-white transition-colors whitespace-nowrap" href="/offerings">Offerings</a></li><li><a class="text-base font-display font-semibold text-white/80 hover:text-white transition-colors whitespace-nowrap" href="/arsenal">Arsenal</a></li><li><a class="text-base font-display font-semibold text-white/80 hover:text-white transition-colors whitespace-nowrap" href="/blog">Insights</a></li></ul><div class="flex items-center 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1ox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill="currentColor" d="M16.175 13H4V11H16.175L10.575 5.4L12 4L20 12L12 20L10.575 18.6L16.175 13Z"></path></svg></a></div><div><button class="group inline-flex items-center justify-center whitespace-nowrap gap-2 text-base font-semibold text-center transition-colors focus-visible:outline-white focus-visible:outline focus-visible:-outline-offset-4 disabled:bg-gray-400 disabled:cursor-not-allowed ui-button ui-button--variant-outline border ui-button--color-primary text-white bg-black border-primary hover:text-white hover:bg-primary-shade hover:border-primary focus:bg-primary-shade active:bg-primary-dark ui-button--size-sm h-[40px] leading-[40px] px-0 w-[40px]" aria-label="Open menu" type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-_R_2lb_" data-state="closed"><svg class="w-5 h-5" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" aria-hidden="true"><path fill="currentColor" d="M3 18V16H21V18H3ZM3 13V11H21V13H3ZM3 8V6H21V8H3Z"></path></svg></button></div></div></nav></div></header><div class="ui-navbar flex items-center flex-shrink-0 h-20 lg:h-28 gap-4" aria-hidden="true"></div><main id="main-content" class="main-content"><h1 class="sr-only">About</h1><section class="ui-section relative"><div class="w-full shrink-0 ui-section-body py-12 sm:py-24"><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-large max-w-screen-2xl"><div class="flex flex-col lg:flex-row"><div class="w-full lg:w-2/3"><div class="relative h-full flex flex-col border border-primary"><div class="absolute top-0 left-0 right-0 -translate-y-full -mx-px"><div class="cutout cutout-tr"><nav class="ui-navbar flex items-center flex-shrink-0 h-12 lg:h-16 gap-4 px-4 lg:px-8"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><p class="ui-text ui-text--variant-overline font-display font-semibold text-sm tracking-wide uppercase ui-text--color-primary text-primary">About</p></div></div></nav></div></div><div class="overflow-hidden space-y-8 p-6 md:p-12"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><p class="ui-text ui-text--variant-display font-display font-semibold text-5xl sm:text-6xl md:text-7xl lg:text-8xl ui-text--color-default text-white">Mildly obsessed with simplifying the complex.</p></div></div><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white">Dive into our world, where we untangle complexities, unlock possibilities, and uphold a people-first approach in technology. Come, let's simplify the complex together.</p></div></div></div></div></div><div class="w-full lg:w-1/3"><div class="relative h-full flex flex-col border border-primary -mt-px lg:-ml-px lg:mt-0"><div class="relative"><div class="pl-8 -mt-8"><img alt="Illustration" loading="lazy" width="1650" height="1974" decoding="async" data-nimg="1" style="color:transparent" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fimmutable%2Fmedia%2Fabout-illustration.2djm7sky6q5rm.png&w=1920&q=75 1x, /_next/image?url=%2F_next%2Fstatic%2Fimmutable%2Fmedia%2Fabout-illustration.2djm7sky6q5rm.png&w=3840&q=75 2x" src="/_next/image?url=%2F_next%2Fstatic%2Fimmutable%2Fmedia%2Fabout-illustration.2djm7sky6q5rm.png&w=3840&q=75"/></div></div><div class="absolute bottom-0 left-0 right-0 translate-y-full -mx-px"><div class="cutout cutout-bl"><nav class="ui-navbar flex items-center flex-shrink-0 h-12 lg:h-16 gap-4 justify-end px-6"><a class="flex items-center gap-2 text-xl lg:text-2xl font-display font-semibold text-white hover:text-primary-tint" href="/contact">Get in touch<svg class="w-6 lg:w-8 h-6 lg:h-8 shrink-0" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill="currentColor" d="M16.175 13H4V11H16.175L10.575 5.4L12 4L20 12L12 20L10.575 18.6L16.175 13Z"></path></svg></a></nav></div></div></div></div></div></div></div></section><section class="ui-section relative"><div class="w-full shrink-0 ui-section-body py-12 sm:py-24"><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-large max-w-screen-2xl"><div class="relative overflow-hidden pointer-events-none select-none py-24 pb-24"><div class="absolute z-20 top-0 left-0 w-full h-24 pointer-events-none bg-gradient-to-b from-black/100 to-black/0" aria-hidden="true"></div><div class="absolute z-20 bottom-0 left-0 w-full h-24 pointer-events-none bg-gradient-to-t from-black/100 to-black/0" aria-hidden="true"></div><img alt="World map of avatars" width="1502" height="1355" decoding="async" data-nimg="1" class="w-full max-w-2xl mx-auto" style="color:transparent" srcSet="/_next/image?url=%2F_next%2Fstatic%2Fimmutable%2Fmedia%2Fworld.0j28m_06_1w7i.png&w=1920&q=75 1x, /_next/image?url=%2F_next%2Fstatic%2Fimmutable%2Fmedia%2Fworld.0j28m_06_1w7i.png&w=3840&q=75 2x" src="/_next/image?url=%2F_next%2Fstatic%2Fimmutable%2Fmedia%2Fworld.0j28m_06_1w7i.png&w=3840&q=75"/></div></div><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-small max-w-screen-lg"><header class="mb-8 lg:mb-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h2 class="ui-text ui-text--variant-h1 font-display font-semibold text-4xl lg:text-6xl ui-text--color-default text-white">The Best of One World</h2></div></div></div></div></header><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="space-y-4"><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white font-bold">Local management backed by global talent. Weâre unique in our own way.</p><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white">Our strategists, managers, designers and tech-leads are all US-based, working from our HQ in Scottsdale and in San Diego. Supported by our distributed teams in Europe and Asia, giving you the best outcomes at a reasonable price. Our distributed teams are our own, engulfed in our culture, and following our processes. Our double-gated management approach matches each project with a US-based strategic PM and a remote PM, ensuring your expectations and our availability is always steady.</p><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white">We started AKOS as a generalized firm saying yes to nearly everything from church websites to custom email templates, and organically we started to grow. Clients came to us for more than their online presence; they came to us to make things work.</p><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white">Over time, we began to learn more about the challenges that larger organizations commonly faced: system integrations, outdated platforms, information bottlenecks ââthings that could be solved with technology. The more difficult the problem, the more we wanted to tackle it. (I told you we were weird).</p><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white">Needless to say, our crew of brilliant rascals has evolved into a close-knit team with global operations that helps large-scale institutions build dependable systems and rapidly-growing organizations scale comfortably. We take so much pride in making things possible for our clients and they know it.</p><div class="border-t border-primary h-0 w-36"></div><p class="ui-text ui-text--variant-h5 font-display font-semibold text-2xl md:text-2xl ui-text--color-default text-white">The most commonly used word to describe our team is "Sharp." The second most commonly used word to describe our team is "Caring."</p><p class="ui-text ui-text--variant-h5 font-display font-semibold text-2xl md:text-2xl ui-text--color-default text-white">Technology is for people and lucky for us, we have some of the best ones around.</p></div></div></div></div></div></section><section class="ui-section relative"><div class="w-full shrink-0 ui-section-body py-12 sm:py-24"><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-large max-w-screen-2xl"><header class="mb-12 lg:mb-24"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="space-y-4"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h2 class="ui-text ui-text--variant-h1 font-display font-semibold text-4xl lg:text-6xl ui-text--color-default text-white">Change is hard. We make it easier.</h2></div></div><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white max-w-screen-xl">The world is changing at an ever-faster rate and this fact hits large organizations harder than small ones. Any adjustment in technology whether new or redeveloped can be a difficult process. We make that process easier with systems that we have worked to perfect. Of course, gaining knowledge and developing skill isn't something that stops. We're in a quickly evolving industry and keeping up with best practices, key platforms, and the latest tools is critical to our expertise and our clients' success. It allows us to make the best possible recommendation at the best possible times.</p></div></div></div></header><div class="flex flex-col gap-20"><div class="flex flex-col md:flex-row gap-8 lg:gap-12 items-start md:items-center"><div class="md:w-[400px] shrink-0 flex justify-center items-center"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><svg class="w-auto h-32 lg:h-48" aria-hidden="true" viewBox="0 0 150 189" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M113.677 7L110.47 8.85051L110.51 40.3671L126.095 49.3644L129.302 47.5139L129.244 15.9857L113.677 7Z" fill="#F1F1F1" stroke="#F1F1F1" stroke-width="0.75" stroke-linecap="round" stroke-linejoin="round"></path><path d="M110.51 40.3677L110.47 8.85107L126.037 17.8426L126.095 49.365L110.51 40.3677Z" fill="#D7F4D4" stroke="#30A624" stroke-width="0.75" stroke-miterlimit="10"></path>
1<path d="M83.3979 186.53L83.3979 101.626" stroke="#30A624" stroke-width="0.75" stroke-linejoin="round"></path><path d="M85.9005 185.329C84.4969 184.522 82.2349 184.522 80.8313 185.329C79.4277 186.135 79.4451 187.457 80.8313 188.27C82.2175 189.076 84.4969 189.076 85.9005 188.27C87.3041 187.463 87.3042 186.158 85.8832 185.329" fill="#30A624"></path><path d="M47.2812 6.19105L56.1601 1.12573L67.0719 7.31102L58.1931 12.3763L47.2812 6.19105Z" fill="#F1F1F1" stroke="#30A624" stroke-width="0.75" stroke-linejoin="round"></path><path d="M140.167 141.982L149.051 136.917L148.983 158.94L140.104 164.005L140.167 141.982Z" fill="#F1F1F1" stroke="#30A624" stroke-width="0.75" stroke-linejoin="round"></path><path d="M140.224 122.907L149.102 117.842L149.051 136.917L140.167 141.982L140.224 122.907Z" fill="#F1F1F1" stroke="#30A624" stroke-width="0.75" stroke-linejoin="round"></path><path d="M58.1934 12.3761L67.0722 7.31079L97.6502 24.6398L88.7662 29.7051L58.1934 12.3761Z" fill="#F1F1F1" stroke="#30A624" 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d="M122.445 48.7906L122.347 82.6697L107.737 74.387L107.649 104.448L88.5845 93.6454L88.766 29.7053L122.445 48.7906Z" fill="white" stroke="#30A624" stroke-width="0.75" stroke-linejoin="round"></path><path d="M140.406 58.972L140.224 122.907L107.649 104.448L107.738 74.3868L122.347 82.6695L122.446 48.7904L140.406 58.972Z" fill="#ACDBA7" stroke="#30A624" stroke-width="0.75" stroke-linejoin="round"></path><path d="M4.22525 12L0.710449 14.0303L0.756824 33.7304L17.832 43.592L21.3468 41.5617L21.283 21.85L4.22525 12Z" fill="#F1F1F1" stroke="#F1F1F1" stroke-width="0.75" stroke-linecap="round" stroke-linejoin="round"></path><path d="M0.756824 33.73L0.710449 14.0299L17.7682 23.8799L17.832 43.5916L0.756824 33.73Z" fill="white" stroke="#30A624" stroke-width="0.75" stroke-linecap="round" stroke-linejoin="round"></path><path d="M104.197 159L102.132 160.195L102.161 171.745L112.172 177.528L114.236 176.339L114.196 164.778L104.197 159Z" fill="#F1F1F1" stroke="#F1F1F1" stroke-width="0.75" stroke-linecap="round" stroke-linejoin="round"></path><path d="M102.161 171.745L102.132 160.195L112.137 165.967L112.172 177.528L102.161 171.745Z" fill="white" stroke="#30A624" stroke-width="0.75" stroke-miterlimit="10"></path></svg></div></div></div><div class="grow"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="space-y-4"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h3 class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white">Modular Approach</h3></div></div><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">We modernize what already works by refactoring proven systems into composable, future-ready building blocks, without the risk of risky rewrites.</p></div></div></div></div></div><div class="flex flex-col md:flex-row 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stroke-miterlimit="10"></path><path d="M114.176 0.459595L110.969 2.3101L111.009 33.8267L126.594 42.824L129.801 40.9735L129.743 9.44529L114.176 0.459595Z" fill="#F1F1F1" stroke="#F1F1F1" stroke-width="0.75" stroke-linecap="round" stroke-linejoin="round"></path><path d="M111.009 33.8273L110.969 2.31067L126.536 11.3022L126.594 42.8246L111.009 33.8273Z" fill="#D7F4D4" stroke="#30A624" stroke-width="0.75" stroke-miterlimit="10"></path></svg></div></div></div><div class="grow"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="space-y-4"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h3 class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white">Palantir Architecture</h3></div></div><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">We build directly on Palantir Foundry and AIP, creating a unified operational layer where data, logic, and decisions live together.</p></div></div></div></div></div><div class="flex flex-col md:flex-row gap-8 lg:gap-12 items-start md:items-center"><div class="md:w-[400px] shrink-0 flex justify-center items-center"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><svg class="w-auto h-32 lg:h-48" aria-hidden="true" viewBox="0 0 140 174" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M45.8062 166.483L54.5989 171.46L94.7768 147.904L112.345 157.839" stroke="#30A624" stroke-width="0.75" stroke-linejoin="round"></path><path d="M122.303 158.759L130.841 163.69L139.355 158.77L139.332 148.92L130.795 143.99L122.28 148.909L122.303 158.759Z" fill="white" stroke="#30A624" stroke-width="0.75" stroke-linecap="round" stroke-linejoin="round"></path><path d="M130.818 153.84L139.332 148.92L130.795 143.99L122.28 148.909L130.818 153.84Z" 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104.213 32.5903 104.213 32.5903Z" fill="#F1F1F1" stroke="#F1F1F1" stroke-width="0.75" stroke-linejoin="round"></path><path d="M93.377 34.0961C93.377 43.1347 97.257 58.9357 101.204 61.2119C105.15 63.4882 109.03 52.3581 109.03 43.2862C105.117 41.0247 101.204 34.0961 101.204 34.0961C101.204 34.0961 97.2902 36.3576 93.377 34.0961Z" fill="#F1F1F1" stroke="#30A624" stroke-width="0.75" stroke-linejoin="round"></path></svg></div></div></div><div class="grow"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="space-y-4"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h3 class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white">AI-Driven Process Modernization</h3></div></div><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">We redesign processes around AI from day one, embedding intelligence into workflows so systems learn, adapt, and scale with the business.</p></div></div></div></div></div></div></div></div></section><div class="ui-section-background relative ui-pattern bg-center bg-repeat"><div class="from-transparent via-black to-black bg-[radial-gradient(circle_at_left,_var(--tw-gradient-stops))] via-60%"><section class="ui-section relative"><div class="w-full shrink-0 ui-section-body py-12 sm:py-24"><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-large max-w-screen-2xl"><header class="mb-6"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="relative"><div class="ui-type-effect absolute inset-0"></div><div aria-hidden="true" class="ui-type-effect opacity-[0.15] select-none pointer-events-none"><h2 class="ui-text ui-text--variant-h1 font-display font-semibold text-4xl lg:text-6xl ui-text--color-default text-white">Care is our <br/>
1competitive <br/>advantage.</h2></div></div></div></div></header><div class="ui-grid grid grid-cols-12 flex-wrap w-full gap-4 lg:gap-10"><div class="ui-grid-item col-span-12 md:col-span-6"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="space-y-4"><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">We know this section is supposed to make us look good. We also know that you know this section is supposed to make us look good. Here's the truth: we believe care is a competitive advantage, and we stand out in that department. Our clients are not just our clients. Our clients are people that can come to us for anything. And our team? Well our team is more than family. They're the kind of people that will give you the last roll of sushi. Not even family will do that.</p></div></div></div></div><div class="ui-grid-item col-span-12 md:col-span-6"><div class="ui-grid grid grid-cols-12 flex-wrap w-full gap-4 lg:gap-10"><div class="ui-grid-item col-span-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="space-y-4"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h3 class="ui-text ui-text--variant-h4 font-display font-semibold text-2xl lg:text-3xl ui-text--color-default text-white">Value-aligned hiring</h3></div></div><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">We know that a culture standard is more important than a process standard. The best organizations are full of people that operate based on who they are, not what they're told. We strive to make our company culture a place of innate knowing based on inner truth, and that's only possible when people share the same values.</p></div></div></div></div><div class="ui-grid-item col-span-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="space-y-4"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h3 class="ui-text ui-text--variant-h4 font-display font-semibold text-2xl lg:text-3xl ui-text--color-default text-white">Next-level empathy</h3></div></div><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">We know that a culture standard is more important than a process standard. The best organizations are full of people that operate based on who they are, not what they're told. We strive to make our company culture a place of innate knowing based on inner truth, and that's only possible when people know their values.</p></div></div></div></div><div class="ui-grid-item col-span-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="space-y-4"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h3 class="ui-text ui-text--variant-h4 font-display font-semibold text-2xl lg:text-3xl ui-text--color-default text-white">Progressive learning</h3></div></div><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">Our industry gives more value to experience than formal learning. The willingness to deep-dive into the dirt to earn intrinsic knowledge is a key difference that allows us to develop sharp solutions and share best practices with our industry colleagues. With transparency of progress, we move forward.</p></div></div></div></div></div></div></div></div></div></section></div></div><section class="ui-section relative"><div class="w-full shrink-0 ui-section-body py-12 sm:py-24"><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-large max-w-screen-2xl"><header class="mb-12 lg:mb-24"><div class="ui-grid grid grid-cols-12 flex-wrap w-full gap-4 lg:gap-10"><div class="ui-grid-item col-span-12 md:col-span-6"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h2 class="ui-text ui-text--variant-h1 font-display font-semibold text-4xl lg:text-6xl ui-text--color-default text-white">Our Values</h2></div></div></div></div></div><div class="ui-grid-item col-span-12 md:col-span-6"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">More than phrases posted on the lobby wall, these are the guiding principles of our everyday work.</p></div></div></div></div></header><div class="ui-grid grid grid-cols-12 flex-wrap w-full gap-4 lg:gap-10"><div class="ui-grid-item col-span-12 md:col-span-6 lg:col-span-4"><div class="border border-primary h-full md:min-h-[400px] overflow-hidden p-8 lg:p-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><svg class="w-16 h-16 mb-4" aria-hidden="true" viewBox="0 0 65 65" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M13.9697 51V14L32.4999 32.5L13.9697 51Z" fill="#30A624"></path><path d="M0 46.4086V18.5914L13.9313 32.5L0 46.4086Z" fill="#ACDBA7"></path><path d="M51.0302 51V14L32.5 32.5L51.0302 51Z" fill="#ACDBA7"></path><path d="M65.0002 46.4086V18.5914L51.0688 32.5L65.0002 46.4086Z" fill="#30A624"></path></svg><div class="space-y-4"><h3 class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white">Candid Conversations</h3><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">Leave no questions unasked. Sharp solutions don't always require reinvention - they just need revelation. We question our way to deeper understanding, to greater knowledge, towards meaningful conversations.
1</p></div></div></div></div></div><div class="ui-grid-item col-span-12 md:col-span-6 lg:col-span-4"><div class="border border-primary h-full md:min-h-[400px] overflow-hidden p-8 lg:p-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><svg class="w-16 h-16 mb-4" aria-hidden="true" viewBox="0 0 66 65" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M32.8335 0L0.333496 32.5114V0H32.8335Z" fill="#30A624"></path><path d="M65.3335 32.5L32.8335 65H65.3335V32.5Z" fill="#30A624"></path><path d="M0.333496 32.5L32.8449 65H0.333496V32.5Z" fill="#30A624"></path><path d="M32.8335 0L65.3335 32.5114V0H32.8335Z" fill="#30A624"></path><path d="M32.8449 49.0644C41.9869 49.0644 49.3979 41.6534 49.3979 32.5114C49.3979 23.3695 41.9869 15.9585 32.8449 15.9585C23.703 15.9585 16.292 23.3695 16.292 32.5114C16.292 41.6534 23.703 49.0644 32.8449 49.0644Z" fill="#ACDBA7"></path></svg><div class="space-y-4"><h3 class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white">Intrinsic Optimism</h3><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">Innovation is born of the belief that things can be better. We honor what already exists and build upon our findings to create new possibilities.</p></div></div></div></div></div><div class="ui-grid-item col-span-12 md:col-span-6 lg:col-span-4"><div class="border border-primary h-full md:min-h-[400px] overflow-hidden p-8 lg:p-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><svg class="w-16 h-16 mb-4" aria-hidden="true" viewBox="0 0 66 65" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M19.3003 13.8715L33.1718 0L47.0338 13.8715H19.3003Z" fill="#ACDBA7"></path><path d="M19.3003 13.8715L33.1718 27.7334L47.0338 13.8715H19.3003Z" fill="#30A624"></path><path d="M19.3003 51.1381L33.1718 65L47.0338 51.1381H19.3003Z" fill="#ACDBA7"></path><path d="M19.3003 51.1381L33.1718 37.2665L47.0338 51.1381H19.3003Z" fill="#30A624"></path><path d="M14.5385 46.3668L0.666992 32.5048L14.5385 18.6333V46.3668Z" fill="#ACDBA7"></path><path d="M14.5386 46.3668L28.4005 32.5048L14.5386 18.6333V46.3668Z" fill="#30A624"></path><path d="M51.8052 46.3668L65.6671 32.5048L51.8052 18.6333V46.3668Z" fill="#ACDBA7"></path><path d="M51.8051 46.3668L37.9336 32.5048L51.8051 18.6333V46.3668Z" fill="#30A624"></path></svg><div class="space-y-4"><h3 class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white">Valiant Code</h3><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">Be the one in the arena. As digital scientists, we consistently hypothesize, test, document, and iterate for continuous learning and fearless experimentation.</p></div></div></div></div></div><div class="ui-grid-item col-span-12 md:col-span-6 lg:col-span-4"><div class="border border-primary h-full md:min-h-[400px] overflow-hidden p-8 lg:p-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><svg class="w-16 h-16 mb-4" aria-hidden="true" viewBox="0 0 65 65" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M16.25 48.3862L32.5 32.1458L48.75 48.3958L16.25 48.3862Z" fill="#30A624"></path><path d="M32.5193 32.184L16.4321 16.0968L32.5289 0L32.5193 32.184Z" fill="#ACDBA7"></path><path d="M32.4808 0L48.568 16.0872L32.4712 32.184L32.4808 0Z" fill="#30A624"></path><path d="M38.4387 16.0946L32.2432 9.89905L26.0476 16.0946L32.2432 22.2901L38.4387 16.0946Z" fill="#D7F4D4"></path><path d="M32.5 64.7223L48.75 48.4723L65 64.7223H32.5Z" fill="#ACDBA7"></path><path d="M0 64.7223L16.25 48.4723L32.5 64.7223H0Z" fill="#ACDBA7"></path></svg><div class="space-y-4"><h3 class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white">Active Reliability</h3><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">Steady through change. We're committed to being trusted partners, producing work that people can depend on.</p></div></div></div></div></div><div class="ui-grid-item col-span-12 md:col-span-6 lg:col-span-4"><div class="border border-primary h-full md:min-h-[400px] overflow-hidden p-8 lg:p-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><svg class="w-16 h-16 mb-4" aria-hidden="true" viewBox="0 0 66 42" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M35.5049 41.546L50.419 26.632L65.3331 41.546H35.5049Z" fill="#ACDBA7"></path><path d="M39.8667 22.2608V1.17505H60.9621L39.8667 22.2608Z" fill="#30A624"></path><path d="M60.9719 1.17505V22.2608H39.8765L60.9719 1.17505Z" fill="#ACDBA7"></path><path d="M0.333496 41.7576C0.333496 33.4774 7.04435 26.7761 15.315 26.7761C23.5856 26.7761 30.2965 33.487 30.2965 41.7576H0.333496Z" fill="#30A624"></path><path d="M23.6047 19.998C19.0313 24.5714 11.6079 24.5714 7.03454 19.998C2.46114 15.4246 2.46114 8.00125 7.03454 3.42786L23.6047 19.998Z" fill="#ACDBA7"></path><path d="M7.03467 3.42785C11.6081 -1.14555 19.0314 -1.14555 23.6048 3.42785C28.1782 8.00125 28.1782 15.4246 23.6048 19.998L7.03467 3.42785Z" fill="#30A624"></path></svg><div class="space-y-4"><h3 class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white">Rich Diversity</h3><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">The full spectrum of perspective enriches us all. Our differences in background, preference, identity, and experience is something we seek and respect. We know deeply that this variety of spirit makes us better.</p></div></div></div></div></div><div class="ui-grid-item col-span-12 md:col-span-6 lg:col-span-4"><div class="border border-primary h-full md:min-h-[400px] overflow-hidden p-8 lg:p-12"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><svg class="w-16 h-16 mb-4" aria-hidden="true" viewBox="0 0 66 65" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M46.6488 40.7554C57.1575 40.7554 65.6764 32.3036 65.6764 21.8777C65.6764 11.4518 57.1575 3 46.6488 3C36.1401 3 27.6211 11.4518 27.6211 21.8777C27.6211 32.3036 36.1401 40.7554 46.6488 40.7554Z" fill="#30A624"></path><path d="M33.065 8.44192L6.15576 35.139L33.2282 61.9981L60.1374 35.301L33.065 8.44192Z" fill="#ACDBA7"></path><path d="M19.6947 40.7554C30.2034 40.7554 38.7223 32.3036 38.7223 21.8777C38.7223 11.4518 30.2034 3 19.6947 3C9.18597 3 0.666992 11.4518 0.666992 21.8777C0.666992 32.3036 9.18597 40.7554 19.6947 40.7554Z" fill="#ACDBA7"></path><path d="M33.0659 8.44958L60.1356 35.3059L33.2295 62.0001L33.0659 8.44958Z" fill="#ACDBA7"></path><path d="M19.6947 40.7554C30.2034 40.7554 38.7223 32.3036 38.7223 21.8777C38.7223 11.4518 30.2034 3 19.6947 3C9.18597 3 0.666992 11.4518 0.666992 21.8777C0.666992 32.3036 9.18597 40.7554 19.6947 40.7554Z" fill="#ACDBA7"></path><path d="M33.2777 8.44958L6.20801 35.3059L33.1142 62.0001L33.2777 8.44958Z" fill="#30A624"></path></svg><div class="space-y-4"><h3 class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white">Human Core</h3><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">Responsibility, Adaptation, Authenticity, Heart. We work with machines everyday, but that doesn't mean we let passion eclipse compassion. People still come first.</p></div></div></div></div></div></div></div></div></section><div class="ui-section-background relative ui-pattern bg-center bg-repeat"><div class="from-transparent via-black to-black bg-[radial-gradient(circle_at_right,_var(--tw-gradient-stops))] via-40%"><section class="ui-section relative"><div class="w-full shrink-0 ui-section-body py-12 sm:py-24"><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-large max-w-screen-2xl"><header class="mb-8"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h2 class="ui-text ui-text--variant-overline font-display font-semibold text-sm tracking-wide uppercase ui-text--color-default text-white">All kinds of stack</h2></div></div></header><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="flex flex-row justify-between flex-wrap"><div class="w-1/4 sm:w-1/6 lg:w-[10%]"><figure class="flex flex-col items-center text-center">
1<svg width="100" height="100" viewBox="0 0 100 100" fill="none" xmlns="http://www.w3.org/2000/svg" class="text-gray-600"><path d="M48.323 24.5507C48.9688 24.1896 49.6963 24 50.4362 24C51.1761 24 51.9036 24.1896 52.5494 24.5507C58.9842 28.1863 65.419 31.8207 71.8538 35.454C72.4589 35.7987 72.9627 36.2963 73.3149 36.897C73.667 37.4977 73.8552 38.1805 73.8604 38.8768V60.7504C73.849 61.4734 73.6426 62.1799 73.2631 62.7954C72.8836 63.4108 72.345 63.9124 71.7041 64.2472C65.2911 67.8633 58.8792 71.4805 52.4685 75.0989C51.8106 75.4626 51.0673 75.6434 50.3159 75.6225C49.5644 75.6016 48.8323 75.3798 48.1956 74.9801C46.2729 73.865 44.349 72.7528 42.4239 71.6434C42.0316 71.4094 41.5876 71.2235 41.3106 70.8415C41.5567 70.5111 41.9989 70.4698 42.3431 70.3252C43.1334 70.043 43.8956 69.6875 44.6198 69.2635C44.7109 69.21 44.8158 69.1849 44.9212 69.1913C45.0267 69.1978 45.1278 69.2355 45.2117 69.2996C46.8551 70.2426 48.4813 71.2132 50.135 72.1459C50.4792 72.3489 50.844 72.0788 51.1434 71.9119C57.4371 68.3554 63.7326 64.8025 70.0297 61.2529C70.1422 61.197 70.2353 61.1085 70.2967 60.9989C70.3581 60.8893 70.3849 60.7637 70.3739 60.6386C70.3739 53.4225 70.3739 46.2064 70.3739 38.9903C70.3854 38.85 70.3527 38.7095 70.2805 38.5886C70.2083 38.4677 70.1002 38.3723 69.9712 38.3158C63.58 34.7169 57.1905 31.1158 50.8027 27.5123C50.692 27.4365 50.5609 27.396 50.4267 27.396C50.2925 27.396 50.1614 27.4365 50.0507 27.5123C43.6606 31.1135 37.2717 34.7163 30.8839 38.3209C30.7544 38.3759 30.6456 38.4704 30.5729 38.5909C30.5003 38.7115 30.4676 38.8519 30.4795 38.9921C30.4795 46.2081 30.4795 53.4248 30.4795 60.642C30.4682 60.7663 30.4951 60.891 30.5566 60.9996C30.6181 61.1081 30.7113 61.1953 30.8237 61.2495C32.5285 62.2166 34.235 63.1803 35.9432 64.1405C36.9051 64.6568 38.0839 64.9648 39.144 64.569C39.6073 64.3942 40.0061 64.082 40.2869 63.6741C40.5677 63.2663 40.7171 62.7823 40.7151 62.2872C40.7237 55.1129 40.7151 47.9387 40.7151 40.7663C40.7131 40.69 40.7272 40.6143 40.7566 40.5439C40.786 40.4736 40.8299 40.4102 40.8855 40.3581C40.9411 40.3059 41.0071 40.2661 41.0792 40.2412C41.1512 40.2164 41.2278 40.207 41.3037 40.2139C42.1228 40.2139 42.9437 40.2035 43.7628 40.2139C43.8426 40.2175 43.9206 40.2379 43.992 40.2738C44.0634 40.3097 44.1264 40.3602 44.1769 40.4221C44.2274 40.4839 44.2643 40.5558 44.2852 40.6329C44.306 40.71 44.3104 40.7906 44.2979 40.8695C44.2979 48.0971 44.3066 55.3074 44.2979 62.5264C44.2979 64.4503 43.5098 66.5445 41.7305 67.4858C39.5381 68.6216 36.8277 68.3807 34.6629 67.2914C32.7872 66.3552 30.9992 65.2505 29.1562 64.2489C28.5145 63.9147 27.9752 63.413 27.5956 62.7971C27.2159 62.1811 27.0101 61.4739 27 60.7504V38.8768C27.0054 38.1665 27.2007 37.4706 27.5656 36.8612C27.9305 36.2518 28.4518 35.7511 29.0754 35.411C35.4964 31.7926 41.9123 28.1725 48.323 24.5507Z" fill="currentColor"></path><path d="M53.9163 39.7028C56.7127 39.5307 59.707 39.5961 62.2229 40.9745C64.1709 42.0311 65.2515 44.2441 65.2859 46.4107C65.2496 46.5477 65.165 46.6671 65.0477 46.7468C64.9304 46.8264 64.7883 46.8611 64.6475 46.8443C63.8353 46.8443 63.0248 46.8547 62.2125 46.8443C61.8684 46.8581 61.6687 46.5397 61.6257 46.2351C61.3934 45.2026 60.8273 44.1701 59.8533 43.6745C58.3561 42.926 56.6215 42.9638 54.9901 42.9862C53.7993 43.0499 52.5173 43.1583 51.5088 43.8466C50.7345 44.3766 50.4987 45.4487 50.7758 46.3109C51.0374 46.9304 51.7515 47.13 52.3366 47.3141C55.706 48.1952 59.2784 48.1074 62.5842 49.2673C63.9609 49.7405 65.2911 50.6594 65.7609 52.0929C66.3735 54.0134 66.105 56.309 64.7387 57.8509C63.6305 59.1191 62.0163 59.8109 60.4073 60.186C58.2666 60.6627 56.0433 60.6765 53.8681 60.4631C51.8238 60.2308 49.6951 59.6922 48.1171 58.3C46.7679 57.1281 46.1071 55.3023 46.1725 53.5384C46.189 53.396 46.2609 53.2657 46.3726 53.1759C46.4844 53.086 46.627 53.0438 46.7696 53.0583C47.587 53.0583 48.4045 53.0497 49.2202 53.0583C49.2958 53.0567 49.3711 53.0701 49.4416 53.0978C49.512 53.1254 49.5763 53.1668 49.6307 53.2194C49.6851 53.272 49.7285 53.3349 49.7585 53.4045C49.7885 53.474 49.8043 53.5488 49.8052 53.6245C49.9567 54.6105 50.3215 55.6465 51.1819 56.2315C52.8425 57.3036 54.9265 57.2296 56.828 57.264C58.4026 57.1935 60.1716 57.1728 61.4571 56.1317C62.1351 55.538 62.3364 54.5434 62.1454 53.6899C61.9475 52.9688 61.192 52.6315 60.5433 52.4113C57.2169 51.3581 53.6048 51.7401 50.3094 50.551C48.9723 50.0778 47.6782 49.1847 47.1637 47.8097C46.4461 45.8652 46.7748 43.4594 48.2857 41.9692C49.757 40.4875 51.8823 39.9162 53.9026 39.7114L53.9163 39.7028Z" fill="currentColor"></path></svg><figcaption class="text-sm text-gray-600 mt-3">Node JS</figcaption></figure></div><div class="w-1/4 sm:w-1/6 lg:w-[10%]"><figure class="flex flex-col items-center text-center"><svg width="100" height="100" fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 100 100" class="text-gray-600"><path fill-rule="evenodd" clip-rule="evenodd" d="M83.491 30.567c.025.092.038.186.038.281v14.77a1.069 1.069 0 0 1-.546.935L70.458 53.69v14.147c0 .385-.206.74-.543.934L43.77 83.667c-.06.
1034-.125.056-.19.079-.025.008-.048.022-.074.03a1.106 1.106 0 0 1-.557 0c-.03-.009-.057-.025-.086-.036-.06-.021-.122-.04-.18-.073L16.548 68.771a1.084 1.084 0 0 1-.547-.934v-44.31c0-.097.014-.192.038-.283.008-.031.027-.06.038-.09.02-.057.04-.115.07-.167.02-.035.05-.063.074-.096.032-.043.06-.087.097-.125.031-.03.072-.054.107-.08.04-.033.075-.068.12-.094h.001l13.07-7.448a1.1 1.1 0 0 1 1.088 0l13.07 7.448h.002c.044.027.08.061.12.092.035.027.075.051.106.08.038.04.065.084.098.127.023.033.054.06.073.096.032.054.05.11.071.167.011.03.03.059.038.091.025.092.038.187.038.282v27.675l10.891-6.207V30.847c0-.094.014-.19.038-.28.01-.032.027-.06.038-.091.022-.057.041-.115.071-.168.02-.035.05-.063.074-.095.032-.043.06-.088.097-.125.032-.031.071-.054.107-.081.04-.032.076-.067.12-.093l13.071-7.448a1.099 1.099 0 0 1 1.089 0l13.07 7.448c.045.027.08.06.121.092.035.027.074.05.105.08.038.04.066.084.098.127.025.032.055.06.074.095.03.053.049.11.07.168.013.03.03.059.038.091Zm-2.14 14.428V32.713l-4.574 2.606-6.319 3.6v12.283l10.894-6.207h-.001ZM68.28 67.213v-12.29l-6.215 3.513-17.748 10.027v12.406L68.28 67.213ZM18.18 25.391v41.822l23.96 13.654V68.464l-12.517-7.012-.004-.003-.006-.003c-.042-.024-.077-.059-.117-.088-.034-.027-.073-.049-.103-.078l-.003-.005c-.035-.033-.06-.075-.09-.113-.027-.036-.06-.067-.081-.105l-.002-.004c-.024-.04-.039-.088-.057-.134-.017-.04-.04-.078-.051-.121v-.002c-.014-.05-.017-.105-.022-.157-.005-.04-.016-.081-.016-.121v-28.92l-6.318-3.602-4.573-2.604v-.001Zm11.981-8.069-10.89 6.205 10.888 6.204 10.888-6.206-10.888-6.203h.002Zm5.663 38.721 6.318-3.6V25.391l-4.574 2.606-6.319 3.601v27.053l4.575-2.608Zm33.548-31.4-10.889 6.205 10.889 6.205 10.886-6.206-10.886-6.203ZM68.28 38.92l-6.318-3.601-4.574-2.606v12.282l6.317 3.6 4.575 2.607V38.92ZM43.227 66.598l15.97-9.024 7.984-4.51-10.88-6.2-12.527 7.138-11.417 6.506 10.87 6.09Z" fill="currentColor"></path></svg><figcaption class="text-sm text-gray-600 mt-3">Laravel</figcaption></figure></div><div class="w-1/4 sm:w-1/6 lg:w-[10%]"><figure class="flex flex-col items-center text-center"><svg width="100" height="100" viewBox="0 0 100 100" fill="none" xmlns="http://www.w3.org/2000/svg" class="text-gray-600"><path d="M50.0155 24L26 32.5558L29.6705 64.3288L50.0155 75.6241L70.6841 64.1963L74.6781 32.4044L50.0155 24ZM43.683 55.7161L40.6802 63.2343L35.0893 63.329L50.0155 30.1175L50.0534 30.0331L65.7403 63.4167L60.4058 63.3221L57.1156 55.7161H43.6933H43.683Z" fill="currentColor"></path><path d="M50.0915 40.6935L50.0158 40.8759L45.6432 51.2661H50.0158H55.1507L50.0915 40.6935Z" fill="currentColor"></path></svg><figcaption class="text-sm text-gray-600 mt-3">Angular</figcaption></figure></div><div class="w-1/4 sm:w-1/6 lg:w-[10%]"><figure class="flex flex-col items-center text-center"><svg width="100" height="100" viewBox="0 0 100 100" fill="none" xmlns="http://www.w3.org/2000/svg" class="text-gray-600"><path d="M56.6864 24.0002L49.8034 35.9221L42.9203 24.0002H19.9988L49.8034 75.6238L79.608 24.0002H56.6864Z" fill="currentColor"></path><path d="M56.6864 24.0002L49.8034 35.9221L42.9203 24.0002H31.9206L49.8034 54.9738L67.6861 24.0002H56.6864Z" fill="#A6ADB5"></path></svg><figcaption class="text-sm text-gray-600 mt-3">Vue JS</figcaption></figure></div><div class="w-1/4 sm:w-1/6 lg:w-[10%]"><figure class="flex flex-col items-center text-center"><svg width="100" height="100" viewBox="0 0 100 100" fill="none" xmlns="http://www.w3.org/2000/svg" class="text-gray-600"><path d="M68.7051 40.7324C68.0879 40.5204 67.465 40.3243 66.8362 40.1443C66.9408 39.7217 67.0374 39.297 67.1259 38.8701C68.5395 32.0047 67.616 26.4766 64.4578 24.6557C61.4302 22.9132 56.4792 24.7298 51.4783 29.0816C50.986 29.5173 50.5047 29.9594 50.0342 30.4081C49.7191 30.1061 49.3989 29.8099 49.0736 29.5194C43.8331 24.867 38.5796 22.9067 35.4257 24.7276C32.4024 26.4701 31.5051 31.678 32.7793 38.1818C32.9056 38.8265 33.0508 39.4669 33.2149 40.1029C32.4721 40.3207 31.7533 40.5385 31.0672 40.7781C24.9227 42.9192 21 46.2779 21 49.7585C21 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61.5464C65.4597 64.4782 65.5424 67.1311 65.1569 69.2047H65.1547ZM67.2588 56.8221C66.9408 56.9267 66.6053 57.029 66.283 57.127C65.4716 54.6128 64.4899 52.1567 63.3447 49.7759C64.4491 47.427 65.3951 45.0068 66.1762 42.5315C66.7687 42.7036 67.3459 42.8844 67.9014 43.076C73.2726 44.9252 76.5463 47.6501 76.5463 49.7628C76.5441 52.0063 73.0068 54.9184 67.2631 56.8156L67.2588 56.8221Z" fill="currentColor"></path><path d="M50.0086 54.9447C52.8704 54.9447 55.1904 52.6247 55.1904 49.7629C55.1904 46.9011 52.8704 44.5812 50.0086 44.5812C47.1468 44.5812 44.8269 46.9011 44.8269 49.7629C44.8269 52.6247 47.1468 54.9447 50.0086 54.9447Z" fill="currentColor"></path></svg><figcaption class="text-sm text-gray-600 mt-3">React JS</figcaption></figure></div><div class="w-1/4 sm:w-1/6 lg:w-[10%]"><figure class="flex flex-col items-center text-center"><svg width="100" height="100" viewBox="0 0 100 100" fill="none" xmlns="http://www.w3.org/2000/svg" class="text-gray-600"><path d="M78 22H22V78H78V22Z" fill="currentColor"></path><path d="M59.6178 65.7502C60.7458 67.592 62.2133 68.9458 64.8089 68.9458C66.9893 68.9458 68.3822 67.856 68.3822 66.3502C68.3822 64.5458 66.9511 63.9067 64.5511 62.8569L63.2356 62.2924C59.4382 60.6747 56.9156 58.648 56.9156 54.3636C56.9156 50.4169 59.9227 47.4124 64.6222 47.4124C67.968 47.4124 70.3733 48.5769 72.1067 51.6258L68.0089 54.2569C67.1067 52.6391 66.1333 52.0018 64.6222 52.0018C63.0809 52.0018 62.104 52.9796 62.104 54.2569C62.104 55.8356 63.0818 56.4747 65.3396 57.4524L66.6551 58.016C71.1262 59.9333 73.6507 61.888 73.6507 66.2827C73.6507 71.0204 69.9289 73.616 64.9307 73.616C60.0436 73.616 56.8862 71.2871 55.3413 68.2347L59.6178 65.7502ZM41.0285 66.2062C41.8551 67.6729 42.6071 68.9129 44.4151 68.9129C46.144 68.9129 47.2347 68.2364 47.2347 65.6062V47.7129H52.4969V65.6773C52.4969 71.1262 49.3022 73.6062 44.6391 73.6062C40.4258 73.6062 37.9858 71.4258 36.7449 68.7996L41.0285 66.2062Z" fill="black"></path></svg><figcaption class="text-sm text-gray-600 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text-center"><svg width="100" height="100" fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 100 100" class="text-gray-600"><path opacity=".3" d="M19.585 55.086 44.145 24H33.577L9 55.086h10.585Z" fill="currentColor"></path><path opacity=".6" d="M30.28 55.086 54.84 24H44.145l-24.56 31.086H30.28Z" fill="currentColor"></path><path d="M65 50.504 85.955 24H54.84L30.28 55.09h25.273A12.048 12.048 0 0 0 65 50.505Z" fill="currentColor"></path><path d="m72.82 44.888-6.546 8.435a12.061 12.061 0 0 1-9.53 4.663h-7.5L35.04 75.975h24.484a13.69 13.69 0 0 0 10.742-5.201l20.448-25.886H72.82Z" fill="currentColor"></path><path opacity=".6" d="M38.557 57.994 24.332 76h10.696l14.225-18.006H38.557Z" fill="currentColor"></path><path opacity=".3" d="M27.98 57.994 13.748 76h10.585l14.225-18.006H27.981Z" fill="currentColor"></path></svg><figcaption class="text-sm text-gray-600 mt-3">InstaBug</figcaption></figure></div><div class="w-1/4 sm:w-1/6 lg:w-[10%]"><figure class="flex flex-col items-center 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stop-opacity="0"></stop></linearGradient><linearGradient id="paint1_linear_1936_7343" x1="48.19" y1="48.197" x2="46.922" y2="46.929" gradientUnits="userSpaceOnUse"><stop stop-color="currentColor" stop-opacity=".8"></stop><stop offset=".5" stop-color="currentColor" stop-opacity=".21"></stop><stop offset="1" stop-color="currentColor" stop-opacity="0"></stop></linearGradient><linearGradient id="paint2_linear_1936_7343" x1="25.629" y1="30.266" x2="72.977" y2="77.613" gradientUnits="userSpaceOnUse"><stop stop-color="#fff" stop-opacity=".1"></stop><stop offset="1" stop-color="#fff" stop-opacity="0"></stop></linearGradient></defs></svg><figcaption class="text-sm text-gray-600 mt-3">Firebase</figcaption></figure></div></div></div></div></div></div></section></div></div><section class="ui-section relative"><div class="w-full shrink-0 ui-section-body py-12 sm:py-24"><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-large max-w-screen-2xl"><div class="ui-grid grid grid-cols-12 flex-wrap w-full gap-4 lg:gap-10"><div class="ui-grid-item col-span-12 md:col-span-6"><header class="mb-8"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><h2 class="ui-text ui-text--variant-overline font-display font-semibold text-sm tracking-wide uppercase ui-text--color-default text-white"><abbr title="Frequently Asked Questions" class="no-underline">FAQS</abbr></h2></div></div></header><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white">You have questions! We canât leave you curious for too long, thatâs how cats go to heaven (RIP Martini ð). Here are answers to questions we get asked often.</p></div></div></div><div class="ui-grid-item col-span-12 md:col-span-6"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><div class="w-full w-1/10" data-orientation="vertical"><div data-state="closed" data-orientation="vertical" class="border-t border-primary"><h3 data-orientation="vertical" data-state="closed" class="flex"><button type="button" aria-controls="radix-_R_16glubslb_" aria-expanded="false" data-state="closed" data-orientation="vertical" id="radix-_R_6glubslb_" class="flex flex-1 gap-4 py-4 transition-all [&[data-state=open]>svg]:rotate-180" data-radix-collection-item=""><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white grow text-left">How much does it cost?</p><svg class="w-6 h-6 mr-2 shrink-0 transition-transform duration-200" viewB
1ox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M12 15.4L6 9.4L7.4 8L12 12.6L16.6 8L18 9.4L12 15.4Z" fill="currentColor"></path></svg></button></h3><div data-state="closed" id="radix-_R_16glubslb_" hidden="" role="region" aria-labelledby="radix-_R_6glubslb_" data-orientation="vertical" class="overflow-hidden text-sm transition-all data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down" style="--radix-accordion-content-height:var(--radix-collapsible-content-height);--radix-accordion-content-width:var(--radix-collapsible-content-width)"></div></div><div data-state="closed" data-orientation="vertical" class="border-t border-primary"><h3 data-orientation="vertical" data-state="closed" class="flex"><button type="button" aria-controls="radix-_R_1aglubslb_" aria-expanded="false" data-state="closed" data-orientation="vertical" id="radix-_R_aglubslb_" class="flex flex-1 gap-4 py-4 transition-all [&[data-state=open]>svg]:rotate-180" data-radix-collection-item=""><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white grow text-left">Will you sign my NDA first?</p><svg class="w-6 h-6 mr-2 shrink-0 transition-transform duration-200" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M12 15.4L6 9.4L7.4 8L12 12.6L16.6 8L18 9.4L12 15.4Z" fill="currentColor"></path></svg></button></h3><div data-state="closed" id="radix-_R_1aglubslb_" hidden="" role="region" aria-labelledby="radix-_R_aglubslb_" data-orientation="vertical" class="overflow-hidden text-sm transition-all data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down" style="--radix-accordion-content-height:var(--radix-collapsible-content-height);--radix-accordion-content-width:var(--radix-collapsible-content-width)"></div></div><div data-state="closed" data-orientation="vertical" class="border-t border-primary"><h3 data-orientation="vertical" data-state="closed" class="flex"><button type="button" aria-controls="radix-_R_1eglubslb_" aria-expanded="false" data-state="closed" data-orientation="vertical" id="radix-_R_eglubslb_" class="flex flex-1 gap-4 py-4 transition-all [&[data-state=open]>svg]:rotate-180" data-radix-collection-item=""><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white grow text-left">Will you take over an existing project?</p><svg class="w-6 h-6 mr-2 shrink-0 transition-transform duration-200" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M12 15.4L6 9.4L7.4 8L12 12.6L16.6 8L18 9.4L12 15.4Z" fill="currentColor"></path></svg></button></h3><div data-state="closed" id="radix-_R_1eglubslb_" hidden="" role="region" aria-labelledby="radix-_R_eglubslb_" data-orientation="vertical" class="overflow-hidden text-sm transition-all data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down" style="--radix-accordion-content-height:var(--radix-collapsible-content-height);--radix-accordion-content-width:var(--radix-collapsible-content-width)"></div></div><div data-state="closed" data-orientation="vertical" class="border-t border-primary"><h3 data-orientation="vertical" data-state="closed" class="flex"><button type="button" aria-controls="radix-_R_1iglubslb_" aria-expanded="false" data-state="closed" data-orientation="vertical" id="radix-_R_iglubslb_" class="flex flex-1 gap-4 py-4 transition-all [&[data-state=open]>svg]:rotate-180" data-radix-collection-item=""><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white grow text-left">Who will I be working with?</p><svg class="w-6 h-6 mr-2 shrink-0 transition-transform duration-200" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M12 15.4L6 9.4L7.4 8L12 12.6L16.6 8L18 9.4L12 15.4Z" fill="currentColor"></path></svg></button></h3><div data-state="closed" id="radix-_R_1iglubslb_" hidden="" role="region" aria-labelledby="radix-_R_iglubslb_" data-orientation="vertical" class="overflow-hidden text-sm transition-all data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down" style="--radix-accordion-content-height:var(--radix-collapsible-content-height);--radix-accordion-content-width:var(--radix-collapsible-content-width)"></div></div><div data-state="closed" data-orientation="vertical" class="border-t border-primary"><h3 data-orientation="vertical" data-state="closed" class="flex"><button type="button" aria-controls="radix-_R_1mglubslb_" aria-expanded="false" data-state="closed" data-orientation="vertical" id="radix-_R_mglubslb_" class="flex flex-1 gap-4 py-4 transition-all [&[data-state=open]>svg]:rotate-180" data-radix-collection-item=""><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white grow text-left">Can you help me raise funds?</p><svg class="w-6 h-6 mr-2 shrink-0 transition-transform duration-200" viewB
1ox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M12 15.4L6 9.4L7.4 8L12 12.6L16.6 8L18 9.4L12 15.4Z" fill="currentColor"></path></svg></button></h3><div data-state="closed" id="radix-_R_1mglubslb_" hidden="" role="region" aria-labelledby="radix-_R_mglubslb_" data-orientation="vertical" class="overflow-hidden text-sm transition-all data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down" style="--radix-accordion-content-height:var(--radix-collapsible-content-height);--radix-accordion-content-width:var(--radix-collapsible-content-width)"></div></div></div></div></div></div></div></div></div></section><section class="ui-section relative overflow-hidden"><h2 class="sr-only">Contact us</h2><div class="w-full shrink-0 ui-section-body py-12 sm:py-24"><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-large max-w-screen-2xl"><div class="ui-grid grid grid-cols-12 flex-wrap w-full gap-8 lg:gap-18"><div class="ui-grid-item col-span-12 md:col-span-6"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><header class="mb-8"><div class="relative"><div class="absolute inset-0"></div><div aria-hidden="true" class="select-none pointer-events-none opacity-[0.15]"><p class="ui-text ui-text--variant-h1 font-display font-semibold text-4xl lg:text-6xl ui-text--color-default text-white">Let's Build Something Great Together!</p></div></div></header><p class="ui-text ui-text--variant-h3 font-display font-semibold text-2xl lg:text-4xl ui-text--color-default text-white mb-4">Ready to revolutionize your Industry or Organization?</p><p class="ui-text ui-text--variant-lead text-lg lg:text-xl ui-text--color-default text-white">Fill out the form with as much detail as possible. The more information you provide, the better we can tailor our questions and solutions to fit your unique needs. Let's take the first step towards creating something extraordinary together.</p></div></div></div><div class="ui-grid-item col-span-12 md:col-span-6"><div class="pl-10"><div class="ui-box-effect relative z-0 pb-10 pr-10 mt-10"><div class="ui-box-effect-lines absolute inset-0 w-full h-full border border-primary pointer-events-none z-[-1]" aria-hidden="true" style="opacity:0"></div><div class="ui-box-effect-lines absolute inset-0 w-full h-full border border-primary/60 pointer-events-none z-[-1]" aria-hidden="true" style="transform:none"></div><div class="ui-box-effect-lines absolute inset-0 w-full h-full border border-primary/40 pointer-events-none z-[-1]" aria-hidden="true" style="transform:none"></div><div class="ui-box-effect-lines absolute inset-0 w-full h-full border border-primary/20 pointer-events-none z-[-1]" aria-hidden="true" style="transform:none"></div><div class="ui-box-effect-content"><div class="bg-black border border-primary p-8 lg:p-12 -mb-10 -mr-10"><div class="ui-fade"><div class="transition ease-in-out duration-700 transform translate-y-32 opacity-0"><form><div aria-hidden="true" class="absolute -left-[9999px] top-0 h-px w-px overflow-hidden"><label for="_R_6qilubslb_">Website</label><input id="_R_6qilubslb_" type="text" tabindex="-1" autoComplete="off" name="website"/></div><div class="ui-grid grid grid-cols-12 flex-wrap w-full gap-4 lg:gap-10"><div class="ui-grid-item col-span-12"><fieldset><legend class="block text-white mb-2">What are you?</legend><div role="radiogroup" aria-required="false" dir="ltr" class="flex flex-col sm:flex-row gap-2 p-1.5 bg-primary/25" tabindex="-1" style="outline:none"><button type="button" role="radio" aria-checked="true" data-state="checked" value="Entrepreneur/Startup" class="grow px-3.5 py-2.5 cursor-pointer focus:outline-none focus:ring-primary focus:ring-2 focus:ring-offset-4 focus:ring-offset-black data-[state=checked]:bg-primary" tabindex="-1" data-radix-collection-item="">Entrepreneur/Startup</button><input type="radio" aria-hidden="true" style="transform:translateX(-100%);position:absolute;pointer-events:none;opacity:0;margin:0" tabindex="-1" checked="" value="Entrepreneur/Startup"/><button type="button" role="radio" aria-checked="false" data-state="unchecked" value="Organization/Enterprise" class="grow px-3.5 py-2.5 cursor-pointer focus:outline-none focus:ring-primary focus:ring-2 focus:ring-offset-4 focus:ring-offset-black data-[state=checked]:bg-primary" tabindex="-1" data-radix-collection-item="">Organization/Enterprise</button><input type="radio" aria-hidden="true" style="transform:translateX(-100%);position:absolute;pointer-events:none;opacity:0;margin:0" tabindex="-1" value="Organization/Enterprise"/></div></fieldset></div><div class="ui-grid-item col-span-12"><fieldset><legend class="block text-white mb-2">What are you working on?</legend><div class="ui-grid grid grid-cols-12 flex-wrap w-full gap-4 lg:gap-4"><div class="ui-grid-item col-span-12"><div class="flex items-start gap-2"><input id="Build New" type="radio" class="shrink-0 w-5 h-5 mt-0.5 text-primary bg-black border-white cursor-pointer focus:ring-primary focus:ring-2 focus:ring-offset-black" name="projectType" value="Build New"/><label for="Build New" class="flex-1 block text-base text-white cursor-pointer">Build New! (App, AI Agent, Platform, or Marketplace)</label></div></div><div class="ui-grid-item col-span-12"><div class="flex items-start gap-2">
1<input id="Improve Existing" type="radio" class="shrink-0 w-5 h-5 mt-0.5 text-primary bg-black border-white cursor-pointer focus:ring-primary focus:ring-2 focus:ring-offset-black" name="projectType" value="Improve Existing"/><label for="Improve Existing" class="flex-1 block text-base text-white cursor-pointer">Improve Existing (Takeover, Add Features, or Integrate)</label></div></div><div class="ui-grid-item col-span-12"><div class="flex items-start gap-2"><input id="Not Sure" type="radio" class="shrink-0 w-5 h-5 mt-0.5 text-primary bg-black border-white cursor-pointer focus:ring-primary focus:ring-2 focus:ring-offset-black" name="projectType" value="Not Sure"/><label for="Not Sure" class="flex-1 block text-base text-white cursor-pointer">I'm not sure, just want to talk</label></div></div></div></fieldset></div><div class="ui-grid-item col-span-12"><label for="description" class="block text-white mb-2">Tell us more about your Idea or Startup</label><textarea id="description" rows="4" class="block p-2.5 w-full text-base text-white bg-black border border-white focus:ring-primary focus:border-primary" name="description"></textarea></div><div class="ui-grid-item col-span-12 sm:col-span-6"><div class="flex flex-col gap-2"><label for="full_name" class="block text-white">What's your name?</label><input type="text" id="full_name" class="block w-full p-2.5 bg-black border border-white text-white text-base focus:ring-primary focus:border-primary" autoComplete="given-name" placeholder="John Doe" name="name"/></div></div><div class="ui-grid-item col-span-12 sm:col-span-6"><div class="flex flex-col gap-2"><label for="email_address" class="block text-white">What's your email?</label><input type="email" id="email_address" class="block w-full p-2.5 bg-black border border-white text-white text-base focus:ring-primary focus:border-primary" autoComplete="email" placeholder="[email protected]" name="email"/></div></div><div class="ui-grid-item col-span-12 sm:col-span-6"><div class="flex flex-col gap-2"><label for="phone_number" class="block text-white">Best number to Text you on?</label><input type="tel" id="phone_number" class="block w-full p-2.5 bg-black border border-white text-white text-base focus:ring-primary focus:border-primary" autoComplete="phone" placeholder="000-000-0000" name="phone"/></div></div><div class="ui-grid-item col-span-12 sm:col-span-6"><div class="flex flex-col gap-2"><label for="referred_by" class="block text-white">How did you find us?</label><input type="text" id="referred_by" class="block w-full p-2.5 bg-black border border-white text-white text-base focus:ring-primary focus:border-primary" placeholder="e.g. Google Search" name="referrer"/></div></div><div class="ui-grid-item col-span-12"><div class="flex items-center"><div class="shrink-0"><button class="group inline-flex items-center justify-center whitespace-nowrap gap-2 text-base font-semibold text-center transition-colors focus-visible:outline-white focus-visible:outline focus-visible:-outline-offset-4 disabled:bg-gray-400 disabled:cursor-not-allowed ui-button ui-button--variant-solid ui-button--color-primary text-white border-primary hover:text-white hover:bg-primary-shade hover:border-primary focus:bg-primary-shade active:bg-primary-dark ui-button--size-md px-5 h-[46px] leading-[46px] bg-primary" type="submit"><span>Submit</span><svg class="inline-block w-[1.25em] h-[1.25em] text-[1em] opacity-70 select-none shrink-0" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M20 6L9 17L4 12" stroke="currentColor" stroke-width="2.5" stroke-linecap="square"></path></svg></button></div><div class="ml-4"><p class="ui-text ui-text--variant-caption text-xs ui-text--color-default text-white">By submitting this form, you are agreeing to the <a href="/privacy" target="_blank" class="underline">privacy policy</a>.</p></div></div></div><div class="ui-grid-item col-span-12"><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white">Scared of your submission getting lost in transition? <br/><a href="mailto:[email protected]" class="underline">Just write us an email</a>.</p></div></div></form></div></div></div></div></div></div></div></div></div></div></section></main><!--$--><!--/$--></div><footer class="fixed z-[-1] bottom-0 left-0 right-0 w-full bg-gray-950"><section class="ui-section relative"><div class="w-full shrink-0 ui-section-body py-12 sm:py-24"><div class="w-full mx-auto px-4 sm:px-6 lg:px-8 ui-container ui-container--size-large max-w-screen-2xl"><ul class="ui-grid grid grid-cols-12 flex-wrap w-full gap-4 lg:gap-10" role="navigation"><li class="ui-grid-item col-span-12 lg:col-span-3"><p class="ui-text ui-text--variant-h5 font-display font-semibold text-2xl md:text-2xl ui-text--color-default text-white"><a href="mailto:[email protected]" title="Email us at [email protected]" target="_blank" class="block text-white hover:underline">[email protected]<svg class="w-[1em] h-[1em] inline-block ml-2" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" aria-hidden="true">
1<path fill="currentColor" d="M6.4 18L5 16.6L14.6 7H6V5H18V17H16V8.4L6.4 18Z"></path></svg></a></p></li><li class="ui-grid-item col-span-12 lg:col-span-3"><p class="ui-text ui-text--variant-h5 font-display font-semibold text-2xl md:text-2xl ui-text--color-default text-white"><a href="https://www.linkedin.com/company/akosai/" title="Find us on LinkedIn" target="_blank" rel="noreferrer" class="block text-white hover:underline">LinkedIn<svg class="w-[1em] h-[1em] inline-block ml-2" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" aria-hidden="true"><path fill="currentColor" d="M6.4 18L5 16.6L14.6 7H6V5H18V17H16V8.4L6.4 18Z"></path></svg></a></p></li><li class="ui-grid-item col-span-12 lg:col-span-3"><p class="ui-text ui-text--variant-h5 font-display font-semibold text-2xl md:text-2xl ui-text--color-default text-white"><a href="https://www.facebook.com/akosweb" title="Find us on Facebook" target="_blank" rel="noreferrer" class="block text-white hover:underline">Facebook<svg class="w-[1em] h-[1em] inline-block ml-2" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" aria-hidden="true"><path fill="currentColor" d="M6.4 18L5 16.6L14.6 7H6V5H18V17H16V8.4L6.4 18Z"></path></svg></a></p></li><li class="ui-grid-item col-span-12 lg:col-span-3"><p class="ui-text ui-text--variant-h5 font-display font-semibold text-2xl md:text-2xl ui-text--color-default text-white"><a href="https://x.com/akosweb" title="Find us on X" target="_blank" rel="noreferrer" class="block text-white hover:underline">X (Twitter)<svg class="w-[1em] h-[1em] inline-block ml-2" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" aria-hidden="true"><path fill="currentColor" d="M6.4 18L5 16.6L14.6 7H6V5H18V17H16V8.4L6.4 18Z"></path></svg></a></p></li><li class="ui-grid-item col-span-6 lg:col-span-3"><address class="not-italic space-y-2"><h6 class="ui-text ui-text--variant-h5 font-display font-semibold text-2xl md:text-2xl ui-text--color-default text-white"><a href="https://maps.app.goo.gl/crRtCscnYcmRZ34z9" target="_blank" rel="noreferrer" class="hover:underline">Scottsdale (HQ)</a></h6><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white"><a href="https://maps.app.goo.gl/crRtCscnYcmRZ34z9" target="_blank" rel="noreferrer" class="hover:underline">7720 E Evans Rd STE 209,<br/>Scottsdale, AZ 85260</a></p></address></li><li class="ui-grid-item col-span-6 lg:col-span-3"><address class="not-italic space-y-2"><h6 class="ui-text ui-text--variant-h5 font-display font-semibold text-2xl md:text-2xl ui-text--color-default text-white"><a href="https://maps.app.goo.gl/Y4HvarKNBrFh8Nk98" target="_blank" rel="noreferrer" class="hover:underline">San Diego</a></h6><p class="ui-text ui-text--variant-body1 text-base lg:text-lg ui-text--color-default text-white"><a href="https://maps.app.goo.gl/Y4HvarKNBrFh8Nk98" target="_blank" rel="noreferrer" class="hover:underline">550 W B St 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1<script>self.__next_f.push([1,"5f:I[31919,[\"/_next/static/immutable/chunks/24qflsk9zeoqt.js\",\"/_next/static/immutable/chunks/1vfbymuc0-njy.js\",\"/_next/static/immutable/chunks/0owpfdxp-u-k0.js\"],\"default\"]\n5e:T4b3,{\"@context\":\"https://schema.org\",\"@type\":\"Organization\",\"name\":\"AKOS\",\"legalName\":\"AKOS\",\"url\":\"https://akos.ai\",\"logo\":\"https://akos.ai/icon.png\",\"description\":\"AKOS is a certified Palantir partner and AI \u0026 data studio shipping production-ready systems on Foundry, AIP, and Ontology â in weeks, not quarters.\",\"foundingDate\":\"2015\",\"founder\":{\"@type\":\"Person\",\"name\":\"Sahil Saini\",\"jobTitle\":\"Founder \u0026 CEO\",\"sameAs\":[\"https://www.linkedin.com/in/sahilakos\",\"https://x.com/sahilakos\"]},\"sameAs\":[\"https://www.linkedin.com/company/akosai\",\"https://twitter.com/akosweb\",\"https://www.facebook.com/akosweb\"],\"address\":[{\"@type\":\"PostalAddress\",\"streetAddress\":\"7720 E Evans Rd STE 209\",\"addressLocality\":\"Scottsdale\",\"addressRegion\":\"AZ\",\"postalCode\":\"85260\",\"addressCountry\":\"US\"},{\"@type\":\"PostalAddress\",\"streetAddress\":\"550 W B St 4th Floor\",\"addressLocality\":\"San Diego\",\"addressRegion\":\"CA\",\"postalCode\":\"92101\",\"addressCountry\":\"US\"},{\"@type\":\"PostalAddress\",\"streetAddress\":\"215 Spadina Ave. 4th Floor\",\"addressLocality\":\"Toronto\",\"addressRegion\":\"ON\",\"postalCode\":\"M5T 2C7\",\"addressCountry\":\"CA\"}],\"contactPoint\":{\"@type\":\"ContactPoint\",\"email\":\"[email protected]\",\"contactType\":\"customer service\"}}60:T57d8,*A field guide for Foundry developers, FDEs, and ML engineers shipping agents that live outside the platform but think with it.*\n\n## Who this is for\n\nFoundry developers, FDEs, and ML engineers who already work inside AIP and Agent Studio but are being pushed by a specific model requirement, a custom weights file, or GPU economics into hosting the agent somewhere else. This is also for the platform owner who's been asked \"why can't we just run this in Foundry?\" and needs a defensible answer either direction.\n\nIf you're standing up your first agent and don't have a specific model constraint, this isn't your post yet. Start in [AIP Agent Studio](https://www.palantir.com/docs/foundry/aip-agent-studio) and come back here only when you hit a wall you can name.\n\n## The pattern\n\nThe pattern shows up the same way across our builds. A customer wants an agent. The agent has a real job, not a chat demo, so it has to read and write through the Ontology. We start the scoping conversation inside AIP, where the agent should be. Two weeks later somebody on the call says one of three sentences and the architecture changes:\n\n\u003e \"We've fine-tuned a Llama variant on our own corpus and we need to use it.\"\n\n\u003e \"The model has to run in this specific quantization to fit on the GPU we already own.\"\n\n\u003e \"We've been waiting on H100 capacity in our Compute Module queue for three weeks.\"\n\nThat's it. That's the whole reason this guide exists. The agent still belongs to the Foundry workflow. The Ontology is still the system of record. The write-back, the audit trail, the human-in-the-loop review, all of that stays inside the platform. Only the model and the agent loop move outside. The tools the agent calls reach back into the Ontology over OSDK.\n\nWhat follows is what we've learned doing this on real engagements, with the parts you can copy.\n\n\n## 1. When to host an agent outside Foundry\n\nDefault position: don't. AIP and [Compute Modules](https://www.palantir.com/docs/foundry/compute-modules/overview) cover more agent work than most teams realize, and every layer of platform you give up is a layer you have to rebuild yourself. Governance, RBAC tied to the Ontology, write-back audit, eval harnesses, the AIP Logic surface where business users can read the rules. All of that you keep for free if the agent stays inside.\n\nYou leave the platform for one of two reasons.\n\n### Model control\n\nAIP's catalog covers most agent work. The moment you need a specific open-source model, custom-trained or fine-tuned weights, or a particular quantization, self-hosting starts to look reasonable. Compute Modules can run any container, which technically gets you there, but GPU availability inside a shared platform queue is the part that bites. We've watched a queue back up for days during a tenant-wide busy period and the agent we were trying to ship just sat there.\n\nIf your team has a Llama variant they've fine-tuned on internal docs, or a quantized Qwen they need at a specific INT4 precision to fit the memory budget, or a model that simply isn't in the AIP catalog yet, that's a real model-control problem. Don't fight it. Host the model where you can control the runtime and reach back into Foundry over OSDK.\n\n### GPU e
1conomics\n\nThis one is more nuanced than it looks. Compute Modules share platform-level resource pools. That works fine for CPU-bound workloads and modest inference. The moment you need a specific GPU SKU, or an H100 for throughput, or steady access to a 4-GPU node for a 70B model, you're competing with every other workload on the platform. It can get slow. It can also get expensive in ways that are hard to forecast because the cost shape lives inside platform usage, not a per-hour bill.\n\nOutside Foundry, GPU economics get more transparent but not necessarily cheaper. Lambda Labs, Modal, Replicate, Runpod, the on-prem cluster your IT team set up two years ago, all have their own cost shapes, cold-start behavior, and scale-down knobs. The trade is: you get predictability and control over GPU choice, you give up the integration tax of doing your own networking, secret management, and observability outside the platform.\n\nThe deciding question is rarely cost in isolation. It's *can we get the GPU we need, when we need it, with the model we need*. If yes inside Foundry, stay inside. If no, leave with a plan.\n\n\n## 2. Defining the agent and choosing the model\n\nThis part gets skipped more often than it should. Teams jump from \"we need an agent\" to \"which model do we use\" without writing down what the agent is actually allowed to do.\n\nBefore you touch a model, write down three things.\n\n1. **What is the agent allowed to do.** Read the Ontology? Write back? Call which functions? Hit which compute modules? Trigger which Actions? If you can't enumerate the allowed surface, you can't scope a tool layer.\n2. **What tools does it have.** One per Ontology query you want to expose. One per function. Plus any side tools (search, math, format helpers) that don't touch Foundry. A flowchart helps more than a paragraph here. Draw it.\n3. **What shape are inputs and outputs.** Free text in, structured JSON out, going where. The agent's outputs are the contract with the rest of the system.\n\nThe scope is part of the design. Don't let scope drift into \"the agent can do anything.\" That's how you ship a thing that's confidently wrong on 12% of the cases and nobody notices.\n\n### Choosing the model\n\nOnce the scope is locked, the model choice has three real axes.\n\n**Tool-calling ability.** A model that can write paragraphs and a model that can reliably emit tool calling syntax in the exact format your dispatcher expects are not the same model. Look up the tool-calling benchmark for your candidate before you commit. Open-source models with strong tool-calling priors save you weeks of dispatcher-tuning later.\n\n**Context window.** Be honest about what the agent actually reads in a turn. If you're feeding it the last 5 Ontology object summaries plus the user message, 32k context is fine. If you're stuffing in a full document plus tool outputs from a multi-step chain, you'll feel the cost of 128k context fast. This is also highly relevant for the inference infrastructure you choose which is discussed below.\n\n**Latency under your expected load.** A 70B model running at 4 tokens/sec on a single H100 is unusable for a chat agent and fine for an overnight batch agent. Profile under realistic concurrency before you sign off.\n\n### Deployment framework\n\nFor the runtime, the practical shortlist:\n\n- [llama.cpp](https://github.com/ggml-org/llama.cpp) and [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) for GGUF models, excellent for custom deployment on on demand GPU platforms. \n- [vLLM](https://docs.vllm.ai/) for high-throughput GPU serving. PagedAttention plus continuous batching matters at scale.\n- LM Studio for the laptop iteration loop before you commit to a server. With recent versions adding MLX support, it's also a credible deployment target on its own: Mac or server.\n\n### Memory math, briefly\n\nModel size in VRAM is rarely just the parameter footprint. KV cache grows with context window. If you allow 128k context on a 70B INT8 model, the cache eats serious VRAM before the first request lands. Balance:\n\n- GPU memory budget (the actual SKU you have)\n- Model size and quantization\n- Maximum context window you'll allow\n- Concurrency target (each in-flight request has its own KV cache)\n- Reasoning depth you need from the model\n\nAlso pay attention to GPU memory bandwidth, not just capacity. Throughput-bound workloads on a slower memory subsystem hit a ceiling you can't optimize past in software.\n\n### Respect the model's default config\n\nThis is the part that quietly destroys self-hosted agent quality. Some open-source models ship with a recommended default config: temperature, top_p, repeat penalty, sometimes a specific system prompt template. The model card on Hugging Face usually has it. Many open models are tuned to perform optimally under strict configs. If you copy a temperature setting from a different model because somebody on the team thinks 0.7 is the \"creative\" setting, you get sub-optimal outputs that look fine in dev and drift in production.\n\nBelow is the structural pattern you'll see across open models, treat the values as placeholders and pull the real ones from the model card:\n\n```python\nself.llm = Llama(\n model_path=MODEL_PATH,\n n_ctx=131072,\n n_gpu_layers=-1,\n verbose=True,\n)\nresponse = self.llm(\n prompt,\n max_tokens=16384,\n temperature=0.0,\n top_p=0.95,\n repeat_penalty=1.1,\n)\n```\n\nThree things to notice. `n_ctx=131072` is the full context the runtime allocates per session, which is also what sets the KV cache ceiling. `n_gpu_layers=-1` pushes every layer to GPU; if you're memory-constrained, this gets reduced and inference slows. `temperature=0.0` plus `top_p=0.95` plus `repeat_penalty=1.1` is the recommended config from the model's own card. Use the card. Don't guess.\n\n## 3. Implementing the OSDK-backed tool layer\n\nA Foundry-aware agent that lives outside Foundry is, structurally, a reasoning loop with hands. The hands are the tool layer. The tool layer talks to the Ontology over OSDK.\n\nThere are several tool frameworks: [LangChain](https://www.langchain.com/), [Haystack](https://haystack.deepset.ai/), [LlamaIndex](https://www.llamaindex.ai/), the OpenAI Agents SDK, the Anthropic SDK's tool use loop. They all roughly do the same thing: take a model's tool-call output, dispatch to a Python function, return the result back into context.\n\nBefore you pick a framework, do the two prerequisite steps.\n\n### Expose the access points as OSDK first\n\n
1If you did sections 1 and 2 right, you already know what the agent needs to access. Whether that's an Ontology object set, a Function, a Compute Module, an Action, or a dataset, expose each one through the [Ontology SDK](https://www.palantir.com/docs/foundry/ontology-sdk/overview) and test it from a plain Python script before the agent ever runs. Use the [Python OSDK](https://www.palantir.com/docs/foundry/ontology-sdk/python-osdk).\n\nTwo reasons. First, if the OSDK call fails standalone, debugging it from inside a reasoning loop is miserable. The model's tool-call output, the dispatcher, the framework, and the OSDK call all become suspect at once. Second, the OSDK contract you build here is what the rest of the agent code stabilizes against. Get it right early.\n\n### Create a long-lived Foundry token\n\nCreate a service-account-style token with scope limited to the Ontology entities the agent needs. The OSDK uses a token that is scoped only to the ontological entities your application wants to access, in addition to the user's own permissions. Tighten that scope. The model is going to call these tools without a human in the loop; the token's permissions are the safety boundary.\n\nRotate the token on a schedule. Store it in your hosting platform's secret manager, not in the container image. If your hosting platform doesn't have a secret manager, you picked the wrong hosting platform.\n\n### Write the tools narrow\n\nEach tool is a function. It takes typed arguments. It calls OSDK. It returns JSON the model can read. You can add complex logic inside the function before returning, that's fine, but the surface the model sees is narrow.\n\nOne tool, one purpose. A single tool with twenty optional arguments confuses the model. Two narrow tools the model can pick between work better than one fat tool with a `mode` parameter, except when you genuinely have one operation with three minor variants. The example below is the borderline case where a `mode` parameter is reasonable because the underlying queries differ only by which Ontology query they hit.\n\n```python\ndef my_tool_name(self, arg1, arg2=None, mode=\"default\"):\n \"\"\"\n Wraps an OSDK Ontology query as an agent-callable tool.\n Returns a JSON string the model can read.\n \"\"\"\n try:\n from foundry_sdk_runtime import AllowBetaFeatures\n client = self.get_osdk_client()\n\n with AllowBetaFeatures():\n if mode == \"option_a\":\n result = client.ontology.queries.my_query_a(\n param_1=arg1,\n param_2=arg2 or \"\",\n )\n elif mode == \"option_b\":\n result = client.ontology.queries.my_query_b(\n param_1=arg1,\n )\n else:\n result = client.ontology.queries.my_query_default(\n param_1=arg1,\n param_2=arg2 or \"\",\n )\n\n # Flatten Ontology objects into plain JSON-safe dicts\n results = [{\n \"id\": str(getattr(r, \"id\", \"\")),\n \"label\": str(getattr(r, \"label\", \"\")),\n \"score\": round(float(getattr(r, \"score\", 0)), 4),\n } for r in result]\n\n return json.dumps({\n \"total_found\": len(results),\n \"mode\": mode,\n \"results\": results,\n })\n\n except Exception as e:\n return json.dumps({\"error\": str(e), \"total_found\": 0, \"results\": []})\n```\n\nThree things worth pointing out. The flattening step matters: Ontology objects don't always serialize directly, and a tool that returns a `TypeError` mid-loop kills the agent. The error path returns a structured payload, not a stack trace, so the model can read the failure and decide whether to retry or escalate. \n\n### The description matters as much as the wrapper\n\nThis is where most self-hosted agents are weakest, and where AKOS's [LLM Context Filter](https://akos.ai/arsenal) module came out of repeated tuning passes.\n\nThe tool's docstring or schema description is what the model reads to decide whether to call it. Lead with *when to use* the tool, not *what it does*. Name parameters the way the model would naturally describe them, not the way your database schema does. If you're using an open-source model that doesn't have strong tool-calling priors, include an exact call example inside the description. The model will pattern-match on it.\n\nConcretely, a tool description like *\"Looks up part inventory by part_id\"* underperforms *\"Use this tool when the user is asking about whether a specific part is in stock or how many units are available. Example: lookup_part(part_id='X-4421'). Returns count, location, and last-updated timestamp.\"*\n\n### Pick the framework based on the model\n\nHosted models with native tool-calling (GPT-4 class, Claude, Gemini) work well with LangChain or LlamaIndex out of the box, because the framework's parser is built around their output format.\n\nOpen-source models with custom output formats often fight the framework. Tool-call parsing fails silently, retries cascade, and you end up debugging escape characters in a chat template at 11pm. In that case, rolling your own dispatch loop is cleaner. It's 200 lines of code. You own the parsing logic. You can tune it to whatever output shape your model actually emits, including the malformed shapes it emits 3% of the time.\n\nFor chat-app deployment of the agent inside the customer's Foundry workflow, our [AI Chatbot SDK App](https://akos.ai/arsenal) handles the OSDK-side wiring so the agent surfaces inside a Workshop module without rebuilding the chat UI.\n\n## 4. Network, ingress, and deployment\n\nThe agent works in dev. Now you have to ship it.\n\n### Network policy\n\nWherever you host the model, the egress and ingress policies have to be set up and approved by your administrator. This is the step that derails timelines because nobody scopes it during architecture review. Two specific items:\n\n- **Egress from Foundry to your hosting platform.** If the agent dispatches from inside Foundry (e.g., a Workshop module or a Compute Module makes the API call to your hosted endpoint), the platform needs an outbound network policy allowing your hosting platform's domain. Get the security team involved early.\n- **Ingress to your hosting platform.** Most hosting platforms expose an HTTPS endpoint. Restrict by IP allowlist or token, ideally both. The Foundry token isn't a substitute;
1 it's an OSDK-scope token, not an inbound auth token for your model endpoint.\n\n### Where the call originates\n\nYou have the agent. You know where the agent fits in the workflow. Now you trigger the call. Most commonly, the trigger is one of:\n\n- A Workshop module calls the hosted endpoint via an AIP Logic function or a Compute Module-hosted dispatcher.\n- An OSDK-backed external app (built on Developer Console) calls the endpoint from a server-side handler.\n- A scheduled job inside Foundry kicks the agent on a cadence.\n\nWhichever it is, the endpoint URL, the auth token, and the timeout settings live in Foundry as a Source or in the calling Compute Module's config, not hard-coded.\n\n### Scale-down and concurrency\n\nHosted GPU platforms (Lambda Labs, Modal, Replicate, Runpod, your own k8s cluster) all have cold-start behavior and scale-down knobs. Configure them deliberately:\n\n- **Cold-start tolerance.** First call after scale-down might take 30 seconds or more depending on model size. If your agent is user-facing, that's a UX problem. If it's batch, it's fine.\n- **Scale-down threshold.** How many seconds of idle before the platform shuts down the GPU. Too aggressive and you pay cold-start tax constantly. Too lenient and your bill grows.\n- **Concurrent request capacity.** How many in-flight inferences per replica. Too high and the GPU thrashes its KV cache, latency spikes. Too low and you spin up unnecessary replicas.\n- **Maximum replicas.** Set a ceiling so a bug in a calling loop can't autoscale you into a thousand-dollar surprise.\n\nDocument these settings somewhere the rest of the team can read. We've inherited self-hosted agents from previous vendors where the scale-down was set to 5 seconds and every cold start hit the user. Nobody knew why the agent felt slow.\n\nFor the [Compute Modules scaling docs](https://www.palantir.com/docs/foundry/compute-modules/scaling) if you're keeping the dispatcher inside Foundry. For external hosting, your platform's docs are the source of truth.\n\n## The honest moment\n\nThree things we've burned ourselves on running this pattern.\n\n**Open-source models fight the framework.** We spent days on a build where the model's tool-call output was *almost* valid JSON but with a trailing comma the LangChain parser refused to forgive. We tried a system-prompt patch. We tried a model swap. We ended up writing our own dispatcher, which we should have done on day one. If your open-source model emits a non-standard tool-call format, accept it early. Don't fight the framework into accepting it; replace the framework with a 200-line dispatcher and move on.\n\n**GPU economics are not free, and the bill shape is unintuitive.** We had a project where the scale-down was tuned for a single-user dev workload. The customer's pilot rolled out to 40 users. The bill in week one was 6x what we forecasted because cold-start tax compounded and the platform was keeping a warm pool the entire workday. We fixed it in an evening, but the customer saw the first bill before the fix. That was on us. Now we tune scale-down and concurrency against a realistic traffic shape before go-live, not after.\n\n**We've shipped this architecture when AIP would have done the job.** One build, the customer asked for a self-hosted agent because somebody on their team had a strong preference for an open-source model they'd seen at a conference. We agreed too quickly. Eight weeks later we rebuilt the agent inside AIP Agent Studio because the model in question had been added to the catalog, the team didn't actually need the custom weights they thought they did, and the maintenance overhead of the external infra was eating an FDE's afternoon every week. We lost time. The customer didn't lose anything they were paying for because we ate the rebuild, but the right call would have been \"let's hold for two weeks and check the AIP catalog first.\" We say it now. Sometimes it costs us a phase. It's still the right call.\n\n## What this looks like end to end\n\nThe shape of a real deployment:\n\n1. Foundry hosts the Ontology, the workflow, the Workshop UI, the AIP Logic surface, the HITL review interface (we use the [Human Validation Station](https://akos.ai/arsenal) module here), the audit log.\n2. The agent loop runs on Lambda Labs (or Modal, or Runpod, or your on-prem cluster). The model is whatever the constraint forced you to: a fine-tuned Llama, a quantized Qwen, a custom-trained classifier-plus-LLM combo.\n3. The tool layer is Python. Each tool wraps an OSDK call, returns JSON. A long-lived service token, scoped tight, lives in your hosting platform's secret manager.\n4. Foundry calls out to the hosted endpoint when the workflow needs the agent. The agent reasons, calls tools, the tools reach back into the Ontology over OSDK. The agent's final output goes back into Foundry as an Action or a
1write-back to an object.\n5. Every step is logged. Every write is reviewable. Every model and prompt version is tagged.\n\nThat's the architecture. The hard part is none of the boxes. The hard part is the model config, the tool descriptions, and the scale-down threshold. That's where the post should be useful.\n\n## TL;DR\n\n- AIP's catalog covers most agent work cleanly. The reason to host an agent outside Foundry is almost always one of three: a model that isn't in the catalog, custom-trained or fine-tuned weights, or a specific GPU SKU you can't get reliable access to through Compute Modules.\n- If you go outside, the agent still has to be Foundry-aware. That means an OSDK-backed tool layer, a long-lived Foundry token, and tools that return JSON the model can actually parse.\n- Three things separate a working self-hosted agent from one that quietly fails in production: matching the model's default config (temperature, top_p, repeat penalty, context window), keeping each tool narrow with a description that leads with *when* not *what*, and tuning scale-down and concurrency for your real traffic shape.\n- We've shipped this pattern in multiple builds. We've also rebuilt one of them back inside AIP because we didn't need to leave in the first place. The decision tree below is the post-mortem.\n\nIf you're standing one of these up and want a second pair of eyes on the architecture, [talk to us](https://akos.ai/contact). We'll send you the same deploy checklist we use internally. It's the one-page PDF that goes with this post.\n\n61:T5a2a,## Where the demo meets reality\n\nWe built an agent workflow on Foundry: documents in, matching and exception handling, write-back to the system of record. The kind of thing that looks clean in a demo and gets ugly in production if you don't think carefully about four things.\n\nThe first version didn't get all four right. (No version of anything ever does.) In the first real run, the agent processed the same transaction twice because of a transient retry, and a duplicate landed in the downstream system. An hour to unwind. A half-day to explain to the [controller](https://armory.akos.ai/library/test-upload).\n\nThat bug got fixed in forty-five minutes. The conversation with the controller took a week. \n\n**Trust takes longer to rebuild than code does.**\n\n## Why write-back from agents is fundamentally different\n\nRead-only AI summarizes data. The worst it can do is be confidently wrong in a chat window, which is bad for your blood pressure but doesn't break the books.\n\nWrite-back AI changes the state of a system. That's a different physics. Three things stack on top of each other:\n\n1. **Non-determinism.** The Large Language Model (LLM) doesn't always pick the same tool for the same input. Two identical user messages can produce two different agent paths.\n2. **Stateful side effects.** When the agent decides to call an action, something happens in a system that other people depend on. Even if you wanted to take it back, you can't always.\n3. **Multi-system blast radius.** A real \"approve this invoice\" action probably hits the Ontology, hits the accounting system, hits a notification system, hits an audit log, and triggers a downstream workflow. If half of those succeed and half fail, your accounting reality and your operational reality are now different. Hilarious in a demo, catastrophic at close.\n\nRobotic Process Automation (RPA) tools dealt with side effects, but they were deterministic. Each run executed the same scripted path. Agentic AI brings the LLM's reasoning into the loop, which is exactly the point but also exactly the risk. The fix is not \"don't use agents.\" The fix is to make the system around the agent unforgiving in the places it needs to be, and forgiving where it can afford to be.\n\n## The four ways it goes wrong\n\nThese are the four failure modes that have shown up on every write-back-heavy AIP engagement we've done. Different industries, same four. If you only remember four things from this piece, remember these.\n\n### 1. Missing idempotency\n\n**Idempotency** means an operation produces the same result no matter how many times it runs. Press the elevator button once or five times, the elevator still arrives once. Most production systems live or die by this guarantee, and most agentic systems don't have it by default.\n\nThe agent fires the same write twice. Maybe because the underlying LLM call timed out and the orchestrator retried. Maybe because the user clicked twice. Maybe because the agent loop reasoned itself into approving the same invoice in two different c
1onversation turns. Without idempotency, the downstream system either creates a duplicate (most accounting and AP systems will reject the write if the vendor + invoice number combination already exists, [as common ERP API patterns enforce](https://developer.intacct.com/api/)) or, worse, silently accepts both because the idempotency key didn't include all the fields it needed to.\n\n**What this looks like in production:** duplicate AP bills, doubled GL postings, duplicate vendor notifications, deduplication code that's twelve months stale.\n\n### 2. Missing transactional semantics\n\nThe agent's \"approve invoice\" action is actually three writes: an Ontology update on the invoice object, an AP bill creation in the accounting system, and a notification to the vendor portal. The first succeeds, the second fails because the accounting system was rate-limited, the third never runs. Now the Ontology says the invoice is approved, the accounting system says it doesn't exist, and the vendor is waiting for a confirmation that will never arrive.\n\n**What this looks like in production:** internal data showing a different state than the system of record. Reconciliation jobs that pass on paper and fail in reality. CFO meetings that include the phrase \"but it should have.\"\n\n### 3. Missing or shallow audit trail\n\nThe agent approved a $50K invoice. The CFO asks why. The answer needs to be: which user initiated the conversation, which Chatbot session, which Logic function executed, which Action was applied, with what inputs, against what version of the agent's instructions, with what model and prompt, and what the human-in-the-loop (HITL) step (if any) looked like. If any of those questions don't have an answer, you don't have an audit trail. You have a guess.\n\n**What this looks like in production:** a compliance officer who stops trusting the system. A regulator who issues a finding. An internal audit that flags the entire AI program for \"insufficient logging.\"\n\n### 4. Missing permission boundaries\n\nThe agent has access to write across all business units. A user who's only supposed to operate on Unit A asks the agent a question about Unit B, and the agent helpfully pulls and modifies the wrong data. Or worse: a user with read-only authority tricks the agent (intentionally or not) into a write path that the user themselves could never have triggered through the UI.\n\n**What this looks like in production:** an agent that becomes a privilege escalation vector. A SOC 2 (Service Organization Control 2) audit finding. A board member asking \"wait, the AI can do *what*?\"\n\n---\n\n## What DIY agentic frameworks give you (and don't)\n\nQuick surface tour. None of these frameworks are bad. They're built for a different problem.\n\n[LangChain and LangGraph](https://www.langchain.com/langgraph) are the most-used building blocks for stateful agents. LangGraph in particular is genuinely good at the orchestration layer: durable state, checkpointing at each node, HITL approval patterns, retry logic. What it doesn't give you: idempotency at the tool layer (your job), transactional semantics across tool calls (your job), an audit trail that means anything to compliance (your job), or Role-Based Access Control (RBAC) tied to your data model (your job, plus a question about which data model).\n\nAutoGen, CrewAI, and the newer OpenAI Agents SDK and Pydantic AI all sit in a similar shape. They're orchestration libraries. They make the LLM call the right tool at the right time. The tools themselves, and the safety guarantees around them, are entirely your responsibility. The cleaner the library, the more clearly it admits this. The less clean ones gesture at it with words like \"production-ready.\"\n\nThis is fine if your team has the engineering depth to build idempotency, transactionality, audit, and RBAC at the tool layer, plus the eval infrastructure to test all of it, plus the patience to maintain it over the platform's lifetime. Some teams do. Most teams who say they will, don't, and ship the demo anyway.\n\nThe architectural question that matters: *where does each of the four guarantees live*? In a DIY framework, all four live in your application code. In Foundry + Chatbot Studio, three live in the platform and one is mostly platform with a thin
1shell of your own logic.\n\nThat's the productivity claim, and it shows up most clearly when an auditor walks in.\n\n---\n\n## How Foundry + Chatbot Studio handles each one\n\nStack we're going to lean on:\n\n- **[AIP Chatbot Studio](https://www.palantir.com/docs/foundry/chatbot-studio/overview/)** as the agent surface. It used to be called AIP Agent Studio. Same thing, new name.\n- **The Ontology** as the source of truth. Object types, link types, action types.\n- **Action Types** as the canonical write-back unit. Every state-changing operation is an Action.\n- **[AIP Logic](https://www.palantir.com/docs/foundry/logic/overview/)** as the orchestration layer for multi-step actions and for any logic the agent calls as a Function tool.\n- **Compute Modules** to host external system connectors, because external systems aren't Foundry-native and you don't want the agent calling them directly.\n- **[AIP Evals](https://www.palantir.com/docs/foundry/aip-evals/overview/)** for the eval harness on the whole thing.\n\n### Fix 1: Idempotency lives in the Action definition\n\nAn Ontology Action is a structured contract. It has inputs, an applied effect, and a set of validation rules. When you wire an Action into Chatbot Studio as a tool, you don't write the call from scratch every time. The platform calls the Action with the inputs the LLM proposed, the validation logic runs, and either the Action succeeds or it doesn't.\n\nYou build idempotency in by:\n\n1. Defining a deterministic external key on the Ontology object (for an AP bill: vendor ID + invoice number + amount hash).\n2. Using that key as a unique constraint on the object type.\n3. Writing the Action so that if an object with the same external key already exists, the Action returns \"exists\" instead of creating a duplicate.\n\nSet this up once per object type, and every agent that calls that Action gets the guarantee for free. The agent can retry to its heart's content; the system writes once.\n\nThis is a big deal. In LangGraph you'd write this in your tool function, and you'd write it again for every new write-back tool, and you'd test it manually. In Foundry, the same Action is reusable everywhere (Chatbot Studio, AIP Logic, Workshop, Ontology SDK app, Automate), and the guarantee travels with it.\n\n```yaml\n# Sketch of an Action Type definition (Ontology, not full Foundry syntax)\naction: createApBill\ninputs:\n vendor_id: VendorId\n invoice_number: String\n invoice_amount: Decimal\n business_unit_id: BusinessUnitId\n gl_code: String\nexternal_key: hash(vendor_id, invoice_number, business_unit_id, invoice_amount)\nvalidation:\n - no_existing_object_with_external_key\n - business_unit_id in user.allowed_business_units\neffect:\n - create ApBill object\n - mark Invoice object as approved\n - emit AuditEvent\n```\n\n### Fix 2: Transactional semantics live in AIP Logic\n\nMulti-step writes (Ontology update + external AP bill + vendor notification) need to be wrapped in something that either commits all of them or commits none. AIP Logic is the right place to do this, because it has explicit blocks for \"apply action,\" \"call function,\" and \"branch on result,\" and because Logic functions are themselves callable from Chatbot Studio as a Function tool.\n\nThe pattern we use:\n\n1. The chatbot tool is a Function tool that points to a Logic function called `approve_invoice_e2e`.\n2. The Logic function applies the `createApBill` action against the Ontology first (cheap, fast, idempotent).\n3. It then calls a Compute Module-backed function `pushApBillToAccountingSystem` that handles the actual external write.\n4. If the external push fails, the Logic function applies a compensating Action: `markApBillForRetry` (or `cancelApBillCreation` if it's a hard fail).\n5. The vendor notification only goes out if both prior steps succeeded.\n\nThe Logic function itself is a single audited operation from the agent's point of view. The chatbot doesn't even see the multi-step nature. It calls the function, gets a result, surfaces the result to the user. [AIP Logic docs](https://www.palantir.com/docs/foundry/logic/blocks/), calling a Logic function from an Action is what makes the edits actually write back to the Ontology, so the entire path stays inside the platform's transactional semantics.\n\nIn LangGraph, you'd build this as a sequence of nodes with rollback edges. Doable. Just every team that uses LangGraph for this kind of work ends up writing their own compensating-action framework. Foundry hands you one.\n\n### Fix 3: The audit trail is the platform\n\nThis is the part where Foundry's architecture earns its keep on day one.\n\nEvery Action a
1pplied against the Ontology is logged automatically with: timestamp, user, inputs, prior state, post state. Every AIP Logic execution is logged with inputs and outputs. Every Chatbot session is logged with the conversation history, tool calls, model used, and reasoning. Every external write that goes through a Compute Module is logged with request/response payloads.\n\nYou don't write this. You don't maintain it. You point your compliance officer at AIP Observability and the [AIP Threads](https://www.palantir.com/docs/foundry/threads/overview/) view, and you let them poke around.\n\nThe thing this gives you that you can't easily replicate in a DIY framework: a single trace from \"user typed this in chat\" â \"agent decided to call this action\" â \"action wrote to the accounting system\" â \"audit event recorded with these properties.\" One join, not five.\n\n### Fix 4: Permissions live in the Ontology, not in the agent\n\nThis is where Chatbot Studio gets unusual. The agent inherits the user's permissions. If the user can't see Unit B's data, the agent can't either, even if the user asks nicely. If the user can read invoices but not approve them, the agent's \"approve invoice\" Action will refuse to apply.\n\nYou don't configure this at the agent layer. You configure it at the Ontology layer, on the object types and action types themselves, using the marking-based and role-based access controls Foundry already enforces everywhere else.\n\nThis is why the Chatbot Studio docs say the platform's security model is the same one the rest of Foundry uses. It sounds boring. It's the most important sentence in the docs.\n\nIn LangGraph or AutoGen, \"the agent inherits user permissions\" is a sentence in a design doc. In Foundry, it's the default behavior. You'd have to actively bypass it to break it.\n\n---\n\n## End-to-end: the AP scenario on Foundry + Chatbot Studio\n\n```\nUSER (accountant)\n â\n â¼\n[CHATBOT STUDIO]\n â - Action tool: approveInvoiceE2E (calls AIP Logic function)\n â - Object query tool: invoice, vendor, business unit, statement\n â - Function tool: explainException (calls Logic function)\n â - Request clarification tool\n â¼\n[AIP LOGIC: approve_invoice_e2e]\n â\n âââ⺠[ACTION: createApBill]\n â (idempotent, external_key on vendor+invoice+unit+amount)\n â\n âââ⺠[FUNCTION: pushApBillToAccountingSystem]\n â (calls Compute Module; retries with backoff;\n â returns success/failure)\n â\n âââ⺠branch on result\n â success â [ACTION: markInvoiceWrittenBack]\n â failure â [ACTION: markApBillForRetry] + alert\n â\n âââ⺠[ACTION: notifyVendor]\n (only if both prior steps succeeded)\n```\n\nA few specifics worth calling out:\n\n**Chatbot Studio config.** We use [native tool calling](https://www.palantir.com/docs/foundry/chatbot-studio/tools/) where the model supports it, so the agent can call object queries and the approval Function in the same turn. We also use the **Request clarification** tool aggressively, especially for invoices the agent has lower confidence on. Better an extra question than an unwanted write.\n\n**The Action tool's \"Run after confirmation\" setting** is on for every Action that writes externally. This is the difference between a chatbot that talks confidently and a chatbot that talks confidently *and* gives the accountant a single-click chance to veto before the platform actually writes anything. This is a built-in toggle on Action tools.\n\n**Compute Module for the accounting system.** The agent never calls the accounting system directly. Only the Compute Module does. The Compute Module is where we encode all the system-specific quirks: API rate limits, the XML vs. REST decision for Accounts Receivable / Accounts Payable (AR/AP) bills, retry logic with exponential backoff, response parsing. Pushing this complexity off the agent's hot path means the agent's logic stays clean and the connector logic stays testable. (This is a pattern, not a specific Palantir-named feature, just a convention we use.)\n\n**AIP Evals on the whole flow.** Because the chatbot is published as a Function and the underlying AIP Logic function is itself a Function, both can be evaluated in AIP Evals. We build eval suites at three levels:\n\n1. *Agent-level*: given a user message, did the chatbot pick the right tools in the right order?\n2. *Logic-level*: given a structured input to `approve_invoice_e2e`, did the right Actions fire with the right inputs?\n3. *Action-level*: given an Action call, did the Ontology end up in the expected state?\n\nEach level catches a different class of regression. The agent-level evals catch the LLM doing something weird. The Logic-level evals catch orchestration bugs. The Action-level evals catch idempotency or permission regressions. AIP Evals lets you wire all three to a deploy gate, which means a prompt change that breaks any of them just doesn't ship.\n\n---\n\n## A small comparison table\n\nLazy, on purpose. Don't take this as a thorough framework review. It's a frame for the architectural question.\n\n| Concern | LangGraph (DIY) | AutoGen / CrewAI / OpenAI Agents SDK | Foundry + Chatbot Studio |\n|---------|------------------|--------------------------------------|--------------------------|\n| Idempotency at tool layer | You build it per tool | You build it per tool | Lives in Action Type definition, reusable everywhere |\
1n| Multi-step transactional semantics | LangGraph nodes + your rollback logic | Your code | AIP Logic + compensating Actions |\n| Audit trail spanning agent â action â external system | Your logs, your joins | Your logs, your joins | Platform-native, single trace |\n| Permission boundaries tied to data | Your tool's API + your wrapper | Your tool's API + your wrapper | Inherited from Ontology RBAC |\n| Eval harness for full agent + write-back | Your test infra (LangSmith, custom) | Your test infra | AIP Evals at agent, function, and action levels |\n| Time to first production-shaped write-back | Weeks of plumbing | Weeks of plumbing | Days, mostly configuration |\n\nThe phrase to internalize: in Foundry + Chatbot Studio, the platform answers most of these questions. In a DIY framework, you do.\n\nThis isn't a knock on the DIY frameworks. LangGraph is genuinely excellent if you have the team to build the rest of the production surface around it. AutoGen is a fine research tool. They're just doing a different job than Foundry's doing.\n\n## What AKOS adds on top\n\nThe Arsenal has a module we call the [Write-Back Orchestrator](https://akos.ai/arsenal). It sits between AIP Logic and the external system (any Enterprise Resource Planning (ERP) system, accounting system, or system of record you need to write to) and handles three things the platform doesn't quite do out of the box:\n\n1. **External-system idempotency cache.** Even with Ontology Action-level idempotency, the *external* system can have its own quirks. Some systems reject duplicate invoice numbers but are pickier about credit memos. Others have their own dedup rules. The Orchestrator keeps a small cache of recent external-system writes so we can detect and handle these edge cases consistently.\n2. **Retry policy with jitter.** Foundry retries, but the retry policy that's right for one system is different from the one that's right for an internal microservice. The Orchestrator centralizes the policy per external connector.\n3. **A compensation registry.** For each Action that writes to an external system, we register its compensating Action. The Orchestrator makes the compensation easy to wire into AIP Logic flows without re-writing the same try/catch pattern in every Logic function.\n\nWe did not build this because Foundry was missing something foundational. We built it because we shipped four AP-style write-back projects in a row and got tired of re-implementing the same three patterns. Reusable modules earn their place when they save you from your fourth re-implementation, not your first.\n\nIf you're a Palantir partner shop reading this and you've built your own version of this, you've probably hit the same walls. Send me yours, I'll send you ours, we'll both ship faster.\n\n## What we'd still tell you to build yourself\n\nThree things, even on Foundry + Chatbot Studio:\n\n1. **A \"stop the world\" switch.** A single feature flag that pauses all write-back from agents, across all chatbots, instantly. You will need this at 9pm on the last Friday of a month, at least once. Build it before you need it.\n2. **A human-readable change log on the agent itself.** The platform logs every change. Your CFO and compliance officer want a curated timeline of which agent versions wrote what, when, with what model. Format-of-record matters. Build a small Workshop widget for this.\n3. **Eval cases written by the operators, not just engineers.** Engineers write happy paths and obvious edge cases. The accountants on the team know which vendor invoices are weirdest. Pay them an hour a week to write eval cases. The eval suite gets dramatically better.\n\nThese aren't platform deficiencies. They're places where the people closest to the work need to put a finger on the scale.\n\n---\n\n## When DIY is the right answer\n\nSometimes Foundry isn't the right substrate. Specifically:\n\n- **You don't have Foundry, and you won't.** If your org is committed to a different data platform, putting AIP just to host an agent doesn't make sense. Pick the agent framework that fits the platform you're on.\n- **Non-regulated, internal-only, low blast radius.** A coding assistant for your engineering team, a knowledge-base summarizer, a meeting-notes drafter. Theater-level stakes. DIY is fine, the failure modes won't kill you.\n- **You're a research lab.** You're optimizing for novelty, not durability. AutoGen, CrewAI, the latest paper-on-arxiv framework. Go.\n- **You have a 50-person platform team and want to own every layer of the stack.** You can. It just costs what it costs, and that bill is bigger than the AIP one for almost every team I've worked with.\n\nFor everything else, especially anything that touches a system of record where the audit log matters and the wrong write costs real money, the platform-layer-guarantees argument is hard to beat.\n\n## What am I missing?\n\nIf you've shipped write-back from agents to a system of record and you disagree with the four failure modes, or you think LangGraph deserves a better defense, or you've found a Chatbot Studio pattern I haven't, write me. Same offer as on the [agentic theater post](https://akos.ai/blog/agentic-ai-things-to-avoid): I'd rather be told I'm wrong loudly than be polite and miss it.\n\nWe packaged the four failure modes into a one-page [Write-Back Safety Checklist](https://armory.akos.ai/library/4-failure-modes-4-mitigations). Use it as a design review document before any AIP agent in your org is allowed to touch a system of record. Use it on us if we propose something half-baked.\n\n[Talk to us](https://akos.ai/contact) if you have a write-back-heavy workflow you're scoping. We've shipped this shape on Foundry across finance, healthcare, manufacturing, and a few flavors of government. The shapes rhyme more than you'd think.\n62:T38e8,## The stat that should ruin your week\n\nRAND looked at 800+ enterprise AI projects and found that 80.3% fail to deliver business value. Gartner's number for GenAI specifically is uglier: 95% of pilots get abandoned after PoC. McKinsey's late-2025 survey says 88% of organizations now use AI somewhere, but only 39% report any EBIT impact. Eighty-something percent of the market is paying for AI and getting no measurable result.\n\nPause on that. We're four years past ChatGPT. The tooling is mature enough. The models are good. Foundry has been GA'd (reached General Availability), expanded, and partnered out to every Big 4. And the failure rate is still 80%+.\n\nThe failure isn't usually a model failure. It's almost always a *production* failure. The pilot ran. The demo wowed the steering committee. Then someone asked \"ok, can we put it in front of a real claims adjuster\" and the wheels came off because nobody designed for what happens on the 1,000th transaction, the 10th model regression, the third audit, the first wrong answer that c
1osts the company money.\n\nThat gap, between \"the demo worked\" and \"the system holds,\" is what \"production-ready\" is supposed to mean. The phrase has been so abused on vendor websites that it now means nothing. Time to give it a definition you can actually hold a partner to.\n\n## What \"production-ready on day one\" actually means\n\nThis is the part that should be boring and isn't. The reason 80% of projects fail is that the buyer never wrote down what production-ready means *before* signing the SOW. Here's our list. Ten things. If all ten exist before go-live, you have a system. If even one is missing, you have a glorified pilot.\n\n### 1. An eval harness, not a demo script\n\nYou need a test suite that scores model output against a reference set, runs on every change, and blocks deploys when scores drop. In Foundry, [AIP Evals](https://www.youtube.com/watch?v=1F9tSbH1nic) is the native tool for this. If your partner has never used it, that's a tell. If they have, ask to see the eval suite, not a screenshot, the actual suite. Look for: variety in test cases, edge cases, regression coverage, and a pass/fail threshold tied to deployment gates.\n\nA demo script is \"here are five questions that work.\" An eval harness is \"here are 200 questions, of which 12 currently fail, here's why, here's our plan, and here's when it runs next.\"\n\n### 2. Write-back with audit, or the system is read-only theatre\n\nMost \"AI deployments\" never write to anything. They read, summarize, and display. That's a research tool, not a system. \n\n_The hard problem starts when an agent has to write a row to the ERP, defer a PO, update a clinical record, or trigger a payment._\n\n\nReal write-back has: \n1. idempotency keys (so retries don't double-write), \n2. two-phase or compensating-transaction semantics (so partial failures unwind), and \n3. an audit trail that records who/what/when at the row level. \n\nWithout those three, you're one network blip away from a finance disaster and an unanswerable audit question.\n\nThis is the single most underestimated cost in enterprise AI work. We built the [Write-Back Orchestrator](https://akos.ai/arsenal) because we got tired of bolting these patterns on every project.\n\n### 3. Human-in-the-loop that captures the correction, not just the override\n\n\"We have HITL\" is a vendor phrase that usually means \"there's a button the user can click to ignore the AI.\" That's not HITL. That's a kill switch.\n\nReal HITL captures the correction structurally: the original AI output, the human's edit, the reason, and links it back to the underlying record. That signal becomes training data, eval data, and the audit story for the regulator. Our [Human Validation Station](https://akos.ai/arsenal) does this because petition signature work taught us that the auditor will ask, and \"we let the user override it\" is not an answer.\n\n### 4. Rollback paths for models, prompts, ontology, and write-back\n\nFour different things can break in production. The LLM. The prompt. The ontology change someone made on Tuesday. The write-back logic. You need a rollback story for each one, separately. If your partner's answer is \"we'll redeploy the previous version,\" ask them how long that takes, who approves it, and whether the in-flight transactions get rolled back too.\n\nThe right answer is usually: model and prompt are versioned with promotion gates between sandbox and prod, ontology changes go through a review branch, write-back has a \"compensating action\" pattern for transactions already committed. If you don't hear all four, you have a hope, not a plan.\n\n### 5. Model and prompt versioning that an outsider can read\n\nEvery change to a model, prompt, or agent definition gets a version, a diff, and a reason. Stored somewhere a compliance officer who doesn't code can read.\n\nThis is so basic it's embarrassing to write down. Half of partner engagements still don't do it. They edit prompts inline in Agent Studio, deploy, and pray nobody asks what changed last Thursday. When the wrong answer happens (and it will), the post-mortem is a nightmare.\n\n### 6. RBAC tied to the ontology, not just the app\n\nIn Foundry, this is mostly free if you do the ontology work right. Access controls live at the object and link level, not just the UI level. That means a user who shouldn't see customer SSNs in the analyst dashboard also can't see them through the AI agent that queries the same ontology.\n\nAsk the partner to draw the access model from data â ontology â AIP Logic â agent â UI. If they can't, the AI agent is going to leak something the dashboard wouldn't have.\n\n### 7. Observability on the LLM layer, not just the app layer\n\nFoundry has tamper-proof audit logs for every LLM call and data access. That doesn't help if nobody's looking at it. Production-ready means there's a dashboard, an on-call rotation, and a threshold-based alert when token cost spikes, hallucination rate climbs, or write-back failure rate moves.\n\nIf the partner can't show you the dashboard during the pitch, it doesn't exist yet.\n\n### 8. Runbooks that someone on your team can execute at 2am\n\nSix months in, your partner's lead engineer is on a beach in Portugal. The agent stops responding. The on-call SRE on your side has never touched Foundry. What do they do?\n\nIf the answer is \"call the partner,\" congratulations, you've signed a managed service disguised as a build engagement. That's fine if you priced it that way. It's a problem if you thought you were buying a system that your team would own.\n\nReal runbooks have: how to restart, how to roll back, how to escalate, who owns what, and the four most likely failure modes with their fixes. PDF or Confluence, doesn't matter, just has to exist before launch.\n\n### 9. SLAs measured in operator outcomes, not system uptime\n\n\"99.9% uptime\" is the lowest possible SLA bar and it's the only one most partners commit to. It's table stakes. The SLAs that matter are: how fast does the AI respond under load, what's the acceptable hallucination rate on the eval suite, what's the maximum write-back latency, what's the resolution SLA when a model regression is detected.\n\nGet those four into the contract or you have no leverage when things drift.\n\n### 10. A deprecation plan\n\nThis one will get me hate mail. A production-ready system has a plan for how it ends.\n\nMaybe the model gets replaced when Claude 5.5 ships. Maybe the workflow gets absorbed back into the ERP in 18 months. Maybe the agent gets retired when the underlying process changes. None of these are bad outcomes. What's bad is buying a system without thinking about how you'd swap it out, then discovering five years later that it's load-bearing and the partner who built it has been acquired twice.\n\nIf a partner's never been asked this question, watch how they answer. The good ones will smile and walk you through it because they think about it constantly. The bad ones will say \"why would you want to deprecate it?\"\n\n---\n\n## The 7-question partner interview\n\nTen attributes is a lot. Here's the short form. Seven questions, what a good answer sounds like, what a bad answer sounds like. Use this on any Palantir vendor. Use it on us. We've answered worse.\n\n### Q1: \"Show me the eval suite for the closest project you've shipped to ours.\"\n\n\u003e **Good answer:** They share a screen, walk you through 50-200 test cases, name the model versions tested, point to the pass/fail thresholds, and tell you which cases currently fail.\n\n\u003e **Bad answer:** \"We use AIP Evals, here's a tutorial link.\" (They've never used it on a customer project.)\n\n\u003e **Worse answer:** \"Evals are part of the next phase.\" (There is no next phase. There's just this phase, forever.)\n\n### Q2: \"Walk me through the worst write-back failure you've had in production and how it got fixed.\"\n\n\u003e **Good answer:** A specific story with a date, a system (ERP/EMR/OMS), a root cause (idempotency missed, schema mismatch, etc.), and what changed afterward. Bonus points if they admit the fix took longer than they're proud of.\n\n\u003e **Bad answer:** \"We haven't had any failures.\" (Then they haven't run write-back at scale.)\n\n### Q3: \"What's your HITL capture model? Show me the schema.\"\n\n\u003e **Good answer:** They draw or pull up a data model showing original output, corrected output, user, timestamp, reason code, and link back to the source record.\n\n\u003e **Bad answer:** \"Users can edit the output.\" (That's a text field, not a HITL pattern.)\n\n### Q4: \"If your lead engineer disappears for two weeks, can my team operate the system?\"\n\n\u003e **Good answer:** Yes, here are the runbooks, here's the training plan, here's the read-only admin tooling we build for your ops team.\n\n\u003e **Bad answer:** \"We provide 24/7 support during the contract.\" (Translation: you don't own this, we do, and we will keep billing you.)\n\n### Q5: \"How do you version a prompt change, and can an auditor read the change log?\"\n\n\u003e **Good answer:** Branch + PR + reviewer + commit message, stored in Foundry or a connected repo. A compliance officer can read it without help.\n\n\u003e **Bad answer:** \"Our engineers track changes.\" (No they don't.)\n\n### Q6: \"Show me the production observability dashboard from a current client.\"\n\n\u003e **Good answer:** Live dashboard (or a redacted screenshot) showing token cost, response latency, eval pass rate, write-back success rate, alert history.\n\n\u003e **Bad answer:** A pitch slide showing what they \"could\" build. The dashboard either exists or it doesn't.\n\n### Q7: \"What's the deprecation plan for this system in three years?\"\n\n\u003e **Good answer:** A real consideration of swap-out paths, vendor lock-in concerns, data portability. They'll tell you what's hard to undo and what's easy.\n\n\u003e **Bad answer:** \"Why would you want to deprecate it?\"\n\nIf a partner aces six of seven, you have a real shortlist candidate. If they ace fewer than four, they're selling something that isn't yet built.\n\n---\n\n## The anti-checklist: what looks production-ready but isn't\n\nA few traps that have burned us, our customers, and people we know.\n\n**A polished demo with hardcoded answers.** If the demo never fails, it's been rehearsed. Ask them to enter a new question in front of you, off-script. Watch their hands.\n\n**\"It works in Foundry sandbox.\"** Sandbox is a museum. Production is a kitchen at dinner rush. Different physics. Ask to see something running against live data with real users.\n\n**A team of generalists with one Palantir certification.** Palantir certifications are real but a single Foundry App Dev cert across the team isn't depth. Ask the team makeup. Ratio of Palantir-certified engineers to
1subcontractors is the number that matters.\n\n**\"AI-first\" branding with no actual model deployments.** This is the easiest tell. Ask what models they've deployed inside Foundry beyond LLM calls. Real answers include things like YOLO, paddle table extractors, custom OCR. If the answer is \"we orchestrate GPT,\" they're a prompt engineering shop, not an AI partner.\n\n**Quote-per-phase pricing with no production milestone.** Read the SOW. If there's no \"production go-live\" milestone with acceptance criteria, the project will never end. Add one. Tie payment to it.\n\n---\n\n## Where AKOS fits (and where we don't)\n\nTo save you time on the pitch: we built the [Arsenal](https://akos.ai/arsenal) because we were tired of solving the same ten problems on every Foundry engagement. Idempotent write-back. HITL with audit. Eval scaffolding. Context filters. Quality conductors that route low-confidence agent output back for regeneration. Real CV models running inside Foundry against media sets. Handwriting OCR for the 40-year-old paper records nobody else wants to touch.\n\nWe are most useful when:\n\n- You have Palantir already, or you're seriously evaluating it, and you need a partner who treats it as the operating system, not a BI tool.\n- Your problem has hard edges. Regulated, audited, write-back-heavy, real-time, or all four.\n- You want to own the system after we leave. We build runbooks. We document. We don't subscribe you to ourselves.\n\nWe are not the right fit when:\n\n- You want a 6-month exploratory phase to \"see what's possible with AI.\" We are bad at this. We will get bored, push you toward an outcome, and you will feel rushed. Hire a strategy firm for that phase. Come back to us when you have a problem to solve.\n- You want pure managed services with no internal team involvement. We can do it. We'd rather not. Forward-deployed means we work with your people, not around them.\n\n## Use this on us\n\nHonestly. Use the 7 questions on AKOS. Send us a Loom of a competitor's pitch and we'll point out what's missing. Ask us about the worst write-back bug we've shipped (it's in the petition signature project, and the fix is in the [Arsenal](https://akos.ai/arsenal) because of it).\n\nMost of the 80% of AI projects that fail die quietly. The buyer doesn't tell anyone. The vendor doesn't tell anyone. Both sides move on. The next buyer has no idea the same trap was set six months ago.\n\nIf we can take even a small bite out of that, by helping you write better RFPs, ask sharper questions, and define \"production-ready\" before signing anything, this post did its job. Even if you don't pick us.\n\n[Talk to us](https://akos.ai/contact). Or take the [7-Question Partner Interview PDF](https://armory.akos.ai/library/the-7-question-partner-interview) and run it on whoever's in your funnel.\n\nWhat am I missing? If you've shipped Foundry to production and disagree with any of the ten attributes, hit reply, we'll either fold it in or argue back. Either is fine.63:T4b10,## TL;DR\n\n- Compute Modules are the piece of Foundry that opens up the workloads the rest of the platform can't handle natively: GPU inference, third-party Python libraries, long-running services, anything containerized.\n- They're powerful and underused. The docs are less mature than the rest of Foundry, the deployment ergonomics are a step behind Transforms and Functions, and most teams discover the rough edges the hard way.\n- Four lessons that save you the most pain: start with the workflow not the container, get the deployment checklist right the first time, plan the OSDK plug-in around real data-transfer limits, and tune replicas and concurrency for your actual workload before you ship.\n- This is a field guide written across countless deployments. Use it as a checklist on your first Compute Module, or as a sanity check on your tenth.\n\n## Who this is for\n\nFoundry developers, Forward-Deployed Engineers, and ML/data engineers who have shipped Transforms, Functions, and Workshop apps and are now staring at a workload that none of those can host. If the phrase \"we need a GPU\" or \"we need to wrap an existing Python library\" or \"we need a long-running service inside Foundry\" has come up in the last week, this is for you.\n\n## 1. Start with the workflow, not the container\n\nThe first instinct when you discover Compute Modules is to build the container. Wrap the library. Make a Hello World deploy. Run a smoke test. Feel productive.\n\nThis is a trap. The container is the easy part. The hard part is where the container plugs in, what it returns, and how the rest of the workflow stays clean around it.\n\nBefore writing the Dockerfile, walk the pipeline end to end. Identify the *exact step* where the Compute Module belongs. Maybe it's a slow Python Transform that would be ten times faster on a GPU. Maybe it's a Workshop button that needs to call a library Foundry can't host. Maybe it's an AIP Chatbot tool that needs to invoke a model that's too custom for AIP Logic. Whatever it is, find the one step.\n\nThe trap to avoid: do not rebuild the surrounding pipeline to suit the container. If the existing Workshop app expects an Ontology object back, the Compute Module should return something that maps cleanly to that object. Not a tuple. Not a giant blob. A structured response your downstream Action Type can ingest.\n\nA few sub-decisions that fall out of this:\n\n- **Function execution mode vs. pipeline execution mode.** Function mode is what you want for interactive workloads called from Workshop, Slate, OSDK, or AIP Logic. Pipeline mode is for autonomous data jobs that read from inputs and write to outputs continuously. The [execution modes doc](https://www.palantir.com/docs/foundry/compute-modules/execution-modes/) walks through both. \n- **What input you accept.** The agent or app passes you the reference, the Compute Module pulls the bytes via the Foundry API. Smaller payloads, cleaner audit trail.\n\nIf you can sketch the input and output shape on a whiteboard in five minutes, you're ready to write the container. If you can't, keep walking the workflow.\n\n## 2. Get the deployment checklist right, then deploy\n\nThe first deployment is where most teams lose two days. There's a sequence of small things, and each one has to be right. Here's the version we run.\n\n### Project structure\n\nMirror the structure from the [getting started doc](https://www.palantir.com/docs/foundry/compute-modules/get-started/):\n\n```\nMyComputeModule/\nâââ Dockerfile\nâââ requirements.txt\nâââ src/\n âââ app.py\n```\n\n`src/app.py` is where your logic, processes, and the `@function`-decorated handlers live. The model weights either get baked into the image (simple, but bloats the image) or downloaded at container startup (cleaner image, slower cold start). \
1n\nIf you want to add weights from your custom trained models, place them under a new folder within the root. In this case: `MyComputeModule`. Bake them in based on how fast you want the compute modules to warm up. Generally it is better to have it downloaded at startup for anything small or anything that updates frequently. For large weights that don't need to be updated often, better to bake it in before pushing it to registry.\n\n\n### Dockerfile \n\n```dockerfile\nFROM --platform=linux/amd64 python:3.12\n\nCOPY requirements.txt .\nRUN pip install -r requirements.txt\nCOPY src .\n\n# Foundry requires a non-root, numeric USER\nUSER 5000\nCMD [\"python\", \"app.py\"]\n```\n\nThree things go wrong here repeatedly:\n\n1. **Wrong platform.** The image must match the Foundry resource queue's architecture. `--platform=linux/amd64` is the default. If your team builds on Apple silicon and skips the platform flag, the image fails on deploy in a way that's not immediately obvious.\n2. **USER not numeric or not set.** The container must run as a non-root *numeric* UID. `USER 5000` is the convention. `USER appuser` will fail.\n3. **Missing the entry point.** The CMD must run a script that starts the Compute Module client (the SDK handles this if you call `start()` or use the `@function` decorator with a main block).\n\n### requirements.txt\n\n```\nfoundry-compute-modules\npyarrow\nnumpy\u003e=1.24,\u003c2.0\npandas\nsentence-transformers\n# plus whatever else your code needs\n```\n\nThe [foundry-compute-modules](https://pypi.org/project/fou
1ndry-compute-modules/) library is what gives you the `@function` decorator and the event/context pattern. The rest are just an example libraries and should be customized based on your needs.\n\n### The function code\n\n```python\nimport logging, sys, time\nfrom dataclasses import dataclass\nfrom typing import List\n \nfrom compute_modules.annotations import function\n \nlogging.basicConfig(level=logging.INFO, stream=sys.stdout, force=True)\nlog = logging.getLogger(__name__)\n \n# ---- Startup: runs once when the container boots ---------------------------\nlog.info(\"Compute Module initializing...\")\nstart = time.time()\n \n# Load models, indices, reference data into module-level globals.\n# Anything heavy goes here â not inside the @function.\nASSETS = {\n # \"model\": load_model(\"/app/model\"),\n # \"index\": load_index(\"/app/data\"),\n}\n \nlog.info(f\"Ready in {time.time() - start:.2f}s\")\n \n \n# ---- Typed payload (input) + result (output) (become data types) ------------------------\n@dataclass\nclass SearchPayload:\n partition: str\n query: str\n limit: int\n \n \n@dataclass\nclass Result:\n record_id: str\n label: str\n score: float\n \n \n# ---- Exposed function (callable from Workshop, Actions, AIP, Pipelines) ----\n@function\ndef search(context, event: SearchPayload) -\u003e List[Result]:\n log.info(f\"search: partition={event.partition}, query='{event.query}'\")\n try:\n # Your logic â use globals loaded at startup\n # results = ASSETS[\"index\"].query(event.query, event.limit)\n return []\n except Exception as e:\n log.info(f\"Error: {e}\", exc_info=True)\n raise\n...\n```\nThe two parameters are non-negotiable: `context` and `event`. `context` carries credentials and metadata (user tokens, source credentials, OSDK access). `event` carries the input data. If you use a `TypedDict` or `dataclass` for the event type, the SDK can automatically infer the function schema and register it as a Foundry Function once the Compute Module is running. Skip the typing and you'll be doing manual schema registration. Don't skip the typing.\n\nPlease pay close attention to your dataclass structure and type. if you have done the step 1 and walked through the workflow and integration, you should know exactly what type and structure of data you need for the output. \n\n### Publishing to the Foundry Artifact repository\n\nTwo paths:\n\n1. **Local build, push to a Foundry Artifact repository.** Build locally with Docker, follow the in-platform instructions in the Compute Module's **Documentation** tab to push to the registry. \nFor this path, the docker image has to be built and pushed to the registry using the command that look somewhat similar to this:\n```\ndocker build --platform linux/amd64 -t \u003cname\u003e-container-registry.palantirfoundry.com/{image-name-tag}\nexport REPOSITORY= {AUTO GENERATED RID FROM FOUNDRY COMPUTE MODULE}}\nexport TOKEN= {AUTO GENERATED TOKEN FROM FOUNDRY COMPUTE MODULE}\necho \"$TOKEN\" | docker login -u \"$REPOSITORY\" --password-stdin \u003cname\u003e-container-registry.palantirfoundry.com\ndocker push --platform=linux/amd64 \u003cname\u003e-container-registry.palantirfoundry.com/{image-name-tag}\n```\n\n\n2. **Code Repositories with Python Compute Module template.** Skip the local Docker step entirely. Foundry handles the build and the image push. Faster iteration loop for Python-only Compute Modules. We use this path more often than the local one for new modules.\n\n\n### Module configuration\nIf your container needs to call out (an external API, a model registry, your own GPU model hosting), use Foundry [Sources](https://www.palantir.com/docs/foundry/compute-modules/sources/) for the credentials. For each Source you import into the Compute Module configuration, a pre-configured client is generated for you. Do not bake credentials into the image. We've seen it. It never ends well.\n\n#### GPU and resource configuration\n\nThe replica defaults are 1 vCPU, 4 GB memory, and no GPU. For ML models, you'll bump CPU and memory and might want to request a GPU on the replica config. Per-replica config matters because it sets the cost floor and the performance ceiling for every parallel call your module handles.\n\nA rough heuristic: start with the smallest GPU your model can run on, the lowest concurrency limit that supports your latency target, and adjust from there. Over-provisioning early is more painful than under-prov
1isioning early, because cost compounds and tuning down is harder than tuning up.\n\n### Start and smoke-test\n\nOnce configured, start the Compute Module from the **Overview** page. Use the **Query** panel at the bottom to test the function before wiring it into Workshop or AIP Logic. If the function doesn't show up in the Functions tab, the schema inference didn't run, which usually means the SDK isn't started correctly in your entry point, or your function signatures aren't typed. In this case, main culprit most likely be in `app.py` and should be the first place to check. \n\nA few failure modes worth knowing about when working with `@function` handlers and custom logic in Compute Modules:\n\n1. **Dataclass objects use Foundry-unsupported types.** The Compute Module SDK introspects your `@dataclass` payload and result types to register them with the Ontology, and it only supports a specific set of primitives (`str`, `int`, `float`, `bool`, `lists` of those, and nested dataclasses). If you use types like `datetime`, `Optional[X]`, or `Any` types in your dataclass fields, registration silently fails and the function never appears. Rewrite the object to stay as close as possible to supported types; convert timestamps to ISO strings, replace Optional with a sentinel value (-1.0 for floats, \"\" for strings), and flatten dicts into explicit fields or a `List[KeyValue]` nested dataclass.\n2. **Heavy assets are loaded inside the decorated function instead of at module level.** If you're loading a large custom trained model or pulling reference data, do it at the top of the file, that is, outside and above the `@function` handler so it runs once when the container boots. _Anything inside the function body re-runs on every invocation,_ which not only kills latency but can push individual calls past their timeout and make the function look broken in the Functions tab. The pattern is: load into module-level globals at import time, then read from those globals inside the handler.\n3. **Not waiting long enough after publishing.** Even after the container reports healthy, it can take another 1â2 minutes for Foundry to pick up the registered functions and surface them in the Functions tab. Before you start tearing your code apart looking for a bug, give it a couple of minutes and refresh. If you jump straight into changing the dataclass or rewriting the handler, you're likely chasing a problem that would have resolved itself and then your \"fix\" triggers another rebuild and another wait cycle.\n\nThis checklist sounds long. It is. It's also the difference between a two-hour deploy and a two-day debugging session. Run it the same way every time.\n\n---\n\n## 3. Plug in: OSDK, Workshop, and the data-transfer trap\n\nOnce the module is running and the functions are registered, you can call them from anywhere a Foundry Function is callable: Workshop, Slate, AIP Logic, AIP Chatbot Studio (as a Function tool), OSDK applications, and Code Repositories.\n\nThe pattern we use most: register the function, then expose it via OSDK to a Workshop widget or an external OSDK app. The [OSDK integration doc](https://www.palantir.com/docs/foundry/compute-modules/osdk-integration/) walks through the wiring. The short version is: the Compute Module function becomes a callable in the generated OSDK client, and your app invokes it like any other Foundry Function.\n\nA few things to be careful about here.\n\n### OSDK data-transfer limits\n\nCompute Module function responses serialize over a request/response interface, which imposes practical limits on payload size per call. If you're tempted to return in both directions a base64-encoded image, video, or anything larger than ~100 MB, stop and batch the payload instead. Note that this also means the logic under your `@function` decorator needs to be restructured to process requests in batches.\n\nIn our experience, the limit sits around 100 MB, and Foundry's error log is generic and unhelpful for diagnosing which OSDK call to your Compute Module function is failing. If your Compute Module runs fine standalone but OSDK calls fail, this is a likely culprit.\n\n### Cold start latency\n\nIf you set the minimum replicas to zero (the scale-to-zero option), the first call after a quiet period will incur a cold start. For most workloads this is fine. For interactive Workshop calls that need to feel instant, set the minimum to one. The cost of a warm replica is the price of predictable latency. Make the call deliberately.\n\n### Container-warmed model state\n\nA single replica can keep your model loaded in memory across calls. Two replicas have tw
1o model copies. State *does not* travel across replicas. Don't rely on accrued state between calls hitting different replicas.\n\n### Function permissions\n\nFunction execution mode supports two permission modes: no platform permissions (the function runs without OSDK access) and application permissions (a service user the function runs as for platform calls). If your function needs to read Ontology objects or write to a dataset, pick application permissions. If it's pure compute on the event payload, no platform permissions is simpler.\n\n---\n\n## 4. Manage replicas and plan for scale\n\nThe platform handles horizontal scaling for you, but only well if the configuration matches your workload. The [scaling doc](https://www.palantir.com/docs/foundry/compute-modules/scaling/) has the precise numbers. The shorthand:\n\n### Replicas\n\n- **Minimum replicas.** Zero means scale to zero when idle (saves cost, costs cold-start latency). Non-zero means always-warm.\n- **Maximum replicas.** The hard cap. Set it based on cost ceiling and the expected peak.\n\n### Concurrency limit\n\nThe maximum number of requests a single replica can process simultaneously. Default is 1, which means sequential processing per replica. For CPU-bound workloads, increasing concurrency can be efficient. For GPU-bound workloads (like CV models), the GPU is usually the bottleneck, and a concurrency above 1 just queues requests on the GPU. Most of our GPU-backed Compute Modules run concurrency 1, with horizontal scaling doing the work.\n\n### Auto-scaling thresholds\n\n- Scale-up triggers when load (current running jobs divided by current replicas times concurrency limit) is at or above 0.75 for one minute.\n- Scale-down triggers when load is below 0.75 for thirty minutes.\n\nThe asymmetry matters. Scaling up is fast (one minute); scaling down is deliberate (thirty minutes). This is the right shape for interactive workloads where you'd rather pay for a few extra warm replicas than serve a slow first call.\n\n### Predictive scaling\n\nCompute Modules track historic query load and try to scale up before anticipated demand. This is helpful for predictable load patterns (business hours, end-of-quarter document processing) and irrelevant for spiky unpredictable load. Monitor it for the first few weeks of production and adjust.\n\n### Scheduled overrides\n\nIf your Workshop app is mostly used 8am-6pm in one timezone, set a scheduled override to bump minimum replicas during those hours and let it drop to zero overnight. The cost difference adds up over a quarter.\n\n## A few things the docs don't tell you, but we've learned\n\nA short list, in no order, for the team about to deploy their first one.\n\n- **The function schema only appears in the Functions tab after the module is running.** If your function isn't showing up, the module isn't actually running or your entry point isn't calling `start()`. Restart, check the logs.\n- **The Compute Module's logs are your best friend.** They show up in the Overview page once the module is running. Print liberally during the first deploy. Strip the noise later.\n- **Image size affects deploy time, not just storage.** Bake only the model weights you need. Multi-stage Dockerfiles help.\n- **Test locally before pushing.** `docker run` with the same `--platform=linux/amd64` flag will catch 80% of the issues that would otherwise show up only after a push and start.\n- **The `Query` panel in the Compute Module UI is the fastest test loop.** Use it before wiring the function into Workshop. Catch input/output shape mismatches early.\n\n---\n\n## Where this fits in our Arsenal\n\nWe use Compute Modules for almost every workload that needs to bring a non-Foundry-native library into the platform. The [Arsenal](https://akos.ai/arsenal) modules that ship as Compute Modules include the YOLO Modeling Objective, the Paddle Detection + Cropping Pipeline, the Paddle Table Structure Extractor, the Handwritten Row Extractor, and Custom fine tuned LLM agent. Each is a Compute Module under the hood, configured around the patterns above, and stitched into the rest of Foundry through Action Types, AIP Logic functions, and Workshop widgets.\n\nReusing those modules saves us the work of deploying a new Compute Module from scratch on every engagement. The shape of the deploy stays the same; the contents change.\n\n## What am I missing?\n\nIf you've shipped Compute Modules at scale and have a workflow trick that's not in this guide, write me. I'd rather collect a real list of edge cases from people who've debugged them in production than re-discover them on the next deploy. Same offer if you think we're wrong on any of the recommendations above. Containers and configurations move fast; the field guide should move with them.\n\nIf you're standing up your first Compute Module and want a second pair of eyes on the architecture before you start the deploy, [talk to us](https://akos.ai/contact). 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