[{"data":1,"prerenderedAt":1211},["ShallowReactive",2],{"site-nav-content":3,"blog:/blog/nimbus-vs-dust":163,"blog-index-copy":507,"blog:/blog/nimbus-vs-dust:surround":528,"hiring-banner-content":1180,"site-cta-content":1192},{"header":4,"productNav":9,"nav":42,"footer":61,"askAI":116,"id":147,"title":148,"archived":149,"authors":150,"badge":150,"body":151,"date":150,"department":150,"description":155,"extension":158,"eyebrow":150,"faqHeader":150,"faqs":150,"footerBand":150,"headline":150,"image":150,"industry":150,"jobType":150,"listed":149,"location":150,"navigation":115,"openRoles":150,"pageLayout":150,"path":159,"relatedHeading":150,"seo":160,"series":150,"sitemap":115,"status":150,"stem":161,"subhead":150,"tags":150,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":162},{"productLabel":5,"loginLabel":6,"contactLabel":7,"contactSalesLabel":8},"Product","Log in","Contact","Get started for free",[10,14,18,22,26,30,34,38],{"label":11,"to":12,"description":13},"Overview","/overview","Seven layers. One closed loop.",{"label":15,"to":16,"description":17},"Conflux","/product/conflux","Where your team, workstreams, and agents meet.",{"label":19,"to":20,"description":21},"Agent Teams","/product/agent-teams","Specialist teams - governed from day one.",{"label":23,"to":24,"description":25},"Lifecycle Graph","/product/lifecycle-graph","Intelligence that compounds across every interaction.",{"label":27,"to":28,"description":29},"Company Wiki","/product/wiki","Playbooks and policies where expertise stays.",{"label":31,"to":32,"description":33},"Workstreams","/product/workstreams","From brief to signed-off deliverable on one canvas.",{"label":35,"to":36,"description":37},"Perception Console","/product/perception","Ask your whole business in plain English.",{"label":39,"to":40,"description":41},"Governance","/product/governance","Frontier AI you can actually sign off on.",[43,46,49,52,55,58],{"label":44,"to":45},"Models","/models",{"label":47,"to":48},"Pricing","/pricing",{"label":50,"to":51},"Integrations","/integrations",{"label":53,"to":54},"Security","/security",{"label":56,"to":57},"Partners","/partners",{"label":59,"to":60},"Insights","/blog",{"productHeading":5,"companyHeading":62,"legalHeading":63,"docsLabel":64,"docsUrl":65,"statementLines":66,"copyright":69,"companyLinks":70,"legalLinks":85,"socialLinks":95,"bottomLinks":105},"Company","Legal","Docs","https://docs.gonimbus.ai",[67,68],"Stop training someone else's model.","Control your AI.","© 2026 Nimbus Intelligence, Inc. All rights reserved.",[71,72,73,74,75,77,80,83],{"label":47,"to":48},{"label":50,"to":51},{"label":53,"to":54},{"label":59,"to":60},{"label":76,"to":57},"Partner Program",{"label":78,"to":79},"Careers","/careers",{"label":81,"to":82},"System status","/status",{"label":7,"to":84},"/contact",[86,89,92],{"label":87,"to":88},"Terms of Service","/terms",{"label":90,"to":91},"Privacy Policy","/privacy",{"label":93,"to":94},"Compliance","/compliance",[96,99,102],{"label":97,"href":98},"LinkedIn","https://www.linkedin.com/company/gonimbusai/",{"label":100,"href":101},"X","https://x.com/gonimbusai",{"label":103,"href":104},"Instagram","https://www.instagram.com/gonimbus_ai/",[106,108,110,111,112],{"label":107,"to":88},"Terms",{"label":109,"to":91},"Privacy",{"label":93,"to":94},{"label":81,"to":82},{"label":113,"to":114,"external":115},"LLMs.txt","/llms.txt",true,{"text":117,"prompt":118},"Ask AI about Nimbus",{"I'm researching enterprise intelligence platforms and want to know how Nimbus combines perception, collaboration, and autonomous agents to drive strategic decision-making":119,"platforms":121},{" Summarize the highlights from Nimbus's website":120},"https://gonimbus.ai",[122,127,132,137,142],{"name":123,"label":124,"icon":125,"hrefPrefix":126},"chatgpt","ChatGPT","simple-icons:openai","https://chatgpt.com/?prompt=",{"name":128,"label":129,"icon":130,"hrefPrefix":131},"perplexity","Perplexity","mdi:magnify","https://www.perplexity.ai/search/new?q=",{"name":133,"label":134,"icon":135,"hrefPrefix":136},"grok","Grok","simple-icons:x","https://x.com/i/grok?text=",{"name":138,"label":139,"icon":140,"hrefPrefix":141},"claude","Claude","simple-icons:anthropic","https://claude.ai/new?q=",{"name":143,"label":144,"icon":145,"hrefPrefix":146},"google-ai","Google AI","simple-icons:google","https://www.google.com/search?udm=50&aep=11&q=","content/shared/nav.md","Site navigation",false,null,{"type":152,"value":153,"toc":154},"minimark",[],{"title":155,"searchDepth":156,"depth":156,"links":157},"",2,[],"md","/shared/nav",{"title":148,"description":155},"shared/nav","p6IsjEfjcrwkjwvGsPcuEkpoJSeUn2vJXPTxLWQfw9M",{"id":164,"title":165,"archived":149,"authors":150,"badge":166,"body":171,"date":495,"department":150,"description":496,"extension":158,"eyebrow":150,"faqHeader":150,"faqs":150,"footerBand":150,"headline":150,"image":150,"industry":150,"jobType":150,"listed":149,"location":150,"navigation":115,"openRoles":150,"pageLayout":150,"path":497,"relatedHeading":150,"seo":498,"series":499,"sitemap":115,"status":150,"stem":500,"subhead":150,"tags":501,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":506},"content/blog/nimbus-vs-dust.md","Nimbus vs Dust: A Shared Agent Studio or a Place Departments Finish Work?",{"label":167,"10|authors":168},"Comparisons",[169],{"name":170,"to":120},"Nimbus Research",{"type":152,"value":172,"toc":476},[173,184,187,196,201,259,263,266,287,290,305,312,315,318,322,325,333,336,340,343,349,356,365,369,374,377,381,386,390,393,397,400,404,407,411,414,418,421,425,428,432,447,451],[174,175,176,183],"p",{},[177,178,182],"a",{"href":179,"rel":180},"https://dust.tt",[181],"nofollow","Dust"," is a shared agent studio. Your people write agents in plain language, connect them to Slack, Notion, Google Drive, GitHub, Salesforce, Zendesk and other apps, and pick which AI model each agent should use. Nimbus is the place those drafts have to survive contact with the rest of the company: go-to-market, finance, and ops on one job, with a sign-off and a record of what changed.",[174,185,186],{},"Dust’s centre of gravity is the agent itself: create it, share it, run it. Nimbus’s centre of gravity is the job. That is not a slight. Studios are how teams stop trapping a useful helper in one person’s chat history. Jobs are how several departments finish something that still makes sense next quarter. If you buy a studio and expect a ledger, you will be disappointed in the studio. If you buy a work OS and expect a flexible agent workshop as the main experience, you will be disappointed in the OS.",[174,188,189,190,195],{},"Dust is a French company, and that is part of why European buyers look at it closely. GDPR still applies when agents process personal data in company tools. Teams share agents, so the same helper is not trapped in one person’s history. Admins get company sign-in, roles for who can build or run an agent, and activity logs. Engineers can plug Dust agents into other developer tools. Model choice is real: OpenAI, Anthropic, Google, Mistral and others. Dust’s ",[177,191,194],{"href":192,"rel":193},"https://docs.dust.tt/docs/user-documentation/getting-started/dust-rollout-guide/welcome-to-dust",[181],"rollout guide"," describes that studio as an enterprise AI platform connecting models to internal knowledge, tools, and workflows.",[197,198,200],"h2",{"id":199},"words-youll-hear","Words you’ll hear",[202,203,204,212,223,229,235,241,247,253],"ul",{},[205,206,207,211],"li",{},[208,209,210],"strong",{},"Agent studio."," A place to build, share, and run custom agents. Dust’s product. Useful. Not the same as a place several departments finish one job.",[205,213,214,217,218,222],{},[208,215,216],{},"Multiplayer agents."," Dust’s term for agents that are not trapped in one person’s chat history. Real. Still primarily ",[219,220,221],"em",{},"shared agents",", not a lasting record of what finance approved.",[205,224,225,228],{},[208,226,227],{},"Model choice."," Dust works with OpenAI, Anthropic, Google, Mistral and others, so you are not locked to one chatbot brand. Nimbus does the same, and treats the choice as an operating decision: do not use the most expensive model for every small task.",[205,230,231,234],{},[208,232,233],{},"Workstream."," In Nimbus, a shared workspace for one job, with the right people, tools, and approval rules.",[205,236,237,240],{},[208,238,239],{},"Wiki."," Official playbooks agents must follow.",[205,242,243,246],{},[208,244,245],{},"Write-back."," Changing a live system. In Nimbus, connectors stay read-only until a named person signs.",[205,248,249,252],{},[208,250,251],{},"Lifecycle Graph."," The causal record of what the AI did, who approved it, and what changed.",[205,254,255,258],{},[208,256,257],{},"CNIL."," France’s data-protection authority. Dust is a French company; GDPR still applies when agents process personal data in company tools.",[197,260,262],{"id":261},"why-the-difference-matters","Why the difference matters",[174,264,265],{},"Teams can share Dust agents, so the same helper is not trapped in one person’s history. That “multiplayer” claim is fair. Admins get company sign-in, roles for who can build or run an agent, and activity logs. Engineers can plug Dust agents into other developer tools. Dust’s rollout guide is written as a programme: connect models to internal knowledge, tools, and workflows. That is a studio you roll out, not a toy.",[174,267,268,269,273,274,277,278,282,283,286],{},"Whether finance ever sees a discount field depends on how disciplined you were about who can invoke that agent, and whether anyone filed the run somewhere finance actually looks. Activity logs tell you that an agent ran. They do not automatically become a signed-off version of a CRM change. In Nimbus, go-to-market and finance sit on the same ",[177,270,272],{"href":271},"what-is-an-ai-workstream","workstream",". An ",[177,275,276],{"href":20},"agent team"," drafts against the ",[177,279,281],{"href":280},"what-is-a-company-wiki-for-ai-agents","wiki",". Customer records stay read-only until someone who is allowed to approve writes actually does. The ",[177,284,23],{"href":285},"what-is-a-lifecycle-graph"," keeps the signed-off version, not only the chat that produced it.",[174,288,289],{},"Dust searches connected sources and whatever you put in an agent’s knowledge. That works well when the files are clean. It gets fragile when the same fact lives in Slack, a deck, and a CRM field, and nobody is the official owner. If your failure is “the agent answered from an outdated Notion page,” Dust’s freshness and permission model matter most. If your failure is “we ran this last quarter and nobody can find the version finance signed,” you need a record of the job, not another shared agent.",[174,291,292,293,298,299,304],{},"Because Dust is French, the natural data-protection authority is the ",[177,294,297],{"href":295,"rel":296},"https://www.cnil.fr/en/ai-system-development-cnils-recommendations-to-comply-gdpr",[181],"CNIL",". CNIL is clear that GDPR still applies when you develop and run AI that processes personal data, including when those systems later connect to company tools. A shared Salesforce agent is not “just a helper.” It is processing with a purpose. ",[177,300,303],{"href":301,"rel":302},"https://www.cnil.fr/en/ensuring-security-ai-systems-development",[181],"CNIL’s security sheet"," puts Article 32 in plain language: security of processing is a risk-based obligation. European origin does not exempt you from deciding who may change production data. Dust’s buyers often arrive with that question already on the table — which is healthy.",[174,306,307,308,311],{},"Model choice is a shared strength. Dust lets you pick a model per agent. Nimbus treats that choice as an operating decision: do not use the most expensive model for every small task. See ",[177,309,310],{"href":45},"models",". The difference is whether the choice sits on an agent you published, or on a step inside a job with a budget in NTUs (work credits).",[174,313,314],{},"The fork is practical by role. A team lead who wants reusable helpers on Slack, Notion, and Drive will feel at home in Dust — publishing an agent is the product. An engineer who wants Dust sitting in the middle of existing tools has a path; that is a hub, not a COO login. Finance cares whether a discount field changed, who signed, and which playbook applied. Legal and a DPO in Europe will read CNIL and still ask purpose, retention, and who can write. Ops eventually wants one canvas for a cross-department job, not a catalogue of agents each team invented.",[174,316,317],{},"The hidden cost in Dust is operational: who owns the write policy when an agent can change production data. The hidden cost in Nimbus is adoption: operators must run workstreams, not only chat. Pick the cost you can staff.",[197,319,321],{"id":320},"when-dust-is-a-better-fit","When Dust is a better fit",[174,323,324],{},"Choose Dust when your job this quarter is “let teams publish reusable agents on our Slack, Notion, and Drive,” you are happy for knowledge to live in those source systems, and you want a flexible studio rather than an opinionated place to finish cross-department work.",[174,326,327,328,332],{},"Dust is also the better match if you have engineers who want Dust sitting in the middle of your existing tools, and you do not want a workstream-and-record layer yet. It is a strong alternative to ",[177,329,331],{"href":330},"nimbus-vs-chatgpt-enterprise","ChatGPT Enterprise"," when you need company context and custom agents you can share — especially in Europe and the mid-market.",[174,334,335],{},"Many teams start in an agent studio and later need sign-off, a ledger, and department-shaped work. That is the path Nimbus is built for — not an insult to Dust. You can keep Dust at the edge for engineering-tool agents and put Nimbus on the business jobs that need a sign-off. Coexistence is a policy: Dust agents do not hold production write passwords for money-moving systems; those writes wait in Nimbus.",[197,337,339],{"id":338},"how-this-shows-up-in-nimbus","How this shows up in Nimbus",[174,341,342],{},"You are not buying a folder of shared agents. You are buying a place go-to-market can draft, finance can review, and the company can still explain the change six months later.",[174,344,345,348],{},[177,346,347],{"href":51},"Connectors"," are scoped to the workspace and kept read-only until a write is approved. Nimbus publishes 2,000+ integrations; Dust publicly emphasises 70-plus, plus custom developer plug-ins. Count is not the whole story. Dust’s set on Slack, Notion, Drive, GitHub, Salesforce, and Zendesk may be exactly what a studio needs. Nimbus’s catalogue matters when the job spans a longer tail — and when the write path is a release, not an invocation.",[174,350,351,352,355],{},"You can ",[177,353,354],{"href":12},"set this up yourselves",". You do not need vendor engineers sitting with your team for months. Dust’s own rollout guide is still a rollout. Run that if you are buying a studio. Do not wait for it to grow a Lifecycle Graph.",[174,357,358,359,361,362,364],{},"See ",[177,360,39],{"href":40}," and the ",[177,363,23],{"href":24},".",[197,366,368],{"id":367},"questions-people-actually-ask","Questions people actually ask",[370,371,373],"h3",{"id":372},"can-nimbus-replace-dust","Can Nimbus replace Dust?",[174,375,376],{},"If Dust is a handful of shared agents on Notion and Slack, yes — you move the jobs into workstreams and the playbooks into the wiki. If you have invested heavily in Dust as a hub for engineering tools, keep Dust at the edge and put Nimbus on the business jobs that need a sign-off.",[370,378,380],{"id":379},"does-nimbus-lock-you-to-one-ai-vendor","Does Nimbus lock you to one AI vendor?",[174,382,383,384,364],{},"No. Both products let you choose models. Dust lets you pick a model per agent. Nimbus treats that choice as an operating decision. See ",[177,385,310],{"href":45},[370,387,389],{"id":388},"is-dust-more-multiplayer-than-nimbus","Is Dust more “multiplayer” than Nimbus?",[174,391,392],{},"Dust coined multiplayer for shared agents, and that is real. Nimbus’s shared surface is the workstream: humans and agent teams across departments on one job, with the Lifecycle Graph as shared memory. Both are multiplayer. Nimbus includes the sign-off and the record.",[370,394,396],{"id":395},"dust-vs-chatgpt-enterprise-vs-nimbus","Dust vs ChatGPT Enterprise vs Nimbus?",[174,398,399],{},"Dust is the closer peer: a shared agent studio with connectors. ChatGPT Enterprise is the default assistant plus team-owned agents inside OpenAI’s product. If you are leaving ChatGPT because you need shared, model-choice agents, Dust is the usual next stop. If you are leaving because you need write gates and a ledger, skip the studio. Three products, three centres of gravity: chat, published agent, job.",[370,401,403],{"id":402},"how-should-we-think-about-cost","How should we think about cost?",[174,405,406],{},"Dust is typically seats plus usage on a workspace of agents. Nimbus meters the work you run, in NTUs (work credits). Compare a real workload — one programme that updates customer records — not list price per seat. The hidden cost in Dust is operational: who owns the write policy when an agent can change production data. The hidden cost in Nimbus is adoption: operators must run workstreams, not only chat.",[370,408,410],{"id":409},"does-dusts-french-base-make-gdpr-easier","Does Dust’s French base make GDPR easier?",[174,412,413],{},"It makes the conversation more natural. CNIL’s guidance still applies: GDPR applies when you develop and run AI that processes personal data, and security of processing is a risk-based obligation. Publishing an agent is not a sign-off on the write.",[370,415,417],{"id":416},"can-we-run-dust-agents-that-draft-and-nimbus-that-releases","Can we run Dust agents that draft and Nimbus that releases?",[174,419,420],{},"Yes. Treat Dust as the place teams publish helpers for knowledge work. Feed anything that must change a live system into a Nimbus workstream. Do not let the published agent hold the write password.",[370,422,424],{"id":423},"who-should-own-dust-vs-nimbus","Who should own Dust vs Nimbus?",[174,426,427],{},"Platform or IT often owns a studio: who may build, who may run, which models, which connectors. Line operators own Nimbus workstreams because they own the outcome. If engineers want Dust in the middle of developer tools, let them — and keep money-moving writes off that hub.",[197,429,431],{"id":430},"related-reading","Related reading",[174,433,434,438,439,443,444,364],{},[177,435,437],{"href":436},"what-is-multi-agent-ai","What is multi-agent AI",", ",[177,440,442],{"href":441},"what-is-write-back-governance","What is write-back governance",", and ",[177,445,446],{"href":330},"Nimbus vs ChatGPT Enterprise",[197,448,450],{"id":449},"sources","Sources",[202,452,453,458,464,470],{},[205,454,455],{},[177,456,182],{"href":179,"rel":457},[181],[205,459,460],{},[177,461,463],{"href":192,"rel":462},[181],"Dust rollout guide: Welcome to Dust",[205,465,466],{},[177,467,469],{"href":295,"rel":468},[181],"CNIL, AI system development: recommendations to comply with the GDPR",[205,471,472],{},[177,473,475],{"href":301,"rel":474},[181],"CNIL, Ensuring the security of an AI system's development",{"title":155,"searchDepth":156,"depth":156,"links":477},[478,479,480,481,482,493,494],{"id":199,"depth":156,"text":200},{"id":261,"depth":156,"text":262},{"id":320,"depth":156,"text":321},{"id":338,"depth":156,"text":339},{"id":367,"depth":156,"text":368,"children":483},[484,486,487,488,489,490,491,492],{"id":372,"depth":485,"text":373},3,{"id":379,"depth":485,"text":380},{"id":388,"depth":485,"text":389},{"id":395,"depth":485,"text":396},{"id":402,"depth":485,"text":403},{"id":409,"depth":485,"text":410},{"id":416,"depth":485,"text":417},{"id":423,"depth":485,"text":424},{"id":430,"depth":156,"text":431},{"id":449,"depth":156,"text":450},"2026-08-17","Dust is a shared studio for building and publishing AI agents; Nimbus is where departments finish a job with a named signer and a lasting record.","/blog/nimbus-vs-dust",{"title":165,"description":496},"comparisons","blog/nimbus-vs-dust",[499,502,503,504,505],"dust","enterprise-ai","agents","governance","NqfuVK0DzAxN3YIuQXXm9YlK5KTrksOvcSLPIIgskEA",{"hero":508,"id":510,"title":511,"archived":149,"authors":150,"badge":150,"body":512,"date":150,"department":150,"description":516,"extension":158,"eyebrow":517,"faqHeader":150,"faqs":150,"footerBand":518,"headline":150,"image":150,"industry":150,"jobType":150,"listed":149,"location":150,"navigation":115,"openRoles":150,"pageLayout":150,"path":60,"relatedHeading":524,"seo":525,"series":150,"sitemap":115,"status":150,"stem":526,"subhead":150,"tags":150,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":527},{"filename":509},"u2221455217_Flat_design_of_a_futuristic_minimalist_landscape__5d589295-cdea-4ea9-a262-be766881accf_1.png","content/blog/index.md","Exploring the future of intelligence.",{"type":152,"value":513,"toc":514},[],{"title":155,"searchDepth":156,"depth":156,"links":515},[],"Deep dives into pre-cognitive intelligence, sentient enterprises, and the evolving landscape of AI-driven business transformation.","Latest Research",{"headline":519,"description":520,"primaryLabel":521,"primaryTo":522,"secondaryLabel":523,"secondaryTo":12},"Stay at the frontier.","Subscribe for product updates and new insights.","Subscribe","/newsletter","Explore the platform","More research",{"title":511,"description":516},"blog/index","eK1RCXdDW8nfLSyKRXGB1mJm9FAmhAO6GWXwNKOMNVE",[529,841],{"id":530,"title":531,"archived":149,"authors":150,"badge":532,"body":535,"date":495,"department":150,"description":832,"extension":158,"eyebrow":150,"faqHeader":150,"faqs":150,"footerBand":150,"headline":150,"image":150,"industry":150,"jobType":150,"listed":149,"location":150,"navigation":115,"openRoles":150,"pageLayout":150,"path":833,"relatedHeading":150,"seo":834,"series":499,"sitemap":115,"status":150,"stem":835,"subhead":150,"tags":836,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":840},"content/blog/nimbus-vs-glean.md","Nimbus vs Glean: Finding the Deck vs Running the Job That Deck Implies",{"label":167,"10|authors":533},[534],{"name":170,"to":120},{"type":152,"value":536,"toc":814},[537,545,552,560,562,609,611,626,633,636,639,642,645,648,652,659,662,665,668,670,673,689,695,701,703,707,713,717,720,724,727,731,734,738,745,749,760,764,767,771,774,776,787,789],[174,538,539,544],{},[177,540,543],{"href":541,"rel":542},"https://www.glean.com/",[181],"Glean"," is workplace search that grew a work assistant on top. It finds files across Drive, Slack, Confluence, and the rest of the workplace — and it respects who is allowed to see them. Nimbus is the place you then run the job those files imply: update the customer record, file the next step, get a person to sign.",[174,546,547,548,551],{},"Glean’s centre of gravity is ",[208,549,550],{},"find the right object, for the right person, at the right time",". Nimbus’s is a signed-off outcome. Both products will say “knowledge” and “agents.” They are not the same purchase. Search that does not leak is a hard, years-long problem. A release on a live system is a different hard problem. Many enterprises have both. They should not pretend one vendor covers both because both say “knowledge graph.”",[174,553,554,559],{},[177,555,558],{"href":556,"rel":557},"https://www.glean.com/product",[181],"Glean’s product"," connects to Google Workspace, Microsoft 365, Slack, Salesforce, Confluence, Jira, and many more — publicly, 100+ workplace apps — then builds an index that respects the same permissions as the source system. Under the hood that is a mix of classic keyword search and meaning-based search, plus a map of people, documents, and activity. Glean has added assistants and agents so the search layer can also draft and automate. That architecture is why Glean wins large-enterprise search bake-offs. Identity, crawl, and permission fidelity are the hard problems, and Glean has spent years on them.",[197,561,200],{"id":199},[202,563,564,570,576,582,588,593,598,603],{},[205,565,566,569],{},[208,567,568],{},"Workplace search."," An index across the apps the company already uses, so people stop hunting for files.",[205,571,572,575],{},[208,573,574],{},"Permission mirroring."," If a document is restricted in Drive, Glean should not surface it in a chat answer. That is the product.",[205,577,578,581],{},[208,579,580],{},"Knowledge graph (Glean)."," A map of people, documents, and activity for retrieval. Not the same as Nimbus’s Lifecycle Graph.",[205,583,584,587],{},[208,585,586],{},"Crawl."," The programme of connecting apps, mapping identity, and keeping the index fresh. Why IT sponsors Glean. Why it takes time.",[205,589,590,592],{},[208,591,233],{}," In Nimbus, a shared workspace for one job — not a search result.",[205,594,595,597],{},[208,596,245],{}," Changing a live system. Search products add agents on top of the index. Nimbus treats the write as a first-class release.",[205,599,600,602],{},[208,601,251],{}," A causal record of AI work: what ran, who approved it, what changed. Not an index of every file an employee ever touched.",[205,604,605,608],{},[208,606,607],{},"Copilot."," Microsoft’s assistant inside Office. Glean searches across many apps, including Microsoft. Neither is a governed execution layer.",[197,610,262],{"id":261},[174,612,613,614,619,620,625],{},"If you have ever watched a naive chatbot answer from a restricted Drive folder, you understand why Glean exists. Permission mirroring is not a slogan. It is the product: if a document is restricted in Drive, Glean should not surface it in a chat answer. ",[177,615,618],{"href":616,"rel":617},"https://csrc.nist.gov/pubs/sp/800/53/r5/upd1/final",[181],"NIST SP 800-53 Rev. 5"," Access Control (the AC family) is the control-catalogue reason: the system must enforce who may see what. ",[177,621,624],{"href":622,"rel":623},"https://www.glean.com/security",[181],"Glean’s security page"," describes that enforcement as a product: permission mirroring, encryption, and compliance claims. Glean is search that does not leak. It is not an AI risk-management framework, and it does not, by itself, put a named signer on a customer-record write.",[174,627,628,629,632],{},"Glean’s primary object is a document, message, ticket, or person. Nimbus’s primary object is a job, an agent team, a release. Glean’s success metric is time-to-answer. Nimbus’s is time-to-signed-off outcome. Glean’s write path is secondary — agents on top of the index. Nimbus’s write path is first-class: ",[177,630,631],{"href":441},"read-only until you open it",", then a person on the change.",[174,634,635],{},"Ask whether agents are a feature of search, or search is a feature of agents. Glean is the first. Nimbus is the second. Assistants on a permission-aware index are a reasonable next step for a search company. They still orbit findability. A workstream does not orbit a search result. It orbits a finish line, a budget, and a write policy.",[174,637,638],{},"The crawl is why IT sponsors Glean and why it takes time: connecting apps, mapping identity, keeping the index fresh, proving permission QA. That is the right model for a 20,000-person corpus. It is the wrong model if you needed a signed CRM update this quarter and were told to wait until 2019’s files had finished indexing. Nimbus assumes you can already find the policy, or that you will attach the systems this job needs. It does not wait for a company-wide crawl of every historical file.",[174,640,641],{},"The two “graphs” are the usual confusion. Glean’s knowledge graph is mostly an index of people and content for retrieval. Nimbus’s Lifecycle Graph is an operational ledger of work, agents, and releases. Collapsing the terms in a vendor meeting is how you buy the wrong one. You can run both graphs. You cannot substitute one for the other.",[174,643,644],{},"Role by role, the fork is practical. A CIO running a knowledge programme wants a universal search bar across 100+ apps, with permission mirroring as the non-negotiable. That is Glean. A Head of RevOps wants an agent to propose opportunity updates with a named signer — search will find the deck; it will not be the release. Security cares that Glean does not leak restricted files, and still cares who may change Salesforce. Knowledge workers want time-to-answer. Operators want time-to-signed-off outcome. Legal will not accept “the assistant found it” as the story of why a customer field moved.",[174,646,647],{},"A healthy split: Glean for findability across the sprawling workplace; Nimbus for execution on the jobs that mutate systems of record. Do not stretch Glean into an operating layer because it added agents. Do not stretch Nimbus into a crawl of every Confluence page because Perception can answer questions about the record.",[197,649,651],{"id":650},"when-glean-is-a-better-fit","When Glean is a better fit",[174,653,654,655,658],{},"Choose Glean when the corpus is huge, permissions ",[219,656,657],{},"are"," the product, and you need a universal search bar across 100+ apps before you invent agent teams. Choose Glean also if the executive sponsor is the CIO’s knowledge programme rather than a line-of-business operating model.",[174,660,661],{},"Deployment is a crawl programme: identity mapping, permission QA, often with Glean or partner engineers in the building. That is the right model for a 20,000-person corpus. Do not treat that implementation cost as a reason Glean is “worse.” It is the cost of doing permission-aware search well.",[174,663,664],{},"Do not choose Glean as a stealth agent operating layer. You will spend a year on crawl quality and still lack workstreams, specialist teams, and change control on writes.",[174,666,667],{},"Some organisations will run Glean for findability and Nimbus for execution. That is a coherent architecture if you do not pretend one graph is the other. Keep Glean as the place people find the deck. Put the job the deck implies — update the customer record, file the next step, get a person to sign — in a Nimbus workstream. Connectors in Nimbus are not a substitute for Glean-scale historical crawl. A Glean assistant is not a substitute for a quoted write.",[197,669,339],{"id":338},[174,671,672],{},"Search exists inside Nimbus as a way to ask about the record, your playbooks, and the systems you attached. It is not a company-wide crawl of 2019.",[174,674,675,676,678,679,681,682,684,685,688],{},"The ",[177,677,281],{"href":28}," is what the company asserts. ",[177,680,347],{"href":51}," are scoped per ",[177,683,272],{"href":32},". Agents do not get “search everything this user could theoretically open” as the default tool. They get the systems you attached, in the mode you allowed (usually read), until a human releases a write. ",[177,686,687],{"href":36},"Perception"," is ordinary language over that scoped world, not a second Glean.",[174,690,351,691,694],{},[177,692,693],{"href":12},"set Nimbus up yourselves",": a workspace, wiki, connectors, a first workstream. You do not wait for an index of every historical file to finish.",[174,696,358,697,361,699,364],{},[177,698,39],{"href":40},[177,700,23],{"href":24},[197,702,368],{"id":367},[370,704,706],{"id":705},"does-glean-require-more-implementation-than-nimbus","Does Glean require more implementation than Nimbus?",[174,708,709,710,712],{},"Usually yes. Permission-aware crawl at enterprise scale is a programme. Nimbus is self-service for most buyers: you are not waiting on vendor engineers sitting with your team for months to get collaboration, sign-off, and a causal record. That is not a claim that Glean is slow for no reason. Crawl and permission QA ",[219,711,657],{}," the work.",[370,714,716],{"id":715},"does-nimbus-replace-glean","Does Nimbus replace Glean?",[174,718,719],{},"Only if your Glean usage is a thin Q&A bot on a small corpus. It does not replace Glean as permission-aware enterprise search across a sprawling workplace. If you need the latter, keep a search product.",[370,721,723],{"id":722},"both-mention-knowledge-graphs-are-they-the-same","Both mention knowledge graphs. Are they the same?",[174,725,726],{},"No. Glean’s graph is mostly an index of people and content for retrieval. Nimbus’s Lifecycle Graph is an operational ledger of work, agents, and releases. Collapsing the terms in a vendor meeting is how you buy the wrong one.",[370,728,730],{"id":729},"can-nimbus-search-drive-and-slack","Can Nimbus search Drive and Slack?",[174,732,733],{},"Connectors bring live systems into agent context and into questions you ask about the record. That is targeted operational retrieval, not a Glean-scale crawl of every historical file. If you need the latter, keep a search product.",[370,735,737],{"id":736},"glean-vs-copilot-vs-nimbus","Glean vs Copilot vs Nimbus?",[174,739,740,741,364],{},"Copilot is productivity inside Microsoft that respects Microsoft permissions. Glean is search across many apps (including Microsoft) that respects source permissions. Nimbus is governed execution. A Microsoft-first company may still need Glean if SharePoint search is not enough, and may still need Nimbus if Copilot Studio is not an operating layer. See ",[177,742,744],{"href":743},"nimbus-vs-microsoft-copilot","Nimbus vs Microsoft Copilot",[370,746,748],{"id":747},"can-we-run-glean-and-nimbus-together","Can we run Glean and Nimbus together?",[174,750,751,752,755,756,759],{},"Yes. That is the intended coexistence for organisations that have both a findability problem and an execution problem. Do not give Glean agents production write credentials because the index is permission-aware. Permission to ",[219,753,754],{},"see"," is not permission to ",[219,757,758],{},"change",". Route mutations through Nimbus governance.",[370,761,763],{"id":762},"who-should-own-which-product","Who should own which product?",[174,765,766],{},"The CIO’s knowledge programme typically owns Glean: identity mapping, crawl, permission QA. Line operators own Nimbus workstreams because they own the systems of record those jobs touch. Security reviews both — leak prevention on the search side, write gates on the execution side.",[370,768,770],{"id":769},"if-glean-has-agents-now-why-add-nimbus","If Glean has agents now, why add Nimbus?",[174,772,773],{},"Because agents on an index are still a feature of search. Time-to-answer is not time-to-signed-off outcome. If the job is to change a customer record with a person on the write, you need a workstream, a wiki clause, and a graph — not another way to find the deck.",[197,775,431],{"id":430},[174,777,778,438,782,443,785,364],{},[177,779,781],{"href":780},"what-is-enterprise-rag","What is enterprise RAG",[177,783,784],{"href":285},"What is a lifecycle graph",[177,786,744],{"href":743},[197,788,450],{"id":449},[202,790,791,796,802,808],{},[205,792,793],{},[177,794,543],{"href":541,"rel":795},[181],[205,797,798],{},[177,799,801],{"href":556,"rel":800},[181],"Glean product",[205,803,804],{},[177,805,807],{"href":622,"rel":806},[181],"Glean security",[205,809,810],{},[177,811,813],{"href":616,"rel":812},[181],"NIST SP 800-53 Rev. 5 (Access Control)",{"title":155,"searchDepth":156,"depth":156,"links":815},[816,817,818,819,820,830,831],{"id":199,"depth":156,"text":200},{"id":261,"depth":156,"text":262},{"id":650,"depth":156,"text":651},{"id":338,"depth":156,"text":339},{"id":367,"depth":156,"text":368,"children":821},[822,823,824,825,826,827,828,829],{"id":705,"depth":485,"text":706},{"id":715,"depth":485,"text":716},{"id":722,"depth":485,"text":723},{"id":729,"depth":485,"text":730},{"id":736,"depth":485,"text":737},{"id":747,"depth":485,"text":748},{"id":762,"depth":485,"text":763},{"id":769,"depth":485,"text":770},{"id":430,"depth":156,"text":431},{"id":449,"depth":156,"text":450},"Glean is permission-aware workplace search; Nimbus is the place an agent can change a customer record — with a person signing off.","/blog/nimbus-vs-glean",{"title":531,"description":832},"blog/nimbus-vs-glean",[499,837,838,839,504],"glean","enterprise-search","knowledge-graph","GaY9-99viUbh6e-P2OUDGOvCd7i49ockwlAhmKjb4TE",{"id":842,"title":843,"archived":149,"authors":150,"badge":844,"body":847,"date":495,"department":150,"description":1173,"extension":158,"eyebrow":150,"faqHeader":150,"faqs":150,"footerBand":150,"headline":150,"image":150,"industry":150,"jobType":150,"listed":149,"location":150,"navigation":115,"openRoles":150,"pageLayout":150,"path":1174,"relatedHeading":150,"seo":1175,"series":499,"sitemap":115,"status":150,"stem":1176,"subhead":150,"tags":1177,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":1179},"content/blog/nimbus-vs-claude.md","Nimbus vs Claude: A Thinking Partner or a Place Thinking Becomes an Approved Change?",{"label":167,"10|authors":845},[846],{"name":170,"to":120},{"type":152,"value":848,"toc":1154},[849,868,871,880,882,928,930,939,942,953,961,968,975,988,991,995,998,1001,1004,1011,1013,1019,1032,1037,1041,1043,1047,1050,1054,1064,1068,1071,1075,1081,1085,1088,1092,1095,1099,1102,1106,1109,1113,1116,1118,1128,1130],[174,850,851,852,855,856,863,864,867],{},"“Claude” is three buying decisions that get collapsed in meetings. The ",[208,853,854],{},"model"," is what you call from your own software. ",[208,857,858],{},[177,859,862],{"href":860,"rel":861},"https://www.anthropic.com/news/claude-for-work",[181],"Claude for Work"," is Anthropic’s hosted assistant: chats, Projects, artifacts, connectors, and admin controls. Claude also shows up ",[208,865,866],{},"inside other products",". Nimbus is none of those. It is the place a strong analysis becomes an approved change in Salesforce, billing, or the policy pack — with a named person on the release.",[174,869,870],{},"This article compares Nimbus to Claude for Work, and to the idea that “we standardised on Claude, so we are done.” The API is complementary: Nimbus can send a step to Claude rather than replacing it. Standardising on a model is not the same as standardising on a place departments finish work.",[174,872,873,874,879],{},"Claude for Work is a serious product. It is built around careful long-context analysis, writing, coding help, and a safety posture enterprises actually cite in questionnaires. Projects give a person or a small team a durable bundle of files and instructions. Artifacts give you something you can look at besides a wall of prose. Connectors and company admin exist because Anthropic knows enterprises will not live in a personal ",[177,875,878],{"href":876,"rel":877},"https://claude.ai",[181],"claude.ai"," tab forever. That is a thinking partner with a workspace. It is still a thinking partner.",[197,881,200],{"id":199},[202,883,884,890,896,902,907,912,917,922],{},[205,885,886,889],{},[208,887,888],{},"Claude for Work."," Anthropic’s hosted workspace: chats, Projects, artifacts, connectors, and company admin.",[205,891,892,895],{},[208,893,894],{},"Project."," A durable bundle of files and instructions in Claude. Excellent context for one person or a small team. Not a company job.",[205,897,898,901],{},[208,899,900],{},"Constitutional AI."," Anthropic’s research on training models to refuse and to be careful. That is model behaviour. It is not a company operating layer.",[205,903,904,906],{},[208,905,233],{}," In Nimbus, a shared workspace for one job, with the people, tools, and approval rules attached.",[205,908,909,911],{},[208,910,239],{}," Official playbooks agents must follow — including a discount floor, a journal policy, or a write rule.",[205,913,914,916],{},[208,915,245],{}," Changing a live system. Connectors stay read-only until a named person signs.",[205,918,919,921],{},[208,920,251],{}," The causal record of what ran, who approved it, and what changed.",[205,923,924,927],{},[208,925,926],{},"Model routing."," Sending each step to the right model. Claude is often right for long, careful writing and wrong for a routine extract.",[197,929,262],{"id":261},[174,931,932,933,938],{},"Claude for Work is excellent at what Anthropic optimises: careful long-context analysis, writing, coding help, and a safety posture enterprises actually cite in questionnaires. ",[177,934,937],{"href":935,"rel":936},"https://www.anthropic.com/legal/privacy",[181],"Anthropic’s privacy policy"," is the product’s own account of how the hosted assistant handles data. Read it as the privacy story for Claude-the-product. It is not a company operating system. Privacy of the assistant and provenance of a company action are related concerns. They are not the same control.",[174,940,941],{},"In Claude, the organisational unit is the conversation (and the Project). Memory is whatever Anthropic’s product retains for that workspace, plus the files you uploaded. When the Project is deleted, or the person leaves, the “memory” is a pile of chats. That is fine for analysis that was always meant to be personal or small-team. It is fragile for a discount exception that finance must still explain in two years.",[174,943,944,945,273,947,949,950,952],{},"In Nimbus, the same job is a ",[177,946,272],{"href":271},[177,948,276],{"href":20}," drafts against the wiki clause that states the rule. Finance and legal can sit on the same canvas. Customer records stay read-only until a human releases the change. The ",[177,951,23],{"href":285}," keeps what ran, who approved it, and what changed — so next quarter you are not hunting Slack for “the Claude doc.” A Project is context. A signed change is the record.",[174,954,955,960],{},[177,956,959],{"href":957,"rel":958},"https://www.anthropic.com/research/constitutional-ai-harmlessness-from-ai-feedback",[181],"Anthropic’s Constitutional AI"," work is why enterprises cite Claude’s safety posture. The research is about training models to refuse and to be careful. That is valuable. A model that refuses a harmful request is not a named signer on a CRM write. A brilliant Project is still context. Those refusals still do not give you a company operating layer. Do not let a safety questionnaire stand in for a release process.",[174,962,963,964,967],{},"If the executive summary is “we bought Claude, therefore we have enterprise AI,” you have bought a model and a workspace. You have not bought a place departments finish work. That gap is where people start pasting Claude output straight into Salesforce — ungoverned ",[177,965,966],{"href":441},"write-back"," with extra steps. Careful analysis that becomes an unsigned field is still an unsigned field.",[174,969,970,971,364],{},"Claude’s long context is a model feature. A Lifecycle Graph is a product feature. Do not confuse a very large window with ",[177,972,974],{"href":973},"what-is-institutional-memory-in-enterprise-ai","institutional memory",[174,976,977,978,982,983,987],{},"The fork is practical by role. Analysts, counsel, and writers get a thinking partner that is unusually good at long documents — Claude for Work is often the right daily surface. Engineers may want the Claude API so they can own orchestration, evaluation, and tool policy: a build, not an operating layer for operators. RevOps and finance need the analysis to land as a quoted change with a signer, not another Project named after last quarter’s close. Security will like Constitutional AI as a model-behaviour story and still ask who can change production records. A COO should not be told that desktop and coding agents (Claude Cowork and similar) are the company AI programme; those personal runtimes are closer to ",[177,979,981],{"href":980},"nimbus-vs-openclaw","OpenClaw"," or ",[177,984,986],{"href":985},"nimbus-vs-hermes","Hermes"," than to a company operating layer.",[174,989,990],{},"The job split is therefore: Claude thinks; Nimbus finishes. You can use Claude as the model behind a Nimbus step. You should not use a Claude Project as the system of record for a decision that hit the CRM.",[197,992,994],{"id":993},"when-claude-is-a-better-fit","When Claude is a better fit",[174,996,997],{},"Choose Claude for Work when the job is high-quality reasoning and writing on documents you can put in a Project, you do not need gated writes into the systems that hold customer and money data, and you want Anthropic as both model vendor and the place people work.",[174,999,1000],{},"Choose the Claude API when you have engineers to own orchestration, evaluation, and tool policy. That path is honest: you are building. Do not pretend the API is a workstream product with a wiki and a graph.",[174,1002,1003],{},"Desktop and coding agents (Claude Cowork and similar) are personal runtimes. Choose them for a person who will look after their own machine. Do not standardise the company on them and call it governance.",[174,1005,1006,1007,1010],{},"Using Nimbus does not mean abandoning Claude. It means Claude stops being the place the company thinks the work finished. A healthy coexistence: people keep Claude for careful analysis; Nimbus routes some steps ",[219,1008,1009],{},"to"," Claude when the task is long, careful writing; anything that must change Salesforce, billing, or the policy pack is a workstream with a signer. Paste is a last resort, not the integration.",[197,1012,339],{"id":338},[174,1014,1015,1016,1018],{},"Nimbus is not a Claude competitor in the model sense. Claude is often the right model for long, careful writing. It is the wrong default for a routine extract, and the wrong abstraction for “how we run AI at work.” See ",[177,1017,310],{"href":45},". Routing is how you avoid using the most expensive model for every small task, and how you keep Claude in the loop without making Claude the loop.",[174,1020,1021,1022,1024,1025,1028,1029,1031],{},"Nimbus keeps three planes operators actually run: asserted policy (",[177,1023,281],{"href":28},"), live systems (",[177,1026,1027],{"href":51},"connectors",", read-only by default), and derived memory (Lifecycle Graph). ",[177,1030,687],{"href":36}," lets anyone ask in ordinary language — “what did we approve for this account last quarter?” — without hoping someone named the chat correctly. That is the opposite of a Project whose title only the author remembers.",[174,1033,351,1034,1036],{},[177,1035,693],{"href":12},". You are not waiting on vendor engineers to invent an operating layer around Claude. If you already have Claude for Work, keep it. Attach the systems of record in Nimbus. Put the playbook in the wiki. Let Claude draft; let a human release.",[174,1038,358,1039,364],{},[177,1040,39],{"href":40},[197,1042,368],{"id":367},[370,1044,1046],{"id":1045},"does-nimbus-compete-with-anthropic","Does Nimbus compete with Anthropic?",[174,1048,1049],{},"At the application layer, yes. At the model layer, no. Nimbus is a customer of frontier models. Routing is how you avoid using the most expensive model for every small task. A Claude-shaped step inside a workstream is complementarity. A Claude Project used as the company archive is substitution you will regret.",[370,1051,1053],{"id":1052},"can-i-bring-my-own-claude-api-key","Can I bring my own Claude API key?",[174,1055,1056,1057,1060,1061,364],{},"Model routing and bring-your-own-key options are part of how Nimbus treats models as infrastructure. Talk to ",[177,1058,1059],{"href":84},"sales"," for the current enterprise path. Do not assume a one-to-one replica of Anthropic’s consumer chat inside Nimbus; the experience is workstreams and Perception, not a clone of ",[177,1062,878],{"href":876,"rel":1063},[181],[370,1065,1067],{"id":1066},"is-nimbus-just-a-wrapper-on-claude","Is Nimbus “just a wrapper on Claude”?",[174,1069,1070],{},"No. A wrapper is a chat window. Nimbus ships a Lifecycle Graph, specialist agent teams, and a read-only-default path for changing records. If the model behind a step is Claude, that is routing, not wrapping. If you removed Claude and routed the same step elsewhere, the workstream, wiki, and graph would still be there.",[370,1072,1074],{"id":1073},"claude-vs-chatgpt-enterprise-vs-nimbus","Claude vs ChatGPT Enterprise vs Nimbus?",[174,1076,1077,1078,1080],{},"Claude and ChatGPT are rival assistants (and rival APIs). Nimbus can sit on either. Shortlist Anthropic vs OpenAI on model and workspace grounds — writing quality, safety posture, company knowledge, Workspace Agents versus Projects. Shortlist Nimbus when the work includes production writes and a ledger. See ",[177,1079,446],{"href":330},". Buying the assistant bake-off and skipping the operating-layer question is how you end up with two chats and still no signer.",[370,1082,1084],{"id":1083},"will-claude-for-work-update-salesforce-for-us","Will Claude for Work update Salesforce for us?",[174,1086,1087],{},"Not as a release manager for enterprise writes. Thinking in Claude and pasting into Salesforce is productivity. It is also drift. If the plan is “an agent team drafts, a human releases, the graph stores the artefact,” you are specifying Nimbus — and you can still use Claude as the model behind the draft.",[370,1089,1091],{"id":1090},"is-a-project-the-same-as-a-workstream","Is a Project the same as a workstream?",[174,1093,1094],{},"No. A Project is a durable bundle of files and instructions. Excellent context for one person or a small team. A workstream is a shared workspace for one job, with people, tools, approval rules, a budget, and a finish line. Projects do not give you connector-level least privilege, quoted writes, or a Lifecycle Graph. Useful for research. Not an operations unit.",[370,1096,1098],{"id":1097},"does-constitutional-ai-mean-we-can-skip-write-back-gates","Does Constitutional AI mean we can skip write-back gates?",[174,1100,1101],{},"No. Constitutional AI is model behaviour: refusal and care in training. Write-back governance is a product control: the connector cannot write until a named person signs the quoted change. A careful model can still propose a bad field update. The gate is what stops the proposal becoming a fact.",[370,1103,1105],{"id":1104},"can-we-use-claude-for-work-beside-nimbus-without-duplicating-memory","Can we use Claude for Work beside Nimbus without duplicating memory?",[174,1107,1108],{},"Yes, if you are strict about what each remembers. Claude remembers the analysis in a Project. Nimbus remembers the job, the playbook version, the signer, and the change. Do not copy the CRM into a Project “for context” and then treat that copy as official. Point at the live system from the workstream.",[370,1110,1112],{"id":1111},"who-owns-claude-vs-who-owns-nimbus","Who owns Claude vs who owns Nimbus?",[174,1114,1115],{},"Knowledge workers and often legal or research own Claude for Work as a thinking surface. Engineers may own the API. Operators who own systems of record own Nimbus workstreams. 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Join the team building the Sentient Enterprise.","content/shared/hiring.md","Hiring banner",{"type":152,"value":1185,"toc":1186},[],{"title":155,"searchDepth":156,"depth":156,"links":1187},[],"/shared/hiring",{"title":1183,"description":155},"shared/hiring","-6bioYD7lKYokGUVU3ff4hHTvB-sDyOMuCptKHnojfk",{"fold":1193,"id":1197,"title":1198,"archived":149,"authors":150,"badge":150,"body":1199,"date":150,"department":150,"description":155,"extension":158,"eyebrow":150,"faqHeader":150,"faqs":150,"footerBand":1203,"headline":150,"image":150,"industry":150,"jobType":150,"listed":149,"location":150,"navigation":115,"openRoles":150,"pageLayout":150,"path":1207,"relatedHeading":150,"seo":1208,"series":150,"sitemap":115,"status":150,"stem":1209,"subhead":150,"tags":150,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":1210},{"headline":1194,"description":1195,"primaryLabel":8,"primaryTo":1196,"secondaryLabel":523,"secondaryTo":12},"Run frontier AI your business actually owns.","Governed agent swarms, 2,000+ integrations, and a knowledge graph that stays inside your walls. Free 7-day trial.","/checkout","content/shared/cta.md","Site CTAs",{"type":152,"value":1200,"toc":1201},[],{"title":155,"searchDepth":156,"depth":156,"links":1202},[],{"headline":1204,"description":1205,"primaryLabel":8,"primaryTo":1196,"secondaryLabel":1206,"secondaryTo":84},"See what governed AI looks like on your stack.","Connect your tools, run a workstream, and keep every decision on your ledger - free for 7 days.","Talk to our team","/shared/cta",{"title":1198,"description":155},"shared/cta","YHK6Fb8AvCPR1zZq7R_xiXUG0hwhP5UxHA8Ix52JQp4",1787194070096]