[{"data":1,"prerenderedAt":1241},["ShallowReactive",2],{"site-nav-content":3,"hiring-banner-content":163,"blog:/blog/nimbus-vs-claude":175,"blog-index-copy":563,"blog:/blog/nimbus-vs-claude:surround":584,"site-cta-content":1222},{"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",{"enabled":149,"message":164,"linkLabel":78,"linkHref":79,"id":165,"title":166,"archived":149,"authors":150,"badge":150,"body":167,"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":171,"relatedHeading":150,"seo":172,"series":150,"sitemap":115,"status":150,"stem":173,"subhead":150,"tags":150,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":174},"We're hiring! Join the team building the Sentient Enterprise.","content/shared/hiring.md","Hiring banner",{"type":152,"value":168,"toc":169},[],{"title":155,"searchDepth":156,"depth":156,"links":170},[],"/shared/hiring",{"title":166,"description":155},"shared/hiring","-6bioYD7lKYokGUVU3ff4hHTvB-sDyOMuCptKHnojfk",{"id":176,"title":177,"archived":149,"authors":150,"badge":178,"body":183,"date":553,"department":150,"description":554,"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":555,"relatedHeading":150,"seo":556,"series":557,"sitemap":115,"status":150,"stem":558,"subhead":150,"tags":559,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":562},"content/blog/nimbus-vs-claude.md","Nimbus vs Claude: A Thinking Partner or a Place Thinking Becomes an Approved Change?",{"label":179,"10|authors":180},"Comparisons",[181],{"name":182,"to":120},"Nimbus Research",{"type":152,"value":184,"toc":533},[185,208,211,220,225,277,281,290,293,309,317,325,333,346,349,353,356,359,362,370,374,381,396,403,408,412,417,420,424,434,438,441,445,453,457,460,464,467,471,474,478,481,485,488,492,505,509],[186,187,188,189,193,194,203,204,207],"p",{},"“Claude” is three buying decisions that get collapsed in meetings. The ",[190,191,192],"strong",{},"model"," is what you call from your own software. ",[190,195,196],{},[197,198,202],"a",{"href":199,"rel":200},"https://www.anthropic.com/news/claude-for-work",[201],"nofollow","Claude for Work"," is Anthropic’s hosted assistant: chats, Projects, artifacts, connectors, and admin controls. Claude also shows up ",[190,205,206],{},"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.",[186,209,210],{},"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.",[186,212,213,214,219],{},"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 ",[197,215,218],{"href":216,"rel":217},"https://claude.ai",[201],"claude.ai"," tab forever. That is a thinking partner with a workspace. It is still a thinking partner.",[221,222,224],"h2",{"id":223},"words-youll-hear","Words you’ll hear",[226,227,228,235,241,247,253,259,265,271],"ul",{},[229,230,231,234],"li",{},[190,232,233],{},"Claude for Work."," Anthropic’s hosted workspace: chats, Projects, artifacts, connectors, and company admin.",[229,236,237,240],{},[190,238,239],{},"Project."," A durable bundle of files and instructions in Claude. Excellent context for one person or a small team. Not a company job.",[229,242,243,246],{},[190,244,245],{},"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.",[229,248,249,252],{},[190,250,251],{},"Workstream."," In Nimbus, a shared workspace for one job, with the people, tools, and approval rules attached.",[229,254,255,258],{},[190,256,257],{},"Wiki."," Official playbooks agents must follow — including a discount floor, a journal policy, or a write rule.",[229,260,261,264],{},[190,262,263],{},"Write-back."," Changing a live system. Connectors stay read-only until a named person signs.",[229,266,267,270],{},[190,268,269],{},"Lifecycle Graph."," The causal record of what ran, who approved it, and what changed.",[229,272,273,276],{},[190,274,275],{},"Model routing."," Sending each step to the right model. Claude is often right for long, careful writing and wrong for a routine extract.",[221,278,280],{"id":279},"why-the-difference-matters","Why the difference matters",[186,282,283,284,289],{},"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. ",[197,285,288],{"href":286,"rel":287},"https://www.anthropic.com/legal/privacy",[201],"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.",[186,291,292],{},"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.",[186,294,295,296,300,301,304,305,308],{},"In Nimbus, the same job is a ",[197,297,299],{"href":298},"what-is-an-ai-workstream","workstream",". An ",[197,302,303],{"href":20},"agent team"," 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 ",[197,306,23],{"href":307},"what-is-a-lifecycle-graph"," 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.",[186,310,311,316],{},[197,312,315],{"href":313,"rel":314},"https://www.anthropic.com/research/constitutional-ai-harmlessness-from-ai-feedback",[201],"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.",[186,318,319,320,324],{},"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 ",[197,321,323],{"href":322},"what-is-write-back-governance","write-back"," with extra steps. Careful analysis that becomes an unsigned field is still an unsigned field.",[186,326,327,328,332],{},"Claude’s long context is a model feature. A Lifecycle Graph is a product feature. Do not confuse a very large window with ",[197,329,331],{"href":330},"what-is-institutional-memory-in-enterprise-ai","institutional memory",".",[186,334,335,336,340,341,345],{},"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 ",[197,337,339],{"href":338},"nimbus-vs-openclaw","OpenClaw"," or ",[197,342,344],{"href":343},"nimbus-vs-hermes","Hermes"," than to a company operating layer.",[186,347,348],{},"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.",[221,350,352],{"id":351},"when-claude-is-a-better-fit","When Claude is a better fit",[186,354,355],{},"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.",[186,357,358],{},"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.",[186,360,361],{},"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.",[186,363,364,365,369],{},"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 ",[366,367,368],"em",{},"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.",[221,371,373],{"id":372},"how-this-shows-up-in-nimbus","How this shows up in Nimbus",[186,375,376,377,380],{},"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 ",[197,378,379],{"href":45},"models",". 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.",[186,382,383,384,387,388,391,392,395],{},"Nimbus keeps three planes operators actually run: asserted policy (",[197,385,386],{"href":28},"wiki","), live systems (",[197,389,390],{"href":51},"connectors",", read-only by default), and derived memory (Lifecycle Graph). ",[197,393,394],{"href":36},"Perception"," 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.",[186,397,398,399,402],{},"You can ",[197,400,401],{"href":12},"set Nimbus up yourselves",". 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.",[186,404,405,406,332],{},"See ",[197,407,39],{"href":40},[221,409,411],{"id":410},"questions-people-actually-ask","Questions people actually ask",[413,414,416],"h3",{"id":415},"does-nimbus-compete-with-anthropic","Does Nimbus compete with Anthropic?",[186,418,419],{},"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.",[413,421,423],{"id":422},"can-i-bring-my-own-claude-api-key","Can I bring my own Claude API key?",[186,425,426,427,430,431,332],{},"Model routing and bring-your-own-key options are part of how Nimbus treats models as infrastructure. Talk to ",[197,428,429],{"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 ",[197,432,218],{"href":216,"rel":433},[201],[413,435,437],{"id":436},"is-nimbus-just-a-wrapper-on-claude","Is Nimbus “just a wrapper on Claude”?",[186,439,440],{},"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.",[413,442,444],{"id":443},"claude-vs-chatgpt-enterprise-vs-nimbus","Claude vs ChatGPT Enterprise vs Nimbus?",[186,446,447,448,452],{},"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 ",[197,449,451],{"href":450},"nimbus-vs-chatgpt-enterprise","Nimbus vs ChatGPT Enterprise",". Buying the assistant bake-off and skipping the operating-layer question is how you end up with two chats and still no signer.",[413,454,456],{"id":455},"will-claude-for-work-update-salesforce-for-us","Will Claude for Work update Salesforce for us?",[186,458,459],{},"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.",[413,461,463],{"id":462},"is-a-project-the-same-as-a-workstream","Is a Project the same as a workstream?",[186,465,466],{},"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.",[413,468,470],{"id":469},"does-constitutional-ai-mean-we-can-skip-write-back-gates","Does Constitutional AI mean we can skip write-back gates?",[186,472,473],{},"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.",[413,475,477],{"id":476},"can-we-use-claude-for-work-beside-nimbus-without-duplicating-memory","Can we use Claude for Work beside Nimbus without duplicating memory?",[186,479,480],{},"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.",[413,482,484],{"id":483},"who-owns-claude-vs-who-owns-nimbus","Who owns Claude vs who owns Nimbus?",[186,486,487],{},"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. Security reviews both.",[221,489,491],{"id":490},"related-reading","Related reading",[186,493,494,497,498,502,503,332],{},[197,495,496],{"href":322},"What is write-back governance",", ",[197,499,501],{"href":500},"what-is-model-routing","What is model routing",", and ",[197,504,451],{"href":450},[221,506,508],{"id":507},"sources","Sources",[226,510,511,516,521,527],{},[229,512,513],{},[197,514,202],{"href":199,"rel":515},[201],[229,517,518],{},[197,519,218],{"href":216,"rel":520},[201],[229,522,523],{},[197,524,526],{"href":286,"rel":525},[201],"Anthropic privacy policy",[229,528,529],{},[197,530,532],{"href":313,"rel":531},[201],"Anthropic, Constitutional AI",{"title":155,"searchDepth":156,"depth":156,"links":534},[535,536,537,538,539,551,552],{"id":223,"depth":156,"text":224},{"id":279,"depth":156,"text":280},{"id":351,"depth":156,"text":352},{"id":372,"depth":156,"text":373},{"id":410,"depth":156,"text":411,"children":540},[541,543,544,545,546,547,548,549,550],{"id":415,"depth":542,"text":416},3,{"id":422,"depth":542,"text":423},{"id":436,"depth":542,"text":437},{"id":443,"depth":542,"text":444},{"id":455,"depth":542,"text":456},{"id":462,"depth":542,"text":463},{"id":469,"depth":542,"text":470},{"id":476,"depth":542,"text":477},{"id":483,"depth":542,"text":484},{"id":490,"depth":156,"text":491},{"id":507,"depth":156,"text":508},"2026-08-17","Claude is Anthropic’s thinking partner for careful analysis; Nimbus is where that analysis becomes a signed change in the tools you already run.","/blog/nimbus-vs-claude",{"title":177,"description":554},"comparisons","blog/nimbus-vs-claude",[557,138,560,561,379],"anthropic","enterprise-ai","Wwzb1hbspRtnBlnF7nJGI1cGOTiMal8P0MxGhdRHl70",{"hero":564,"id":566,"title":567,"archived":149,"authors":150,"badge":150,"body":568,"date":150,"department":150,"description":572,"extension":158,"eyebrow":573,"faqHeader":150,"faqs":150,"footerBand":574,"headline":150,"image":150,"industry":150,"jobType":150,"listed":149,"location":150,"navigation":115,"openRoles":150,"pageLayout":150,"path":60,"relatedHeading":580,"seo":581,"series":150,"sitemap":115,"status":150,"stem":582,"subhead":150,"tags":150,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":583},{"filename":565},"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":569,"toc":570},[],{"title":155,"searchDepth":156,"depth":156,"links":571},[],"Deep dives into pre-cognitive intelligence, sentient enterprises, and the evolving landscape of AI-driven business transformation.","Latest Research",{"headline":575,"description":576,"primaryLabel":577,"primaryTo":578,"secondaryLabel":579,"secondaryTo":12},"Stay at the frontier.","Subscribe for product updates and new insights.","Subscribe","/newsletter","Explore the platform","More research",{"title":567,"description":572},"blog/index","eK1RCXdDW8nfLSyKRXGB1mJm9FAmhAO6GWXwNKOMNVE",[585,882],{"id":586,"title":587,"archived":149,"authors":150,"badge":588,"body":591,"date":553,"department":150,"description":873,"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":874,"relatedHeading":150,"seo":875,"series":557,"sitemap":115,"status":150,"stem":876,"subhead":150,"tags":877,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":881},"content/blog/nimbus-vs-dust.md","Nimbus vs Dust: A Shared Agent Studio or a Place Departments Finish Work?",{"label":179,"10|authors":589},[590],{"name":182,"to":120},{"type":152,"value":592,"toc":855},[593,601,604,613,615,665,667,670,685,688,703,709,712,715,719,722,729,732,734,737,743,749,756,758,762,765,769,774,778,781,785,788,792,795,799,802,806,809,813,816,818,828,830],[186,594,595,600],{},[197,596,599],{"href":597,"rel":598},"https://dust.tt",[201],"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.",[186,602,603],{},"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.",[186,605,606,607,612],{},"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 ",[197,608,611],{"href":609,"rel":610},"https://docs.dust.tt/docs/user-documentation/getting-started/dust-rollout-guide/welcome-to-dust",[201],"rollout guide"," describes that studio as an enterprise AI platform connecting models to internal knowledge, tools, and workflows.",[221,614,224],{"id":223},[226,616,617,623,633,639,644,649,654,659],{},[229,618,619,622],{},[190,620,621],{},"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.",[229,624,625,628,629,632],{},[190,626,627],{},"Multiplayer agents."," Dust’s term for agents that are not trapped in one person’s chat history. Real. Still primarily ",[366,630,631],{},"shared agents",", not a lasting record of what finance approved.",[229,634,635,638],{},[190,636,637],{},"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.",[229,640,641,643],{},[190,642,251],{}," In Nimbus, a shared workspace for one job, with the right people, tools, and approval rules.",[229,645,646,648],{},[190,647,257],{}," Official playbooks agents must follow.",[229,650,651,653],{},[190,652,263],{}," Changing a live system. In Nimbus, connectors stay read-only until a named person signs.",[229,655,656,658],{},[190,657,269],{}," The causal record of what the AI did, who approved it, and what changed.",[229,660,661,664],{},[190,662,663],{},"CNIL."," France’s data-protection authority. Dust is a French company; GDPR still applies when agents process personal data in company tools.",[221,666,280],{"id":279},[186,668,669],{},"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.",[186,671,672,673,300,675,677,678,681,682,684],{},"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 ",[197,674,299],{"href":298},[197,676,303],{"href":20}," drafts against the ",[197,679,386],{"href":680},"what-is-a-company-wiki-for-ai-agents",". Customer records stay read-only until someone who is allowed to approve writes actually does. The ",[197,683,23],{"href":307}," keeps the signed-off version, not only the chat that produced it.",[186,686,687],{},"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.",[186,689,690,691,696,697,702],{},"Because Dust is French, the natural data-protection authority is the ",[197,692,695],{"href":693,"rel":694},"https://www.cnil.fr/en/ai-system-development-cnils-recommendations-to-comply-gdpr",[201],"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. ",[197,698,701],{"href":699,"rel":700},"https://www.cnil.fr/en/ensuring-security-ai-systems-development",[201],"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.",[186,704,705,706,708],{},"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 ",[197,707,379],{"href":45},". 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).",[186,710,711],{},"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.",[186,713,714],{},"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.",[221,716,718],{"id":717},"when-dust-is-a-better-fit","When Dust is a better fit",[186,720,721],{},"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.",[186,723,724,725,728],{},"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 ",[197,726,727],{"href":450},"ChatGPT Enterprise"," when you need company context and custom agents you can share — especially in Europe and the mid-market.",[186,730,731],{},"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.",[221,733,373],{"id":372},[186,735,736],{},"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.",[186,738,739,742],{},[197,740,741],{"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.",[186,744,398,745,748],{},[197,746,747],{"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.",[186,750,405,751,753,754,332],{},[197,752,39],{"href":40}," and the ",[197,755,23],{"href":24},[221,757,411],{"id":410},[413,759,761],{"id":760},"can-nimbus-replace-dust","Can Nimbus replace Dust?",[186,763,764],{},"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.",[413,766,768],{"id":767},"does-nimbus-lock-you-to-one-ai-vendor","Does Nimbus lock you to one AI vendor?",[186,770,771,772,332],{},"No. Both products let you choose models. Dust lets you pick a model per agent. Nimbus treats that choice as an operating decision. See ",[197,773,379],{"href":45},[413,775,777],{"id":776},"is-dust-more-multiplayer-than-nimbus","Is Dust more “multiplayer” than Nimbus?",[186,779,780],{},"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.",[413,782,784],{"id":783},"dust-vs-chatgpt-enterprise-vs-nimbus","Dust vs ChatGPT Enterprise vs Nimbus?",[186,786,787],{},"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.",[413,789,791],{"id":790},"how-should-we-think-about-cost","How should we think about cost?",[186,793,794],{},"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.",[413,796,798],{"id":797},"does-dusts-french-base-make-gdpr-easier","Does Dust’s French base make GDPR easier?",[186,800,801],{},"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.",[413,803,805],{"id":804},"can-we-run-dust-agents-that-draft-and-nimbus-that-releases","Can we run Dust agents that draft and Nimbus that releases?",[186,807,808],{},"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.",[413,810,812],{"id":811},"who-should-own-dust-vs-nimbus","Who should own Dust vs Nimbus?",[186,814,815],{},"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.",[221,817,491],{"id":490},[186,819,820,497,824,502,826,332],{},[197,821,823],{"href":822},"what-is-multi-agent-ai","What is multi-agent AI",[197,825,496],{"href":322},[197,827,451],{"href":450},[221,829,508],{"id":507},[226,831,832,837,843,849],{},[229,833,834],{},[197,835,599],{"href":597,"rel":836},[201],[229,838,839],{},[197,840,842],{"href":609,"rel":841},[201],"Dust rollout guide: Welcome to Dust",[229,844,845],{},[197,846,848],{"href":693,"rel":847},[201],"CNIL, AI system development: recommendations to comply with the GDPR",[229,850,851],{},[197,852,854],{"href":699,"rel":853},[201],"CNIL, Ensuring the security of an AI system's development",{"title":155,"searchDepth":156,"depth":156,"links":856},[857,858,859,860,861,871,872],{"id":223,"depth":156,"text":224},{"id":279,"depth":156,"text":280},{"id":717,"depth":156,"text":718},{"id":372,"depth":156,"text":373},{"id":410,"depth":156,"text":411,"children":862},[863,864,865,866,867,868,869,870],{"id":760,"depth":542,"text":761},{"id":767,"depth":542,"text":768},{"id":776,"depth":542,"text":777},{"id":783,"depth":542,"text":784},{"id":790,"depth":542,"text":791},{"id":797,"depth":542,"text":798},{"id":804,"depth":542,"text":805},{"id":811,"depth":542,"text":812},{"id":490,"depth":156,"text":491},{"id":507,"depth":156,"text":508},"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":587,"description":873},"blog/nimbus-vs-dust",[557,878,561,879,880],"dust","agents","governance","NqfuVK0DzAxN3YIuQXXm9YlK5KTrksOvcSLPIIgskEA",{"id":883,"title":884,"archived":149,"authors":150,"badge":885,"body":888,"date":553,"department":150,"description":1214,"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":1215,"relatedHeading":150,"seo":1216,"series":557,"sitemap":115,"status":150,"stem":1217,"subhead":150,"tags":1218,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":1221},"content/blog/nimbus-vs-chatgpt-enterprise.md","Nimbus vs ChatGPT Enterprise: The Assistant You Love vs the Place Work Gets Recorded",{"label":179,"10|authors":886},[887],{"name":182,"to":120},{"type":152,"value":889,"toc":1195},[890,897,908,916,918,962,964,967,970,987,994,1001,1008,1016,1020,1023,1026,1029,1040,1042,1049,1072,1077,1083,1085,1089,1095,1099,1102,1106,1109,1113,1119,1123,1126,1130,1133,1137,1140,1144,1147,1151,1154,1156,1167,1169],[186,891,892,896],{},[197,893,727],{"href":894,"rel":895},"https://openai.com/business/chatgpt-enterprise/",[201]," (and ChatGPT Business for smaller teams) is OpenAI’s hosted assistant for work: company login, retention controls, and a chat people already know. Nimbus is the place that draft becomes a company action — with a named signer and a record you can reconstruct next quarter. You can keep both. ChatGPT is how people think. Nimbus is how the organisation finishes the job.",[186,898,899,900,903,904,907],{},"That split is easy to miss because both products talk about agents, connectors, and knowledge. The jobs are still different. ChatGPT Enterprise is a governed ",[366,901,902],{},"assistant",": a place knowledge workers already open, with admin, SSO, and a processing agreement. Nimbus is a governed ",[366,905,906],{},"operating layer",": a place a job lives until someone signs a change in a live system. Treat those as one purchase and you will either under-buy the assistant people actually use, or under-buy the ledger finance actually needs.",[186,909,910,915],{},[197,911,914],{"href":912,"rel":913},"https://openai.com/enterprise-privacy/",[201],"OpenAI’s enterprise privacy page"," is the promise that matters for the assistant: OpenAI does not train on Enterprise or Business data by default, and customers control retention. That closes the “personal Plus account on the side” hole. It is not a ledger of what changed in your CRM. Closing shadow chat is a real win. Reconstructing a signed customer-record change is a different win. Most organisations need both, in that order: stop the unofficial accounts, then decide where work that mutates systems of record is allowed to finish.",[221,917,224],{"id":223},[226,919,920,926,932,938,943,948,953],{},[229,921,922,925],{},[190,923,924],{},"ChatGPT Enterprise / Business."," The official company tenant of ChatGPT, with admin, SSO, and a processing agreement.",[229,927,928,931],{},[190,929,930],{},"Company knowledge."," Permission-aware search over workplace sources such as Slack, Drive, SharePoint, Notion, GitHub, HubSpot, and Zendesk before the model answers.",[229,933,934,937],{},[190,935,936],{},"Workspace Agents."," Team-owned agents inside ChatGPT that persist across sessions, can run in the background, and use native connectors. They are off by default; admins enable them with role-based access.",[229,939,940,942],{},[190,941,251],{}," In Nimbus, a shared workspace for one job — people, tools, budget, and a finish line — not a chat thread.",[229,944,945,947],{},[190,946,263],{}," Changing a live system (a CRM field, a journal). In Nimbus, connectors stay read-only until a named person signs the exact change.",[229,949,950,952],{},[190,951,269],{}," Nimbus’s causal record of what ran, who approved it, and what changed.",[229,954,955,958,959,332],{},[190,956,957],{},"Compliance Platform."," OpenAI’s feed of ChatGPT logs and metadata for eDiscovery, DLP, or SIEM. Useful. Still a feed of what happened ",[366,960,961],{},"inside ChatGPT",[221,963,280],{"id":279},[186,965,966],{},"Everyone using ChatGPT is not the same as the company being able to explain last quarter.",[186,968,969],{},"ChatGPT Enterprise is good at the job OpenAI designed it for. People already know the product. IT can put it behind company login. Retention is a customer control rather than a rumour. Company knowledge is the right design if the failure is “the model answered from a file this person could not open.” Workspace Agents persist, can run in the background, and use native connectors, with admins deciding who may turn them on. None of that is trivial. It is why so many organisations standardise on ChatGPT as the default thinking surface.",[186,971,972,973,976,977,982,983,986],{},"The limit is what that surface is ",[366,974,975],{},"for",". Ask: “Show me every customer-record change an agent proposed last quarter, who approved it, and what our playbook said.” OpenAI’s ",[197,978,981],{"href":979,"rel":980},"https://help.openai.com/en/articles/9261474",[201],"Compliance Platform"," gives Enterprise and Edu customers logs from the ChatGPT workspace — useful for eDiscovery, DLP, or a SIEM. Reconstructing ",[366,984,985],{},"your"," Salesforce changes as a business event — across go-to-market and finance, with the signed-off version attached — is a different job. A feed of what happened inside ChatGPT is not a ledger of what happened in the CRM.",[186,988,989,990,993],{},"Company knowledge is permission-aware retrieval. That answers “did this person have a right to see that file?” Limits show up when the same fact lives in Slack, a deck, and a CRM field with no official owner — and when last quarter’s ",[366,991,992],{},"decision"," never became a document. Search cannot invent a signer.",[186,995,996,997,1000],{},"Writes exist in ChatGPT; they are gated by admin policy and often by a per-action confirmation. That is productivity with confirmation fatigue. Nimbus treats the write as a ",[197,998,999],{"href":322},"release",": quote the change, name the signer, store the outcome. Confirmation is a courtesy. A quoted release is a control.",[186,1002,1003,1004,1007],{},"Seat price plus credits for Workspace Agents is a real cost line. Every team inventing agents is spend and a risk surface, and a “just use the flagship model” default burns frontier prices on small tasks. Nimbus meters work in NTUs (work credits) and ",[197,1005,1006],{"href":45},"routes models"," so routine steps do not consume frontier prices. Compare whether you can attribute spend to a job — not only which seat looks cheaper.",[186,1009,1010,1011,1015],{},"The fork is practical by role. A knowledge worker wants a chat they already know, with company files in reach — ChatGPT Enterprise is that product. RevOps wants to know which opportunity fields an agent proposed, who signed, and which playbook version applied. Finance wants a named signer on anything that touches revenue or journals; a Compliance Platform feed shows ChatGPT usage, not a CRM release. Security and legal want SSO, retention, and a processing agreement for the assistant, and still want purpose limitation when recruiting must not see finance forecasts. IT will run identity, company-knowledge crawls, and agent design: a real programme, and not the same programme as standing up workstreams. An official ChatGPT workspace is how you stop people pasting customer data into personal accounts — better than ",[197,1012,1014],{"href":1013},"what-is-shadow-ai","shadow AI",". Nimbus is how you stop the next failure: the draft that became a live field with nobody on the change.",[221,1017,1019],{"id":1018},"when-chatgpt-enterprise-is-a-better-fit","When ChatGPT Enterprise is a better fit",[186,1021,1022],{},"Choose ChatGPT Enterprise when the job is a governed assistant for knowledge workers, company knowledge over Drive and Slack is the main AI win, and you want OpenAI as both model vendor and the place people work.",[186,1024,1025],{},"Choose Workspace Agents when the work should live in ChatGPT or Slack and confirmation-gated connector actions are enough. If the team is writing briefs, summarising threads, and drafting from files they can already open, forcing that into a workstream is ceremony.",[186,1027,1028],{},"Using Nimbus does not mean abandoning ChatGPT. It means ChatGPT stops being the only place work happened. A coherent coexistence looks like this: people keep ChatGPT for personal and team thinking; company knowledge stays the retrieval layer for that assistant; anything that must change a system of record, carry a budget, or be reconstructable next quarter moves into a Nimbus workstream. Drafts can travel. Write credentials should not.",[186,1030,1031,1032,1035,1036,1039],{},"Choose on the verb. If the verb is ",[366,1033,1034],{},"ask and draft",", ChatGPT. If the verb is ",[366,1037,1038],{},"release and remember",", Nimbus. Most companies need both verbs.",[221,1041,373],{"id":372},[186,1043,1044,1045,1048],{},"Nimbus can use OpenAI models for a given step. It does not assume ChatGPT is the operating layer. That is ",[197,1046,1047],{"href":45},"routing",", not a ChatGPT clone.",[186,1050,1051,1052,1055,1056,1058,1059,1061,1062,1065,1066,1068,1069,1071],{},"Operators open ",[197,1053,1054],{"href":298},"workstreams"," themselves. The unit is the job: people, tools, budget, and a finish line. The ",[197,1057,386],{"href":680}," is the playbook agents must follow — a discount floor, a journal policy, a write rule — rather than a prompt someone pasted into a custom GPT. ",[197,1060,741],{"href":51}," are read-only until a write is approved. ",[197,1063,1064],{"href":20},"Agent teams"," are department-shaped specialists on the job. The ",[197,1067,23],{"href":307}," is the record. ",[197,1070,394],{"href":36}," is how you ask that record in ordinary language — “what did we approve for this account last quarter?”",[186,1073,398,1074,1076],{},[197,1075,401],{"href":12},". ChatGPT Enterprise at scale often still looks like a programme — identity, company-knowledge crawls, agent design — with OpenAI or partner engineers in the building. That is a real delivery model. It is not how Nimbus is sold.",[186,1078,405,1079,753,1081,332],{},[197,1080,39],{"href":40},[197,1082,23],{"href":24},[221,1084,411],{"id":410},[413,1086,1088],{"id":1087},"does-nimbus-compete-with-openai","Does Nimbus compete with OpenAI?",[186,1090,1091,1092,1094],{},"At the application layer, yes. At the model layer, no. Nimbus is a customer of frontier models. GPT-class models are often the right choice for a given step — and often they are not. See ",[197,1093,379],{"href":45},". Buying Nimbus does not require leaving OpenAI. It requires stopping the assumption that the chat product is the company operating system.",[413,1096,1098],{"id":1097},"can-nimbus-replace-chatgpt-enterprise","Can Nimbus replace ChatGPT Enterprise?",[186,1100,1101],{},"If ChatGPT usage is a handful of shared GPTs on docs and Slack, a workstream-plus-wiki move is plausible. If you have made ChatGPT the default employee assistant, keep it. Put Nimbus on the business loop where writes, budgets, and the graph matter. Replacing a loved assistant to “standardise on one vendor” is how you recreate shadow Plus accounts.",[413,1103,1105],{"id":1104},"are-workspace-agents-the-same-as-nimbus-agent-teams","Are Workspace Agents the same as Nimbus agent teams?",[186,1107,1108],{},"No. Workspace Agents are team-owned workers inside OpenAI’s product, with durable memory and native connectors. They persist across sessions and can run in the background; admins enable them with role-based access. Nimbus agent teams are department-shaped operators on a workstream, with playbooks, a release path, and a company record. Shared ownership is the overlap. The work loop is not. One lives in ChatGPT. The other lives on the job.",[413,1110,1112],{"id":1111},"how-do-connector-counts-compare","How do connector counts compare?",[186,1114,1115,1116,332],{},"ChatGPT’s native catalogue is on the order of tens to about 90-plus, plus custom tools. Nimbus publishes 2,000+ integrations, scoped to the workspace and read-only until a write is approved. Count is not the whole story. A connector that can write after a confirmation click is a different risk class from a connector that cannot write until a named person signs a quoted change. See ",[197,1117,1118],{"href":51},"integrations",[413,1120,1122],{"id":1121},"is-chatgpt-company-knowledge-the-same-as-a-lifecycle-graph","Is ChatGPT company knowledge the same as a Lifecycle Graph?",[186,1124,1125],{},"No. Company knowledge is an index of files you already have, with each user’s permissions respected. The Lifecycle Graph is a record of work and releases. Collapsing those in a vendor meeting is how you buy search and think you bought memory. Permission-aware retrieval answers “could this person see that file?” A graph answers “who signed this change, and what did the playbook say?”",[413,1127,1129],{"id":1128},"can-we-keep-chatgpt-and-still-put-writes-in-nimbus","Can we keep ChatGPT and still put writes in Nimbus?",[186,1131,1132],{},"Yes. That is the intended coexistence. People think in ChatGPT. They finish in Nimbus. Do not give Workspace Agents production write credentials “because we already have confirmation prompts,” and do not ban ChatGPT because Nimbus exists. Ban unofficial accounts. Route mutations.",[413,1134,1136],{"id":1135},"what-does-the-compliance-platform-actually-give-us","What does the Compliance Platform actually give us?",[186,1138,1139],{},"A feed of ChatGPT logs and metadata for eDiscovery, DLP, or SIEM. That is the right artefact if the question is “what happened inside the ChatGPT workspace?” It is the wrong artefact if the question is “what changed in Salesforce, who approved it, and which playbook applied?” Use both questions. Do not let one answer stand in for the other.",[413,1141,1143],{"id":1142},"who-should-own-which-product","Who should own which product?",[186,1145,1146],{},"IT and the knowledge-worker programme typically own ChatGPT Enterprise: identity, retention, company knowledge, Workspace Agent policy. Line operators — RevOps, finance, shared services — typically own Nimbus workstreams, because they own the systems of record those workstreams touch. Security reviews both.",[413,1148,1150],{"id":1149},"is-confirmation-on-a-connector-action-enough-for-finance","Is confirmation on a connector action enough for finance?",[186,1152,1153],{},"Usually not, if the change hits revenue, pipeline, or journals. Confirmation is easy to click through and hard to reconstruct. Finance wants a quoted payload, a named signer, and a stored outcome. If your writes are low-radius and reversible, ChatGPT’s confirmation model may be enough. If they are not, you are specifying a release.",[221,1155,491],{"id":490},[186,1157,1158,497,1161,502,1163,332],{},[197,1159,1160],{"href":1013},"What is shadow AI",[197,1162,496],{"href":322},[197,1164,1166],{"href":1165},"nimbus-vs-claude","Nimbus vs Claude",[221,1168,508],{"id":507},[226,1170,1171,1176,1182,1189],{},[229,1172,1173],{},[197,1174,727],{"href":894,"rel":1175},[201],[229,1177,1178],{},[197,1179,1181],{"href":912,"rel":1180},[201],"OpenAI enterprise privacy",[229,1183,1184],{},[197,1185,1188],{"href":1186,"rel":1187},"https://help.openai.com/en/articles/10128477-chatgpt-enterprise-edu-release-notes",[201],"ChatGPT Enterprise & Edu release notes",[229,1190,1191],{},[197,1192,1194],{"href":979,"rel":1193},[201],"OpenAI Compliance Platform for Enterprise and Edu",{"title":155,"searchDepth":156,"depth":156,"links":1196},[1197,1198,1199,1200,1201,1212,1213],{"id":223,"depth":156,"text":224},{"id":279,"depth":156,"text":280},{"id":1018,"depth":156,"text":1019},{"id":372,"depth":156,"text":373},{"id":410,"depth":156,"text":411,"children":1202},[1203,1204,1205,1206,1207,1208,1209,1210,1211],{"id":1087,"depth":542,"text":1088},{"id":1097,"depth":542,"text":1098},{"id":1104,"depth":542,"text":1105},{"id":1111,"depth":542,"text":1112},{"id":1121,"depth":542,"text":1122},{"id":1128,"depth":542,"text":1129},{"id":1135,"depth":542,"text":1136},{"id":1142,"depth":542,"text":1143},{"id":1149,"depth":542,"text":1150},{"id":490,"depth":156,"text":491},{"id":507,"depth":156,"text":508},"ChatGPT Enterprise is OpenAI’s hosted work assistant; Nimbus is where a draft becomes a signed company action you can reconstruct later.","/blog/nimbus-vs-chatgpt-enterprise",{"title":884,"description":1214},"blog/nimbus-vs-chatgpt-enterprise",[557,123,1219,561,1220],"openai","workspace-agents","BNAk_9z0vr2RrtYfUThuW9rMiLPt3eJWT4oj9LgCf8A",{"fold":1223,"id":1227,"title":1228,"archived":149,"authors":150,"badge":150,"body":1229,"date":150,"department":150,"description":155,"extension":158,"eyebrow":150,"faqHeader":150,"faqs":150,"footerBand":1233,"headline":150,"image":150,"industry":150,"jobType":150,"listed":149,"location":150,"navigation":115,"openRoles":150,"pageLayout":150,"path":1237,"relatedHeading":150,"seo":1238,"series":150,"sitemap":115,"status":150,"stem":1239,"subhead":150,"tags":150,"video":150,"whyJoin":150,"workplaceType":150,"__hash__":1240},{"headline":1224,"description":1225,"primaryLabel":8,"primaryTo":1226,"secondaryLabel":579,"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":1230,"toc":1231},[],{"title":155,"searchDepth":156,"depth":156,"links":1232},[],{"headline":1234,"description":1235,"primaryLabel":8,"primaryTo":1226,"secondaryLabel":1236,"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":1228,"description":155},"shared/cta","YHK6Fb8AvCPR1zZq7R_xiXUG0hwhP5UxHA8Ix52JQp4",1787194064263]