在为Meta开发AI的八年后，罗希特·帕特尔正在离开公司，TechCrunch只学到了。 Meta超级智能实验室的前任主任有发布 Ndoc，这是他作为人工智能取代Microsoft Office套件或Google工作空间的启动。
Patel是继首席全球事务官Joel Kaplan,AI研究员Andrew Tulloch，以及AI-for Work Emily Dalton Smith的产品负责人出道后
Patel设计了Ndoc 不仅仅是另一个AI工具被分层到现有的工作流程中。 目标是用一个所有同事都能进入的统一工作空间来取代人们跨越Excel、Word、PowerPoint、Tableau、Replit和“ChatGPT复制版”等工具的零散工作方式，即使他们没有为Ndoc报名。
Ndoc 带来三个组件合为一个布局。 Stage分页是关键发现的演示现场，如幻灯片甲板，仪表板，模拟或迷你应用。 叙述性标签有分析、编号引用、生成数字以及数字背后的“原因”。 模型标签是内置数据集和Python脚本的地方。
设计反映了帕特尔的背景。 在Meta任职期间，帕特尔专注于强化学习——一种机器学习，在这种学习中AI agents学会通过试验和出错做出决策并获得最大回报，评价AI输出是否值得信赖。 帕特尔认为，如果AI要建立公司用来决策的电子表格和幻灯片甲板，那么应该有人能够检查它的工作。 例如，用户可以找到用于创建文档的Python脚本。
“即使你不认识任何Python，你也可以要求[AI聊天]解释，我的公式是哪行的？你可以验证这个公式。” " 幕后没有隐藏的东西。
在实践中，用户可以上传一个数据集，并要求恩多克进行分析，并写出有关调查结果的叙述。 他们还可以添加特定文档的背景，包括受众，语气和记忆。 “它在你个人、这一文件、你的团队和你的公司中保持记忆。”
帕特尔还使用称 Ndoc模型的混合物来建造并微调。 由于该平台是从零开始开发的，他称运行的成本大大低于基于前沿AI模型的替代品。
称提到他早期的试用用户，“人们所兴奋的是，他们喜欢在一个地方看到他们所有的端到端。”
Patel更大的野心是重塑文件本身。 He 称文档可以导出为单个的".ndoc.html"文件，可以在任何浏览器中打开，这个格式他计划到开源，这样其他开发者就可以在不锁入Ndoc的情况下，建立与之合作的应用程序，因为这么多人已经锁入了Microsoft或Google多年。
他对开源的渴望这个计划部分地是他离开Meta并决定自己去的原因。 在像Meta这样的大公司里，让每个人都同意开源格式无疑需要“上百人和关于该格式应怎样看的意见”的投入。
帕特尔想快点行动人类只能以人类的速度前进
试图成为企业工作发生地的平台，对于一个拥有3个且没有资金的团队的创始人来说是一个巨大的赌注，特别是当其他AI实验室试图拥有同一个空间时。
OpenAI,Anthropic,Meta,SpaceXAI，和Google都推出了企业AI工具，使用户可以连接到Google工作空间，Microsoft365，以及其他工作场所应用，因此他们的AI模型可以直接在这些工具中工作。 ChatGPT内部的共享工作空间
帕特尔承认太空可能是一种威胁，但称实验室基本上有错误的想法。
“公司基本上在电子表格中附加AI，它为您填满了您的单元格。” “它只是杀了我的灵魂。 这就像如果你刚刚想出电力，然后你决定最好的使用案例是把灯在我的公牛上，直到我的土壤。 号取一拖拉机".
AI实验室试图将模型带到人们已经工作的地方，很难改变习惯，Patel 称他理解，这就是他建造连接器的原因，这样客户就可以从他们已经使用的工具中导入已有的数据。
称表示：「我的酒吧不会放出我不喜欢的东西，我对AI的工作流程非常优化，如果我开始使用这个工具
帕特尔在募集资金前也有一个类似的酒吧称表示
截至星期二，恩多克正在进行企业试点，并向加入等待名单的有限人数的客户开放α版本。 客户将支付使用费和纪念品。 他希望在未来几周内发布一个β版本，并计划在来年发布完整的发布。
对帕特尔来说，越快越好越重要
称说：“执行是极其廉价的。.....所以，“当你制定出最佳计划时，处决将由其他人进行。”
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After eight years developing AI for Meta, Rohit Patel is departing the company, TechCrunch has exclusively learned. The former director at Meta Superintelligence Labs has launched Ndoc, a startup he’s pitching as an AI-native replacement for Microsoft Office suite or Google Workspace.
Patel is the latest in a handful of high-profile departures from Meta following the exits of Chief Global Affairs Officer Joel Kaplan, AI researcher Andrew Tulloch, and Head of Product for AI-for-Work Emily Dalton Smith.
Patel designed Ndoc to be more than just another AI tool layered onto an existing workflow. The goal is to replace the fragmented way people work across tools like Excel, Word, PowerPoint, Tableau, Replit, and “ChatGPT copy-paste” with one unified workspace that all colleagues can access, even if they’re not signed up for Ndoc.
Ndoc brings three components together in one layout. The Stage tab is where presentations of key findings live, like slide decks, dashboards, simulations, or mini apps. The Narrative tab features analyses, numbered citations, generated figures, and generally the “why” behind the numbers. The Model tab is where underlying datasets and Python scripts are housed.
The design reflects Patel’s background. During his tenure at Meta, Patel focused on reinforcement learning — a type of machine learning in which AI agents learn to make decisions through trial and error and maximize rewards — and evaluating whether AI output is trustworthy. Patel believes that if AI is going to build the spreadsheets and slide decks that companies use to make decisions, someone should be able to check its work. For instance, users can find the Python scripts used to create the documents.
“Even if you don’t know any Python, you can ask [the AI chat] to explain, ‘Hey, what does this script do? Which line has my formula?’ And you can verify that formula,” Patel said. “There’s nothing hidden behind the curtain here.”
In practice, users can upload a dataset and ask Ndoc to perform an analysis and write a narrative about the findings. They can also add document-specific context, including audience, tone, and memory. “It maintains a memory across your person, that particular document, your team, and your company,” Patel said.
Patel also says Ndoc is built and fine-tuned using a mixture of open source models. Because the platform was developed from scratch, he says it costs considerably less to run than alternatives built on frontier AI models.
“I thought that’s what people would be excited about, but people don’t care about that,” he said, referring to his early trial users. “What people are excited about is that they love seeing all their end-to-end in one place.”
Patel’s larger ambition is to reinvent the file itself. He says documents can be exported as a single “.ndoc.html” file that can be opened in any browser, a format he plans to open-source so other developers can build apps that work with it without being locked into Ndoc the way so many have been locked into Microsoft or Google for years.
His desire to open-source this project was partly why he left Meta and decided to go it alone. At a big company like Meta, getting everyone to agree on an open source format would undoubtedly require the input of “hundreds of people and opinions on how the format should look.”
Patel wanted to move quickly. “Humans can only move at human pace, whereas…I can move at the pace of what AI can do.”
Trying to become the platform where enterprise work happens is a huge bet for a founder with a team of three and no funding, especially when other AI labs are trying to own the same space.
OpenAI, Anthropic, Meta, SpaceXAI, and Google have all come out with enterprise AI tools that allow users to connect to Google Workspace, Microsoft 365, and other workplace apps so their AI models can work directly in those tools. And last month, OpenAI launched Space, a shared workspace inside ChatGPT that’s a more direct competitor to those established tools.
Patel acknowledges that Space could be a threat, but says the labs largely have the wrong idea.
“Companies are basically attaching AI to spreadsheets and it fills your cells for you,” he said. “It just sort of kills my soul. This is like if you just figured out electricity, and then you decided the best use case is to put lights on my bulls that till my soil. No. Get a tractor.”
There’s a reason the AI labs have tried to bring their models to where people are already working, though. It’s hard to change habits and replace entire workflows. Patel says he understands that, which is why he built connectors so customers can import existing data from the tools they already use.
“My bar was that I’m not going to release anything I don’t love using,” he said. “I have very optimized AI workflows for everything I do. If I start using this tool, and I keep going back to my workflows, I haven’t built a good enough tool…I have a graveyard of tools that did not meet that bar.”
Patel has a similar bar for gaining customer traction before he tries to raise funds. “I didn’t want to take money until I had some proof that people love the product,” he said.
As of Tuesday, Ndoc is conducting an enterprise pilot and opening an alpha version to a limited number of customers who join the wait list. It’s free to sign up, but customers will pay for usage and tokens. He hopes to release a beta version in the coming weeks, with a full launch planned for next year.
For Patel, moving as fast as possible is more important than having the perfect product from the get-go.
“Execution is extremely cheap…so go execute on a suboptimal plan and worry about it later,” he said. “By the time you make the best plan, the execution will be done by somebody else.”
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