A former director at Meta Superintelligence Labs has founded Ndoc, a startup positioned as an AI-native alternative to Microsoft Office and Google Workspace. Patel's departure is one of several notable exits from Meta, following Chief Global Affairs Officer Joel Kaplan, AI researcher Andrew Tulloch, and Head of Product for AI-for-Work Emily Dalton Smith.

Patel built Ndoc to do more than simply bolt AI onto existing workflows. The aim is to replace the scattered approach of working across Excel, Word, PowerPoint, Tableau, Replit, and "ChatGPT copy-paste" with a single unified workspace accessible to all colleagues—even those without an Ndoc account.

Ndoc combines three elements in one interface. The Stage tab houses presentations of key findings such as slide decks, dashboards, simulations, or mini apps. The Narrative tab contains analyses, numbered citations, generated figures, and essentially the "why" behind the numbers. The Model tab holds the underlying datasets and Python scripts.

This design mirrors Patel's background. At Meta, he concentrated on reinforcement learning—a machine learning approach where AI agents learn decision-making through trial and error to maximize rewards—and on evaluating whether AI output can be trusted. Patel believes that if AI is going to generate the spreadsheets and slide decks companies use for decisions, someone must be able to verify its work. Users can, for instance, locate the Python scripts used to create 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 upload a dataset and ask Ndoc to run an analysis and write a narrative about the findings. They can also supply 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 notes that Ndoc is built and fine-tuned using a mix of open-source models. Since the platform was developed from scratch, he says it costs considerably less to operate 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 of early trial users. "What people are excited about is that they love seeing all their end-to-end in one place."

Patel's broader ambition is to reinvent the file itself. Documents can be exported as a single ".ndoc.html" file openable in any browser—a format he intends to open source so other developers can build compatible apps without being locked into Ndoc the way many have been locked into Microsoft or Google for years. His desire to open source the project was partly why he left Meta to go it alone. At a large company like Meta, getting everyone to agree on an open-source format would undoubtedly require input from "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."

Attempting to become the platform where enterprise work happens is a massive bet for a founder with a three-person team and no funding—especially when other AI labs are pursuing the same territory. OpenAI, Anthropic, Meta, SpaceXAI, and Google have all released enterprise AI tools that let users connect to Google Workspace, Microsoft 365, and other workplace apps so their models can operate directly within those tools. Last month, OpenAI launched Space, a shared workspace inside ChatGPT that competes more directly with those established tools. Patel acknowledges Space could pose a threat, but argues the labs largely have the wrong approach. "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 bringing their models to where people already work: changing habits and replacing entire workflows is hard. Patel says he understands this, which is why he built connectors so customers can import existing data from 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 applies a similar standard to gaining customer traction before raising 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 running an enterprise pilot and opening an alpha version to a limited number of customers who join the wait list. Signing up is free, 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 matters more than having a perfect product from the start. "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."