Rohit Patel’s Ndoc challenges Microsoft Office and Google Workspace

TechCrunch reported that Rohit Patel left Meta after eight years to found Ndoc, a three-person, unfunded startup testing an AI-native rival to Microsoft Office and Google Workspace.

TechCrunch called the attempt a huge bet for a founder with a team of three and no funding, especially as other AI labs try to own the same space. Patel said he wants to replace the applications companies use to make decisions, rather than add another chat window beside them.

His pitch targets the file itself, alongside the spreadsheet, slide deck and analysis behind those decisions. TechCrunch said Patel, a former director at Meta Superintelligence Labs, is the latest in a run of high-profile Meta exits. Those departures include Chief Global Affairs Officer Joel Kaplan, AI researcher Andrew Tulloch and Head of Product for AI-for-Work Emily Dalton Smith.

What is Ndoc built to replace?

Patel says Ndoc is more than AI layered onto an existing workflow. He wants one unified workspace for work spread across Excel, Word, PowerPoint, Tableau, Replit and “ChatGPT copy-paste.” All colleagues can use that workspace, Patel said, even if they have not signed up for Ndoc.

Patel divided the product into three tabs:

• Stage presents key findings through slide decks, dashboards, simulations and mini apps.
• Narrative contains analyses, numbered citations and generated figures, along with the “why” behind the numbers.
• Model stores the underlying datasets and Python scripts.

Together, Patel says, the tabs carry a piece of enterprise work from underlying data through analysis to presentation. The result, in his design, is one shared place for the evidence, its explanation and the decision-ready output, without moving each step into a separate application.

How does Ndoc make AI work inspectable?

During his Meta tenure, Patel focused on reinforcement learning and on evaluating whether AI output could be trusted, TechCrunch said. The publisher defined reinforcement learning as machine learning in which agents learn decisions through trial and error and maximize rewards.

That background shaped Ndoc’s design, according to TechCrunch. Patel said if AI builds the spreadsheets and slide decks companies use, someone should be able to check its work. 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.”

Patel says users can upload a dataset, ask Ndoc to analyze it 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.

Why does open source matter?

Patel built and fine-tuned Ndoc with a mixture of open-source models. Because he developed the platform from scratch, Patel says it costs considerably less to run than alternatives built on frontier AI models.

The lower running cost did not excite his early trial users most. “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 export as one “.ndoc.html” file that opens in any browser. Patel plans to open-source the format so other developers can build compatible apps without locking users into Ndoc, as many have been locked into Microsoft or Google for years.

That desire partly drove Patel to leave Meta and start alone. Patel said agreement at a big company would require input from “hundreds of people and opinions on how the format should look.” He wanted to move quickly.

“Humans can only move at human pace, whereas…I can move at the pace of what AI can do.”

Who else is competing for enterprise work?

TechCrunch reported that OpenAI, Anthropic, Meta, SpaceXAI and Google have each released enterprise AI tools. Those products connect to Google Workspace, Microsoft 365 and other workplace apps, allowing their models to work directly inside the tools employees already use.

TechCrunch described Ndoc’s larger goal as becoming the platform where enterprise work happens. Last month, OpenAI launched Space, a shared workspace inside ChatGPT, according to TechCrunch. The product is a more direct competitor to the established enterprise tools.

Patel acknowledges that Space could threaten Ndoc, but says the labs 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.”

The labs have a practical reason to put models where people already work, TechCrunch said. Changing habits and replacing entire workflows is hard.

Patel said he understands that constraint, which is why he built connectors that let customers import data from tools they already use. The lab tools extend existing applications; Ndoc tries to bring data, analysis and presentation into one interface while still supporting imported work.

Patel also applies a personal standard before release.

“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.”

When can customers use Ndoc?

As of Tuesday, Ndoc is running an enterprise pilot and an alpha for a limited number of customers on its wait list, TechCrunch reported. Sign-up is free, but customers will pay for usage and tokens. Patel hopes to release a beta in the coming weeks, with a full launch planned for next year.

Patel wants customer traction before trying to raise funds. “I didn’t want to take money until I had some proof that people love the product,” he said. For him, speed matters more than reaching a perfect product at launch.

“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.”

Patel’s coming-weeks beta will test whether enterprises will move beyond Microsoft 365 and Google Workspace for an inspectable workflow. Ndoc still has three people and no funding, while Space and the rival lab tools compete for the same workplace.