To build the autonomous machines of the future, sometimes your model needs a model.
Companies developing self-driving cars, robots manipulating the physical environment, or autonomous construction equipment collect thousands, if not millions, of hours of video data for evaluation and training.
Organizing and cataloging that video is now a job for humans, who have to watch all of it. Even fast-forwarding, that doesn’t scale. NomadicML, a startup founded by CEO Mustafa Bal and CTO Varun Krishnan, wants to solve problems for customers who have 95% of their fleet data sitting in archives.
The challenge becomes harder when looking for edge cases — the most valuable data depicts events that rarely occur and can befuddle inexperienced physical AI models.
Nomadic is working to solve that problem with a platform that turns footage into a structured, searchable dataset through a collection of vision language models. That, in turn, allows for better fleet monitoring and the creation of unique datasets for reinforcement learning and faster iteration.
The company announced an $8.4 million seed round Tuesday at a post-money valuation of $50 million. The round was led by TQ Ventures, with participation from Pear VC and Jeff Dean, and will allow the company to onboard more customers and continue refining its platform. Nomadic also won first prize at Nvidia GTC’s pitch contest last month.
The two founders, who met as Harvard computer science undergrads, “kept running into the same technical challenges again and again at our jobs” at companies like Lyft and Snowflake, Bal told TechCrunch.
Techcrunch event
San Francisco, CA
|
October 13-15, 2026
“We are providing folks insight on their own footage, whatever drives their own AVs [and] robots,” he said. ”That is what moves these autonomous systems builders forward, not random data.”
Imagine, for example, trying to fine-tune an AV’s understanding that it can run a red light if a police officer is directing it to do so, or isolating every time that vehicles drive under a specific type o …