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Human Archive, a startup founded by UC Berkeley and Stanford researchers, is paying gig workers in India to wear camera-equipped caps and sensor devices to collect the real-world physical training data that AI and robotics labs are racing to acquire.
In the last few years, India’s online food delivery market has grown significantly, with both Zomato and Swiggy going public and the number of cloud kitchens increasing. Meanwhile, startups working on home services, such as on-demand household staffing platforms like Urban Company, Snabbit, and Pronto, have gained popularity.
Silicon Valley-based startup Human Archive is tapping into this trend, partnering with these companies to have workers wear special caps with cameras to collect egocentric (first-person point of view) video data of everyday tasks that could be used to train robots.
Without naming specific partners, the startup said it is working with companies in the home services, hotel, and restaurant sectors to collect egocentric data, and it says it has more than 1,000 active headsets deployed across multiple locations.
On the back of that traction, Human Archive said Tuesday it has raised $8.2 million in funding from Wing Venture Capital, NVP Capital, Y Combinator, and angels from OpenAI, Nvidia, Google, Mercor, AfterQuery, BAIR, SAIL, Brad Boa, and Meta.
The startup was founded by three students from UC Berkeley and one from Stanford — Samay Maini, Rushil Agarwal, Shloke Patel, and Raj Patel, the latter two being cousins. (Raj Patel is CEO.) All four have research backgrounds spanning robotics, hardware, and tactile data.
The company’s founding is a direct bet on where the AI industry is heading. As robotics labs and frontier AI companies race to build machines that can perform physical tasks in the real world, they face a critical bottleneck — a shortage of high-quality, real-world training data showing humans doing everyday work. Human Archive’s bet is that the workers staffing India’s booming gig economy represent an untapped and scalable source of exactly that data.