India's online gig economy has witnessed substantial growth in recent years, particularly within the online food delivery sector, marked by Zomato and Swiggy going public and a proliferation of cloud kitchens. Concurrently, on-demand home service platforms, including prominent players like Urban Company, Snabbit, and Pronto, have similarly surged in popularity.
Capitalizing on this expanding landscape, Silicon Valley startup Human Archive is innovating by partnering with these companies. Their method involves equipping gig workers with specialized caps containing cameras to gather egocentric (first-person point of view) video data of routine tasks, intended for training advanced robotic systems.
While refraining from disclosing specific partners, the startup confirms its collaborations span the home services, hostel, and restaurant industries for egocentric data acquisition. Human Archive reports having over 1,000 active headsets currently deployed across various locations.
Building on this momentum, Human Archive announced on Tuesday that it has successfully secured $8.2 million in funding. This round saw participation from Wing Venture Capital, NVP Capital, Y Combinator, and a notable group of angel investors associated with OpenAI, Nvidia, Google, Mercor, AfterQuery, BAIR, SAIL, Brad Boa, and Meta.
The company was founded by four individuals with strong academic backgrounds from Berkeley and Stanford: Samay Mani, Rushil Agarwal, Shloke Patel, and Raj Patel, with the latter two being cousins. All four founders bring extensive research experience in robotics, hardware, and tactile data.
Human Archive's establishment is a strategic move, anticipating the future trajectory of the AI industry. As robotics laboratories and leading AI firms endeavor to create machines capable of performing real-world physical tasks, they encounter a significant challenge: a scarcity of high-quality, real-world training data illustrating human execution of everyday work. Human Archive's core proposition is that the workforce powering India's thriving gig economy offers an untapped and scalable reservoir of precisely this kind of essential data.
Despite its current partnerships, Human Archive faced initial resistance, with several prominent Indian home services companies, including Pronto and Urban Company, declining collaboration on data collection efforts.
These rejections became a topic of public discussion last weekend, following a report by the Indian publication Entrackr. The report indicated that Pronto is actively seeking partnerships for robotics training data collection, and that Snabbit had engaged in preliminary discussions with Human Archive before the proposed project ultimately did not materialize.
The situation escalated when Urban Company CEO Abhiraj Singh Bhal publicly stated on X that his company would not participate in such data collection arrangements. This prompted Raj Patel to retort, suggesting Urban Company would soon be compelled to reconsider or risk losing market relevance due to customer churn. Co-founder Rushil Agarwal's response was even more direct, recalling that Pronto founder Anjali Sardana had "laughed" at him and called him "stupid" when he initially proposed a data partnership. Pronto acknowledged these past conversations but affirmed its decision not to proceed.
While other startups nationwide are also gathering egocentric data from diverse work environments, including factory floors, Human Archive distinguishes itself through advanced methods. The company is developing and deploying additional devices such as tactile gloves, full-body motion capture suits, and wrist cameras. These tools capture a broader spectrum of data, including motion and tactile force, which is synchronously aligned with RGB-D (real-time color imagery paired with depth information), for sale to AI labs. The startup posits that video data alone is insufficient, asserting that its value significantly increases when combined with other sensor data.
Raj Patel shared with TechCrunch that the inspiration to combine video with tactile force data arose while presenting their project to other researchers. Engaging with various labs, the founders recognized the burgeoning market for egocentric and sensor-based data, leading them to establish Human Archive.
Initially, Human Archive relied on makeshift setups and off-the-shelf rigs for data capture. The company has since progressed to developing custom hardware designed to work cohesively and collect diverse data types. Currently, more than 50 different devices are deployed to gather a wide array of data points.
"To capture data, we started with iPhones, then we built our own custom rigs and caps. Now we have more than seven different hardware products that we use interchangeably across different modalities. After data collection from different devices, we worked on synchronizing data from all these different sources," Patel explained during a call.
The company is also focused on developing methodologies to fine-tune AI models using its proprietary data and testing them on robots to assess task effectiveness. This internal validation process allows Human Archive to demonstrate the quality of its data to prospective clients and to post-train its own models.
Zach DeWitt, a partner at Wing VC, highlighted Human Archive's distinct advantage in its multi-sensor data collection capabilities.
"No one else in the world has been able to synchronize and collect headset RGB-D, force feedback, full-body motion capture, and synchronized chest and wrist camera data at scale. They’ve been doing internal model training on this data, and every major lab and university is interested in running experiments on it due to the novelty of the sensors and the scale of the new dataset they are releasing soon," he informed TechCrunch.
Despite the initial setbacks with major home services companies, Human Archive forged alliances with smaller startups. This strategy enables them to offer discounted services to customers. Through an in-app option, consumers can choose to pay a reduced price in exchange for consenting to data collection, or opt for the full price for a service performed without recording.
Raj Patel noted that customers have shown a preference for the discounted, recorded option. This is largely because disputes over service quality are common, and video recordings can serve as an effective tool for resolution.
Workers participating in Human Archive's egocentric data collection are compensated at a base rate of $1 per hour. This contrasts with a report from the Economic Times, which suggests other companies pay between ₹250–₹400 per hour (approximately $2.63–$4.20). Patel acknowledged that competitors offer higher pay but stated that Human Archive's robust on-the-ground presence in India allows it to maintain a lower compensation structure.
"Human Archive’s network provides immediate, flexible earning opportunities globally, lowering the barrier to participating in the AI economy. We see this as a critical bridge that funds immediate livelihoods while building the infrastructure for a safer, more productive future," DeWitt remarked.
Beyond the compensation aspect, data collection via video recording raises significant privacy concerns. While it remains unclear what specific details Human Archive provides to workers regarding the usage of their footage, the company asserts that its commercial contracts adhere to India’s Digital Personal Data Protection (DPDP) Act. This compliance includes displaying a privacy policy notice and consent information that outlines the purpose of data collection and its processing. Furthermore, Human Archive states that all collected data is anonymized, and faces are blurred from recordings. Last week, Moneycontrol reported that India’s Ministry of Electronics and Information Technology is actively scrutinizing the consent mechanisms and data collection practices employed by startups gathering egocentric data from home service workers.
Although Human Archive's primary data collection efforts are concentrated in India, the company has commenced expansion into Southeast Asia and the U.S. It is also developing a platform designed to allow broader participation in data collection for earning opportunities. In the U.S., Human Archive is piloting programs to offer services such as cleaning or cooking, where customers receive the service in exchange for consenting to data collection by participating workers, though these initiatives are still in early stages.
Numerous well-funded startups are currently engaged in a competitive race to develop physical AI. Achieving this ambitious goal necessitates vast quantities of training data depicting humans performing various tasks. Human Archive positions itself as a key player in meeting this critical demand. The scalability of its approach will ultimately depend on the strategic partnerships it secures and its ability to collect unique and voluminous data sufficient to satisfy the growing appetite of physical AI laboratories.
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