Waymo, Google and Microsoft Alums Raise $30M to Build the Data Layer for Physical AI
● Maxinsights pays everyday people to wear cameras while they cook, clean, and do their jobs, then turns the footage into training data robots learn from
● MaxLoop, the company's proprietary AI platform, automates quality control of footage as it arrives and cuts the human review team by 90%
● MaxGlove, a sensor glove, records how hard a hand grips and how heavy an object is, which a camera can't see
SANTA CLARA, Calif., October 22, 2026--Maxinsights, the company building the data layer for physical AI, today announced $30 million in seed funding led by PeakXV, with participation from Tola Capital, South Park Commons, INCE Capital, and Alumni Ventures. The company also launched MaxLoop, an agent-in-the-loop system that scores and corrects data collection while it happens, and MaxGlove, a sensor glove for capturing tactile touch and force for a new era of data collection.
Investors put $47.4 billion into physical AI in the first half of 2026, nearly four times the total for the second half of 2025. The capital is flowing faster than the data that exists to train it. Robots can only learn to fold a shirt or load a dishwasher by watching people do it thousands of times, and nobody has captured that kind of work at anything close to the scale the models require.
The full data stack in one place
Competitors stage tasks in mock environments and ask operators to slow their hands so the footage is easier to process. Maxinsights records real work at natural speed in real homes, restaurants, and hotels, so models keep seeing new settings and unplanned moments. Every step from capture to delivery happens in-house
Maxinsights operates in five steps:
Collect. First-person (egocentric) footage of real people working, captured in the wild by our own global network across varied places, objects, and tasks.
Check. Automated and human review of every clip: capture quality, task completion, visibility, sync, metadata.
Annotate. Proprietary models label each clip across four layers (environment, task, subtask, instruction) plus hand pose, body motion, and object interactions.
Curate. Search and rebalance the corpus by task, scene, geography, behavior, complexity, or whatever a client needs.
Deliver. Ship raw or processed datasets, then feed client and model feedback into the next collection cycle.
The company has captured more than 2 million hours, with 1.5 million hours model-ready and available for immediate delivery. Maxinsights serves over 30 customers, including frontier AI labs and foundation model companies, with eight on recurring contracts.