DeepReach: A distributed data network for Physical AI

DeepReach builds a distributed data network for Physical AI. It equips local data partners with wearable stereo-capture devices, software, quality checks and payments so they can record real-world human work across many environments for robotics and world-model customers.

What it does

DeepReach’s YC profile ↗ describes the bottleneck as diverse physical-world data: robots need demonstrations of how people work in warehouses, farms, kitchens, workshops and repair shops, not only hours collected in one lab. DeepReach supplies the hardware and platform while local partners own relationships and run collection as a business.

The model is a network decision, not just a data-labeling service. Diversity comes from many local operators reaching different environments; the platform needs quality control, payments and a steady customer demand layer to make that network useful. The YC profile reports 475 devices, 100-plus partners, nearly 150,000 clips in three months and production use with frontier-model and robotics companies. The launch reports different, larger counts—150-plus entrepreneurs, 1,000-plus local experts and 500,000-plus clips—so these should be treated as company-published snapshots rather than a single reconciled metric.

The partner page ↗ displays an illustrative rate of about $7 per approved hour and warns that actual earnings depend on task availability, location and accepted data quality. That is a partner-side illustration, not guaranteed income. A data customer should ask about consent, worker privacy, task specifications, quality review and the chain of rights for captured demonstrations.

Founder context and tradeoffs

The YC profile identifies Chris Liu as CTO, with Meta research, computer vision and a PhD from USC, and describes the broader company history around workforce and data-network building. The launch also names Tim Li as founder/CEO. The public record supports a technically ambitious data-network thesis; it does not prove every partner count or contract value.

Pricing for customers is not public. The buyer should ask how diverse the requested data is, how quickly new environments can be reached, how rejected clips are handled and whether the capture process is safe and lawful for workers.

Editorial take

I would shortlist DeepReach for a Physical AI team whose main gap is environmental and human diversity, not simply more labeled frames from a controlled lab. The first pilot should define a task, environment mix and quality threshold. If the model only needs one predictable setting, a distributed network may be unnecessary.

Quick facts

Sources checked

Cohort context

DeepReach Inc. is listed in Summer 2026. In our 2026-09-18 directory snapshot, 119 of 232 listed companies in that cohort have YC’s primary industry label B2B (51.3%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.

Public website snapshot

Observed 2026-09-19 16:18 UTC in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.