Robotics

DeepReach Inc.

Website
Founded 2025Origin source ↗Checked 2026-09-15

Overview

Physical AI — robots and world models — is bottlenecked on one thing: diverse,

real-world data at massive scale showing how humans actually do work in the

physical world. It was never written down, and you can't buy it, because no one

has ever captured it. Only real local experts, the people who do this work every

day, can produce data this authentic and valuable.

DeepReach builds the network that reaches them. We design and manufacture

wearable stereo capture devices, then place them with local data partners who

run collection as their own business — in warehouses, workshops, farms, kitchens

and repair shops. They own the local relationships; we supply the hardware, the

software platform, the quality pipeline and the payments. Anyone can run a data

business.

That structure is what produces diversity. Instead of thousands of hours inside

one environment, we deliver hours spread across thousands of environments —

which is what actually makes robot policies generalize.

475 devices in the field and 100+ data partners are live across 7 countries, adding new

partners every week. They've collected nearly 150,000 clips in the past three

months, with volume roughly doubling month over month. Our data is in production

with multiple frontier model and robotics companies, and we're scaling to

10,000+ devices across 50 countries.

Company description As recorded 2026-11-08

Founders & team

Chris Liu

Founder/CTO

Co-founder at DeepReach Inc. Founding MLE & tech lead of Aven scaling to #1 in DM volume in the US. Research Scientist at Meta working on core ML systems. PhD in computer vision from USC. B.S. from Peking University.

Founder profile As recorded 2026-09-15
6team members reported
Source-reported As recorded 2026-09-15

Founded 2025 · Source

Origins

Chris and I are from the same hometown and have known each other for ten years, long before either of us thought about robots. In that time I built two companies around the same problem: how do you organize people you'll never meet, in places you'll never visit, to do consistent work? The first was HireIO, a cross-border staffing platform out of Silicon Valley. The second was Talex, an expert network for LLM data annotation — around 70,000 people across ten countries. Chris went the other direction: a CS PhD at USC in computer vision, ML infrastructure at Meta, then founding ML engineer at Aven. Running an annotation network for language models, I kept hitting the same wall from the supply side. Text was running out, and the next generation of models wasn't going to learn from the internet. It was going to learn from the physical world — and nobody had captured it. What a warehouse picker, a line cook or a phone repair technician knows is real expertise, and none of it was ever written down. You can't buy that data, because it doesn't exist yet. Chris was hitting the same wall from the model side, watching what was missing from training sets. The default answer at the time was to put a lab in one city and record thousands of hours in the same room. That produces volume, not diversity, and robot policies don't generalize from volume. What the models needed was the opposite shape: a few hours each from thousands of different real workplaces, all over the world. Which meant the bottleneck wasn't an AI problem. It was a distributed human network problem — reaching thousands of ordinary businesses across dozens of countries, and giving the people already working in them a reason to capture what they do. That was the problem I'd spent two companies learning to solve. We started DeepReach in 2025. Chris owns perception and the data engine; I own the network, hardware operations and the commercial side. Ten years of knowing each other means we skipped most of the negotiation that takes co-founders their first year.

Founder account As recorded 2026-09-15

News & product activity

Sources & observations

Y Combinator

Profile depthenriched profile
Website observationwebsite reachable
Last website check2026-11-11
Location · source statedSan Francisco, CA, USA
Pricing evidenceA pricing page was detected
Maturity: Not assessed · 4 of 6 areas documented

Source-based assessment · automated evidence rules

The retained text does not establish product availability or customer use.

Maturity is an evidence-based assessment, not a success rating. It may understate the current stage where evidence is limited. Coverage records documented areas, not a complete or independently audited company history.

Stage definitions
independent websitesource corroborationreachable websitedescriptive evidenceclassified market and modelsourced peoplesourced founding yearpricing page observed