Robots learn from the world. Most of the world was never recorded.

Physical AI has a data problem, not a model problem.

Language models learned from a written record that took centuries to build. There is no equivalent record of how people actually fold a shirt, guide fabric under a needle or wipe down a car. The movements that make up real work were never written down. They live in people's hands.

So teams improvise, and every shortcut costs them somewhere.

The result is a quiet tax on every physical AI team: months spent sourcing, cleaning and clearing data before the first training run, and models that look brilliant in the lab and hesitate everywhere else.

The richest training data already exists. It happens every day. It just isn't recorded.

Every day, people spend billions of hours on skilled manual work: sewing, ironing, packing, cooking, washing up, assembling, cleaning, welding. Every one of those hours holds thousands of small decisions about grip, force, order and recovery. That is exactly what a robot needs to learn, in the messy, unscripted places it will one day have to work.

We started Billion Hours to capture that work properly: from the worker's own point of view, with their informed consent, labelled precisely enough to train on, and delivered in the format your stack already reads.

The whole pipeline, built by us, so you don't have to stitch one together.

Three steps from "we need data" to a training run.

1. 01

   Tell us what you're training

   The task, the setting, the sensors and the format you need.

2. 02

   We scope, capture and annotate

   Recorded in real workplaces to your spec, checked in QA and labelled in-house.

3. 03

   You train on data you can defend

   Delivered with a full dataset card and a consent record for every clip.