rescaling — real-world data for physical AI

01 — The gap

Foundation models learned to read the internet. The next ones have to learn how the physical world actually works — from the people already doing the work.

What we do

Field capture → model-ready data

01 / capture

In the field

Trained field teams record real tasks from the worker's own point of view — hands, tools, materials, decisions — across dozens of verticals.

02 / structure

Model-ready

Raw footage becomes labeled, synced and segmented datasets — aligned to task, quality-checked, and consented at the source.

03 / deliver

At scale

Robotics and AI teams get physical-world data they can't scrape or synthesize — on the timeline and format their training runs need.

Where we capture

retail counterscommercial kitchenswarehousesrepair & maintenancelogisticsstreet vendingassembly linesfield services retail counterscommercial kitchenswarehousesrepair & maintenancelogisticsstreet vendingassembly linesfield services

02 — Why real-world data

The internet ran out.

Physical AI needs data that was never online.

Robots and embodied models don't learn to fold a box, plate a dish or wire a panel from text and web images. They learn it from people doing it — the grip, the sequence, the recovery when something slips, the thousand small decisions inside real work.

That data isn't sitting in a scrape. It has to be captured, from the worker's point of view, with their consent, in the places the work happens. That's the layer we build.

Foundation models learned to read the internet. The next ones have to learn how the physical world actually works — from the people already doing the work.

What we do

The internet ran out.

Let's talk.