Industrial data for Physical RSI.

We capture how physical things are made, from the first design to the final assembly, and turn it into training data for frontier AI and robotics models.

Mission

Toward Physical RSI.

Physical RSI is recursive self-improvement for the physical world. AI helps engineers design and plan, robots help build and inspect, and each generation makes better machines, lines and factories for the next.

Over time, that loop makes physical goods abundant. It starts with knowing how things are designed and made today.

Backed by angels from

Problem

Physical AI is limited by real-world data.

Language models learned from the internet. How things are made isn't there. The reasoning behind a design and the skill on the line live with engineers and operators, and most of it is never written down.

Robots are only part of the story.

Physical AI covers the whole chain of making things, from design intent to the operator's hands. Robotic manipulation is only the last mile of that chain.

The lab is not the factory.

Generalist models need diverse data, and diversity can't be staged. Every line, part and operator is different, and things go wrong in ways no lab scripts. We record the work where it actually happens.

What we capture

The whole manufacturing process, captured.

Every part moves through design, planning and production, and each stage produces knowledge a model needs. We capture all three.

01
Design

Requirements, design decisions, iterations and analysis.
Linewise Engineering
02
Planning

How the part will be made: setups, sequences and the process plan handed to the line.
Linewise Engineering
03
Production

Skilled physical work on real production lines, and inspection against the specification.
Linewise Robotics

Linewise Research sits behind every stage of capture and quality control, delivering data to frontier-lab spec.

From design to the line, captured to one standard.

Two datasets, built and measured by Linewise Research.

Linewise Robotics

Skilled physical work, captured on real production lines: egocentric video, UMI data, dense action annotation, and hand pose.

Linewise Engineering

The decisions behind a part: requirements, design iterations and analysis, process plans and inspection records, including rejected designs.

Linewise Research

Methods for capture, quality control, annotation and model evaluation, and agent-native data infrastructure, all built around real industrial tasks.

Methods

Every delivery can be checked.

Research teams commit training runs to our data. Each delivery can be traced to where it came from and checked against what it was meant to be.

Reviewed by practitioners: Each delivery is checked against its specification by someone who works in that discipline.
Known provenance: We record where each file and recording came from, and screen for copied material before delivery.
Consent before capture: Site agreements and participant consent are in place before capture begins.
Failures are kept: Rejected designs and failed inspections stay in the record. They show how decisions were made.

Discuss a data requirement.

Tell us what your model needs to learn. Every engagement starts with a sample and a technical conversation.

Email info@linewise.io Contact