Reinforcement learning for the factory floor

Fast, cheap, reliable intelligence for industrial automation.

Reinforcement learning policies, trained in simulation, that work alongside the classical controls you already trust to get more out of every station.

Start a pilot How it works

Policy size: 50K–1Mparams
Runs on: CPUno GPU at the cell
Control rate: 50Hz

pose_error / trial classical + RL policy

in tolerance — to —

Pick & placeInsertionAssemblyMachine tendingKittingDeburring

01 — The problem

Automation works until the world varies.

Small errors, big failures

A few millimeters of pose error or an unexpected contact turns a reliable motion program into a missed grasp, a jam, or a scrapped part.

Yield ramps take months

New stations often launch well short of target yield. Closing the gap means endless edge-case tuning, fixtures, and re-teaching.

Big models aren't the answer

General-purpose robot foundation models are slow, costly, and hard to certify. A welding cell doesn't need a humanoid's brain.

02 — Our approach

Reinforcement learning that works alongside your controls.

In manufacturing you already know the part, its CAD, the robot, and the task. We train reinforcement learning policies in simulation on thousands of variations of your cell, then run them alongside your existing controller to maximize efficiency, yield, and uptime.

Your controller stays in charge. The PLC and robot program keep running; the policy adds intelligence where it pays off.
Lightweight by design. Policies are 50K to 1M parameters and run on ordinary industrial hardware.
Trained in simulation. Thousands of randomized variations of your cell, in minutes on a single GPU.

new

RL policy

50K–1M params · trained in simulation

yours

Nominal controller

PLC / robot program · trajectories, IK

Efficient, reliable automation

Higher yield, faster cycles, fewer stops

03 — From cell to policy

A pilot in weeks, not quarters.

01
Model the cell

We import your robot, gripper, part CAD, and existing motion program into a physics simulator.
02
Randomize reality

Pose error, friction, mass, sensor noise, and latency are varied across thousands of parallel runs.
03
Train & verify

A compact policy is trained and scored on paired trials against your baseline, failure by failure.
04
Deploy at the edge

The policy ships as a small artifact that runs beside your controller on standard industrial hardware.

04 — Who it's for

Built for the people who ship automation.

System integrators

Add robot learning to your offering without hiring a research team. Win the stations your competitors walk away from.

Manufacturers

Raise yield on existing lines and shorten bring-up on new ones, with the equipment you already own.

Primes & OEMs

Handle high-mix, high-value parts where every drop or misalignment is expensive, and where determinism matters.

Have a station that won't hit yield?

Tell us about the part, the robot, and where it fails. We'll tell you whether a trained policy can fix it.

Get in touch