Use cases

One loop, three kinds of buyer.

The gym is the same everywhere: an agent and a goal go in, an evaluated policy comes out. What changes is the workload.

Language models

For teams improving the model itself. Rubrics, checkers, and promotion gates instead of preference guesswork — on your hardware.

Browsers, terminals, codebases, ticket queues — trained against stubs of your real systems and scored on whether the work got done.

Humanoids, arms, quadrupeds, drones — trained in simulation at scale. A robot model and a task go in; evaluated policies come out.

Behavior inside engines and open worlds — single characters or whole populations that learn instead of being scripted.

Image, voice, and video models trained in loops where quality is a measurement, not a vibe — on the hardware where your data lives.

Agents that drive simulators, lab protocols, and discovery loops — domains where success is read off an instrument.

Markets, fleets, teams — cooperating and competing populations where the system, not the individual, is what has to work.

Agents put into real work — healthcare, finance, logistics, operations. Trained on your systems, proven against your definition of success.

Work with us

We are taking a small number of design partners. The bar is a real workload, not a logo. Everything runs on your hardware, and you keep the machine.