Use case · Multimodal models
Evaluators decide what better means.
Image, voice, and video models trained in loops where quality is a measurement, not a vibe — on the hardware where your data lives.
The shape of the work
Generative quality — in images, speech, or video — is subjective right up until you write it down. The gym turns your definition of better — fidelity, similarity, artifact rates, listener metrics — into evaluators, then trains against them with the same adversarial discipline as any reward: attacked before the model learns to game it. Training data never leaves your building.
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Evaluator-defined quality
Your definition of better, written as measurements the loop can optimize.
02
The adversarial audit
Evaluators are attacked before the model is trusted with them.
03
Data with provenance
Every generation scored and traced — a dataset your next model trains on.
04
On your hardware
Voice and image data are the crown jewels. They stay home.
Work with us
Bring an agent and a goal.
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.