We engineer the environments — data, tasks, and evaluation — that make AI agents reliable on closed or open source models.
Targeted datasets that expose the behaviors you actually want to train — curated, verified, and free of the noise that quietly degrades a model.
Tasks, tools, and reward signals assembled into environments where agents can be trained — and honestly measured — against frontier-grade difficulty.
Closing the loop between evaluation and behavior — diagnosing failure modes and hardening agents until reliability is the default, not the exception.
We start from the task the agent must master — its tools, its edge cases, the conditions where it breaks.
Data, reward, and evaluation are designed together so that what we measure is what we mean to improve.
Every change is held to a repeatable bar. We ship the gains that survive scrutiny — and document the ones that don't.
A capable agent and a dependable one are not the same thing. The difference is decided long before deployment — in how the training ground was designed, and how honestly it was measured.
That is the whole of our work: stress testing agents before they break in production.