We build verifiable reinforcement-learning environments, evaluations, and proprietary training datasets to improve frontier AI models and robot policies. We focus on complex, underexplored domains where high-quality training data is scarce and existing models fall short. Our long-term goal is to develop self-improving training infrastructure that can autonomously generate increasingly challenging tasks, evaluate performance, and continuously advance model capabilities toward recursive self-improvement.
The company was founded by Sardor Rahmatulloev; Danyal Ahmad. It is part of YC Spring 2026. It is based in San Francisco, CA, USA.