Twolabs

Frontier AI models and robot policies tend to fall short in complex, specialized domains such as PCB and chip design, embedded systems, robotics, and niche coding environments. These areas lack the high-quality training data and evaluation infrastructure that more mainstream domains already have, which limits how well existing models can perform on them. Teams working in these fields are left without the verifiable environments or datasets needed to meaningfully improve model capabilities in their area.

Twolabs builds verifiable reinforcement-learning environments, evaluations, and proprietary training datasets aimed at improving frontier AI models and robot policies in underexplored domains. The company focuses specifically on complex technical areas like PCB and chip design, embedded systems, robotics, and specialized coding, where data is scarce. Its longer-term goal is to build self-improving training infrastructure that can autonomously generate increasingly challenging tasks, evaluate model performance on them, and use that feedback loop to push model capabilities toward recursive self-improvement.