Enact

Companies building physical AI and robotics models need their policies to work reliably in the real world, but models often fail in specific, hard to predict states. Without targeted data on these failure modes, teams lack a clear way to improve model robustness beyond general demonstration data. Enact's site frames this as the core problem it addresses: identifying where policies fail and generating the data needed to fix those specific failures.

Enact positions itself as the post-training data layer for physical AI. It rolls out existing policies on real tasks, identifies the specific states where those policies fail, and then generates targeted demonstration and recovery datasets aimed at those failure points. Enact also evaluates model performance in-house to verify that the data it produces actually addresses the identified failures. The company offers this data to robotics teams through a direct request process via email.