Responsibility

Before any model or autonomous system reaches the real world, whether it’s making a decision on a screen or directing a machine on the floor, we evaluate it against the failure modes that matter physically: bad actuation, unsafe motion, and misjudged material limits. Capabilities are staged and gated against those thresholds before a system earns the right to act on more.

Any AI decision with a physical consequence, a part that gets cut, a tool that gets moved, a process that gets automated, stays reviewable and overridable by a qualified human until we’ve earned the trust to hand over more autonomy. Oversight scales down only as reliability is proven, not assumed.

We validate our systems against what actually happens in the physical world, not just held-out test sets, so a "correct" output means it held up when it met real material, real tolerances, and real machines. A model earns trust only once its behavior matches intended, verifiable outcomes, physical or otherwise.

We publish the datasets, scores, and rankings behind our claims rather than asking anyone to take them on faith, for software decisions and physical AI performance alike, so outsiders can verify our safety and capability claims independently.

Deployment isn’t the finish line, especially once AI is acting on physical systems. We track performance in the field after release, investigate near-misses before they become incidents, and roll out mitigations quickly to every system already in the world.