Decisions where bits meet matter.
Physical AI is what happens when a learned policy controls a real-world actuator. A robot arm, a power converter, a chemical reactor, a satellite. Errors are no longer cost-of-quality; they are events. The decision to put a model in the loop with matter is governed by a different standard than any pure-software roll-out.
Helios Brain for Physical AI & Embodied Systems.
Physical AI is where every assumption gets paid in joules, newtons and lives. The bitter lesson does not exempt you from physics, regulation or accountability. The architecture that makes a learned policy admissible at the interface with matter is the architecture we build.
6 decisions, tested before they are made.
Sim-to-real transfer evaluation
Project the gap between simulated policy performance and real-world behaviour across distribution shift, sensor noise and actuator wear. Identify the deployments where transfer holds versus where it silently degrades.
Online-learning policy boundary
Test which adaptations the deployed policy is allowed to make in situ, and which require offline review. Calibrate the autonomy of learning to the consequence of error.
Calibrated abstention and fall-back control
Rehearse the conditions under which the model defers to a deterministic controller or to a human. Identify the abstention surface that holds safety without paralysing operations.
Hardware-software co-design trade-offs
Test alternative compute, sensor and actuator stacks against latency, power and thermal envelopes. Identify the architecture that lets the policy actually run.
Outcome attribution under hybrid control
When humans, deterministic controllers and learned policies share the loop, attribute outcomes correctly. For learning, for liability, for audit.
Embodied safety case
Construct an end-to-end safety case across hazards, failure modes, mitigations and evidence. Defend it against a regulator who knows physical systems but not learned policies.
Three desks, one substrate.
Quantifies the sim-to-real gap.
Distribution shift, sensor noise, actuator wear simulated against real telemetry.
Calibrates abstention and fall-back.
Operating-envelope deferral rules tested across failure modes.
Attributes outcomes under hybrid control.
Human, deterministic, learned-policy actions ledgered separately and jointly.
Built for the regulations that govern your sector.
We rehearse the policy change in their model first, then walk into the credit committee with the full picture. The questions get answered before they are asked.