From pixels to physics.

The synthetic world and the real world need to be in sync.

Physical AI is reshaping every industry the way software did. The models are getting there. The data is growing. But all of it is built on pixels that capture appearance, and on synthetic data with approximate physics. Both fail the moment real hardware enters the picture.

It’s not a perception problem. It’s a physics fidelity problem. We are building infrastructure that gives Physical AI validated and persistent physical grounding: physics that is measured, versioned, and corrected across every deployment.

We have three goals: Capture reality, build memory and close the loop with the real world.

01 — Real world capture

Clap: Capture, label, analyze, process

Environments, objects, task execution, and sensor data — any form reality takes, Clap reads it. Structured, labeled output flows directly into Realm for digital twin creation.

02 — Neural memory

Realm: Physics-accurate manipulation memory

A persistent, structured memory layer for robot foundation models. How real-world objects behave; their physics, their interactions, their edge cases. Validated once, reusable everywhere.

03 — Proving ground

Veron: Closing the loop with the real world

Training orchestration for Physical AI. Veron loads Realm-qualified environments into Isaac Lab, runs vectorized training under calibrated physics, and feeds corrections from deployment back into both the object’s physics and the robot’s policy.

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Calibrated physics, not randomised guesswork. When deployment fails, corrections are routed back: 2/20 → 10/10 after one routed correction. Each run starts closer to reality.