Building the memory architecture for Physical AI. Real-world knowledge that compounds across every robot, every simulation, every deployment.
Physical AI will reshape every industry the way software did. Not through bigger models, but through infrastructure that makes deployment as repeatable as code.
The models are getting there. The data is growing. What’s still missing is a validated layer of real-world knowledge that carries across every robot, every simulator, and every deployment.
That’s what we’re building.
Capture. Remember. Train. Remember.
01 — Real world capture
Camera capture, scene reconstruction, and sensor capture tools that turn raw input into qualified neural data for training and validating Physical AI.
02 — Neural 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
Training orchestration for Physical AI. Forge loads Realm-qualified environments into Isaac Lab, runs multi-variant training across physics configurations, and feeds corrections from deployment back into both the object’s physics and the robot’s policy.
