Trained
on
Reality.
Physical AI must observe the real world in real time, convert that into structured data, train in the virtual world, then act. Terranex makes that loop possible.
The world becomes the training input.
Physical AI can only be trusted if it is trained on what it will actually encounter. Terranex instruments real environments in real time — capturing motion, spatial structure, cause, and consequence as they actually occur.
Observations become structured intelligence.
Raw sensor data is worthless without structure. Terranex converts continuous real-world observations into canonical intelligence records — timestamped, cross-modal, and causally annotated — ready for training pipelines.
Virtual training.
Reality-derived fidelity.
Structured intelligence from real environments drives simulation at scale. Thousands of agents train simultaneously on grounded scenarios — not synthetic guesses, but reality-derived environments that generalise to production edge cases.
Simulation becomes real-world action.
Policies trained in simulation on reality-grounded data are transferred directly to physical systems. Every deployment feeds observations back into the training loop — the system never stops learning from the real world.
The gap between simulation and reality has always been the problem.
Most physical AI systems are trained on curated demonstrations or synthetic environments. They fail when they encounter the real world. Terranex closes the loop — real-time observation feeds simulation, simulation trains policy, policy executes in the real world, and real-world outcomes feed observation.
TNX-1
TNX-1 is the first physical system running the Terranex training pipeline. It observes, converts, trains in simulation, and deploys the full loop, in hardware. More information coming soon.