Our moat is data collected
in real industrial environments.
Not a lab. Not simulation. The real thing.
The deployed fleet is the moat.
Deploy robots into live industrial environments
Real deployments reveal exactly which tasks and movements matter
This tells us which egocentric data to collect — and we built the infrastructure to collect and process it
Operational teleops data + targeted egocentric data trains VLA and WAM models
Better models unlock greater autonomy
Greater autonomy enables more deployments
The deployed fleet is the moat.
Teleoperation & Deployment Stack
Built a versatile teleoperation SDK for Unitree G1, hardware-agnostic by design to be compatible with major humanoid robots. Deployment stack consisting of real-time perception and low-latency VR control with real-time safety systems.
Egocentric Data Processing Infrastructure
Built a SOTA infrastructure to convert egocentric RGB videos into VLA/WAM model training datasets and simulation environment assets.
Full Teleoperation
Human operator controls every movement remotely via VR. Robot handles hazardous zone tasks immediately, with current hardware, at industrial scale.
Semi-Autonomous
Policy models and VLA models handle routine task segments autonomously. Operator monitors and steps in for complex decisions only. One operator manages multiple deployments simultaneously.
Full Autonomy
Foundational WAM models and VLA models trained on real-world industrial data from Phase 1 and 2 deployments. Fully autonomous rounds on familiar routes. Human oversight for novel situations only.
Simulation is useful. Real deployment is irreplaceable.
A model trained in simulation learns simulation performance. A model trained on real valve operations — with real corrosion, real pressure, real conditions no lab anticipated — learns the actual job.
Tesla's fleet is its training set. Every car on the road collects edge cases no test track produces. The more cars deployed, the better the models. Humanoid robotics works the same way. The operators who collect real industrial deployment data in the next three years will train models their competitors cannot match — not because they had better labs, but because they deployed first.