Physical AI

From pixels to physics to policy.

Physical AI isn’t one workload — it’s a loop of very different ones. AtomereAI keeps the whole loop on one platform, so data, simulation, and training never wait on a cross-cloud hand-off.

The Physical AI loop

Each stage stresses infrastructure differently: graphics-heavy rendering, massively parallel simulation, data-hungry training, and latency-bound evaluation. Running them on a single, mixed fleet means each stage gets the right hardware — and the data stays put.

Graphics and compute GPUs in the same environment
Shared high-throughput storage for sensor data and checkpoints
Simulation fleets that scale out and back in by the hour

Capture & curateIngest multi-camera video, lidar, IMU, and teleoperation logs. Index, label, and search them with GPU-accelerated pipelines.

Reconstruct & simulateTurn real-world captures into digital twins, then run physics simulation across thousands of parallel environments.

Generate synthetic dataUse world models and rendering to create the rare, risky, and long-tail scenarios real fleets can’t safely collect.

Train policies & world modelsTrain vision-language-action models, reinforcement-learning policies, and world models on combined real and synthetic data.

Evaluate in the loopRun software- and hardware-in-the-loop evaluation at scale before anything ships to a robot or vehicle.

Deploy & learnDistill and serve models to the edge, then feed field results back into the next iteration.

Who it’s for

Teams building machines that act.

Robotics & humanoids

Manipulation, locomotion, and generalist robot foundation models.

Autonomous mobility

Driving, drones, and logistics — where long-tail scenarios decide safety.

Industrial & digital twins

Factories, warehouses, and infrastructure modeled and optimized in simulation.

Building for the physical world?

Tell us about your robots, vehicles, or facilities — we’ll help you design the infrastructure for the full loop.

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