Physical AI for Robotics Industry

We provide hardware validated edge models built on open PyTorch and optimized for robotic compute platforms. Each includes reproducible optimization and compilation recipes, compiled artifacts, and runtime components, ensuring what works in the lab also works on the robot.

Our models are smaller, faster, and more energy efficient, and can be fine tuned for robotics use cases such as multimodal perception, on device reasoning, task planning, human robot interaction, remote operations support, and autonomy enhancement.

Embedl Deploy provides hardware specific model conversion, explicit quantization, and compilation workflows that transform PyTorch models into optimized on-device binaries. Robotics teams avoid lengthy cycles of debugging unsupported operators, dealing with opaque compiler rewrites, and struggling with unstable quantization results.

Instead, they gain a transparent and reproducible workflow from model to validated artifact, enabling faster iteration and predictable performance on target hardware.

The Embedl Hub offers a centralized platform for model management, benchmarking, traceability, and collaboration across robotics development teams. Engineers can compare models across supported hardware, track artifacts through the compile and validation lifecycle, and maintain documentation required for safety critical or regulated environments.

Available as SaaS or on prem, the Hub integrates into robotics R and D workflows while supporting strict data control and internal device farms.