Physical AI development
Photon Echo focuses on data and simulation infrastructure for physical AI. It builds environments, behaviors, and structured datasets that help systems perceive, decide, and act in the physical world with more confidence and control.
Focus on physical systems
Physical AI systems need to see and understand their surroundings, predict outcomes, and take actions that respect safety and constraints. Photon Echo models the environments, agents, and interactions that matter for these systems so that developers can explore behavior before hardware is committed.
Simulation areas
- Perception tasks that rely on structured scenario data and clear annotations
- Robotics and manipulation tasks that involve contact with objects and surfaces
- Autonomous navigation and planning in structured or semi structured environments
- Monitoring and inspection tasks where small changes can signal important events
From environment to artifact
Work on physical AI in Photon Echo follows a simple pattern
- define the environment and assets that are relevant to a use case
- model how agents and systems behave inside that environment
- design scenarios that surface the questions and edge cases that matter
- run simulation passes that produce structured artifacts and labels
- review results, refine models, and repeat