Train with raw radar data, real or synthetic. Our radar connects to your vehicle & your world model through its digital-twin. Create infinite scenes, test edge cases, & evaluate closed-loop.
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End-to-end models eliminate artificial boundaries between perception and planning. A single system processes raw sensor data directly, learning a unified representation of the world.
By operating directly on raw radar data, the system maintains situational awareness even when vision-based sensors fail.
Congruent — the only radar with raw data, real or synthetic.
Congruent bridges real & synthetic worlds. Engineers can generate, manipulate, & inject synthetic actors directly into real sensor data—without breaking physical consistency.
Raw radar data may be difficult to interpret, but it contains a wealth of information. Congruent embraces this complexity, turning active signals into actionable understanding.
Evan is a machine learning expert who earned his doctorate from UT Austin. He developed physics-learned models for multi-sensor satellite data in Greenland & developed perception pipelines for autonomous systems.
Clement earned his doctorate in Structural Mechanics from UC Berkeley. He has developed advanced sensor systems for both automotive and structural health monitoring applications.
