Automotive

The challenge

Automotive systems share a fundamental constraint: AI must run in real time, on-device, within tight power and latency budgets, without cloud dependency.

Road conditions change continuously and driving style varies between trips and within a single journey. A controller tuned once in a lab cannot respond to these variations. It delivers average performance, never optimal.

The same logic applies to driver monitoring: a system that builds a model of each driver's normal behaviour detects fatigue and distraction far earlier and reliably than general-purpose models.

PRAESC builds first-principles adaptive AI that closes this gap: whether the system controls a guidance module or assesses a driver, the same framework applies — a generative model of the system, real-time belief updates, and a clear confidence signal.

Our approach

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  Generative model per system. Whether modelling vehicle dynamics or driver behaviour, we build a principled world model — not a black-box regressor. The agent actively infers the system state and minimises prediction error in real time, adapting without retraining as conditions change.
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  Edge-native inference. All inference runs on resource-constrained embedded hardware. Millisecond-level latency for control loops; real-time video processing for perception tasks.
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  Validated before deployment. Control systems validated in physics simulation; perception systems fine-tuned on instrumented field data. No guesswork when real hardware is involved.
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  Data sovereignty by design. Sensor feeds, operational logs, and inference outputs stay on-device. GDPR-clean, no cloud upload — critical for OEM protecting process know-how and driver privacy.