Detecting anomalies in physical and industrial systems: car batteries, wind turbines, antennas, and more, is critical for fast investigation and timely corrective action.

 

We build a digital twin of the system’s nominal (healthy) behaviour and use it as a reference to detect any deviation from normal operation.

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Our approach is grounded in scientific evidence: anomalies are identified by measurable deviations from expected behavior.

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Explainability is not required to trust the result, because detection relies on objective, physics-consistent evidence rather than black-box narratives.