Physics-informed AI models that make intelligent systems viable where cloud reliance fails — remote industrial sites, latency-critical processes, and harsh environments.
Standard AI models trained on data alone fail in industrial environments where operating conditions vary, sensor coverage is sparse, and edge hardware constraints limit model size. EWOTA's physics-informed approach encodes the governing physical equations of the domain — corrosion electrochemistry, structural mechanics, power dynamics — into the learning architecture itself.
The result: models that generalise from far less data, maintain accuracy under sensor degradation, and remain interpretable to process engineers.
94% accuracy corrosion and pipeline degradation detection from acoustic emission, temperature, and resistance signals — using existing sensor arrays, with no dedicated corrosion hardware required.
The EWOTA inference engine is domain-agnostic — the same physics-informed architecture applies to quality inspection, vibration analysis, thermal monitoring, and predictive maintenance across any industrial sensor modality.
Our engineers will assess your sensor environment and design an inference deployment that works without cloud dependency.