We capture electrical signals at ultra-high-speed 20-nanosecond resolution, enabling the detection of even the most transient anomaly patterns. This precision provides highly granular data that conventional logging systems cannot capture, forming the foundation for accurate diagnostics and prediction.

Our AI models identify invisible, early-stage anomalies long before they escalate into failures. By analyzing vast multimodal data—including electrical, thermal, and physical signals—the system uncovers patterns that traditional methods cannot detect, enabling proactive intervention.

Our AI continuously refines its understanding through repeated multi-layer processing of high-frequency data. This iterative learning approach improves prediction accuracy over time, delivering increasingly reliable insights into system behavior.

We forecast failures earlier than CAN, impedance, or IR-based diagnostics, which typically detect issues after degradation has progressed. Even in the presence of ECU or BMS errors, our system continues to monitor and predict anomalies without interruption, ensuring consistent visibility into system risks.

By combining early prediction with actionable insights, we enable not only preventive maintenance but also continuous optimization of system performance. This ensures electrified assets operate safely, efficiently, and at peak reliability.