We record electrical signals at an ultra-high- speed 20-nanosecond resolution, enabling the detection of even the most fleeting anomaly patterns. This precision provides engineers with highly granular data that conventional logging systems simply cannot match, 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 amounts of multimodal data, the system uncovers patterns that human eyes or traditional methods cannot see, allowing proactive intervention.

We forecast failures faster than CAN, impedance, or IR-based diagnostics, which often detect issues only after degradation has progressed. Our method shortens the reaction window, giving operators valuable time to act and prevent costly downtime or safety incidents.

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