What Vizgenix analyzes?
Vizgenix evaluates battery and vehicle usage data to identify hidden risk signals behind EV battery performance, degradation, and resale confidence. Instead of relying on a single SOH value, Vizgenix combines multiple battery-related indicators into a clear risk profile for used EV decisions.
Model thousand-cycle degradation.
Our predictive physics-informed machine learning models simulate extreme thermal stress in milliseconds to calculate exact residual value risk.
Anode Degradation
Thermal Stress
State of Health
Outputs a bankable diagnostic score verified against rigorous physical laboratory testing and real-world fleet baselines.
Tracks microscopic lithium plating and physical structural decay within the cell chemistry under varying load profiles.
Simulates localized temperature spikes and high-draw fast charging cycles to map accelerated degradation behaviors.
Zero hardware footprint.
Integrate directly with existing fleet telematics platforms or standard OBD-II data streams. Our API processes raw telemetry and returns a certified health score in under ninety seconds.
Technical Parameters
Data Ingestion: JSON API payload or OBD-II telematics stream. Processing Latency: Under 90 seconds per vehicle. Security Standard: SOC 2 Type II certified data pipeline.