Correlate ambient conditions, regional fault patterns, and supply-chain signals in one operational intelligence layer. Sensor-level failure prediction, not just DTC counting. Built for fleet optimization and capital allocation.
Active Regions
Avg Failure Rate
Samples (30D)
Market Risk Exposure
Each new connected vehicle strengthens the prediction model. This network effect creates a compounding data advantage that grows exponentially.
842K+ diagnostic events with sensor-level telemetry — not just fault codes but the raw signals that predict failures before they happen.
Machine learning models trained on multi-sensor correlation patterns. Every vehicle adds training data — competitors can't bootstrap this overnight.
Unique dataset mapping ambient temperature, humidity, and altitude to failure rate curves. Regional specificity no generic data provider offers.
More vehicles → better predictions → more users → more data. Once the flywheel spins, the marginal cost of each new prediction approaches zero.
Intensity = failure_rate × log(sample_count). Click a region for predictive breakdown.
Translate OBDindex predictive improvements into annual fleet savings. Model based on aggregated failure-rate reduction and downtime prevention.