Every vehicle decision, verified.
From a single OBD-II device in a driver's car to an OEM fleet of millions, Vantage cross-validates every sensor reading against the physics of how the vehicle actually works, so a failing sensor never reads 'normal' and a warranty claim is never a guess.
Where automotive systems go wrong.
Silent sensor drift
A degrading O2 or coolant sensor reads plausible values for weeks while the real fault grows unseen.
Unprovable warranty claims
Was it a defect or misuse? Without an auditable trail, OEMs pay for both.
Reactive breakdowns
The check-engine light arrives after the damage, not before it.
Proof, not probability.
Cross-validated readings
Every signal checked against a physics model of the powertrain before it is trusted.
Context-aware alerts
Altitude, season and driving pattern factored in so legitimate extremes do not trigger false alarms.
Signed Evidence Bundles
Each verdict sealed cryptographically, defensible in a warranty dispute or recall review.
Where it's headed
Connected vehicles generate oceans of telemetry that nobody can fully trust. As regulators and buyers demand provable safety decisions, verification moves from optional to mandatory. Automotive is where Vantage proves the model at scale.
How it deploys
Two ways in: a consumer device-plus-app for drivers and owners, or an OEM PaaS integration layer that plugs into existing connected-vehicle infrastructure with no new hardware.
The physics and standards behind the verdicts.
11 subsystem models
Powertrain, thermal, battery & battery ageing, brakes, tires, drivetrain, HVAC, tribology, fluid dynamics and material fatigue — the same governing equations used in engineering practice, from Arrhenius ageing to rainflow fatigue counting.
OBD-II & CAN native
Standard SAE J1979 PIDs plus manufacturer extensions, decoded and cross-checked in real time. Tuned for Indian-market vehicles: Maruti Suzuki, Tata, Hyundai, Mahindra, Toyota and more.
Built for the rulebook
Aligned with where vehicle regulation is going: UN R155 and India's AIS-189 demand field monitoring, sensor-attack detection and forensic-grade records — exactly what verification with signed evidence provides.
A model for your asset, not a generic one.
Per-asset baseline
From the first moment it is connected, Vantage protects the asset using proven models trained across many assets, while it spends 7 calendar days learning how that specific asset behaves. The personal model then trains and the first Evidence Bundle runs automatically. From there it verifies every active run and retrains on multiple triggers: detected drift, your feedback, and a schedule you can configure, so accuracy keeps improving.
Private by architecture
Shared models improve across many assets through federated learning: the system learns from each asset locally and combines only the learnings, never the raw data. No asset is ever tied to a specific dataset. See the four protection layers →
On-device option for fleets
Commercial fleets running edge-capable hardware can run basic anomaly detection directly on the device, so raw telemetry need never leave the vehicle. Useful where bandwidth is limited or a local-first posture is required. How privacy is built in →
Verify what matters in automotive.
Bring your hardest failure case. We will show you where verification moves the needle.