Prediction you can prove.
A compositional neuro-symbolic verification platform. It does not just estimate the state of a physical system, it verifies that estimate against the laws governing the system, and signs the result so anyone can audit it later.
Four layers, one verdict.
Each layer is independently composable, so verification scales across domains without re-architecting the core.
Sensing & ingestion
Continuous capture of all available signals from the asset, normalized into a unified state representation regardless of source or protocol.
Physics simulation
A real-time, physics-grounded model computes the expected state of the system, the ground truth that observed readings are measured against.
Neuro-symbolic reasoning
Learned models and symbolic rules cross-validate observation against physics, weighing context, GPS, season, and multi-sensor correlation so legitimate extremes don't trigger false alarms.
Evidence & proof
Every verdict is sealed in a cryptographically signed Evidence Bundle: a tamper-evident record of what was decided, on what basis, and when.
Why it holds up where others fail.
Cross-validation first
No single sensor is ever trusted in isolation. Every reading is checked against physics and its peers.
Context-aware
A reading that looks anomalous in a lab may be normal on a Himalayan road. Vantage reasons about context to avoid false positives.
Compositional
The same verification core recomposes across automotive, industrial, and aerospace without a rebuild.
Tamper-evident
SHA-256 signing across processes means an Evidence Bundle cannot be quietly altered after the fact.
Auditable reasoning
Not a black box. The basis for each verdict is recorded and inspectable.
Two delivery models
Consumer hardware-plus-app, or an OEM PaaS layer with no hardware. Same engine underneath.
Protected on day one. Personalized within a week.
Every asset follows the same lifecycle: immediate protection from global models, a short baseline period to learn the individual asset, then a continuously self-improving per-asset model.
From connection, the asset is verified at reduced capacity using pre-installed global models, so protection starts before anything is trained locally.
In parallel, Vantage collects asset-specific data (7 calendar days for automotive; a short baseline period for other domains) to characterize the individual asset.
Once the baseline threshold is crossed, training through to the first signed Evidence Bundle and report runs end to end, with no manual step.
The per-asset model runs every active cycle and retrains on detected drift, feedback, and a configurable schedule, keeping accuracy current.
Learns from everyone. Exposes no one.
Vantage gets smarter across an entire fleet without ever pooling raw data in one place. The privacy is built into how the system learns, not bolted on afterward.
Two paths from here.
OEMs, fleets, and operators across every vertical can book a technical briefing to see the architecture against their own stack. Drivers can join the waitlist on the For Drivers page.