Heavy iron, held to physics.
Mining equipment runs hard in brutal conditions where a failure halts an entire operation. Vantage models the physics of haul trucks, crushers and conveyors and verifies their real condition, predicting failures before they stop production.
Where mining systems go wrong.
Brutal-duty wear
Components degrade fast and unpredictably under extreme load and dust.
Production-halting failure
One downed haul truck or crusher stalls the whole operation.
Inspection blind spots
Remote sites and harsh access make manual checks infrequent and partial.
Proof, not probability.
Duty-aware physics models
Expected behavior under real load and conditions, not lab assumptions.
Failure prediction with proof
Anomalies caught early, with the reasoning recorded.
Operational evidence trail
Signed verdicts for maintenance planning and accountability.
Where it's headed
Mining is electrifying and automating its fleets, adding sensors and complexity. Verified equipment intelligence keeps that complexity from becoming unplanned downtime.
How it deploys
Vantage integrates as a verification layer on existing fleet-management and asset telemetry.
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 a short baseline period 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, operator feedback, and a configurable schedule, 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 →
Verify what matters in mining.
Bring your hardest failure case. We will show you where verification moves the needle.