Generation and grid, verified.
Turbines, transformers and grid assets fail expensively and often silently. Vantage models the physics of each asset and continuously verifies its real condition, catching degradation before it becomes an outage or a safety incident.
Where energy systems go wrong.
Silent asset degradation
A turbine or transformer drifts toward failure inside nominal readings.
Outage risk
One unmonitored asset cascades into wider grid instability.
Costly over-maintenance
Servicing on schedule rather than on real condition wastes capital.
Proof, not probability.
Asset-physics modeling
Each asset's expected behavior computed and checked against reality.
Cascade-aware analysis
A failing asset traced through the systems it can destabilize.
Audit-ready verdicts
Signed evidence for regulators, insurers and operators.
Where it's headed
The energy transition adds complexity and intermittency faster than legacy monitoring can absorb. Verified condition intelligence is becoming essential to reliability.
How it deploys
Vantage integrates as a verification layer on existing energy telemetry and historian systems.
The physics and standards behind the verdicts.
Wind, solar, storage — one core
Gearbox contact stress per ISO 6336 and fatigue damage-equivalent loads via rainflow counting for turbines; Arrhenius-kinetics degradation and Coffin-Manson IGBT cycling for solar; automotive-grade battery electrochemistry scaled to grid storage.
Grid-native
SCADA and OPC-UA gateways with historian backfill, building per-asset baselines across an entire farm or feeder.
The compound-state frontier
NERC CIP governs grid cybersecurity and the EU Battery Passport (2027) demands auditable storage state-of-health. The 2025 Iberian blackout showed why per-signal thresholds are not enough. Read the analysis →
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 energy.
Bring your hardest failure case. We will show you where verification moves the needle.