Keep the line running.
Rotating machines, pumps, compressors and motors fail in ways their own sensors hide. Vantage models the physics of each asset and verifies its real state continuously, turning unplanned downtime into scheduled maintenance.
Where industrial systems go wrong.
Vibration masked as noise
Early bearing wear hides inside normal operating ranges until catastrophic failure.
Unplanned line stoppage
One unmonitored motor takes a whole production line down without warning.
Maintenance guesswork
Calendar-based servicing replaces healthy parts and misses failing ones.
Proof, not probability.
Physics-grounded thresholds
Limits derived from how the machine actually behaves, not generic rules of thumb.
Cascade tracing
A failing component is followed through every downstream system it threatens.
Auditable maintenance trail
Evidence Bundles document why each intervention was called, for compliance and insurance.
Where it's headed
Industry 4.0 deployed sensors everywhere but left the trust problem unsolved. The next wave is verified condition monitoring: not just more data, but provable answers about asset health.
How it deploys
Vantage integrates as a software verification layer on top of your existing SCADA and historian data. No rip-and-replace.
The physics and standards behind the verdicts.
Rotating machinery, held to ISO
Bearing life via ISO 281 L10 and Hertzian contact stress, vibration defect frequencies (BPFO/BPFI) against ISO 10816 severity zones, compressor surge margins, tool-life models for machining.
Speaks factory protocols
Modbus RTU/TCP, OPC-UA, PROFINET and EtherNet/IP via existing gateways, with historian backfill for day-one context. No rip-and-replace.
Asset management, provable
Built toward ISO 55000 asset management and IEC 62443 industrial cybersecurity expectations: auditable decisions, tamper-evident records, zero-trust treatment of every signal.
Proof, not promise: a complete bottling-line vertical — filler to pasteurizer, 37 signals, 34 physics-verification rules — runs on the Vantage core today, stood up as configuration. How the platform composes →
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 industrial.
Bring your hardest failure case. We will show you where verification moves the needle.