Real-time AI diagnostics for gas turbines, hybrid-electric propulsion, and eVTOL powertrains.
Detect combustion instability, blade fouling, and bearing degradation
weeks before failure — zero hardware changes.
A zero-latency pipeline from plant floor to AI inference — with no new hardware.
Your historians are already capturing this data. You're just not acting on it fast enough.
No new hardware. No rip-and-replace. API connectors for every major industrial historian and control system — live in days.
Isolation Forest and LSTM autoencoder models trained on GE Frame 5/6/7 and LM-series operating patterns. Detects subtle cross-parameter deviations invisible to threshold-based alarms.
Bayesian RUL models output a degradation curve with confidence intervals for each asset. Maintenance windows ranked by criticality and operational context.
Spectral analysis of dynamic pressure and cross-correlation with fuel-air ratio to flag lean blowout precursors and thermoacoustic oscillation onset.
Continuous compressor performance monitoring using modified Flett efficiency map methods to detect fouling accumulation and stage stall margin erosion.
Envelope analysis and kurtosis trend monitoring on raw vibration time-series. Detects inner/outer race defects and roller element spalling weeks ahead of threshold breach.
Every turbine builds its own operational baseline. Models retrain nightly on each plant's operational history, accounting for fuel quality, ambient conditions, and load profile — creating a proprietary dataset moat that strengthens over time.
Built for plant engineers — not data scientists. Actionable, prioritized, and explained in plain language.
Connect to your PI historian or SCADA system, import your tag list, and receive your first scored anomaly alert — without writing a single line of code.