AI that catches failure before the shutdown
Real-time anomaly detection across flowmeter and rotating-equipment telemetry — faults flagged seconds after they emerge, alerts routed straight into your SCADA, downtime cut 40%.
A Hominis app module · your oil & gas data, app-ified
Catch the fault before it stops production
Anomaly detection and predictive maintenance across flowmeter, rotating-equipment and well-log telemetry.
Flowmeter & sensor anomaly detection
Stream thousands of telemetry points per second and surface irregular flow patterns, drift and degradation as they emerge — not on the next inspection round.
95% detection at <2% false positives
Predictive maintenance on rotating equipment
Move pumps, compressors and turbines from calendar-based servicing to condition-based intervention, scheduling work before a fault becomes a failure.
40% less unplanned downtime
Subsurface & well-log interpretation
The WellLogStack instrument reads well logs alongside production telemetry to flag formation and completion anomalies that manual interpretation misses.
WellLogStack · upstream instrument
SCADA-native alerting & triage
Models sit on top of the legacy SCADA and historian systems already in your facilities, routing actionable alerts to maintenance teams without a rip-and-replace.
Deployed across multiple facilities
Reactive-to-proactive operations
Each model is specialized for point, contextual or collective anomalies, so crews triage genuine events instead of chasing noise across distributed assets.
25% lower maintenance cost
Built for noisy telemetry and brownfield plants
Why crews kept trusting the alerts — and why it shipped across existing facilities.
Built for noisy, multi-asset telemetry
A hybrid of statistical process control and deep-learning autoencoders, with a feature pipeline that extracts temporal patterns at multiple time scales — so it tells normal operational variation apart from a real fault even when baselines differ across flowmeter types. That false-positive discipline is what kept crews trusting the alerts.
Drops onto legacy SCADA, not over it
The system integrates with the SCADA and historian infrastructure already running at client sites instead of demanding a new control stack. In oil & gas the data — and the risk of touching it — is the gravity; meeting plants where they are is what made the deployment shippable across facilities.
One method, tuned for oil & gas
Assess, Transform, Sustain — the cycle every organization runs, dropped one level deeper for your sector's pains and sticky aspects.
Assess
Telemetry, historian and SCADA audit across heterogeneous assets
We map your flowmeter, rotating-equipment and well-log data sources, profile streaming quality and reliability, and establish per-asset baselines that differ across equipment types and operating conditions — then rank the highest-downtime failure modes by recoverable value. 4–6 weeks to a ranked anomaly-detection and predictive-maintenance roadmap scoped to your existing control infrastructure.
Transform
Hybrid anomaly models wired into the existing control stack
We build the SPC-plus-autoencoder ensemble — specialized models for point, contextual and collective anomalies on a multi-timescale feature pipeline — and harden it from pilot to production against real streaming throughput, integrating with legacy SCADA and historian systems so alerts land in the maintenance workflows your crews already use.
Sustain (AIOps)
Drift-aware retraining tuned to false-positive tolerance
Operations AI lives or dies on alert trust, so we monitor for sensor drift and shifting operating regimes and retrain to hold detection sensitivity high while keeping false positives below the threshold crews will act on. Tuned to your maintenance rhythms and turnaround windows, it sustains the reactive-to-proactive shift across every monitored facility.
Anomalies caught before they become downtime
AI-powered anomaly detection for IoT flowmeters — 40% less downtime across plants.