VÖRNTEC is an industrial AI platform that detects early equipment failures by learning normal behavior from existing sensors, surfacing anomalies weeks before traditional alarms trip. The system automatically converts detected issues into prioritized work orders in tools like SAP and IBM Maximo, with a median scoring latency of 1.2 seconds across thousands of assets.
Funding
Funding not disclosed
Founders
Product
Problem
Industrial operations rely on threshold-based alarms that only trigger after a failure has already begun, missing slow-developing degradation patterns that build over hours, days, or weeks. This reactive approach leads to unplanned downtime, costly emergency repairs, and production losses—particularly in energy sectors where equipment failures also risk methane leaks with significant climate impact.
Solution
VÖRNTEC provides a multi-layer AI platform that continuously learns the normal behavior of every asset from existing sensor data, then scores anomalies in real time across the full sensor field rather than per-channel thresholds. The system detects subtle failure signatures—such as gradual current rise with harmonic vibration shifts—and predicts failures up to 14 days in advance with root-cause attribution. When an anomaly is confirmed, VÖRNTEC automatically raises prioritized work orders in enterprise systems like SAP, IBM Maximo, and Oracle, complete with diagnostic context, recommended actions, and crew assignments. This end-to-end automation from sensor reading to work order eliminates manual monitoring and enables condition-based maintenance at scale.
Target Audience
Primary customers are industrial operators in upstream oil and gas, midstream, gas processing, chemicals, and power generation who need early failure detection and automated maintenance workflows across their asset base.
Features
- Self-learning baseline that requires no tagged failure data, detecting any deviation from learned normal behavior across all asset types
- Multi-layer AI detection stack combining anomaly scoring, isolation, clustering, and sequence analysis for root-cause attribution
- Real-time scoring with median latency of 1.2 seconds across thousands of assets, with 14-day failure prediction lookahead
- Automated work order generation in SAP, IBM Maximo, Oracle, and custom ERP systems with diagnostic context and prioritization
- Alert routing to MS Teams, Slack, and email with configurable severity and confidence thresholds
- Detection catalogue covering gas lift valve failure, leaks, slugging, compressor surge, valve degradation, and foaming in amine contactors
- 83%+ precision on real, unlabelled failures validated against historical incident benchmarks