Anomaly detectionin drone pusher motors.
Drone manufacturers needed automated fleet health monitoring — detecting power consumption anomalies without manual inspection, on a private data architecture keeping all flight logs securely in-house.
Before and after AI-Native
Manual fleet-health inspection was the bottleneck. Here's what flipped when intelligence became the engine.
- 5,000+ hours of flight logs, reviewed by hand
- Anomalies spotted only after failure reports
- No model — only spreadsheets and SME intuition
- Zero ability to generalize across aircraft types
- Autoencoder-style models on private flight data
- Reconstruction-error threshold pinpoints outliers
- Alerts raised before operational impact
- One model generalizes across the fleet
Every byte stays on-prem.
Telemetry moves straight from the fleet into a private vector store. The model trains and scores entirely inside the client perimeter — no external data sharing, no public LLM.
The numbers after a full deployment.
Every metric below is measured on client infrastructure, on client data, with zero external inference calls.
Three phases. Zero surprises.
From proprietary data foundation to autonomous anomaly detection — delivered as a single intelligent system, not a patchwork.
- P-01
Private Data Foundation
- 5,000+ hours of proprietary flight logs processed on-premise
- No external data sharing; model trained on client data only
- Audit-ready outputs for regulatory compliance
- P-02
AI Approach
- DWT selected for efficiency and accuracy
- Isolation Forest & SVM evaluated as benchmarks
- Reconstruction error threshold of 0.25 established
- P-03
Outcomes
- 100% automated defect categorization
- Zero manual review dependency
- Model generalizable across aircraft types
Anomaly detection, on your fleet, in your walls.
Book a 30-minute demo. We'll show you how Private Vector Foundations and DWT anomaly scoring map to your flight telemetry.