Core Service
Anomaly Detection
Quality control through unsupervised learning and outlier identification
Overview
Anomaly detection systems identify rare, suspicious, or defective patterns in data without requiring extensive labeled examples. Our solutions leverage autoencoders, isolation forests, and deep learning techniques to provide real-time quality control for manufacturing, operations, and security applications—deployed both in cloud and on edge devices for immediate detection and response.
Key Features
Autoencoders & Isolation Forests
Unsupervised learning approaches that learn normal patterns and flag deviations without requiring labeled defect examples, ideal for rare anomaly scenarios.
Real-Time Inference on Edge Devices
Optimized models deployed on edge hardware for sub-millisecond detection latency, enabling immediate alerts and process interventions.
Explainable Anomaly Localization
Attention maps and saliency techniques pinpoint exactly what triggered the anomaly flag, enabling operators to quickly understand and address issues.
Adaptive Thresholding
Dynamic anomaly score thresholds that adapt to changing operational conditions, minimizing false alarms while maintaining high detection rates.
Technical Approach
Our anomaly detection pipeline ensures robust, explainable detection:
- Data Collection: Gather comprehensive normal operation data across diverse conditions and variations
- Model Selection: Choose between autoencoders, VAEs, GANs, isolation forests, or one-class SVM based on data characteristics
- Training & Validation: Learn normal patterns and validate on synthetic anomalies or known rare events
- Threshold Calibration: Set detection thresholds balancing false positive vs. false negative rates for operational constraints
- Deployment & Monitoring: Deploy to edge devices with continuous performance monitoring and model updates
Use Cases
Anomaly detection transforms quality and security operations:
- Manufacturing QA: Detect surface defects, dimensional deviations, and assembly errors in real-time production lines
- Predictive Maintenance: Identify early signs of equipment degradation from sensor data before catastrophic failures
- Cybersecurity: Flag unusual network traffic patterns, unauthorized access attempts, and potential intrusions
- Financial Fraud: Detect suspicious transactions and account behaviors indicative of fraud or money laundering
Expected Outcomes
Anomaly detection delivers immediate operational impact:
- 95%+ anomaly detection rate with <1% false positive rate
- Real-time inference (<10ms) on edge devices for immediate response
- 60-80% reduction in defects reaching customers through early detection
- Explainable alerts accelerating root cause analysis and remediation
Ready to Enhance Quality Control?
Let's discuss how anomaly detection can improve your operations and reduce defects.