AI-Powered Anomaly Detection
Our AI models spot deviations invisible to the human eye. From product inspection to security footage, anomaly detection prevents costly failures and enhances quality control across industries.
- Superhuman Vision
- Real-Time Processing
- Unsupervised Learning
- Continuous Improvement
Industry Applications
Preventing failures and enhancing quality across critical operations
Manufacturing QC
Automated visual inspection for defects in production lines
Fraud Detection in Visual Data
Identify fraudulent documents, images, and security footage
Infrastructure Monitoring
Detect cracks, corrosion, and structural anomalies in assets
Advanced Detection Techniques
Leveraging cutting-edge deep learning architectures for maximum accuracy
🔬
Autoencoders
Neural networks that learn to compress and reconstruct normal patterns, flagging anomalies through reconstruction error
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Isolation Forest
Tree-based algorithm that isolates anomalies by randomly partitioning data—outliers require fewer splits
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GAN-Based Detection
Generative models trained on normal data; anomalies fail to be accurately generated or discriminated
Quality Excellence
40% Reduction in False Negatives
A leading electronics manufacturer deployed AI-powered visual anomaly detection, reducing false negatives by 40% and achieving 99.5% defect detection accuracy while cutting inspection time by 300x.
40%
Fewer Misses
99.5%
Accuracy
300x
Faster
- Reduced defect escape rate from 40% to 0.5% in production
- Inspection time decreased from 30 seconds to 0.1 seconds per unit
- Detected microscopic defects invisible to human inspectors
- Saved $1.8M annually in warranty claims and rework costs