FALCON: Real-Time Model Monitoring & Performance Assurance
Ensuring AI Model Reliability with Intelligent Monitoring & Error Detection
Unlock the Unpredictable World
AI models in production face challenges such as data drift, model degradation, and operational uncertainties—all of which can compromise performance and decision-making. FALCON is AvaWatz's real-time AI monitoring solution, designed to track, diagnose, and enhance model performance. By continuously analyzing predictions, detecting errors, biases, and inconsistencies, and providing actionable insights, FALCON ensures that AI systems remain reliable, adaptive, and high-performing throughout their lifecycle.
Powerful Features for Model Monitoring
Automated Error Detection at Deployment Time
Real-time Performance Monitoring
Degradation Detection and Alerting
Root Cause Analysis
Alerting for Model Updates
Case Study 1
Real-Time AI Monitoring in Security & Surveillance
Problem
Surveillance AI faced performance degradation due to environmental changes and adversarial tactics, increasing false negatives.
Solution
FALCON continuously monitored model performance, detecting drift early and triggering retraining on edge cases.
Results
Model Performance: Achieved high accuracy with Mean Average Precision over 80% for FOD detection and classification.
- 50% reduction in false negatives
- Proactive AI adaptation, preventing security failures
- Improved response time for threat detection
Case Study 2
Medical Imaging AI Performance Monitoring
Problem
A hospital AI system degraded over time due to scanner variations and demographic shifts, leading to misdiagnoses.
Solution
FALCON flagged uncertain cases for expert review, ensuring continuous model updates and improved accuracy.
Results
Model Performance: Achieved high accuracy with Mean Average Precision over 80% for FOD detection and classification.
- 35% improvement in diagnostic accuracy
- Lower bias across different patient demographics
- Enhanced trust in AI-assisted diagnoses
Case Study 3
Detecting Debris on Roads & Runways for Degradation Monitoring
Problem
AI-powered road and runway monitoring systems failed to detect subtle degradation, increasing maintenance costs.
Solution
FALCON continuously monitored model performance, detecting drift early and triggering retraining on edge cases.
Results
FALCON's continuous monitoring flagged performance drops and triggered retraining on newly observed debris types.
- 40% increase in debris detection accuracy
- Automated alerts for early hazard detection
- Reduced maintenance costs with proactive AI retraining
