From face recognition to intrusion detection, Visylix deploys 12 specialized self-learning AI models that improve daily, plus Radha AI Copilot for natural language control. All models run on-premise with GPU-accelerated inference at 200+ FPS or CPU-only at 15-20 FPS.
Each model is purpose built and optimized for its domain, delivering best in class accuracy and performance.
Enterprise-grade facial detection and identity verification with anti-spoofing, multi-face tracking, and sub-200ms database matching against 1M+ identities.
Multi-camera re-identification system that maintains consistent identities across overlapping and non-overlapping camera networks using deep appearance embeddings.
Automatic number plate recognition (ANPR/ALPR) supporting 80+ country formats with night-vision IR capability and high-speed capture up to 250 km/h.
Real-time body skeleton detection tracking 17 keypoints per person for safety monitoring, movement analysis, fall detection, and gesture-based interaction.
Enterprise-grade computer vision identifying and localizing 80+ object classes with custom class support via transfer learning and persistent tracking across frames.
AI-powered density estimation and people counting using regression-based density maps, accurate from sparse to ultra-dense environments where traditional detection fails.
Specialized PPE detection for industrial environments identifying hard hats, vests, gloves, glasses, and boots with real-time compliance scoring and violation alerts.
Transforms person-detection data into color-coded spatial overlays revealing movement patterns, dwell times, traffic flows, and engagement hotspots.
Intelligent motion analysis using adaptive background modeling and shadow suppression with configurable zones, tripwires, and directional rules for precise alerting.
De-duplicated visitor counting combining person detection with Re-ID embeddings for accurate footfall analytics that reflect actual engagement, not inflated headcounts.
Zone-based unauthorized entry detection using AI-powered virtual perimeters with instant alerts, eliminating the need for physical sensors or laser barriers.
Directional boundary monitoring that counts and alerts when people or vehicles cross defined virtual lines, with configurable direction rules and counting analytics.
Identifies vehicle type (sedan, SUV, truck, bus, motorcycle), make, color, and direction of travel alongside license plate reading. Search every vehicle detection by appearance.
Our models are trained, tested, and deployed for production grade video intelligence.
Battle tested models deployed across enterprise environments with proven reliability.
GPU-accelerated inference at 200+ FPS for object detection. Supports 500+ streams per GPU. CPU-only mode handles up to 50 streams at 15-20 FPS for testing and small deployments.
Optimized model variants for edge compute devices with hardware-accelerated inference.
Per camera model assignment with adjustable sensitivity, zones, and scheduling.
Visylix AI does not just detect. It learns your environment and continuously improves without manual retraining.
From day one, the AI builds a behavioral model of your environment. It learns what normal looks like: shift patterns, traffic flow, peak hours, regular visitors. False positive rates drop by 60-80% in the first week as the system calibrates to your site.
When a face is matched multiple times, the system selects the highest quality embedding and discards lower quality captures. Face recognition accuracy improves by 15-25% after 30 days compared to initial enrollment, with zero manual retraining.
Person tracking Re-ID models learn the unique appearance characteristics of your environment including lighting and camera angles. Accuracy improves from ~70% to ~90% after two weeks as the system maps your camera network.
Every acknowledged alert teaches the system what is important and what is noise. ANPR builds a plate database that grows more accurate with repeated observations. Crowd thresholds auto adjust based on time of day patterns.
AI learns which alert types are most actionable for your team. Repeated noise alerts are automatically suppressed while critical alerts like unknown faces at restricted areas are prioritized higher over time.
All self-learning happens on your hardware. Your video data never leaves your building. No cloud dependency for model improvement. This is fully on-premise continuous learning that no competitor offers.
Schedule a live demo to see how Visylix AI models perform on your video feeds.