Visylix delivers intelligent video analytics through 22 specialized AI models, from face recognition to abandoned object detection, plus Radha AI Copilot for natural language control. Around 10 of the 22 models learn your site and get quieter over time, bounded and operator-supervised. All models run on-premise with GPU-accelerated inference or CPU-only at 15-21 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 fast database matching against large-scale identities.
Per-camera tracking that maintains consistent identities within each camera view, plus cross-camera appearance search (v1): pick a person and Visylix assembles their journey across your other cameras as operator-reviewed candidates.
Automatic number plate recognition (ANPR/ALPR) reading global plate formats with an India-optimized pipeline, night-vision IR capability, and moving-traffic capture.
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 that combines person detection and tracking with in-memory de-duplication for accurate footfall analytics, 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.
Automatically detects camera sabotage such as defocus, blackout, blinding, and obstruction, so a blinded or covered camera raises an alert instead of silently going dark.
Detects unattended bags, packages, and objects left in a monitored area, using owner and dwell-time analysis to cut false alarms in busy environments.
Detects two or more people passing through a secured door on a single credential (piggybacking), one of the most common access-control breaches, and alerts security in real time. Runs on any camera covering a door, turnstile, or mantrap with no extra hardware.
Measures queue length and average wait time per zone in real time, alerting when lines exceed your thresholds so staff can open a new counter before customers walk out. Built for retail, banking, and transport hubs.
Monitors each parking slot for a vehicle and reports overall lot occupancy in real time, alerting when the lot reaches capacity. Uses existing overhead or wide-area cameras with no per-space sensors, and ignores cars merely driving past.
Reports real vehicle speed from your cameras using a one-time per-camera calibration and alerts when a vehicle exceeds the limit you set. Works alongside license-plate recognition so an over-speed event can carry the vehicle plate, with no radar or in-road sensors.
Learns what normal looks like for each individual camera and alerts on activity that deviates from it, with no rules, zones, or labels to configure. A semantic per-camera memory built from the scene's own visual embeddings catches unusual activity and out-of-place objects, not just raw pixel change.
Spots visible flame and smoke on any camera and raises an alert in real time, so a fire is seen the moment it starts rather than when a ceiling sensor finally reacts. Runs on the cameras you already have, with no dedicated fire or smoke sensors, and can be limited to the areas that matter.
Detects visible firearms and knives on any camera and raises an immediate critical alert for operator review. It confirms a weapon across several frames before alerting to suppress false alarms, and runs on the cameras you already have with no special hardware.
Listens to camera audio for gunshots, breaking glass, and screaming and raises an immediate critical alert for operator review. It confirms a sound across several consecutive seconds before alerting to suppress false alarms, and works on any camera that has a microphone.
Our models are trained, tested, and deployed for production grade video intelligence.
Battle tested models deployed across enterprise environments with proven reliability.
GPU-accelerated real-time inference for object detection. CPU-only mode is available 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 learns your site and gets quieter over time, on-premise, bounded, and always operator-supervised. Around 10 of the 22 models adapt in place: face recognition, the fire, weapon, PPE, and object detectors, and the motion, audio, crowd, camera-tampering, and anomaly scene baselines. The rest are conventional trained models that do not self improve in place. Nothing trains automatically, nothing leaves your building, and every adaptation is reversible.
Motion learns each camera's ambient movement so foliage and traffic stop triggering it, audio learns the ambient noise ceiling, crowd thresholds auto-calibrate to each scene, and camera-tampering learns the day-to-night light cycle so dark nights and IR switches stop firing false blackout alarms.
Face recognition refines each enrolled identity's template from confirmed matches over time, with anti-poisoning drift guards that reject low-quality or inconsistent captures. On-premise and reversible, with no manual retraining.
When an operator dismisses a false alert, that camera's threshold tightens automatically within bounded limits. For safety detectors (fire, weapon, PPE) it can only make them more conservative, never less, and a drift-freeze guard reverts a runaway camera to its baseline.
Anomaly detection builds a per-camera memory from the scene's own visual embeddings, catching unusual activity and out-of-place objects rather than raw pixel change. It keeps separate day and night memories and learns from operator dismissals.
Acknowledging an alert, or dismissing it with a one-tap reason (false alarm, fog or steam, known person, duplicate), teaches the system. A one-click apply-retune on the alarm-quality board raises a noisy source's threshold a bounded step.
An opt-in, human-approved fine-tune queue lets your own reviewed examples improve the detector models, with eval-before-swap so a new model only ships if it beats the old one. Nothing trains automatically, nothing leaves the premises, and all state survives restarts.
Follow a person or a vehicle across your site, not just within one camera view. Available in plain language through Radha (“where did this person go?”, “track that vehicle across the site”).
Pick a person from any detection and Visylix finds appearance matches on your other cameras within a time window, assembling a chronological path with travel times between cameras. It is appearance-based and returns candidates for operator review, not automatic identity assertions. A purpose-trained re-identification model for higher precision is in our training pipeline.
Because a plate is an exact identifier, vehicle journeys are precise: the same plate seen at camera A then camera B is stitched into one cross-camera path with the travel time between them, for enforcement, investigation, and site-flow analysis.
Schedule a live demo to see how Visylix AI models perform on your video feeds.