AI Video Intelligence

Traditional CCTV records. AI Video Intelligence thinks. Where a conventional system stores footage for someone to review later — often days later — this system analyzes every frame in real time using convolutional neural networks. It detects, classifies, and tracks objects, people, and events as they happen. The output is not video. It is structured operational data: who entered where, when, for how long, and whether that violates any rules you have defined.

RTSP or ONVIF — any brand works. No hardware swap. No re-cabling. The inference engine connects to your existing IP camera feeds and processes them at the edge. We have deployed on Hikvision, Dahua, Axis, Bosch, Hanwha, and dozens of others. If it streams video over IP, it works.

95%+ across standard conditions. But the real question is false positive rate — and ours is under 2% because we use multi-frame temporal analysis, not single-frame guessing. A shadow does not trigger an alert. A plastic bag blowing in the wind does not trigger an alert. The system requires consistent detection across multiple frames with high confidence before firing.

Yes. Full air-gap deployment — zero data leaves your network. Every frame processed locally on your hardware. This is not an option we added later; the architecture was designed edge-first. Cloud is available for multi-site aggregation, but it is never required.

Standard x86 servers with NVIDIA GPUs — T4, A2, L4, or equivalent. A single edge node handles 16-32 simultaneous camera streams at 30+ FPS depending on the GPU and resolution. For 100+ cameras, cluster multiple nodes behind the management console. No proprietary hardware.

No hard limit. Single edge node: 16-32 streams. Clustered deployment: organizations routinely run 100-500+ cameras across multiple sites from one management interface. The largest deployment we have configured manages 800+ cameras across 12 facilities.

Pre-trained models — ANPR, fire detection, PPE compliance, crowd counting — deploy within hours. Not days. Hours. Custom detection models for specialized objects (specific machinery, branded packaging, rare animal species) require 2-4 weeks for training data collection and model fine-tuning.

REST APIs and webhook endpoints for SOC, BMS, access control, and enterprise dashboards. Pre-built connectors for Milestone, Genetec, Lenel, and common VMS platforms. MQTT for IoT integration. Syslog for SIEM. If your system accepts HTTP or MQTT, it integrates.

Five-stage pipeline. Cameras encode H.264/H.265 streams, ingested via RTSP/ONVIF. A CNN backbone extracts spatial features from every frame — not sampled, every frame. Our AI Agent identifies and classifies objects with bounding boxes and confidence scores. Detected objects are evaluated against zone rules, temporal patterns, and behavior models. Confirmed events dispatch alerts and index metadata for forensic search. Total pipeline latency: under 200ms.

Motion detection fires on any pixel change — wind, shadows, rain, animals, flickering lights. Our system fires on classified objects exhibiting defined behaviors in defined zones. That is a fundamentally different approach. Multi-frame tracking means a single anomalous frame gets ignored. The result: false alarm rates drop from 94-98% (industry average for motion-based systems) to under 2%.

Yes. The management console is built for multi-site operations. Each site runs its own edge inference — no bandwidth bottleneck shipping video to a central location. The console aggregates alerts, analytics, and health monitoring across all sites. Role-based access means site managers see their cameras while central security sees everything.

Configurable per camera and per zone. Face blurring, body anonymization, and selective redaction run in real time on the edge — before any recording. Retention policies enforce automatic deletion after configurable periods. Full GDPR compliance: data subject access requests, right to erasure, and data processing agreements are built into the platform, not bolted on after the fact.