AI Live Insight

NVR/VMS systems record and play back video but do not analyze it. AI Live Insight adds AI detection, classification, alerting, and structured event records on top of existing NVR/VMS infrastructure. The system understands what is happening in the video, not just that video exists.

Camera-embedded analytics are limited by onboard processing power and locked to that vendor's ecosystem. VIDIZMO runs centralized GPU-powered AI across cameras from any manufacturer, supports custom model training, and scales independently of camera hardware refresh cycles.

YOLOv5/YOLOv8 architectures achieve 95% accuracy with less than 3% false positive rates on standard categories. Activity recognition achieves 92%+ accuracy. Configurable confidence thresholds let operators tune sensitivity per detection type to balance thoroughness and false positives.

DOT agencies use AI Live Insight for pothole detection, road hazard identification, illegal dumping monitoring, copper wire theft prevention, and infrastructure condition assessment. Detections generate structured 311 requests with location, severity, and linked video for field verification.

Each AI Live Server instance handles 32 simultaneous camera streams. Scale horizontally by adding GPU server instances -- 5 cameras need one server, 100 cameras need four. Phased deployment lets organizations prove value on a small scale before expanding.

Phased deployment means rolling out incrementally -- one corridor, district, or facility at a time. This reduces upfront investment, proves ROI on a small scale, aligns with budget cycles, and lets organizations refine detection models before expanding to additional camera groups.

AI Live Insight deploys fully on-premises with no internet dependency. All AI models run locally, detections stay within the secure network, and the management interface operates without cloud connectivity for classified or high-security installations.

AI Live Insight requires GPU-equipped servers for AI processing. Organizations can use existing GPU servers or add dedicated hardware. Each server instance handles 32 camera streams, so infrastructure planning is straightforward based on camera count.

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Yes. Phased deployment starts with a single corridor, facility, or use case using one AI Live Server instance (up to 32 cameras). Organizations validate detection accuracy, alert workflows, and operational fit before committing to broader rollout across additional camera groups.