Loading...
Loading...
Computer Vision
Turn every camera into a decision engine
Capabilities
Process feeds from thousands of cameras simultaneously with GPU-accelerated inference. Detect objects, track movement, and classify events without manual monitoring.
Configure rule-based and AI-driven alerts that trigger SMS, email, or API callbacks within 200ms of event detection. Eliminates false positives through multi-frame verification.
Search hours of footage by object type, color, direction, or behavior pattern. Find specific incidents in seconds rather than scrubbing through hours of video.
Run inference at the edge for sub-second response while syncing metadata to the cloud for cross-site analytics and long-term pattern recognition.
Connect with existing access control, building management, and ERP systems through RESTful APIs and webhook support for over 40 third-party platforms.
Use Cases
According to ASIS International, corporate campuses with AI-integrated video management reduce security incidents by 67% compared to traditional CCTV monitoring. The AI-VMS platform processes feeds from 500+ cameras across multi-building campuses, identifying unauthorized access attempts, tailgating at entry points, and suspicious loitering in restricted zones. A 2024 Deloitte security operations study found that AI-driven VMS platforms reduce the average incident response time from 14 minutes to under 90 seconds. The system correlates events across cameras to track individuals across the entire campus, providing security teams with actionable intelligence rather than raw footage. Automated shift reports summarize daily activity patterns, highlight anomalies, and flag cameras requiring maintenance or repositioning for optimal coverage.
The National Retail Federation reports that retail shrinkage cost the industry $112 billion in 2023, with organized retail crime accounting for $93 billion. AI-VMS addresses this by detecting shoplifting behaviors, concealment patterns, and suspicious group movements in real time. A 2025 study by IHL Group found that retailers deploying AI-powered video analytics experience a 45% reduction in inventory shrinkage within the first six months. Beyond loss prevention, the same camera infrastructure delivers foot traffic heatmaps, dwell time analytics, and queue length monitoring. Store managers receive daily reports on peak hours, conversion zones, and staff positioning effectiveness. The system processes 200 transactions per second across POS-linked cameras to flag sweethearting and return fraud patterns without disrupting legitimate customer experiences.
According to McKinsey Global Institute, cities deploying AI-integrated video surveillance achieve a 30-40% reduction in crime rates within monitored zones. The AI-VMS serves as the central nervous system for municipal surveillance operations, aggregating feeds from traffic cameras, public space monitoring, and critical infrastructure sites into a unified command dashboard. A 2024 World Economic Forum report found that smart city video platforms process an average of 15,000 simultaneous feeds while maintaining sub-second alert latency. The platform supports inter-agency coordination by routing alerts to police, fire, and emergency medical services based on event classification. Automated incident correlation across jurisdictions helps identify crime patterns spanning multiple precincts, while privacy-preserving features like automatic face blurring in non-investigation footage ensure compliance with local data protection regulations.
Frequently Asked Questions
A single AI-VMS server cluster supports up to 10,000 simultaneous camera feeds with GPU-accelerated inference. The architecture scales horizontally — add server nodes to handle additional cameras without performance degradation. Most enterprise deployments start with 200-500 cameras and expand as coverage requirements grow.
Yes. AI-VMS supports ONVIF and RTSP protocols, which means it integrates with over 95% of IP cameras on the market including Hikvision, Dahua, Axis, Bosch, and Hanwha. Analog cameras work through IP encoders. No camera replacement is necessary — the AI layer runs on dedicated compute hardware, not on the cameras themselves.
Event detection-to-alert latency is under 200 milliseconds in edge deployment mode and under 500ms in cloud mode. Multi-frame verification adds 1-3 seconds for complex event classification but reduces false positives to below 2%. Critical alerts like fire detection bypass multi-frame verification for immediate notification.
AI-VMS includes configurable privacy zones that permanently mask defined areas in both live and recorded footage. Automatic face blurring is available for non-investigation footage. All video data is encrypted at rest (AES-256) and in transit (TLS 1.3). Access controls support role-based permissions with complete audit trails for regulatory compliance.
AI-VMS offers three deployment models: on-premises for organizations requiring full data sovereignty, cloud for distributed multi-site operations, and hybrid for organizations that need edge inference with centralized management. The hybrid model is most popular — it processes video locally for fast response while syncing analytics metadata to a central dashboard.
Tell us what you're trying to solve. We'll show you exactly how AI-VMS fits your operations.