AI LiveSight Analytics

AI LiveSight Analytics is a software-only layer that connects to existing IP cameras and VMS platforms via standard RTSP/ONVIF protocols. It receives video streams, runs AI detection in real time, and generates alerts -- without touching or replacing any installed camera hardware.

AI in construction is transforming infrastructure maintenance by automating visual inspections, detecting defects in real time, and predicting failures before they happen. Using technologies like computer vision and machine learning, AI-powered systems analyze video feeds to identify cracks, surface deformations, lighting outages, and other anomalies across roads, bridges, and utilities, dramatically improving accuracy and response times. 

VOD is pre-recorded and stored before viewers see it, so they can watch anytime, pause, and rewatch. Live streaming delivers video in real time as the event happens, with viewers tuning in during the broadcast.

It depends on frame sampling rate, resolution, and latency tolerance more than raw hardware specs. On a consumer card like the RTX 5090, a realistic range is roughly 32 to over 100 cameras, and the trade-off between accuracy and camera count is tunable per use case.

Adaptive bitrate streaming (ABR) is a video delivery technique that encodes content into multiple quality levels and lets the player switch between them in real time based on network conditions. This prevents buffering on slow connections while maximizing quality on fast ones. Both HLS (HTTP Live Streaming) and MPEG-DASH are widely used ABR protocols.

Anomaly detection is the process of identifying data patterns or behaviors that deviate from the expected norm. It works by establishing a baseline of normal activity and then flagging deviations that may signal threats, failures, or compliance risks.

Traditional monitoring relies on human operators who fatigue and miss incidents. AI LiveSight Analytics runs 24/7 without attention gaps, detects threats in real time, and generates structured event data for accountability. Organizations reduce response times and staffing costs for monitoring rooms.

Accuracy depends heavily on the threat type, lighting, camera placement, and whether the model has been fine-tuned for the specific site. Mature categories like person detection and ALPR routinely operate above 95% precision in production, while newer categories like behavioral anomaly detection still benefit from site-specific fine-tuning to reach operational thresholds. The honest framing is that AI handles the high-volume detection work better than humans can, while humans still adjudicate edge cases.