97.8% detection accuracy · 0.18% false positive rate · 40+ concurrent feeds
Overview
A classified government client required a perimeter intelligence system capable of detecting, classifying, and alerting on threats across 40+ concurrent camera feeds — entirely offline, with zero cloud dependency.
Challenge
The system had to operate in an air-gapped LAN environment with no internet access. All AI inference had to run on-premise on hardened GPU hardware. False positive rates had to be below 0.3% to avoid alert fatigue in a high-stakes operational context.
Solution
We deployed a YOLOv8x-based detection pipeline optimised for edge inference on NVIDIA A100 clusters. Custom training data was generated from the installation's actual camera feeds. A React-based control room dashboard provided real-time alerts, camera management, and audit trails — all running on an isolated intranet.
Outcome
The system achieved 97.8% detection accuracy with a 0.18% false positive rate. Deployed across 3 installation zones. Now manages 40+ simultaneous feeds at sub-200ms latency. 100% LAN-only — no data leaves the installation perimeter.