Perimeter Monitoring Accuracy Hit 99.2% — Replacing 40 Contract Guards

False alarm rates from their legacy camera system were running 84%. Security teams were responding to deer, tumbleweeds, and shadows 20+ times per night. Real intrusion attempts were getting lost in the noise. After a genuine perimeter breach went undetected for 47 minutes, the client's CISO demanded a complete overhaul within 90 days.

The Challenge

This Costa Mesa defense tech company's autonomous perimeter monitoring product was losing contracts to competitors. Their computer vision models, trained on generic security datasets, couldn't handle the diverse environments their DoD and critical infrastructure clients operated in — desert, coastal, arctic, urban. Each deployment required weeks of manual calibration that erased their margins.

What We Built

We rebuilt their detection pipeline with a multi-modal sensor fusion architecture — combining camera feeds, LIDAR, thermal imaging, and ground vibration sensors. Custom models trained per-environment learn local baselines (wildlife patterns, weather effects, vegetation movement) and adapt automatically. Our overnight engineering team handles new deployment calibrations and model updates across time zones.

Results

✓Detection accuracy improved from 68% to 99.2%

✓False alarm rate dropped from 84% to 3.1%

✓New deployment calibration time reduced from 3 weeks to 3 days

✓Won $34M in contracts previously lost to competitors

✓Client reduced physical guard force by 40 positions (reassigned to rapid response)

The system now knows the difference between a coyote at 800 meters and a person at 2 kilometers in fog. Our guards used to spend their nights chasing animals. Now they respond to real threats.

— Director of Product Engineering

Timeline20 weeks (including DoD compliance)

Team4 ML engineers + 3 overnight deployment ops

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