Stop replaying tape after the fact.Move to real-time detectionalertsanalysisresponseAI retail security.
For US brick-and-mortar retail, where theft and violent incidents are rising fast, we built an end-to-end stack: AI behavior recognition, multi-camera live monitoring, risk event classification, real-time alert push, and store operations analytics.
Brick-and-mortar
is still the main
battleground, but risk is rising fast.
As of 2024, about 81.5% of US retail revenue still comes from physical stores, a market worth over $5.93 trillion. Yet theft and violent incidents keep climbing, and traditional security systems can no longer keep up.
Store theft incidents are up 93% vs 2019, with direct annual losses around $42.6 billion.
We didn't treat this as
"just another AI camera".
The hard part is: teaching the AI to actually understand risky behavior. We redesigned the action recognition model, the multi-camera fusion logic, edge inference, the real-time alert pipeline, and the cloud-edge-device architecture. The system doesn't just record video — it identifies risk in real time, interprets intent, classifies alerts, drives fast response, and powers long-term operations analytics.
A real-time
five-step AI security pipeline
From camera to control-room dashboard, every step is designed for real-time response and intent understanding.
Multi-camera live video ingest
Keep store state live
AI action recognition engine
From anomaly detection to behavior understanding
Edge inference and instant alerts
Risk to response in under 3 seconds
Multi-camera fusion and occlusion recovery
What one camera misses, the others fill in
Security operations platform
Managers finally see store risk
From camera to dashboard,
this is how the system runs.
Eight core screens covering live monitoring, alert triage, operations analytics, and multi-store management — all designed around what store managers actually do.
