Problem
Shoplifting is rarely a single dramatic moment. It is a sequence of behaviors across aisles, blind spots, and exits. Manual CCTV review cannot follow those sequences at store scale.
Business impact
Loss prevention teams chase incomplete evidence. Repeat offenders exploit blind spots. Stores absorb shrink while managers lack a consistent review queue.
Current approaches
- After-the-fact VMS search
- Exit guards and EAS tags
- POS exception matching
- Periodic LP audits
Why existing methods fail
- Footage review is slow and selective
- EAS false alarms train staff to ignore signals
- POS and video stay disconnected
- No shared playbook across stores
How AI solves it
- Vision models flag suspicious movement patterns as review candidates
- Multi-camera context follows a person across zones when configured
- Evidence packs include time, zone, and clip for LP review
- Human confirmation remains required before escalation
With AnomaAI, detections become business events with evidence—so teams review what matters and leaders see outcomes, not camera walls.
Business outcomes
- Shorter investigation cycles
- Higher-quality review queues
- Better multi-store consistency
- Less blind-spot leakage
Frequently Asked Questions
Can AI prove someone stole?
No. Responsible systems surface candidates and evidence for trained human review.
Does this work with existing cameras?
Yes—AnomaAI is built for existing retail CCTV, mapped to aisles, exits, and stockrooms.
What about privacy?
Deployments should follow local law, retailer policy, and purpose limitation. Events should support operations—not unrestricted surveillance.
Related use cases
Next step
Ready to see this on your cameras? Book a demo or explore See Retail AI.
