AI-Theft Detection
Learn how AI models detect suspicious behaviour and send alerts to staff.
Suspicious Behaviour Monitoring and Alert
AI analytics detect loitering, concealment, and suspicious actions near high-value items. Real-time alerts notify staff for immediate response, preventing incidents before escalation. This proactive approach shifts from passive CCTV monitoring to intelligent detection, minimising false alarms while safeguarding assets and enhancing security efficiency across retail operations.
Facial Recognition for Repeat Offender Alerts
Facial recognition identifies known shoplifters upon entry and alerts staff instantly. This proactive measure deters repeat offenders, ensuring a safer environment for shoppers and employees. Integration with existing CCTV systems makes it scalable and cost-effective, helping retailers reduce theft while strengthening store-wide security and customer confidence.
Blacklisting
Retailers can centralise offender data across multiple branches. Once flagged, individuals are automatically identified network-wide, preventing exploitation of location gaps. Effective against organised retail crime groups, blacklisting ensures all stores remain informed, creating a strong collaborative defense and reducing repeated losses across the retail chain.
Internal Theft Detection
By analysing video streams, Centroid flags irregular staff activity, such as cash drawer access without transactions, inconsistent stock handling, or unauthorised entry into restricted areas. Linked with attendance and access logs, alerts integrate into SOPs, ensuring systematic, discreet investigations. Transparent monitoring deters misconduct, protects assets, and reinforces operational trust.
ePOS Audit
Integrating video with ePOS data allows automatic verification of every sale, refund, or void. The system highlights discrepancies, fraudulent refunds, or under-ringing items while providing video evidence. Automated audits reduce manual checks, strengthen financial controls, and give retailers effective protection against transaction-level fraud and shrinkage.