CPO Lost Prevention
CPO Lost Prevention brings computer-vision retail loss prevention to the sales floor, flagging shrinkage and suspicious activity as it happens so losses are stopped in the moment, not just discovered on footage later.
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CPO Lost Prevention is a retail loss prevention solution that applies computer vision to store surveillance, analyzing existing CCTV feeds to detect the behaviors and events behind shrinkage — concealment, shelf sweeps, unusual movement around high-value goods, and gaps between what is scanned and what is taken. Instead of reviewing footage after an incident, floor and loss-prevention teams get alerts while there is still time to act, and every alert carries the video behind it as evidence.
Beyond stopping theft, the system doubles as a shrinkage analytics and store-intelligence layer. Using CCTV AI for retail, it tracks movement patterns, processing times and conversion rates across the floor, turning ordinary camera coverage into precise, structured data on how the store is actually used. That lets managers see where customers dwell, where queues and process times build up, and how foot traffic converts, alongside the loss signals it raises.
CPO Lost Prevention keeps its data records organized and integrated rather than scattered across separate camera and point-of-sale systems, and it is built to prevent trickery in the field by correlating what the cameras see with transaction data at the till and self-checkout. Cross-checking camera events against POS records helps separate genuine risk from normal shopping and catch scan avoidance, reducing the false alarms that otherwise overwhelm floor teams.
Because it runs on the cameras a store already has, CPO Lost Prevention adds intelligence without a hardware rip-and-replace, and it aggregates incidents into real-time trends by store, zone and time. Loss-prevention leaders can target prevention where shrink is highest, build evidence against repeat offenders, and use the same movement-pattern and conversion data to inform staffing and layout — so the platform pays back on both loss reduction and operational insight.
Real-time shrink alerts
Flags likely theft and concealment as it happens so staff can intervene on the floor rather than review losses afterward, shortening the gap between an event and a response to near real time.
Suspicious activity detection
Recognizes movement patterns and behaviors linked to loss around shelves and high-value zones — sweeps, loitering and unusual handling — and raises them for attention while the person is still in store.
POS correlation
Cross-checks camera events against transaction data to catch discrepancies between items taken and items scanned at manned tills and self-checkout, helping prevent trickery in the field and scan avoidance.
Video evidence capture
Attaches the relevant footage to every alert, giving loss-prevention teams a clear, organized record for response, investigation and building a case against repeat offenders.
CCTV integration
Runs on existing store cameras as a CCTV AI retail layer, adding shrinkage analytics and detection without replacing installed hardware or rewiring the store.
Loss analytics
Aggregates incidents and store activity into real-time trends by store, zone and time, including movement patterns, process times and conversion rates, so prevention is targeted where shrink is highest.
Frequently asked questions
How does CPO Lost Prevention differ from ordinary CCTV recording?
Ordinary CCTV records footage that someone has to review after a loss is noticed. CPO Lost Prevention adds a computer-vision layer on top of those same cameras that analyzes the feed live, recognizes the behaviors behind shrinkage, and raises an alert while staff can still intervene. Each alert comes with the clip that triggered it, so the team is not scrubbing hours of video.
Do we need to replace our existing store cameras?
No. The system is built to run on the CCTV a store already has, integrating as a CCTV AI retail layer rather than a hardware replacement. That keeps rollout cost and disruption low while adding detection and analytics to installed cameras.
How does it catch losses at self-checkout?
It correlates what the cameras see with point-of-sale transaction data, so it can spot discrepancies between the items a shopper handles or bags and the items actually scanned. This POS correlation is how it flags scan avoidance and ticket switching at both self-checkout and manned tills, helping prevent trickery in the field.
Besides theft, what business data does it provide?
The same vision engine measures movement patterns, processing times and conversion rates across the floor. Managers get precise data on how the store is used — where customers dwell, where queues form, and how traffic converts — which supports staffing, layout and service decisions alongside loss prevention.
How does it help build a case against repeat offenders?
Every alert is stored with its video evidence in organized, integrated records, and incidents are aggregated into trends by store, zone and time. That gives loss-prevention teams a clear evidence trail to investigate patterns and support action against repeat-offender activity across one or many stores.