Give loss prevention the context to act.
Explore shoplifting detection events, configuration considerations and a simulated detection preview.
Loading recorded detections…
Rule: Store activity threshold: 2 · 0.75s confirmation
Recorded elevated view · Person detections and example review alerts.
Recorded demonstration with synchronized detections and example alert rules. Review the event details to see what this camera view can establish.
Start with the operational problem.
Staff cannot watch every aisle, and isolated gestures can be misleading.
The EyeSpot approach
Define monitored merchandise zones, review event context and tune alerts with store staff. An alert is not a determination of theft.
What shoplifting detection brings into view.
Possible concealment
Event clips
Review queue
Zone configuration
A clear path from capture to action.
Define monitored merchandise zones, review event context and tune alerts with store staff. An alert is not a determination of theft.
Your cameras
IP CCTV · RTSP · NVR
Secure ingestion
Connect supported streams
AI processing
Edge · Cloud · On-premise
Event engine
Rules · Metadata · Clips
Your workflows
Dashboard · Alerts · APIs
Choose around your operating conditions.
Local response, intermittent connectivity and reduced video transfer.
Device capacity, updates and physical access need an operational owner.
Cloud AICentral management, scalable compute and shared reporting.
Stream bandwidth, connectivity, cloud cost and data access require planning.
Hybrid AIFleets, distributed sites and local inference with central insight.
Plan event synchronization, retry behavior and device fleet management.
On-PremiseControlled networks, local retention and sensitive infrastructure.
Your team owns capacity, availability, backups and upgrades.
Private CloudDedicated access controls and centralized enterprise governance.
Networking and infrastructure ownership must be agreed during design.
Deliver context to the people who act.
Assess compatible camera streams and connect configured events to APIs, webhooks and notification workflows.
Measure it where it will run.
Detection quality depends on camera placement, image quality, lighting and occlusion. Validate the configured scenario on representative footage before operational use.
Agree on review workflows, access controls and retention. Analytics support human decisions and do not independently establish safety or regulatory compliance.
See our evidence approachPlanning shoplifting detection?
01How should we begin with shoplifting detection?+
Define monitored merchandise zones, review event context and tune alerts with store staff. An alert is not a determination of theft.
02Can we use our existing cameras?+
Compatible IP cameras and recorder streams can be assessed using their available protocols, codec, resolution and network access. Camera suitability is validated during the pilot.
03Does all video have to go to the cloud?+
No. Edge and on-premise processing can keep raw video local while forwarding metadata, events and selected clips according to your configuration.
04How do you validate a detection?+
We define the event, collect representative footage with permission and compare model outputs against manually reviewed examples. Lighting, occlusion and placement are included in the evaluation.
Your cameras already see it. Make it actionable.
Tell us about your environment. Let’s design the right video analytics pilot.
Book a live demo Your cameras. Your requirements. A clear next step.