AI CCTV Platform for Multi-Store Retail Operations
How Electro AI Lab built a computer vision platform that turns retail CCTV into real-time operational and loss-prevention insights.
The challenge
RetailMax operated multiple store locations with extensive CCTV coverage, but footage was reviewed manually and rarely used for timely operational decisions. Queue buildup, crowding, and loss-prevention signals were discovered too late to act.
Our solution
Electro AI Lab designed and delivered an AI CCTV platform that analyzes camera streams, detects operational events, and presents alerts and trends in a web dashboard used by store and regional operations teams.
Our approach
We started with a discovery workshop to prioritize detection scenarios, then built a pipeline for stream processing, model inference, and a role-aware operations interface. Iteration focused on alert quality, false-positive reduction, and store-level usability.
Key features
- Queue and occupancy detection
- Loss-prevention alert workflows
- Multi-store operations dashboard
- Historical trend reporting
- Role-based access controls
Technologies used
Technical architecture
Camera streams feed a processing layer where OpenCV and ML models extract events. Structured events are stored and exposed through FastAPI services. A Next.js dashboard presents live alerts, store filters, and historical trends hosted on AWS.
Development process
- 1Discovery and camera scenario prioritization
- 2Model and pipeline architecture
- 3Dashboard UX for operations teams
- 4Pilot stores and alert tuning
- 5Rollout and monitoring
Outcomes
Operational visibility from CCTV streams
Centralized monitoring in one workspace
Alert workflows for loss-prevention teams
Project gallery
“The platform helped our operations team move from reviewing footage after the fact to acting on what is happening across stores.”
Operations Lead · RetailMax Solutions
Frequently asked questions
What problems does the AI CCTV platform solve?
It helps retail teams detect queues, occupancy patterns, and loss-prevention signals from existing camera infrastructure, then surface those events in a shared operations dashboard.
Which technologies were used?
The solution uses Next.js for the dashboard, Python and OpenCV for computer vision, FastAPI for services, PostgreSQL for structured data, and AWS for cloud hosting.
Can the platform support multiple locations?
Yes. The workspace was designed for multi-store filtering, role-based access, and centralized alert review across locations.
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