Case study 05 · Retail
Give every shelf a voice.
Existing store cameras detect empty shelves and misplaced products, then help the team move from an alert to replenishment.
- Sector
- Retail
- Geography
- UK · France
- Hardware
- Existing cameras
- Scope
- Alerts to inventory
01 / The challenge
Spot shelf issues sooner.
Empty shelves and misplaced products are difficult to track through manual checks alone. Retail teams needed a way to identify those issues and route them to staff using the cameras already in their stores.
02 / The approach
Connect shelf visibility to action.
Computer vision analyzed store-camera feeds to detect empty shelf spaces and misplaced items. Staff alerts connected those detections to the people responsible for replenishment.
The broader Smart Shelves system added inventory recommendations and replenishment management, connecting shelf monitoring with the next operational steps.
03 / The outcome
From a camera feed to a replenishment task.
The project covered stores in the UK and France. It connected shelf detection, staff alerts, and replenishment support using existing camera infrastructure.
What we built
The system, in parts.
- 01
Shelf vision
Analyzes existing camera feeds for empty shelves and misplaced products.
- 02
Staff alerts
Routes detected shelf issues to store staff for attention.
- 03
Smart Shelves
Connects monitoring with inventory recommendations and replenishment management.
Technology used
- Computer Vision
- Python
Selected work delivered across GOT AI, Rocket Farm Studios, and Full Cortex in engineering, technical leadership, and advisory roles. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. OpenCV and its logo are trademarks of their owner. Logos identify technology the project used and link to their makers; no endorsement is implied.
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