Computer vision shelf auditing automates planogram compliance and in-store merchandising across retail networks.
- Shelf inspection time
- Materially reduced
- Planogram violations
- Detected accurately
- Field team expansion
- Avoided
What we shipped
A mobile-first platform where reps capture shelf photos that computer vision analyzes against planograms in real time.
Consumer goods companies rely on in-store execution for visibility and sales. Sales reps and merchandisers visit stores to inspect shelves, verify planogram compliance, and find placement opportunities.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Defined SKU taxonomy, planogram definitions, and the capture workflow for reps in store.
- AI Pilot
Trained computer vision on shelf imagery to detect products, facings, and misplacements, validated in live stores.
- Production launch
Deployed the planogram comparison engine with real-time in-store guidance on mobile.
- Scale and operate
Added out-of-stock alerts, promo effectiveness analysis, competitor monitoring, and planogram optimization.
Business, technical, and governance outcomes.
- Faster in-store audits per visit.
- Real-time corrective guidance for reps.
- Network-wide retail execution analytics.
- Lower need to grow field sales headcount.
- Python
- PyTorch
- YOLO
- FastAPI
- PostgreSQL
- Mobile SDKs
Image and audit trails per store visit, structured planogram comparison logs, and data pipelines for retailer negotiations.
Working on something similar?
Schedule a call. We will tell you honestly whether AI is the right move.
Reference calls available under NDA after the second working session.