Funding
Funding not disclosed
Founders
Product
Problem
Retailers and their suppliers face challenges in accurately and efficiently monitoring in-store product availability, pricing, and planogram compliance. Manual audits are labor-intensive, prone to human error, and lack real-time visibility, leading to missed sales opportunities and suboptimal shelf execution.
Solution
FocusOnShelf offers an AI-driven image recognition platform that automates retail shelf analysis using standard smartphone photography. The system processes images in near real-time, extracting critical data points such as stock levels, product facing, price accuracy, and adherence to planograms. This automated approach significantly reduces the need for manual labor and minimizes data entry errors. Insights are delivered through customizable dashboards, enabling data-driven decision-making for optimizing in-store operations and improving retail execution.
Target Audience
The primary customers are hypermarkets, supermarkets, convenience stores, and their respective suppliers who require automated, data-driven insights into in-store product presentation and inventory management.
Features
- AI-powered image recognition leveraging deep learning for automated shelf analysis.
- Real-time data extraction for stock availability, product facing, OOS (Out-of-Stock) detection, and price compliance.
- Planogram compliance verification through comparison with model photos.
- Mobile-first data capture requiring only smartphone photos and minimal time per rack (approx. 10 seconds).
- Secure, automatic data transmission to cloud servers with deletion from mobile devices to prevent data tampering.
- Customizable dashboards providing visualizations and metrics tailored to various user roles and needs.
- Data anonymization and aggregation for benchmarking and competitive analysis.
- Scalable infrastructure supporting unlimited photo uploads on a flat-rate model.