Development of an AI-Powered Shelf and Retail Analytics Platform for Enhanced Store Auditing

The client faces significant challenges in maintaining accurate shelf displays and inventory data, leading to inefficient manual audits, increased operational costs, and delayed insights. Heavy traffic periods strain existing systems, causing stability issues. There's a need for a scalable, reliable, and automated solution that can quickly capture shelf data, identify SKU placement and pricing, and provide in-depth analytics to optimize in-store merchandising.

A large-scale retail chain seeking to modernize its store auditing processes through advanced image recognition and data analytics to improve shelf management and compliance.

• Develop an AI-powered application capable of capturing store shelf images and automatically generating comprehensive SKU and pricing lists.
• Enhance system performance to handle peak traffic loads with improved stability and reliability.
• Implement advanced data processing capabilities for real-time analytics on shelf share, compliance, and inventory levels.
• Ensure seamless migration and integration with existing cloud infrastructure with zero downtime.
• Reduce resource costs through optimized data model utilization and efficient processing workflows.
• Train and mentor internal teams to maintain and extend the system post-deployment.

• Image Capture Module: Enables store staff to quickly photograph shelves using mobile cameras or fixed devices.
• Image Recognition Engine: Utilizes advanced AI to identify SKUs, prices, and shelf share from captured images in real-time.
• Data Analytics Dashboard: Offers in-depth insights into shelf share, product placement, and pricing accuracy.
• Performance Optimization: Implements data model enhancements to improve query efficiency and reduce latency.
• High Availability Architecture: Designed for stability under heavy traffic, with auto-scaling and load balancing.
• Cloud Migration Tooling: Custom services to facilitate data and application migration across cloud platforms with minimal disruption.
• User Management & Training Modules: Supports team onboarding, role management, and ongoing training to ensure long-term system sustainability.

• Existing store management systems for SKU and inventory data synchronization
• Cloud storage solutions for scalable file management
• Security protocols for data integrity and privacy during migration
• Real-time messaging systems for handling load spikes

• System should support high traffic loads with auto-scaling capabilities during peak hours
• Target response time for image recognition and analytics generation under 5 seconds
• Zero-downtime migration between cloud providers
• Secure data handling compliant with industry standards
• Cost-effective resource utilization during both normal and peak operation

The implementation aims to significantly improve operational efficiency by automating shelf data collection and analysis, reducing manual effort, and minimizing errors. Expected outcomes include faster audit cycles, real-time insights into shelf share and compliance, a reduction in resource costs through optimized data processing, and enhanced stability during high-traffic periods, ultimately leading to better inventory management and increased sales performance.