Retail IQ AI Management System

This paper presented the complete design, implementation, and empirical evaluation of the RetailIQ AI Management System—a full-stack intelligent retail management platform combining a Java Spring Boot backend, HTML/CSS/JavaScript frontend, MySQL relational database, AI-powered item-based collaborative filtering recommendations, and a real-time inventory intelligence layer within a unified three-tier web application. Comprehensive functional testing across ten system modules confirmed 100% pass rates, with all API response times below 300 ms and AI recommendation accuracy reaching 94.0%. Performance benchmarking under 50-concurrent-user load validated the system’s suitability for small and medium retail deployment environments. The comparative analysis confirmed that RetailIQ uniquely combines all eight evaluated retail intelligence dimensions at a fraction of the cost of generic e-commerce platforms. Future enhancements planned for subsequent development iterations include: (i) integration of UPI, credit card, and net-banking payment gateways through Razorpay or Stripe APIs for end-to-end digital transaction support; (ii) real-time order tracking with GPS-enabled delivery status updates; (iii) Android and iOS mobile application development using React Native for improved customer accessibility; (iv) deep learning-based demand forecasting using LSTM time-series models trained on historical sales data; (v) customer review and star-rating system with sentiment analysis for product quality monitoring; and (vi) cloud-native containerised deployment on AWS or Azure Kubernetes Service for horizontal scalability beyond the current single-server architecture.

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