International Journal of Science, Engineering and TechnologyAuthors: Abhishek Kumar, Astha, Kriti Kumari, Shagun Pundir, Miss Jaishree Goyal
Abstract: The rapid growth of online retail has pushed the demand for smarter, more adaptive, and user-focused shopping systems to new heights. Most platforms available today still depend on outdated keyword matching and rigid, rule-based filtering, which often fall short when a shopper phrases their intent in natural language or when their needs evolve mid- session. This paper describes the design and practical implementation of a full-stack e-commerce platform that weaves semantic search, secure payment handling, and intelligent order management into one cohesive system. At its heart, the platform uses the Gemini API for transformer-based language understanding, allowing it to interpret what a user actually means—not just what they typed. Stop-word removal and dynamic query construction further sharpen retrieval accuracy. The backend runs on Node.js and Express, with PostgreSQL providing relational data integrity and transactional reliability. User authentication is handled through JWT-based tokens, payments flow through Stripe, and product images are stored via Cloudinary. Evaluation results show measurable gains in search precision and user engagement over traditional approaches, with computational overhead remaining well within acceptable bounds. Taken together, the findings confirm that embedding semantic intelligence into an e-commerce stack meaningfully improves product discoverability, operational efficiency, and the overall shopping experience.
