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Gigaversity
Gigaversity.in • 2m
We built an e-commerce platform that worked well initially. But as the product catalog grew, users started facing issues—search results were slow and often not relevant. This led to frustration and a drop in engagement. To solve this, we upgraded the search experience using AI-powered semantic search with vector databases, making results faster and more accurate. The Problem We Faced - Search results were based on basic keyword matching, which often ignored user intent. - Users struggled to find what they sought, especially when using natural language queries. - Product data changes weren’t reflected in real-time, resulting in outdated listings in search results. - The system couldn’t scale well with growing inventory and user base. Our Solution To fix the outdated and irrelevant search experience, we built an AI-driven search system focused on speed and accuracy: - Semantic Search Integration - We used AI vector databases like Pinecone and Typesense to match search intent with product meaning. - Optimized Data Processing - Product descriptions were preprocessed with NLP and cached using Redis for faster results. - User Feedback Loop - Real-time user actions (clicks, views, carts) helped fine-tune relevance over time. - Real-Time Inventory Sync - Background workers ensured updated product data was instantly reflected in search results. Tools & Technologies Used Frontend: Next.js, Tailwind CSS Backend: Node.js, PostgreSQL AI & Search: Pinecone/Typesense (vector databases), NLP models Caching: Redis Async Processing: Background workers (Node worker threads / BullMQ)
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