Smarter Product Search with AI

The Challenge

A client managing a specialized product catalog needed a better way to connect users with the right items. Their existing keyword-based search struggled with vague or complex queries ("lightweight modular knee joint," "compatible sleeve for model X"), leading to repetitive manual filtering and incomplete results.

The Solution

Meritocra designed and implemented a Retrieval-Augmented Generation (RAG) pipeline that connects product data from APIs, documents, and descriptions into a unified semantic index. Uses OpenAI or AWS Titan models to create product embeddings, stores them in an OpenSearch vector index for similarity-based retrieval, and integrates with a LangGraph-powered backend that interprets user intent and refines search results conversationally.

The Results

• Noticeably improved search relevance for natural language queries
• Reduced manual filtering and faster browsing for end users
• A flexible foundation for future applications — including knowledge retrieval, internal support bots, and domain-specific Q&A
• Built with modularity in mind, enabling reuse across retail, healthcare, and industrial contexts