AI-Powered Product Search Engine
Built an intelligent search system that understands product queries semantically. Improved accuracy in product discovery for both staff and customers — even with phrased or misspelled inputs.
PROJECT CASE
6/16/20241 min read
AI-Powered Product Search Engine with RAG and Vector Embeddings
One of our standout projects involved building a smart product search engine for a company managing a large, complex product database. The existing search tools were failing — too dependent on exact keywords, and unable to handle misspellings, abbreviations, or loosely phrased queries.
We developed a solution using a Retrieval-Augmented Generation (RAG) approach combined with vector embeddings and a multi-agent AI pipeline.
Here’s how it worked:
Vector Embedding: Every product in the database was embedded into a vector space using a powerful language model. This allowed the system to understand semantic similarity rather than relying on string matches.
RAG (Retrieval-Augmented Generation): Instead of only returning fixed results, we used RAG to pull the most relevant product matches and let AI generate context-aware responses based on that data.
Multi-Agent Pipeline: We deployed multiple AI agents with different responsibilities — one specialized in ranking relevance, another in matching based on features, and a third for context interpretation.
The system provided:
Highly relevant top matches, even for vague or misspelled queries
Similar product suggestions with match scores for transparency
Dynamic responses grounded in the company’s real database, not general internet knowledge
This not only improved search accuracy but also gave both employees and customers confidence in the results. The solution became a powerful internal tool that scaled with the company’s growing data.