AI-Powered Search & Recommendation Engine

I will build a semantic search and recommendation system for your application — covering embedding generation for your content catalogue, vector database setup, semantic similarity search replacing or augmenting your existing keyword search, and a recommendation API surfacing related content or products based on embedding similarity. Traditional keyword search fails when users describe what they want differently from how your content is labelled; semantic search using vector embeddings finds conceptually related results even when the exact words don't match, dramatically improving search relevance and enabling "more like this" recommendation functionality that keyword matching cannot provide.

The build covers content embedding generation (OpenAI Embeddings or an open-source alternative), vector database setup (Pinecone, Weaviate, or pgvector), a semantic search API with relevance ranking, a recommendation endpoint returning similar items based on embedding distance, and integration with your existing application search or product pages.

Designed for e-commerce, content platforms, and any application with a searchable catalogue who want search and recommendations that understand meaning rather than just matching keywords.