I will build an ai powered rag system with semantic search

Want your app to understand and retrieve information intelligently instead of relying on basic keyword search? I build Retrieval-Augmented Generation (RAG) systems that combine GPT models with vector search the same approach I used to build an AI-powered skill-matching platform, where user profiles and project requirements are semantically matched using embeddings and cosine similarity.

What I bring:

• RAG pipeline design document/data ingestion, chunking, embedding generation, and retrieval
• Vector database integration (PGVector) for fast, accurate semantic search
• GPT-powered extraction and understanding turning unstructured text into structured, usable output (I've shipped this for real-world document processing)
• REST APIs so your RAG system plugs cleanly into your existing app
• Clear reasoning behind model/embedding choices so the system stays cost-efficient and accurate, not just "wired together"

Whether you need a document Q&A system, a recommendation engine, or semantic search over your own data, I'll scope the pipeline clearly before starting.

Message me with your use case and I'll confirm the best approach.