I build end-to-end Large Language Model (LLM) solutions with Retrieval-Augmented Generation (RAG) — connecting your private data to AI models for accurate, grounded, hallucination-resistant responses.
What you get:
• A fully working RAG pipeline: document ingestion, chunking, embedding, vector storage, retrieval, LLM generation
• Integration with your data sources — PDFs, Word docs, wikis, databases, Notion, Confluence, Google Drive
• API endpoints ready to plug into your app, chatbot or internal tool
• Evaluation and testing suite to measure answer quality
• Deployment docs and architecture diagram
Typical use cases:
• "Chat with your docs" — internal knowledge base Q&A
• Legal/compliance document search and summarization
• Product documentation assistant for support teams
• Research and analysis tools over large document collections
• Domain-specific AI assistants (medical, financial, technical)
Tech stack: OpenAI API/Chat GPT, Claude, Gemini, LangChain, LlamaIndex, Pinecone, ChromaDB, Weaviate, FAISS, Python, FastAPI, PostgreSQL + pgvector.
I've built RAG systems that process thousands of documents with sub-second retrieval. I focus on production-grade quality — proper chunking strategies, metadata filtering, re-ranking, and evaluation metrics — not just a quick demo.
Deliverables are clean, documented, tested Python code you can maintain and extend.