What it is
I build retrieval-augmented generation (RAG) systems and AI chatbots grounded in your real data — documents, knowledge bases, product catalogs, internal wikis, support histories. The whole pipeline: data ingestion, chunking strategy, embedding model selection, vector store setup, retrieval logic, and the LLM response layer on top. Built to handle the messy, real-world questions your actual users ask — not just the clean queries from your demo script.
Who it's for
Businesses with information locked in documents or internal systems that users or employees need fast access to. Support teams fielding the same questions from the same knowledge base every week. SaaS products that want to add AI-powered Q&A over their own content or user data.
What you get
- Full RAG pipeline from ingestion to response
- Chunking and embedding strategy matched to your specific data type and query patterns
- Hybrid search (semantic + keyword) for better retrieval accuracy across different question types
- Eval setup so you can measure whether retrieval is actually working before you ship
- Typical result: chatbot that handles 70–80% of common queries accurately, with traceable source citations
Common questions
Because the chunking and retrieval strategy is almost always an afterthought. Most demos chunk naively, embed everything the same way, and retrieve purely on cosine similarity. Real RAG requires thinking about how your data is structured, what your users’ queries actually look like, and what “correct retrieval” means for your specific use case.