Generic chatbots hallucinate. A properly built Retrieval-Augmented Generation (RAG) system grounds AI responses in your actual data: documents, knowledge bases, databases, and internal tools.

I build custom RAG pipelines that let your team or customers ask questions and get accurate, sourced answers from your own content. No hallucinations. No generic responses. Just your data, made searchable and conversational.

Document Q&A systems — upload PDFs, docs, or knowledge bases and get accurate, cited answers

Internal knowledge assistants — AI that searches across your company's Notion, Confluence, Google Drive, or custom databases

Customer-facing AI support — chatbots grounded in your product docs, help center, and support history

Semantic search engines — replace keyword search with meaning-based retrieval across large content libraries

Hybrid RAG pipelines — combine vector search, keyword search, and structured database queries for maximum accuracy

Most RAG implementations fail because of bad chunking, wrong embedding models, or no evaluation pipeline. I handle the full stack:

Data ingestion — parse, clean, and chunk your documents with strategies optimized for your content type

Embedding & indexing — select the right embedding model and vector database for your scale and accuracy needs

Retrieval optimization — implement hybrid search, re-ranking, and metadata filtering to surface the right context

Generation layer — prompt engineering with citation, confidence scoring, and hallucination guardrails

Evaluation — automated testing against ground-truth Q&A pairs so you can measure accuracy over time

OpenAI Embeddings, Cohere, Pinecone, Weaviate, Supabase pgvector, ChromaDB, LangChain, LlamaIndex, Next.js, Node.js, Python.

Companies sitting on valuable content that's hard to search or access

SaaS products that want to add AI-powered search or Q&A features

Teams drowning in documentation who need instant, accurate answers