RAG & Knowledge Systems
When the answer to most of your team's questions lives in a Notion workspace, a folder of PDFs, or a Confluence wiki, a properly built RAG system pays for itself within weeks. We build the ingestion pipeline (chunking, embedding, deduplication), the vector store (pgvector or Pinecone), the retrieval layer (hybrid sparse plus dense), and the chat or search UI. Citations are non-negotiable — the LLM only answers from sources it can point you at.
What we offer
Capabilities
Tech stack
What we reach for
Our process
How we deliver
Map the corpus
What docs, what update cadence, what ACLs, and what the queries actually look like.
Ingest & embed
Pipeline that chunks, embeds, and refreshes — handles updates and deletes.
Retrieval & answer
Sparse plus dense hybrid, reranker, prompt that always cites.
Eval & ship
Golden-set Q&A library, eval gates, citation accuracy targets before launch.
Selected work
Related case studies
Talk to us
Interested in this service?
Tell us what you're building. We'll let you know whether it's a fit, and where it isn't.