We build production RAG systems that give your LLM access to your private docs, knowledge base, or product catalogue — with citations, access control, and accuracy you can measure. Not a weekend vector-DB demo — a pipeline your customers actually trust.
Every component — from chunking to citation UI — built, measured, and deployed as a system.
Recursive chunking with overlap, OpenAI or Cohere embedders, metadata extraction — tuned per corpus, not left on defaults.
pgvector if you already run Postgres, Pinecone or Qdrant for scale. Schema design, indexing strategy, and hybrid (BM25 + dense) retrieval.
Cohere Rerank or cross-encoder models that lift retrieval accuracy 10–20 points over cosine-only search. The step most teams skip.
Every answer links back to the source chunk with page/section references. Your users — and your compliance team — see where facts come from.
Org-level and user-level ACLs on the retrieval layer so each customer sees only their data. Critical for B2B SaaS RAG.
100-query scored eval set built in week one. Every chunking, embedding, or prompt change is measured against it before merge.
No discovery phase that never ends. Each step has a deliverable, a date, and a demo.
We profile your documents — format, length distribution, structure, metadata density — and pick the chunking + embedding strategy that fits.
Ingest → chunk → embed → store → retrieve → rerank → prompt → generate. Each hop instrumented, each step configurable.
Run the 100-query eval set. Iterate chunking, prompt, and reranker until accuracy clears the bar your team sets.
Production deploy with citation UI, hallucination detection, fallback paths, and observability (LangSmith or Langfuse).
Opinionated defaults — we swap components when your corpus or scale calls for it.
A demo retrieves the top few chunks by cosine similarity and pastes them into a prompt. A production system adds a reranker, hybrid retrieval combining dense and sparse search, chunking tuned to your document structure rather than a fixed token count, metadata filters so a user only ever retrieves what they are permitted to see, and citations that point at a specific passage rather than a whole document.
The reranker is the single highest-leverage component and the one most often skipped. Retrieving twenty candidates and reranking to five typically moves answer accuracy ten to twenty points versus taking the top five directly — because embedding similarity is a decent first filter and a poor final judge.
Almost always retrieval, not generation. If the right passage never reaches the prompt, no model can answer correctly. Diagnose by measuring retrieval recall separately from answer quality: take fifty real questions, check whether the correct source document appears in the retrieved set at all, and fix that number first. Teams that jump straight to prompt engineering are tuning the wrong stage.
The second cause is chunking that splits meaning. A table cut in half, a clause separated from its heading, or a procedure split mid-step retrieves as noise. Chunk along the document's own structure, keep an overlap, and carry the section heading into each chunk as context.
If you already run Postgres, use pgvector. One database to operate, transactional consistency between your documents and their embeddings, and it comfortably handles millions of vectors — which is more than most products ever reach. Reach for Pinecone or Qdrant when you genuinely need scale beyond that, or when you want managed operations more than you want one fewer system.
The database is rarely the thing that determines whether your RAG system works. Chunking strategy, retrieval quality and reranking decide that, and they are portable across every store.
Filter at retrieval time using metadata on each chunk, never by asking the model to withhold information it has already been shown. A model instructed to ignore context it can see is one prompt injection away from repeating it. Every chunk carries its access scope, and the retriever applies the user's permissions before anything reaches the prompt.
For regulated data, add a zero-retention agreement with your model provider, keep embeddings in-region, and log which chunks were retrieved for each answer so an auditor can reconstruct what the system saw.
A 30-minute call. We'll talk scope, timelines, and what a realistic first release looks like. NDA signed before we start.