Retrieval-augmented generation, built to be right in production.
Most RAG demos work on the three questions someone rehearsed and fall apart on the fourth. Mine are built to hold up on the questions you didn't rehearse — hybrid retrieval that actually finds the right passage, reranking that puts it first, and grounded, source-cited answers on your own data, without that data leaking anywhere it shouldn't.
Most "RAG" I get called in to fix is a vector database bolted to a chatbot, and it fails in the same predictable way: the right chunk exists in the index, but it never makes it into the prompt. That's not a model problem. That's a retrieval problem, and no amount of prompt tuning fixes it.
I build the retrieval layer first, because it's the part that decides whether the rest of the system is even worth running. Hybrid search catches what pure embeddings miss — exact terms, IDs, part numbers, names. Reranking puts the actually-relevant passage at the top instead of the third-closest cosine match. And every answer ships with the citation that produced it, so you can check the model's work instead of trusting it.
What a production RAG system actually needs
Three things separate a system your team relies on from a demo that impresses once and gets quietly abandoned.
- Hybrid retrieval — keyword and vector search combined, tuned to your content, so exact terms and semantic meaning both surface the right passage.
- Reranking — a second pass that reorders candidates by actual relevance to the query, not just embedding distance, before anything reaches the model.
- Grounding and citations — answers constrained to what the retrieved sources actually say, with a citation on every claim, so wrong answers are visible instead of confidently invisible.
Your data, without the leaks
The other half of the job is the boundary around the data. Chunking that respects document structure, access controls enforced at retrieval time — not just at the UI — and no client's documents ever surfacing in another client's answers. I design the index and the permission model together, not as an afterthought once the demo works.
A RAG system that can't cite its source is a guessing machine with better vocabulary. I build ones that show their work on every answer.
The four layers of a production RAG system.
Twelve weeks, docs to grounded answers.
Find the question worth answering
We map the corpus and the real questions your team asks it, and agree on the KPI — answer accuracy, time-to-answer, deflected tickets — before a line of code is written.
Design retrieval, reranking, and grounding
Chunking strategy, hybrid index, reranker, and citation format are designed together against your real documents — fixed scope and price, no surprises.
Ship into production, not a slide deck
We build in sprints with weekly demos on your real corpus, deploy into your environment, and harden retrieval until it holds up under real questions.
Prove it against the KPI
We run evals against a held-out test set, track the system against the number we set, and keep tuning retrieval until the value crosses the cost.
Let's build retrieval that's actually right.
If you have a corpus your team keeps asking questions of, that's where we start. I'll tell you what accurate retrieval is worth before we build it.
