RAG systems built for production accuracy.
A RAG system is only useful if its answers are traceable. We build systems that retrieve from your real data, cite sources, and stay accurate as your content changes.
Cited answers Evaluated for accuracy Maintainable over time
What a production-quality RAG system needs
A RAG demo is easy: vector DB, embeddings, LLM, done. A production RAG system is hard: you need chunking strategy that fits your content, retrieval that returns the right context (not just the most similar), citation tracking, evaluation against a test set, and a retraining loop for when content changes.
We build RAG systems the second way. Every deployment includes an eval harness, so you can quantify accuracy rather than hoping.
How we build RAG
Content audit
Structure of source content (markdown, PDFs, HTML, database rows) — chunking strategy per type.
Retrieval design
Embedding model choice, vector DB, hybrid search if needed, re-ranking.
Generation layer
LLM with strict grounding instructions. Citation tracking from day one.
Evaluation
Held-out question/answer set, accuracy scored, regression-tested after every change.
Good fit for
Good fit
- Internal knowledge bases (50+ pages of docs)
- Product catalogs with complex specs
- Customer-facing chatbots needing domain grounding
Not a good fit
- Creative generation (RAG is for grounded retrieval)
- Super small corpora where long context is enough
Questions we hear all the time
Which vector DB do you use?
Pinecone, Weaviate, Qdrant or pgvector on Postgres. We pick based on your infra preferences and scale.
How do you measure accuracy?
We build a 50–200 question evaluation set with known correct answers, run the system against it, and score. Any change requires re-running the eval.
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Let's see if we're the right fit.
30-minute call. We'll tell you honestly whether we can help — or if someone else is a better fit.
Or email us at contact@unlockmanagement.com