RAG & Knowledge Systems
A retrieval system is only as good as what it retrieves. We build the full pipeline: ingestion, structure-aware chunking, hybrid search, reranking and context assembly — then measure it against a golden dataset so quality is a number, not an impression.
What this includes
Structure-aware ingestion
PDFs, Markdown, wikis, tickets and spreadsheets parsed with their hierarchy intact so chunks keep the context they sat under.
Hybrid retrieval
Vector similarity fused with full-text search via reciprocal rank fusion — semantic recall plus exact-term precision.
Reranking
A second-stage relevance pass over the candidate pool, cutting irrelevant context before it reaches the model.
Query rewriting
Follow-up questions resolved into standalone queries so conversational context does not break retrieval.
Citation tracking
Every claim traceable to the passage that produced it, surfaced in the UI.
Continuous evaluation
Retrieval precision, answer faithfulness and refusal accuracy tracked per release.
What you end up with
- Retrieval quality measured, not assumed
- Sub-second search over millions of passages
- Re-indexing without downtime
- Clear separation of knowledge from model
Tools we reach for
Frequently asked
How much content do we need?
Useful results start at a few dozen pages. The bigger lever is quality and structure, not raw volume.
How often can the index update?
From nightly batches to near-real-time on document change. We size this to your content churn.
What about documents that contradict each other?
We surface conflicts rather than silently picking one, and support recency and authority weighting so newer or canonical sources win.
Other work in this practice
Let's talk about what you're building
Tell us the problem. We'll tell you honestly whether AI is the right tool, and what it would take.