RAG & Knowledge Systems

RAG lets a model answer from your content rather than its training data. It is the most reliable way to make AI useful over proprietary knowledge — and the quality of a RAG system is determined almost entirely by retrieval, not by the model.

Retrieval quality is the whole game

If the right passage is not retrieved, no model can produce a correct answer. Most disappointing RAG systems are retrieval failures misdiagnosed as model failures. We measure retrieval separately from generation so problems are attributed correctly.

Chunking and embedding decisions matter

How documents are split materially affects results. Chunks that are too small lose context; too large and they dilute relevance. We tune chunking, overlap, and embedding choice against your actual content and query patterns rather than applying a default.

Citations make answers checkable

Every answer links to the source passages it drew on. This lets users verify claims, gives your team a way to diagnose wrong answers, and substantially increases trust in the system.

Keeping the index current

Knowledge changes. We build ingestion pipelines that keep the index synchronised with source systems, because a confidently delivered outdated answer is worse than no answer.