RAG systems grounded in your permissioned business knowledge
We build retrieval-augmented generation systems that find relevant, current sources and make evidence visible to users instead of asking a model to guess.
What this means in practice
RAG retrieves selected source material at query time and supplies it to a language model. A production implementation needs document lifecycle controls, access filtering, retrieval evaluation, citations and a clear response when the available evidence is insufficient.
Problems this work is designed to solve
- Important knowledge scattered across documents and tools
- Generic assistants answering without company context
- Outdated or unauthorised sources entering answers
- No way to measure retrieval quality or inspect evidence
Typical delivery scope
Knowledge ingestion pipeline
Parse, clean, version and index approved sources with ownership and update rules.
Permission-aware retrieval
Filter results using user identity and source permissions before content reaches the model.
Grounded answer experience
Show useful citations, communicate uncertainty and route unsupported questions appropriately.
Retrieval evaluation
Measure whether the right passages are found and whether answers are supported by those passages.
How a project moves from idea to production
Discover
Document the current workflow, baseline cost and failure modes, then agree the outcome and guardrails.
Prove
Build a narrow working slice with real data and evaluate quality, latency, cost and operational fit.
Deploy and improve
Integrate the system, add monitoring and human controls, then improve it against measured production behaviour.
Questions teams ask before starting
RAG or fine-tuning: which do we need?
Use RAG when answers depend on current or private knowledge that must be retrieved and cited. Fine-tuning is more suitable for changing model behaviour or output patterns. Some systems use both, but neither should be selected without a task-level evaluation.
Can RAG respect document permissions?
Yes, when identity and source permissions are applied during retrieval. Post-filtering after an unauthorised passage has reached the model is not a sufficient security boundary.
How do you know whether retrieval is working?
Create representative queries with expected sources, then measure retrieval relevance and answer groundedness. Production feedback and unanswered-query analysis extend that test set over time.