RAG Development
Answers grounded in your own data, with citations — and an accuracy number you can hold us to.
Most RAG systems fail at retrieval, not generation. Chunking splits a table in half, embeddings miss the one exact term that mattered, and the model confidently answers from the wrong document. The model is rarely the problem.
We treat retrieval as the engineering problem it is: structure-aware document processing, hybrid keyword and semantic search, reranking, and an evaluation set that scores answer quality against known-good responses before anything reaches users.
What's included
- Document ingestion pipeline with structure-aware chunking
- Hybrid semantic and keyword retrieval with reranking
- Citations linking every answer to its source
- Evaluation set scoring retrieval and answer accuracy
- Incremental re-indexing as source documents change
- Access controls so retrieval respects user permissions
Core capabilities
How we work
- 1
Corpus audit
Review the real documents — formats, structure, update frequency and access rules — before choosing an approach.
- 2
Retrieval design
Chunking, embedding and hybrid search tuned against your content rather than a generic default.
- 3
Evaluate
Score retrieval and answers against a known-good set so quality is a measurement, not an opinion.
- 4
Ship & monitor
Deploy with citation display, feedback capture and re-indexing built in.
Frequently asked questions
RAG or fine-tuning — which do I need?+
RAG in almost every business case. It keeps answers current as documents change, cites sources, and respects per-user permissions. Fine-tuning changes tone and format, not knowledge, and it goes stale the moment your content does. Many production systems use RAG for knowledge and light fine-tuning for style.
How accurate can a RAG system be?+
It depends on the corpus, which is why we build an evaluation set first and report a real number. Well-tuned retrieval over clean documents typically reaches the high eighties to mid nineties on answer accuracy. We would rather show you the measurement than promise a figure.
Can it respect who is allowed to see what?+
Yes. Permissions are enforced at retrieval time, so a user's query can only ever match documents they are entitled to read. Filtering after generation is not safe and we do not do it.
What does RAG cost to run?+
Ongoing cost is driven by query volume and context size, not document count. We size this during scoping and build in caching and context limits so spend stays predictable.
Tell us what you're building
We'll assess the idea, the technical requirements and the fastest realistic path to production — and tell you honestly if we're not the right fit.