RAG Consulting
RAG consulting: retrieval-augmented generation grounded in your documents, with vector databases, citations, and access controls, so AI answers from your data.
What this service covers
- RAG consulting
- retrieval augmented generation
- RAG implementation
- vector database consulting
- enterprise knowledge management AI
- LLM grounding
- enterprise RAG
Frequently asked questions
What is retrieval-augmented generation (RAG)?
RAG connects a language model to your own documents and data so it answers from your sources instead of its training data. The model retrieves the relevant material first, then answers with a citation. It is how you get an AI assistant that knows your policies, contracts, and product details rather than guessing at them.
Do we need a vector database?
Usually yes. A vector database stores your documents as embeddings so the model can retrieve the passages closest in meaning to a question, not just keyword matches. We select and stand up the right one for your scale and latency needs, and design the chunking and embedding strategy that decides retrieval quality.
How is this different from just using ChatGPT?
A general chatbot answers from what it was trained on, which is not your business. RAG grounds the model in your own documents, returns a citation, and respects who is allowed to see what. The difference is a system you can trust with an internal or customer-facing question, instead of one that sounds confident and is sometimes wrong.
How do you stop the model from leaking documents to the wrong people?
We enforce access controls on the retrieval layer, not just the interface. The model can only retrieve and cite documents a given user is permitted to see, mirrored from your existing permissions. Anything sensitive stays behind the same wall it already sits behind.
Do you handle document processing and messy PDFs?
Yes. Intelligent document processing is part of the work. We turn PDFs, scans, contracts, and wiki pages into clean, structured, retrievable content, because retrieval quality is only as good as what you feed it. Skipping this step is why most internal AI search tools disappoint.
How long until a RAG system is in production?
A scoped RAG capability on a defined document set typically ships in 6 to 10 weeks, including retrieval design, access controls, and evaluation. Broader rollouts across many sources take longer, but we sequence the work so you get a governed, usable result on the first document set early.
Request the 30-day Analytics Truth Audit to scope this engagement for your environment.