AI Solutions
Knowledge Retrieval (RAG)
Answering questions from your own documents and data, with every answer traceable back to a real source — not a plausible-sounding guess.
RAG · Vector Search · Grounded Q&A
What it is
Retrieval-augmented generation grounds a model's answers in your actual content — internal docs, policies, product manuals, past cases — instead of relying on what it happened to learn during training. The point isn't a chatbot; it's a Q&A system whose answers you can actually check.
How it works
- 01Index your content
Your documents get chunked and embedded into a vector store — the retrieval layer that finds the actually relevant passage instead of a keyword match.
- 02Ground every answer in a source
The model answers from retrieved passages and cites them, so a wrong-sounding answer can be checked against the source it came from.
- 03Tune for your content's structure
Retrieval quality depends on how your content is actually organized — chunking and ranking are tuned against your real documents, not a generic default.
- 04Keep the index current
A defined process for re-indexing as source documents change, so answers don't quietly drift out of date.
Benefits
- Answers grounded in your actual documents, with sources attached
- Less time spent searching internal wikis and shared drives for the right doc
- A system whose answers can be checked, not just trusted
Frequently asked
How is this different from just using ChatGPT on our docs?
A general-purpose assistant doesn't have your content unless you paste it in, and gives no reliable way to verify what it says. This indexes your actual documents and cites the source behind every answer.
What if our documents are inconsistent or out of date?
That gets surfaced during the indexing pass, since retrieval quality depends on it — worth fixing before launch either way, and part of what the initial review catches.
Also under AI Solutions
Not sure this is the right fit yet?
A scope call is a lower-commitment way to find out before anything gets built.