RAG & Knowledge
Ask your own documents a question in plain language and get an answer with the source attached.
Retrieval-augmented generation systems that let your team query internal documents, contracts, manuals, and records in plain language, with every answer citing where it came from.
The problem
The answer is in a PDF somewhere on a shared drive, and the person who knew which one has left. Search matches words, not meaning, so nobody finds it and the question gets asked again.
What you get
- Document ingestion from the formats you actually have, including scanned PDFs
- Every answer returns its source document and section, so it can be checked rather than trusted
- Semantic search, so a question finds the right passage even when the wording differs
- Access control, so people only retrieve from documents they are already allowed to read
- "I could not find that" as a real answer, instead of a plausible invention
- A re-indexing process, so the assistant does not slowly go stale as documents change
What it costs
Scoped and quoted per project after a discovery call. Scope varies too much here for a single published figure to be honest — tell us what you have and what you need, and we will put a number in writing.
Book a free consultationHow we work
- Step 1
Content and access audit
We map what documents exist, where they live, and who should be able to see what.
- Step 2
Ingest and index
We process the documents into a searchable index and check retrieval quality on real questions.
- Step 3
Build the assistant
We put a plain-language interface over it, with citations on every answer.
- Step 4
Handover and refresh
We document how new material gets indexed so it stays current after we leave.
RAG & Knowledge Assistants questions
What is RAG, in plain language?
Retrieval-augmented generation. Instead of relying on what a language model already knows, the system first searches your own documents for the relevant passage, then answers using that passage — and tells you which document it came from. It is the difference between an assistant that guesses and one that looks it up.
Can it read our scanned PDFs?
Usually yes, though scan quality decides how well. Text-based PDFs are straightforward. Scanned or photographed documents need character recognition first, and we will test that on a sample of your worst documents before quoting, so nobody is surprised later.
Can we control who sees which documents?
Yes, and it should be set up that way from the start. Retrieval respects the permissions you define, so someone asking a question only gets answers drawn from material they are already allowed to read.
What happens when our documents change?
The index needs refreshing, or the assistant slowly goes stale and starts citing superseded material. We set up and document that refresh process as part of the build, so it keeps working after we hand over.
Tell us what you need
A thirty-minute call, no charge, no obligation. We will tell you honestly whether we are the right fit.