RAG knowledge assistants
Your documents, finally answerable
Companies accumulate knowledge in places nobody can search: policy PDFs, service manuals, price lists, past quotations, WhatsApp groups. A retrieval-augmented assistant makes that material answerable in plain language — for staff who need the answer now, or for customers asking the same twenty questions. The retrieval part is what matters: the assistant quotes your material rather than improvising.
Built on the same foundations as our live assistant work — conversational answering with handoff, and AI that reads business data into an actionable answer.
The engagement
What we deliver
Knowledge ingestion
Your documents, manuals and records prepared and indexed for retrieval.
Natural-language answers
Questions answered in plain language, in the language asked.
Sources shown
Every answer points at the document it came from, so it can be verified.
Access control
Staff see what staff should see; customers see only what is public.
Keeping it current
A process for updating the index as your documents change.
How it runs
From first conversation to live agent
- 01
Find the job
We look for one repetitive, text-heavy job with a clear right answer. Not a strategy deck — a job.
- 02
Design the agent
What it decides, what data it can read, which tools it can call, where it must stop and ask a person.
- 03
Build and connect
The agent, grounded in your own data, wired into the systems it needs — CRM, WhatsApp, sheets, accounting.
- 04
Deploy with a human in the loop
It goes live handling a slice of the work, with a person reviewing what it does before the loop widens.
- 05
Operate and improve
We watch traces of real behaviour and tighten it. An agent is a system you run, not a project you finish.
FAQ
Common questions
What is RAG, in one sentence?
Retrieval-augmented generation: before answering, the system retrieves the relevant passages from your own documents and answers from those — which is why it can cite a source instead of guessing.
Our documents are messy and out of date. Is that a problem?
It is the main problem, and we will say so honestly. An assistant over contradictory documents produces confident contradictions. Part of the work is deciding what is authoritative.
Can staff and customers use the same assistant?
Same foundation, different access and tone. Internal assistants can be franker; customer-facing ones need tighter constraints.
Also from our AI practice
Thinking about rag knowledge assistants?
Tell us the job. We'll tell you honestly whether an agent should do it, and what it would take to run it properly.