RAG Systems
AI That Cites Its Sources
Retrieval Augmented Generation. AI that searches your own documentation, knowledge base or database and answers from what it found — with the source alongside, so anyone can check it.
The Problem
A Generic AI Doesn’t Know Your Documents
Ask an ordinary model about your own policies and it will answer confidently and be wrong. It has never seen them.
It Makes Things Up
A model with no access to your documents still answers. Fluently, plausibly, and sometimes completely wrongly, which is worse than saying nothing.
No Way To Check The Answer
Without a source you cannot tell a right answer from a confident one. For anything contractual or regulated that makes it unusable.
The Knowledge Is Scattered
Policies in one place, contracts in another, procedures on somebody's drive. Nobody can search all of it at once, so people ask a colleague instead.
It Goes Out Of Date
A model trained once is frozen. Your version 3 policy supersedes version 2 and the answer has to change the day it does.
What We Build
Answers From Your Own Documents, With The Source Attached
It searches your documentation, knowledge base or database first, then answers from what it found — and shows you where it came from.
Searches Your Own Material
Your documentation, knowledge base or database. The answer comes from what you have written, not from what a model absorbed off the internet.
Every Answer Cited
The document and the passage it came from, next to the answer. Anybody can click through and check it, which is what makes it usable for real work.
Says When It Does Not Know
If nothing in your material answers the question it says so, rather than filling the gap. That is the whole point of retrieval.
Updates When You Do
Replace a policy and the answers change the same day. There is no retraining and no waiting for a new model.
Respects Who Can See What
Someone only gets answers from material they are allowed to read. Permissions are applied at retrieval, not bolted on afterwards.
Handles What You Actually Have
PDFs, Word documents, spreadsheets, a database, a wiki. We index what exists rather than asking you to reorganise it first.
Our Process
How We Build A RAG System
- 01
Discovery
We find out what questions people actually ask and which material answers them. That decides what gets indexed and what does not.
- 02
Data Preparation
We clean, structure and prepare your documents for retrieval — the step that decides whether the answers are any good.
- 03
Build & Cite
We build the retrieval and the answering, and wire the citation so every reply carries its source.
- 04
Launch & Tune
It goes live, you see the questions and the sources it used, and we tune the retrieval on the ones it got thin.
"indexed": 142,
"cites_source": true,
"says_when_unknown": true,
"permissions": "inherited",
"reindex": "on change"
}
Our Work
Recent Responsive Projects
Common Questions
Common Questions About RAG Systems
Let's Build Something
That Actually Works.
Book a free consultation. We will review your current setup, identify quick wins, and give you a clear proposal with fixed pricing. No obligation.