Copilots & Knowledge Assistants
Answers from your own data, for your team and your customers.
We build retrieval-augmented (RAG) copilots grounded in your documents, policies and systems: internal assistants that stop staff pinging experts for the same answers, and customer-facing assistants that deflect repetitive tickets, always with citations, so you can trust the output.
- Self-serve answers from your own knowledge, with sources
- Faster support with far fewer repetitive tickets
- Experts freed from answering the same questions
- →RAG copilot grounded in your data, with citations
- →Secure, private deployment on your stack
- →An evaluation set to measure and trust answer quality
- →Integration into Slack / Teams / your site + training
Ground
Connect and index your documents, policies and systems.
Evaluate
Test answers against real questions with known-good responses.
Deploy
Ship into the tools your people and customers already use.
The hidden cost of answers living in people's heads
In most organisations, the real knowledge base isn't the wiki, it's a handful of experienced people who get interrupted all day with the same questions. How do we handle this refund case? What's the policy on that? Where's the current version of this document? Every interruption costs the asker time waiting and the expert their focus, and when that person is on holiday or leaves, the answers walk out with them. It's a tax you pay in minutes that never shows up on a budget line.
The instinct is to write more documentation, but documentation only helps if people can find the right passage at the moment they need it, and they usually can't. The information exists; it's just scattered across a wiki, a shared drive, a policy PDF, a ticketing system, and three chat channels. A copilot's job is to make that existing knowledge answerable in plain language, so the answer comes to the person instead of the person hunting for it.
How a grounded RAG copilot actually works
We build retrieval-augmented (RAG) assistants, which is the difference between a chatbot that guesses and one you can trust. Rather than relying on whatever the model absorbed in training, the copilot retrieves the relevant passages from your own documents, policies, and systems at question time, and answers from those, with citations pointing back to the source. If it can't find grounding, it says so instead of inventing an answer. That's what makes the output safe to act on.
The build has three stages. First we ground it: connect and index your knowledge, whether that's Confluence, SharePoint, a document store, a database, or a support history, and handle permissions so people only see what they're allowed to. Then we evaluate: we assemble a set of real questions with known-good answers and measure the copilot against them, so answer quality is something we can prove and tune rather than hope for. Finally we deploy into the tools people already live in, Slack, Teams, your website, or an internal portal, so using it takes no change of habit.
What you get, and why the citations matter
Two shapes of copilot cover most of the value. Internal assistants stop staff pinging experts for the same answers and let new joiners get productive without shoulder-tapping, and customer-facing assistants deflect the repetitive tickets that clog support so your team spends its time on the cases that genuinely need a human. Both stand on the same grounded, cited foundation.
Citations are not a nice-to-have; they're the whole trust mechanism. When every answer links to the passage it came from, a user can verify it in a click, an auditor can trace it, and you can tell instantly when the underlying document is out of date. You own the deployment: it runs privately on your stack, indexes your data without shipping it off to train someone else's model, and comes with the evaluation set and documentation so your team can extend it. If your knowledge is too thin or contradictory to ground good answers, we'll tell you what to fix first rather than paper over it.
Who it's for, and when it isn't the answer
Copilots are for support, knowledge, and IT teams buried under repetitive questions, and for any organisation where valuable answers are locked in a few people's heads or scattered across systems nobody wants to search. If you can picture the same question being asked twenty times a week and a correct answer already existing somewhere in your files, that's the sweet spot.
It's not the right tool when the questions need real-time judgement, negotiation, or empathy that shouldn't be delegated, or when the underlying knowledge simply doesn't exist yet, a copilot retrieves answers, it doesn't invent policy you never wrote. It's also a poor fit if your source material is so inconsistent that even a human expert couldn't give a reliable answer; there, the honest first step is cleaning up the knowledge, and we'll say so before building anything.
Questions we hear before the first call
How is this different from just using ChatGPT?+
A general chatbot answers from what it learned in training and will confidently guess when it doesn't know. A RAG copilot answers only from your own documents and systems, retrieved at question time, with citations you can check. It knows your policies, your products, and your data, and it tells you when it doesn't have a grounded answer.
Is our data safe? Does it train someone else's model?+
No. The copilot runs privately on your stack and retrieves your data to answer questions; it isn't used to train a public model. We also honour your existing permissions, so people only get answers from documents they're already allowed to see.
What if it gives a wrong answer?+
Every answer carries citations, so a user can verify it in a click and a wrong or outdated source is easy to spot. The evaluation set we build measures accuracy against known-good answers so we can tune it, and the copilot is built to say 'I don't have a grounded answer' rather than invent one.
How much of our knowledge needs to be documented first?+
It works with what you have across wikis, drives, PDFs, and ticket histories; it doesn't need a perfect knowledge base. But it can only retrieve answers that exist somewhere. If a topic is genuinely undocumented or contradictory, we'll flag it so you can fix the source rather than expect the copilot to invent it.
Find out where AI actually pays off for you
Tell us a bit about your business. We'll come back within one business day with a short, honest read on the highest-ROI AI opportunities we see, and whether it's even worth starting yet.