Blog

Most organisations are treating “AI governance” like it’s a document-writing exercise. Write a policy. Form a committee. Add a disclaimer to the chatbot. Maybe run a training session. Job done. Except it isn’t. If you’re deploying AI in regulated or operational environments, the uncomfortable reality is this: compliance is going to be audited like finance . Not “do you have a policy?” but “show me the evidence.” And in practice, that evidence lives (or dies) inside your knowl

index is not “just an AI layer on top of your docs” If you’ve taken some time to look at what we do and thought, “Hang on… can’t we just add a copilot / RAG / search upgrade and call it done?”, you’re forgiven, because we've heard this a few times now - hence the blog post!

Here’s the reality in 2025/2026: there isn’t one universal “where does AI gets its facts from?” because most frontier labs don’t fully disclose their training mixes anymore. But we can  anchor this in what’s publicly documented and what regulators/researchers keep pointing at. You know what people get wrong about AI? They think it “looks things up” like a clever librarian. Most of the time it doesn’t. Most of the time it’s answering from whatever got baked into it during trai

You ask an AI a question (ChatGPT or other - they're all as guilty as one and other) and it replies instantly with something that sounds like it came from a well read expert who also happens to be polite, structured, and suspiciously confident. It's fluent. It's tidy. It even gives you bullet points. And then you discover one awkward detail. It is wrong.