Blog
How AI works in practice: language models, retrieval, agents, evaluation and the safeguards that make them trustworthy.
Four practical ways schools and colleges can use AI to take routine work off staff, and the safeguards that keep people in charge and children's data protected.
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Invoices, forms and records are often still re-keyed by hand. How document AI turns them into structured data, and how to make the results accurate enough to trust.
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What AI agents are, how they differ from chatbots and rule-based automation, which tasks suit them today, and the guardrails they need before they act for you.
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Better prompts, retrieval, fine-tuning or tools? A practical guide to working out what your AI feature is missing, and choosing the simplest change that fixes it.
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A demo shows an AI feature on a good day. Here is a practical way to test it on real cases, agree what good looks like, and keep checking once it is live.
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AI features run on your data. How to classify it, trace where it goes, choose where the model runs and put simple controls in place before launch, so you keep personal and business data under your control.
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Retrieval-augmented generation lets an AI assistant answer from your organisation's own documents and show its sources. How it works, what decides its quality, where it fails and when it fits.
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What a large language model actually does, why its answers can be fluent and still wrong, and how to judge where it fits in your organisation's work.
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Most AI value in operations comes from a few well-chosen, well-measured automations. A four-step method to find the first one and prove it works, with a worked example.
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