5 articles
AI search visibility
How assistants choose what to cite, what llms.txt does and does not do, and where generative engine optimisation genuinely differs from the search work you already fund.
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Measuring AI visibility: the method, and our own baseline
Nine buying questions, 69 cited sources, 64 distinct domains. What the answer layer in this niche is made of, and what it says about a site three days old.
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Why ChatGPT recommends your competitor
How assistants decide which companies to name, why invisibility is usually a sourcing problem rather than a ranking one, and what you can influence.
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llms.txt and structured data: what actually matters
What llms.txt is, who reads it and who does not as of 2026, the exact format, what schema markup demonstrably does, and where to spend an hour if you only have one.
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Writing content that assistants actually cite
The page structures that survive being retrieved and extracted: answer-first openings, self-contained sections, real numbers, and claims a machine can attribute.
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GEO vs SEO: what actually changes
What genuinely differs when assistants answer instead of linking, what the click data now shows, how much of GEO is renamed SEO, and where the budget should move.
The other folders
- What will this cost, and who should build it? Buying and budgeting AI 15 articles
- Which tool, and why do these things break? Workflow automation 14 articles
- How should the system be put together? Building and running it 16 articles
- How do we know it is any good? Measuring whether it works 11 articles
- What do we have to be able to show? Governance and regulation 4 articles
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