4 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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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 10 articles
- Which tool, and why do these things break? Workflow automation 13 articles
- How should the system be put together? Building and running it 11 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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