SEO, AEO and GEO: being found when AI gives the answer
SEO (search engine optimisation) is the familiar discipline: ranking on Google so a person clicks through to your page. AEO (answer engine optimisation) is about being the source an AI assistant actually cites when someone asks it a direct question — "what's a good BIM consultancy in Pune" no longer returns ten blue links, it can return one confident answer, with or without you in it. GEO (generative engine optimisation) is the broader version of the same idea — making a business legible to generative AI systems in general, not just the chat interfaces people think of first. None of these replace the other two. A page that ranks well on Google but is structured in a way no AI model can parse cleanly is already losing a growing share of the people who never reach a search results page at all.
What actually makes a page machine-readable
The mechanics are less mysterious than the acronyms suggest. A clear, single canonical URL per page, so nothing gets attributed to the wrong place. Structured data — schema.org markup — that states in plain terms what a page is, who wrote it, and what it answers. Content written as direct answers to direct questions, not just as persuasive copy; an FAQ section with real questions and real, specific answers consistently outperforms a page that only describes a service in general terms. A robots.txt file that explicitly allows the AI crawlers — GPTBot, ClaudeBot, PerplexityBot, Google-Extended — rather than silently blocking them by default, which many site templates still do. And increasingly, an llms.txt file at the site root: a short, plain-language index of what the site covers, written for a model to read in one pass rather than crawl page by page.
Where this usually breaks in practice
The most common failure isn't missing content — it's conflicting signals. Two canonical tags on the same page pointing to different URLs. A meta description that's just the page title repeated. Schema markup copy-pasted across every page with the same generic business description and none of the specific answer the page is actually about. Each of these is small on its own and easy to overlook, because a human visitor never notices them — the page looks fine. A crawler or a model reading the raw HTML sees the contradiction immediately, and the safest thing it can do with contradictory signals is ignore the page, or worse, cite the wrong one.
We audit canonical tags, structured data and crawl permissions as part of every website build now — being readable to an AI model is no longer a separate project from being readable to a search engine.
Frequently asked
SEO (search engine optimisation) is the familiar discipline: ranking on Google so a person clicks through to your page. AEO (answer engine optimisation) is about being the source an AI assistant actually cites when someone asks it a direct question — "what's a good BIM consultancy in Pune" no longer returns ten blue links, it can return one confident answer, with or without you in it. GEO (generative engine optimisation) is the broader version of the same idea — making a business legible to generative AI systems in general, not just the chat interfaces people think of first. None of these replace the other two. A page that ranks well on Google but is structured in a way no AI model can parse cleanly is already losing a growing share of the people who never reach a search results page at all.
The mechanics are less mysterious than the acronyms suggest. A clear, single canonical URL per page, so nothing gets attributed to the wrong place. Structured data — schema.org markup — that states in plain terms what a page is, who wrote it, and what it answers. Content written as direct answers to direct questions, not just as persuasive copy; an FAQ section with real questions and real, specific answers consistently outperforms a page that only describes a service in general terms. A robots.txt file that explicitly allows the AI crawlers — GPTBot, ClaudeBot, PerplexityBot, Google-Extended — rather than silently blocking them by default, which many site templates still do. And increasingly, an llms.txt file at the site root: a short, plain-language index of what the site covers, written for a model to read in one pass rather than crawl page by page.
We audit canonical tags, structured data and crawl permissions as part of every website build now — being readable to an AI model is no longer a separate project from being readable to a search engine.