Stop treating AI search as a single target. From Perplexity’s focus on real-time recency to Google’s reliance on existing SEO equity, each engine requires a specific tactical approach. These platform-by-platform playbooks uncover the exact technical configurations and crawler directives needed for each system.
Fan-out mechanics are covered elsewhere. This is the workbook: a repeatable method for mapping your own cluster and building the coverage that actually earns citations.
Ask ChatGPT something with browsing off and you get an answer built from training data alone. That’s not AEO or GEO, it’s a third mechanism, and it moves on a completely different clock.
A baseline is not the first time you ran your prompts. Here is how to build one that holds up when someone asks you to prove impact six months from now.
Visibility score, share of voice, and citation rate measure three different things. Here is what each one actually tells you, and when to trust it.
How to actually measure AI visibility: the metrics that matter, organising prompts by funnel stage, and the mistakes that quietly skew your data.
ChatGPT reads far more of the web than it ever shows you. Here’s what separates the pages it quietly discards from the ones it puts in front of users.
Perplexity tracks Google’s own results far more closely than ChatGPT does, except where it doesn’t. Here’s where it follows the pack and where it goes its own way.
Google says there’s nothing special you need to do to appear in AI Overviews. The data on how citations actually get chosen tells a more complicated story.
ChatGPT Search, Perplexity, Google AI Overviews, Copilot and Claude all retrieve and cite sources differently. Here is how each platform actually works, and which one to prioritise first.