Technical GEO is the plumbing an answer engine has to get through before your content can be considered at all: crawler access, rendering, structured data, llms.txt and the entity signals that let a model work out who you are. It is the least glamorous part of this discipline and reliably the one hiding the largest single problem — a page nobody can fetch cannot be quoted, however good it is.
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Server logs, engine docs and a 137K-site study say the same thing: almost nothing reads the file. Why we ship one anyway — and what it's actually for.
WordPress already renders the HTML AI crawlers need — the rest is configuration. Schema plugins compared honestly, robots.txt, llms.txt in one toggle, and what to skip.
AI answer pipelines extract facts before they synthesize prose. Structured data is how you hand them those facts cleanly — price, specs, ratings — instead of hoping they parse it out of your layout. Here’s the markup that matters, ready to adapt.
Language models don’t think in keywords — they think in entities and the relationships between them. Making your brand a well-defined, consistently-described entity is foundational GEO work that decides whether a model even knows what you are.
You can write the most quotable product page on the internet, but if its facts only materialize after a heavy JavaScript bundle executes, many AI crawlers will fetch a nearly empty page — and quote a competitor whose HTML just says what they are.
The proposed standard for LLM-friendly site maps, separated from the hype: real adoption status, what it can and can't do, and a copy-paste starting template.
Schema markup was built for search engines — but it also makes your facts machine-legible to AI answer pipelines. The types that matter and how to ship them.
Every AI crawler you'll meet in your logs, what each one feeds, and a decision framework for robots.txt that doesn't quietly delete you from AI answers.
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