Guides
Everything we had to work out to build the audit, written down properly. Each guide states what is documented by the operators, what is measured, and what is inference. Sources are linked inline.
Let ChatGPT cite you while blocking AI training
Every major operator now publishes separate robots.txt tokens for training and for retrieval, so allowing citation while denying training is a file you can write today.
llms.txt explained: what the v2 spec actually requires
An H1 is the only element the llms.txt specification requires, llms-full.txt is not in the spec at all, and no answer engine documents reading either file.
AI crawlers and JavaScript: why your page looks empty
A single-page app sends an empty container and a script tag, so a fetcher that does not execute JavaScript receives no text at all; here is how to check and how to fix it.
Should you block AI crawlers? A decision framework
Blocking a training crawler costs nothing measurable today; blocking a retrieval crawler costs documented visibility, which is why the decision is per-token rather than yes or no.
robots.txt for AI crawlers: the complete reference
The exact rules a compliant AI crawler applies to your robots.txt, the four mistakes that quietly void it, and a worked reference file.
How answer engines pick sources: documented vs guessed
No answer engine publishes a citation ranking function; here is exactly what each one does document, and the short checklist that survives the evidence test.
Structured data for AI answers: what is actually evidenced
One property is documented as read by an answer engine for AI output; everything else rests on a transitive argument that is worth making honestly.
AI crawler blocking at the edge: detect it and fix it
The most common invisible failure in AI crawl accessibility: a permissive robots.txt in front of a WAF that challenges anything that looks like a bot.