The situation
The site has hundreds of pages, and nobody can currently say which of them any AI engine has ever visited. Every improvement effort is a guess about where the attention already is.
Why it's a real problem
Without page-level crawl data, effort goes wherever seems reasonable, on a site where most of those reasonable-seeming guesses are probably wasted. A page nobody is improving might already be getting fetched constantly; a page getting all the attention might never be visited by a single AI bot.
What we implement
We analyze your server logs for AI crawler activity page by page - our own crawler-log analysis tooling, the same instrument we run on our own estate - so you see exactly what GPTBot, PerplexityBot, and ClaudeBot fetch, how often, and what they skip.
What you get
- A page-level map of AI crawl demand, so improvement effort goes to pages engines are already visiting instead of a guess.
- Early warning when a previously-fetched page stops being crawled, before a citation drop shows up downstream and someone has to explain it.
- Evidence for whether a low-traffic page is invisible to AI engines or simply not written in a way worth citing - two different problems with two different fixes.
Illustrative scenario
A site with hundreds of thin category pages might find that AI crawlers concentrate almost entirely on a handful of long-form guide pages, ignoring the rest of the catalog completely. (Illustrative scenario - not a measured result.) Whether your own site shows that pattern, or a different one entirely, is what the log analysis actually answers.
What this measures, precisely: retrieval, not citation. A page can be fetched and never cited, or cited from a cached fetch made weeks earlier - this use case establishes the retrieval layer; the AI visibility audit adds citation on top of it.