METRICS
Crawl Log Analysis
IN ONE SENTENCE
Crawl log analysis answers a question nothing else can: did the AI crawlers actually come, and what did they get.
Crawl log analysis answers a question nothing else can: did the AI crawlers actually come, and what did they get.
OUR POSITION
This is the only first-party data source in AI visibility diagnosis. When content is not entering answers, read the logs before rewriting anything — the other order wastes the work.
What to look for in the logs
Group requests and status codes by user agent, keeping retrieval crawlers (OAI-SearchBot, Claude-SearchBot, PerplexityBot and similar) separate from training crawlers.
Look at response sizes. If one URL returns a few KB to a crawler and tens of KB to a conventional search engine, prerendering is branching on user agent — a fault that zeroes out every piece of content work.
Look at changes in crawl frequency. A sharp drop means checking access rules and site availability first, not rewriting content.
Common findings and what to do
No requests at all from a given agent: check robots and the CDN or edge layer, which frequently blocks outside your site config.
Requests present but many 4xx/5xx: fix availability first. It ranks ahead of any content work.
200s with suspiciously small bodies: check the rendering path and confirm key content does not depend on client-side rendering.
Collection notes
Log volume is large. Pre-aggregate by agent and path rather than scanning everything on each analysis.
Crawlers can spoof or change their user agent, so treat the agent string as a lead and corroborate with IP ranges and behaviour when something looks off.
Data behind this page
30–40%
Relative visibility lift from adding statistics / citations / quotations
Source：Princeton GEO paper, KDD 2024, GEO-bench 10,000 queries，2024
Sources
- [1]Optimizing your website for generative AI features on Google Search．Google Search Central．2026-05-15
Updated 2026-08-10