AI audit with Screaming Frog: Claude reads the data cleanly and invents the diagnosis eullrich.com

Claude chews through large piles of crawl exports fast and separates confirmed facts from data gaps. But it turns measurable signals into proven causes and finished priorities that the data doesn't support. A word count under 200 became “thin content” and High priority, with no Search Console, no analytics, no check of search intent. Hand that audit to a client unchecked and you're selling guesses as a diagnosis. The value of the automation lives in the review layer behind it, and that layer belongs to a human. I also pushed the same crawl through a second model: both name the same numbers and both fail at the interpretation, only with different error profiles.