How Jerry grew its AI bot traffic 60% and more than doubled conversions from AI in eleven weeks

Jerry started by using Sitefire to study its whole site. It looked at three things: what buyers ask AI engines, which of Jerry’s pages the engines read, and where competitors get cited instead. Sitefire turned that into a clear to-do list for each page Jerry wanted to improve.

The first improved pages went live on April 23, with more following daily through May and June.

For each page, Sitefire showed what the top-cited content in its category had in common, and the things AI engines reward. Jerry’s writers systematically built that into its pages.

To isolate the effect of the work itself, we compared the pages Jerry improved with Sitefire against the rest of jerry.ai over the same weeks. Both groups sit on the same site and face the same competitive conditions, so the only real difference is the improvements. The chart runs February 9 to June 29, with the dashed line marking April 23, when the first improved pages went live.

Each line shows a group’s weekly AI bot traffic against its own level before March. Both dropped when GPT-5.4 launched on March 5, and both recovered, but not equally. The improved pages averaged 112% of their old level and ended above where they started, while the untouched pages averaged just 72%. The gray line shows what doing nothing looked like - the median untouched page lost 38% of its AI bot traffic over the period, and twice as many improved pages grew as untouched ones.

Where that AI traffic actually lands is worth understanding for anyone planning content for AI search.

Jerry already had what most brands want. It had built a deep, well-ranked library of car-insurance pages over years, tuned to how Google reads the web. Then a new discovery layer formed on top of search, with its own rules for what to surface. Through the spring, AI traffic to older content fell across the industry, while a small set of pages took a growing share of it.

And the layer is hard to see. When someone researches insurance in ChatGPT or Google’s AI Mode, the engine reads specific pages to build its answer, then usually shows the brand’s homepage as the link to click. Using Sitefire, Jerry spotted this in its own data before most of the industry had a name for it.

The number above each bar shows the share of visits landing on the homepage and signup pages.

AI engines spread their reading across the whole site, and the homepage holds steady at 2% to 4% of what they read. Human visits go the other way. From May, about two of every three AI-referred sessions land on the homepage or signup pages, the ones the engines barely read.

In the AI answers themselves, Jerry’s homepage rarely appears without one of Jerry’s content pages cited alongside it. When both appear together, the homepage usually carries the link a reader can click.

A single fitted line explains 85% of the week-to-week variation (R²). Roughly every two to three answers that cite a Jerry content page pull the homepage into one more answer, and with no content-page citations, homepage appearances drop to almost zero.

Using Sitefire, Jerry turned its existing content pages into the ones LLMs rely on. AI bot traffic grew 60%, AI referral traffic grew 37%, and monthly conversions more than doubled, all measured in Jerry’s own logs and analytics. The content pages earn the citation but the homepage collects the click.

If you publish content pages, this split is already happening in your data, and your analytics is crediting the wrong pages. Sitefire shows you which pages AI actually reads and what each one is missing.

Jerry and Sitefire are now building the measurement that’s still missing, to drive the next round of growth. It connects the content pages cited alongside the homepage to the signups they produce, so the next improvements go to the pages that actually grow the business.