If you wanted a single artifact to represent what AI bots actually reward, you could do worse than studying the 395 sites that sit in the top 0.1 percent of our AI crawl dataset. They average 38.8 crawls each. The median site in the same dataset averages 1 crawl. That is a 30-times gap.
Those 395 sites are not a random selection of well-resourced brands. They are not all e-commerce. They are not all blogs. They are not all in tech. They are a mix of dentists, regional ecommerce stores, mortgage brokers, specialty supplement brands, and other small and mid-size businesses across many categories. What they share is technical. Here is the field guide.
29.9 percent of the top cohort has an AEO score of 8 or higher. In the bottom 90 percent of crawled sites, only 0.4 percent do. That is the most discriminating single signal we found.
41.8 percent of the top cohort has 8 or more structured-data signals present. In the bottom 90 percent, only 5.9 percent do. The top cohort is roughly 7 times more likely to have a full structured-data slate than the long tail.
Mean AEO score in the top cohort is 6.26. Mean in the bottom 90 percent is 4.66. A 1.6-point gap is not huge in absolute terms, but it is the gap that separates the 30-times crawl multiplier.
Mean number of structured-data signals in the top cohort is 6.9, against 4.5 in the bottom 90 percent. The cohort difference is concentrated in the signals that are hardest to ship by accident: FAQPage schema, llms.txt, Organization schema with full identity fields, and Article schema with author credentials.
FAQPage schema. The top cohort is dramatically more likely to have FAQ schema wrapped around a real FAQ page that answers the questions their customers actually ask. AI engines treat FAQPage as a high-confidence input because the question-and-answer pairs map directly to how users phrase queries to AI assistants. A well-written FAQ page is one of the highest-leverage things a brand can ship.
An llms.txt manifest. Most top-cohort sites have an llms.txt file at their domain root. The file is a plain-language summary of who the brand is, what it sells, who runs it, and how AI models should think about it. The format is informal. The point is to make a brand legible to AI agents in the same way robots.txt makes it legible to search engine crawlers.
Organization schema with full identity fields. Not just "name" and "url." The top cohort sites have founder fields, foundingDate, sameAs links to verified social and business listings, address, contactPoint, and aggregateRating where applicable. The signal density is the differentiator.
Article schema with author bylines. Every blog post on the top-cohort sites has Article schema. The author field is a real Person object with credentials (jobTitle, sameAs to LinkedIn, alumniOf). E-E-A-T signals matter here in a way they did not matter for traditional SEO, because AI engines explicitly evaluate the credibility of the writer when deciding whether to cite a claim.
An explicit AI crawler allow-list. The robots.txt files on top-cohort sites explicitly allow GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and Applebot-Extended. They do not rely on the absence of a deny rule to mean access is allowed. They write the allow rule explicitly, which is more legible to bots that interpret robots.txt cautiously.
The data we have shows what these brands have in place. It does not show how they got there. We have anecdotal evidence from the brands in our Fix-It tier that the work usually took less time than the team expected. The top cohort is not made up of brands with massive technical staffs. It is made up of brands where one decision-maker decided AI visibility mattered enough to spend a focused week on it.
There is also a survivorship bias in this cohort worth naming. The 395 brands at the top of the AI crawl distribution include some that got there because they did the AEO work, and some that got there because their content is already exceptionally useful to AI engines for reasons unrelated to schema. Local service businesses tend to over-represent in the cohort because their content (here is what we do, where we do it, who we serve, what we charge) is naturally well-structured around the kinds of questions AI assistants are asked.
If you are deciding what to build, start by checking which of the cohort signals you already have. Most sites have Organization schema in some form. Many do not have FAQPage. Almost none have an llms.txt. Very few have author bylines wired up as machine-readable Person objects.
The gap between an average site and a top-cohort site is usually four or five missing signals plus one cleanup pass on the existing schema. That is a single week of work for most teams, or a single day with the right tooling.
The free AEO score at engagemii.com/aeo returns a category-by-category breakdown that maps directly to the cohort signals described here. Knowing which signals you are missing is the first step. The fix list ranks them by impact on your specific score.
The cohort table in this article is Section 3.2 of Engagemii's research brief, generated 2026-05-29. The dataset is 1,187,128 brand domains in the Engagemii directory, of which 395,022 have been observed being crawled by at least one major AI bot. Cohort assignment is by rank on observed crawl volume.
If you want to cite this article, the URL is engagemii.com/blog/395-most-crawled-sites-field-guide. Full methodology at engagemii.com/research/aeo-crawl-drivers.
Ready to find out if AI can cite your brand?
Get Your Free AEO Score