The Top 32: What the Only Sites Scoring 70+ on AEO Rank Do Differently

Out of the 13,385 domains we have scored on AEO Rank as of 2026-05-29, only 32 sites clear the 70-point threshold. Just two clear 80. We pulled every one of the 32 and ran a side-by-side comparison against the corpus median of 47. The pattern that emerges is not what most teams expect. The top tier is not winning on word count, domain age, or backlink profile. They are winning on four specific, replicable structural choices that account for almost the entire gap between a 50 and a 75 on AEO Rank.

Who is in the top 32

The top of the corpus is a small, idiosyncratic group. aeocontent.ai sits at 92 under our ChatGPT engine evaluation - we are one of the two sites in the corpus at 80 or higher. The other is helpsquad.com at 82, evaluated on the instant engine on 2026-05-24 and reflecting our recent client work with the team. Below that, the 70-to-79 cohort holds 30 sites including appsembler.com at 78, nomi.so at 77, knowlify.net and incrediblepizza.com at 74, electro-mech.com and understoodcare.com at 73, and kaelio.com also at 73.

Site	Score	Engine	Date	Note
aeocontent.ai	92	ChatGPT	2026-02-22	Our own site
helpsquad.com	82	instant	2026-05-24	Client case study
appsembler.com	78	instant	2026-03-10
nomi.so	77	Claude	2026-02-27
helpsquad.com	77	Claude	2026-02-26	Same client, Claude engine
knowlify.net	74	instant	2026-05-09
incrediblepizza.com	74	instant	2026-03-10
electro-mech.com	73	instant	2026-05-28	Client; chatgpt baseline 35 in Feb 2026
understoodcare.com	73	instant	2026-05-09	Client; chatgpt baseline 64 in Feb 2026
kaelio.com	73	Claude	2026-02-27

The cohort spans industries - SaaS, live chat, family entertainment, industrial manufacturing, home health care, AI tooling. There is no sector advantage. There is a behavior advantage.

Pattern one: every top site has a named author with a real bio

The single most consistent signal across the top 32 is that articles carry a named author byline, and that author byline links to a real author page with its own schema markup. The median corpus site has either no byline at all (publishes under “Admin” or the company name) or a byline that links nowhere. The top cohort treats authors as first-class entities. The author has a Person schema entity, a photo, a bio, links to other articles, and where relevant external proof of expertise. AI engines weight named authorship heavily because it gives them an entity to attach trust to.

This shows up in the Trust & Authority pillar scores. The corpus average on the pillar is 3.7. The top 32 average above 7.0. The bulk of that gap comes from the Schema.org Structured Data criterion (corpus mean 4.0, top-32 mean above 8) and from Owned Data Density (corpus mean 1.2, top-32 mean above 6).

Pattern two: an llms.txt at the root, every time

We checked all 32 sites for a /llms.txt file in May 2026. The top cohort runs at near-100% adoption. The corpus runs at 17.8% (the inverse of the 82.2% critical-fail share on the llms.txt criterion). That gap alone explains a measurable chunk of the score difference, because llms.txt is a 10-point criterion in the AI Discovery pillar and most of the top cohort scores at or near the ceiling on it.

The llms.txt files in the top cohort are not stubs. They average over 100 lines, with a one-line summary, an About block, a Products & Services block with direct URLs, and a Key Content block linking to the highest-trust pages on the site. helpsquad.com and aeocontent.ai both publish llms.txt files in this shape. The pattern is not novel - llmstxt.org documents it - but the cohort that follows it is small.

Pattern three: structured comparison tables on commercial-intent pages

The third pattern is editorial rather than technical. The top 32 publish comparison tables - product vs product, plan vs plan, methodology vs methodology - on the pages most likely to receive commercial-intent traffic. AI engines treat comparison tables as high-signal answer surfaces because they collapse a multi-paragraph comparison into a quotable grid. The corpus median sites either avoid comparison tables (worried about driving traffic to competitors) or use them as decorative elements without semantic structure.

HelpSquad’s instant-engine v1-to-v6 progression (55 in April to 82 in late May, publicly visible at audit.aeocontent.ai/helpsquad-com) was driven mostly by Content Structure work — the pillar moved from 6.8 to 9.1, and structured comparison tables across product pages were a documented part of that lift. Electro-Mech published equipment spec tables comparing variants of their own product line and now sustains 73 on the instant engine. The lesson is that comparison tables work even when they only compare your own offerings.

Pattern four: Q&A density, not Q&A presence

Almost every site in the corpus has an FAQ block somewhere. The top 32 have Q&A blocks on every commercially relevant page, not just a single /faq page. Their FAQ blocks average 5 to 12 Q&A pairs per page rather than the corpus median of 0 to 2. Each Q&A pair is wrapped in FAQPage JSON-LD so the AI engine sees the structure. Each answer is short and quotable - 40 to 80 words, not 200-word essays. This is the pattern that drives the Answer Readiness pillar from the corpus average of 5.7 toward 8-plus, which is where every top-32 site we measured lands.

Understoodcare’s current 73 on the instant engine (from a 64 chatgpt baseline and a 66 instant baseline in Q1 2026) was anchored on this pattern. The team rewrote service pages to lead with a “short answer” paragraph, then attached a five-to-eight question FAQ block to each one, each block schema-marked. The published progression is queryable at audit.aeocontent.ai/understoodcare-com.

What the four patterns do NOT include

Worth naming what is absent. The top 32 are not winning on raw word count - several of them publish articles in the 1,200-to-2,000-word range, not the 4,000-plus that some SEO playbooks recommend. They are not winning on backlink profile - several are sub-five-year-old startups with limited link equity. They are not winning on domain authority in the traditional sense. They are winning on structural editorial choices that AI engines specifically reward and traditional SEO largely ignores.

How We Tested

The top-32 cohort was assembled by querying aeo_audit_versions for every domain with a most-recent published score of 70 or higher, joined to the canonical aeo_domains row, de-duplicated by registered domain. The result is the 32-site list referenced throughout this article.

For each of the 32 sites we manually verified four signals in May 2026: presence of /llms.txt returning HTTP 200 with non-trivial content (>100 bytes, headings present); presence of a named author byline on the most recent published article; presence of at least one structured comparison table on a commercial-intent page; and FAQ density (count of Q&A pairs on the most recent published article). The “top-32 averages” for the Trust & Authority and Answer Readiness pillars are computed from the same aeo_audit_versions rows used for the corpus comparisons.

Case-study citations (HelpSquad instant 55 to 82, Electro-Mech chatgpt 35 baseline to instant 73, Understoodcare chatgpt 64 baseline to instant 73) reference the public aeo_audit_versions rows for each domain, queryable at the respective audit.aeocontent.ai/<slug> page. Cross-engine baselines are flagged explicitly because each engine evaluates a different signal set and the scores are not directly comparable.

What to do if you are not in the top 32 yet

If your AEO Rank is below 70, the path the top cohort took is documented and replicable. Add named author bylines with schema markup. Publish an llms.txt at the root. Ship structured comparison tables on your commercial pages. Increase Q&A density and wrap each Q&A pair in FAQPage schema. Read the AEO Rank methodology for the full criterion list and weights, or run a site audit to see exactly which of the four patterns your site is missing today.