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AEO audit · May 31, 2026

example.com

on the queryexample domain
28AEO score
Currently uncited
Not cited at top 0·simulated retrieval against 0 candidates
⚡ This Week — top 5 fixes📈 Predict score⬇ CSV⬇ JSON View page

The 30-signal rubric

7 ok · 7 weak · 16 absent

Every signal is measurable from your page. Hover for the detail and a remedy.

Structure

27/100
JSON-LD structured data0
No JSON-LD blocks found. Answer engines key off schema.org markup to ground citations.
Fix: Add at least one schema.org JSON-LD block — Article, Product, FAQPage, or HowTo are the highest-lift types.
JSON-LD validity100
No JSON-LD blocks to validate (the presence signal covers that).
Schema type matches query intent0
No schema types — for a informational query, add one of: Article, BlogPosting, FAQPage, HowTo.
Fix: Add a Article JSON-LD block — the highest-leverage schema type for a "example domain" query.
FAQ / Q&A schema0
No FAQPage / QAPage schema — AI Overviews preferentially cite pages exposing structured Q&A.
Fix: Add a FAQPage schema with 3-5 questions buyers actually ask. The single most direct path to AI Overview citation.
H1 quality60
H1: "Example Domain" (14 chars).
Fix: Adjust H1 length to 20-80 chars — shorter than 20 lacks context, longer than 80 loses retrieval focus.
H2 subtopic coverage0
0 H2 headings — answer engines decompose a buyer query into subtopics and match each to an H2.
Fix: Break the page into at least 4 H2 sections, each answering one aspect of the buyer query. Engines surface specific H2s as citations.
Lists (bulleted / numbered)0
No lists — AI engines preferentially cite list items because they pack into answer summaries cleanly.
Fix: Add 2-3 bulleted lists summarising key points. They're the single most-cited structural element in AI summaries.
Comparison tables50
No tables. Comparison / specification tables are heavily cited for product and technical queries.
Image alt-text coverage100
No images — alt-text coverage trivially complete.
Open Graph metadata0
Open Graph: title ✗, desc ✗, image ✗.
Fix: Add og:title, og:description, og:image meta tags. AI engines use OG for the cited preview card.

Authority

14/100
Outbound links to authoritative sources0
0 links to authoritative domains (.edu, .gov, named sources).
Fix: Add 2-3 outbound citations to .edu / .gov / known publishers. The GEO paper measured this as the #2 citation-lift factor.
Inline citations and attributions0
0 bracketed citations + 0 attribution phrases (per 200 words: 0%).
Fix: Add inline citations ([1], [2]) or attribution phrases ("according to X"). GEO factor #1 for credibility-weighted citation.
Statistic density0
0 numerical facts (~0 per 100 words).
Fix: Add concrete statistics with sources. AI engines preferentially cite paragraphs containing numbers — a GEO paper §4.1 finding.
Expert quotations0
0 blockquotes + 0 long inline quotes.
Fix: Include at least one direct quote from a named expert, ideally in a <blockquote>. Quote-rich pages are 28% more likely to be cited (GEO paper).
Author byline0
No author byline. E-E-A-T penalises anonymous content.
Fix: Add a visible author byline (a `<div class="byline">` or `[rel="author"]`) AND a `<meta name="author">` tag.
Publish + update date markup0
No structured publish date. Freshness ranking can't be applied.
Fix: Add `<time datetime="...">` for published date AND a `<meta property="article:modified_time">` for updates.
Content freshness0
No parseable date — engines treat undated content as stale.
Fix: Add a parseable date — `<time datetime="2026-01-15">` or `<meta property="article:modified_time">`.
Technical / domain vocabulary100
5 multi-syllable words (~29.4% of total).

Content

38/100
Direct answer in the lead50
1 of 2 query terms appear in the first paragraph.
Fix: Open the page with a direct, complete answer to the buyer query — engines extract their citation snippet from the lead paragraph.
Query-term coverage100
100% of query terms appear somewhere on the page.
Named-entity coverage30
3 distinct proper-noun entities mentioned.
Fix: Mention the named entities (people, products, organisations, places) the buyer query implies. Entity coverage feeds knowledge-graph retrieval.
Readability (Flesch Reading Ease)20
Reading-ease score: 21.9 (target 60-70).
Fix: Page reads too academic — shorter sentences and simpler vocabulary lift citation rate by ~12% (GEO §4.3).
Content depth0
17 words. Target 300-2500 — engines bias toward focused depth.
Fix: Page is too short. Expand to at least 500 words covering the query in depth.
Information density30
Unique-word ratio: 88.2% (target 40-60%).
Fix: Vocabulary varies too wildly — content may lack topical focus. Engines prefer focused pages.
Definitional sentences0
0 definitional sentences.
Fix: Include definitional sentences ("X is...", "X refers to..."). Engines specifically extract definitions for AI Overview snippets.

Trust

43/100
HTTPS100
Page served over HTTPS.
Canonical URL0
No canonical link element.
Fix: Add `<link rel="canonical" href="...">`. Engines use it to de-duplicate competing URLs for the same content.
Mobile-friendly viewport100
Viewport meta tag present.
Twitter Card30
No Twitter Card — secondary signal but easy to add.
Fix: Add `<meta name="twitter:card" content="summary_large_image">` and the matching title/description/image.
Meta description0
No meta description.
Fix: Add a `<meta name="description">` summarising the page in 120-160 characters.

The Fix Kit

Currently uncited (28/100) — 14 foundational patches required

Ready-to-paste patches. Schema.org JSON-LD, meta tags, copy guidance. The first three below are unlocked; the rest are gated behind a free account.

Article JSON-LD

A Article schema is the strongest single AEO signal for a informational query. Paste this in the <head>.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Example Domain",
  "description": "Example Domain",
  "url": "https://example.com",
  "datePublished": "2026-05-31",
  "author": {
    "@type": "Person",
    "name": "Add author name"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Example",
    "logo": {
      "@type": "ImageObject",
      "url": "https://example.com/logo.png"
    }
  }
}
</script>

FAQPage JSON-LD

FAQPage is the single most-cited schema type by Google AI Overviews. Add 3-5 Q&A pairs covering what buyers ask.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is Example.com?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "The domain names example.com, example.net, example.org, and example.edu are second-level domain names in the Domain Name System of the Internet. They are reserved by the Internet Assigned Numbers Authority (IANA) at the direction of the Internet (IETF) as special-use domain names"
      }
    },
    {
      "@type": "Question",
      "name": "Why does Example.com matter?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "A 1-2 sentence explanation of the practical value or impact."
      }
    }
  ]
}
</script>

Organization JSON-LD

Organization schema gives the engine an E-E-A-T anchor — pages without one are treated as anonymous.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Example",
  "url": "https://example.com",
  "logo": "https://example.com/logo.png",
  "sameAs": [
    "https://twitter.com/your-handle",
    "https://linkedin.com/company/your-org"
  ]
}
</script>
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