AEO Article Analyzer

The 10 criteria — and what each one measures

Each criterion is based on how AI engines parse, evaluate, and cite content. Each receives pass/fail, a 0–10 score, and a specific improvement suggestion that quotes your actual text.

1. Question-based headlines

   Headlines phrased to match how users actually prompt AI engines — questions, comparisons, "how to" framings — not internal taxonomy labels.

2. Direct answers up front

   A clear TL;DR in the first 40–60 words. AI extraction is biased toward early-paragraph answers; bury the lead and you lose the citation.

3. Clear heading hierarchy

   Logical H1 → H2 → H3 structure that AI parsers can walk. One H1, properly nested subheadings, no skipped levels.

4. Modular sections

   75–300 word chunks per subheading, each self-contained enough to be extracted as a standalone passage. The size band that consistently surfaces in AI answers.

5. FAQ coverage

   Five or more questions with concise 40–80 word answers. Match the phrasing users would actually type into ChatGPT or Perplexity, not internal headings.

6. Source attribution

   Named experts with real credentials cited inline. AI systems are trained on source-rich content and preferentially cite content that itself cites named sources.

7. Data-backed claims

   Concrete figures with attribution, not vague "studies show" or "experts say." Original or primary-sourced numbers materially raise extraction probability.

8. Original insight

   Perspectives, frameworks, or experience-based observations AI can't generate on its own. The signal that makes a page worth citing instead of paraphrasing.

9. Zero filler content

   Avoids vague "experts say" framing, generic takes without point of view, and word-padding. Density of useful content per paragraph.

10. Author credibility signals

    Bio, credentials, and relevant experience clearly shown on the page or linked author profile. E-E-A-T signal that AI engines weight increasingly heavily.