Beyond access and visibility—see technical signals that influence whether AI systems trust and cite your content.
AI systems don’t cite every page they can access. They prioritize sources that appear authoritative, well-structured, and trustworthy. Authority analysis shows you the technical signals AI systems might use to evaluate your page quality—schema validity, page-type classification, structural completeness, and other markers of content credibility.
Important caveat: This is exploratory analysis. We don’t claim to know exactly how AI systems weight these signals.
When AI systems have multiple sources, they likely favor sources with stronger technical authority signals.
If two pages have similar content but one has complete schema while the other has none, AI systems might prefer the technically-complete source. Technical completeness is something you can control and measure.
A summary grade (A–F) based on grouped technical checks. Not a ranking prediction—a diagnostic of how technically complete your page appears.
We attempt to classify your page type based on schema markup, HTML structure, content patterns, and URL patterns.
Why page type matters: AI systems might prioritize certain page types for certain queries—articles for informational, products for shopping, FAQ for direct question-answer. Ambiguous classification reduces AI confidence in how to use your content.
Useful for informational queries, tutorials, commentary, and current events.
Important for shopping and commercial-intent prompts where specs, pricing, and offers matter.
Best aligned with brand, entity, and company-information queries.
Matches queries about capabilities, deliverables, and business offerings.
Structured Q&A format often maps directly to prompt-answer matching.
Matches entity and biographical queries about organizations and individuals.
Functional page type with limited AI citation potential on its own.
Time-bounded content; schema completeness is especially important for discoverability.
Highly structured format with strong schema support—one of the clearest classification signals.
Step-structured content with instructional intent matches procedural prompts well.
Evaluative content with opinions and comparisons; rating schema reinforces classification.
Multimedia content type; VideoObject schema clarifies the primary format to AI systems.
Mixed or unclear signals. Classification is uncertain, which may reduce AI confidence in how to use the page.
What we check: Schema.org presence and type, JSON-LD validation, required and recommended properties, nested schemas, multiple schema types.
Why it matters: AI systems use structured data to understand page context without reading every word of body copy.
What we check: Heading hierarchy (H1→H6), skipped levels, multiple H1s, HTML5 semantic elements, content outline, landmark roles.
Why it matters: Clean structure helps AI systems parse and segment your content correctly.
What we check: ARIA labels, ARIA roles, alt text coverage, form label associations, keyboard navigation markers.
Why it matters: Accessibility markup provides additional machine-readable context about page elements and their purpose.
Important note: This is not a full WCAG audit—we check presence and coverage as structural signals, not compliance.
What we check: Title tag length (50–60 chars), meta description length (150–160 chars), OG tags, Twitter Card, canonical URL, hreflang.
Why it matters: Metadata gives AI systems concise summaries of your page without requiring full content processing.
Each authority check provides pass/fail/warning status, specific evidence found on the page, why the signal matters, fix recommendations, and a severity level.
Valid Article JSON-LD found with required properties present.
{
"@type": "Article",
"headline": "...",
"author": {...},
"datePublished": "..."
}Why it matters: Confirms page type and surfaces key metadata without requiring AI systems to parse body text.
Recommendation: Add dateModified and image properties to strengthen completeness.
Heading levels skip from H1 to H3, with no H2 present.
<h1>Main Title</h1> <h3>Subheading</h3> <!-- H2 skipped --> <h3>Another Section</h3>
Why it matters: Broken hierarchy makes document outline ambiguous and harder for AI systems to segment content correctly.
Fix: Restructure headings to use H2 for primary sections and H3 for subsections within those sections.
Product schema found but missing the offers property, which is required for commercial page classification.
{
"@type": "Product",
"name": "...",
"description": "..."
// offers missing
}Why it matters: Incomplete Product schema may cause AI systems to treat the page as informational rather than commercial.
Fix: Add an offers object with price, priceCurrency, and availability.
Crawlability: Can AI systems access your page at all?
Authority: Once they can access it, will they trust and cite it?
A perfectly crawlable page with no schema and broken structure still scores poorly on authority.
Content Visibility: Is the text of your content actually present in the HTML AI systems see?
Authority: How well-structured and annotated is that visible content?
Visible content without semantic markup is harder for AI systems to interpret confidently.
Performance: Can AI systems fetch your page quickly and reliably?
Authority: Does the page present strong credibility signals after the fetch succeeds?
Fast delivery of a technically weak page still leaves authority gaps.
Reverse Prompting: What queries does your content match based on its subject matter?
Authority: Do the technical signals around that content support trust and citation?
Content-query fit and technical credibility are complementary, not interchangeable.
Scenario: You publish high-quality content but aren’t appearing in AI-generated answers despite good SEO metrics.
Value: Technical completeness improvements you control without rewriting content.
Scenario: You’re adding or updating JSON-LD markup and need to verify correctness and completeness, not just syntax.
Value: Specific evidence pinpointing exactly what’s missing rather than a generic validity pass/fail.
Scenario: You’re auditing client sites for AI readiness and need structured reports covering technical authority gaps.
Value: Structured evidence base for client recommendations beyond traditional SEO metrics.
Scenario: You publish at volume and need consistent technical quality across many article pages at scale.
Value: Template-level fixes that improve authority signals across your entire content library at once.
Authority analysis does not monitor where your pages appear in AI-generated answers or track citation frequency over time. It evaluates technical completeness signals, not outcomes.
We analyze structure, markup, and metadata—not whether your writing is accurate, useful, or well-researched. Content quality is beyond what automated technical checks can assess.
Improving your authority score does not guarantee AI systems will cite your pages. It removes technical obstacles that might reduce citation probability, but many other factors are outside your control.
This is page-level technical signal analysis, not a domain-wide reputation score. We don’t analyze backlinks, domain age, brand recognition, or cross-site trust signals.
Authority analysis is in active development. The checks, scoring, and grade thresholds reflect our current hypotheses about AI citation signals. We’re transparent that AI system behavior is not fully observable and these methods will evolve.
Start with free diagnostics to check access, visibility, and performance. Unlock authority analysis in the beta platform for a deeper view of technical credibility signals.