Generative Engine Optimization (GEO) & AI Citation Audit
GEO goes beyond crawlability: it measures whether generative engines see your site as a trustworthy entity worth citing. Our GEO audit runs 33 checks across entity authority, topical depth, factual density, and AI readability - the signals that decide who gets recommended - then layers on AI citation simulation.
GEO checks across 4 signal groups
AI intelligence features on the Pro tier
point GEO score with trend tracking
What this audit actually checks
Every check below runs on every scan. No black boxes: this is the exact coverage you get.
| Category | Checks |
|---|---|
| Entity authority | 8 |
| Topical depth | 8 |
| Factual density | 8 |
| AI readability | 9 |
Entity authority
Generative engines build a knowledge-graph picture of your brand. These checks measure how complete and credible that picture is - so an LLM resolves you to one distinct, trusted entity.
Author bylines & bios
Verifies explicit authorship signals, such as rel=author and Person schema, that establish human expertise behind the content.
Knowledge Graph entity signals
Checks for @id identifiers in your JSON-LD - the field the Google Knowledge Graph uses to recognize your brand as a distinct entity. Without it, engines can't reliably resolve who you are.
Contact & trust signals
Looks for a verifiable phone, email, postal address, or ContactPoint schema. Generative engines trust entities they can confirm are real and reachable.
Social & sameAs links
Checks the schema links to verified profiles that help engines assemble your brand's knowledge-graph entity.
Brand logo detection
Confirms your logo is declared in schema so AI citations display your official branding.
Publication dates
Validates visible created and modified dates. Freshness is a citation tiebreaker for time-sensitive queries.
About page linked
An accessible About page is a core legitimacy signal engines look for.
Entity disambiguation score (Pro)
Scores how completely your Organization / WebSite schema is filled in - description, foundingDate, founder, sameAs, logo. Sparse data causes LLMs to confuse your brand with similarly named ones.
Topical depth
Generative engines favor sources that own a topic completely: original perspectives, coherent clusters, and depth over one-off posts.
Content depth (word count)
Measures whether coverage is substantial enough to serve as a primary source.
Unique value proposition
Checks whether your opening makes a differentiated claim ("only", "first", "unlike"). AIs prefer to cite original perspectives over commodity, rephrased content.
FAQ schema & sections
Detects FAQ content and FAQPage schema that map directly onto the questions users put to AI assistants.
Subtopic hierarchy (H2/H3)
Verifies a clear heading outline AI systems use to parse and extract sections.
Breadcrumb trails
Checks visible and schema breadcrumbs that reveal your content's topical structure.
Internal link density
Measures the topic clusters that signal domain-level authority to generative engines.
Content uniqueness indicators (Starter)
Detects first-person research ("we tested", "our data"), case studies, and expert quotes - the signals of original content LLMs preferentially cite.
Topical cluster detection (Pro)
Analyzes whether your internal links form a coherent pillar-and-cluster structure around a topic, the architecture generative engines reward, rather than scattered navigation.
Factual density
LLMs cite content they can quote confidently: clean definitions, dated statistics, attributed quotes, and inline-referenced claims.
Definition format detection
Looks for clean "X is..." / "X refers to..." definition patterns - the exact extractable format LLMs lift as a direct answer.
Data point density
Counts concrete figures, statistics, and dates, the raw material of citable content.
Statistic freshness (Starter)
Checks whether your statistics carry a year reference ("in 2025", "a 2024 study"). Undated stats get deprioritized in favor of fresher sources.
Wikipedia-style inline citations (Starter)
Measures whether citations sit inline next to the claim they support, the way Wikipedia does, rather than dumped in a footer - which dramatically increases LLM trust.
Quotes & citations markup
Checks blockquote and cite elements that make attributions machine-readable.
External citations
Verifies your content references sources. A bibliography signals research, not opinion.
Authoritative source links
Detects links to .gov, .edu and academic domains that pass trust by association.
Comparison & "best of" optimization (Pro)
Detects comparison framing (X vs Y, alternatives, pros/cons, "best of") - the single most-cited content type in AI shopping and recommendation queries.
AI readability
How easily can a language model isolate, quote, and attribute your content? Structure, formatting, and semantic markup decide.
Paragraph chunking
Flags rambling paragraphs. LLMs extract short, self-contained 2-4 sentence chunks cleanly.
Semantic HTML landmarks
Checks for <article>, <section>, <aside> and <nav> landmarks that let engines separate your primary content from navigation and boilerplate.
Image captions
Detects images wrapped in <figure>/<figcaption>. LLMs can't see images but they do read captions - the only part they can quote.
Data structuring (tables)
Verifies comparisons live in HTML tables that models parse without error.
List usage
Confirms steps and features are formatted as lists, the structure models reproduce most faithfully.
Pronoun ambiguity
Detects sentences that start with ambiguous "this" or "it", where context is lost the moment AI extracts a single sentence.
Multi-format content score (Starter)
Scores how many content formats you mix - prose, lists, tables, code, captioned images. Pages combining three or more are extracted more reliably.
Heading completeness (Starter)
Checks that headings are descriptive phrases ("How to Install Node.js") rather than terse labels ("Installation") - descriptive headings match how users prompt AI.
Structured summary detection (Pro)
Looks for a TL;DR, executive summary, or key-takeaways block - the section LLMs extract first as a direct-answer snippet.
Everything your team needs to act
A score without a fix list is trivia. Every report pairs measurement with prioritized, copy-ready remediation, including AI-native fix prompts for your coding assistant.
GEO score across four signal groups, with an entity authority card and topic cluster map
Findings organized by plan tier - Free, Starter, and Pro - so you see exactly what each level unlocks
Every finding ships with a plain-language explanation, remediation, and an agent-native AI fix prompt
Citation simulator plus a prompt coverage heatmap (Pro) that maps which user prompts around your topic your content actually answers
Pro AI intelligence: citation simulation, Knowledge Graph gap analysis, a topical-authority depth map, and a competitive positioning report
Competitor GEO comparison to benchmark your citation-worthiness against rivals, with trend tracking scan over scan
From URL to fix list in three steps
Enter any URL
No installs, no code snippets, no DNS changes. Every audit starts from a single URL, yours or a competitor's.
Scanners do the work
Purpose-built engines audit security, SEO, AEO, GEO, brand visibility, and site health, with each check scored and severity-ranked.
Fix with AI-ready prompts
Every finding ships with plain-language remediation and an agent-native fix prompt you can paste straight into your AI coding assistant.
Frequently asked questions
Sources & further reading
- Generative engine optimization- Wikipedia
- Schema.org structured data vocabulary- Schema.org