GEO Factors

Checks whether your content leads with a direct answer instead of filler introductions. AI engines prefer content they can extract immediately.

Validates your heading structure (H1-H6) for logical hierarchy. Proper headers help AI engines parse content sections accurately.

Measures how many of your H2/H3 headers are phrased as questions. Question headers match natural AI queries and improve citation chances.

Counts and evaluates your use of bullet lists, numbered lists, and tables. Structured formats make content highly extractable by AI engines.

Analyzes word count, paragraph structure, and subtopic coverage. Thin content rarely gets cited by AI engines — depth signals authority.

Detects short, self-contained sentences that AI engines can quote directly. More quotable statements means more chances of being cited.

Detects FAQ content on your page with or without schema markup. FAQs provide ready-made Q&A pairs that AI engines love to cite.

Checks alt text on images and captions on videos. Accessible media helps AI engines understand and reference your visual content.

Measures Flesch Reading Ease, sentence length, ambiguous pronouns, and jargon density. Clear writing is more extractable by AI.

Analyzes content uniqueness and detects duplicated sentences, repeated phrases, and boilerplate. AI engines strongly prefer original content.

Detects coverage across 9 intent types: definition, how-to, comparison, examples, troubleshooting, benefits, best practices, cost, and more.

Counts JSON-LD structured data blocks on your page. More schema types means AI engines have richer context about your content.

Detects FAQPage schema markup in your JSON-LD. Proper FAQ schema makes your Q&A content directly consumable by AI engines.

Checks for Article, BlogPosting, or NewsArticle schema with required fields (headline, author, datePublished, image).

Detects Person schema with author credentials like name, jobTitle, url, and sameAs links. Establishes authorship for AI trust signals.

Checks for Organization schema with name, url, logo, and sameAs (social links). Helps AI engines identify and trust your brand entity.

Detects BreadcrumbList schema that communicates your site hierarchy to AI engines. Helps establish page context within your site.

Validates your JSON-LD for syntax errors, missing required properties, and deprecated fields. Invalid schema can hurt AI discoverability.

Validates that your schema markup matches your actual content type and checks for completeness of type-specific required fields.

Detects author attribution via bylines, rel=author links, Person schema, or HTML patterns. Identified authors build AI trust.

Scans author bios for expertise signals: degrees, certifications, years of experience, and professional titles. Expertise builds AI trust.

Detects publication dates from time elements, schema markup, or text patterns. Dated content helps AI engines assess freshness and relevance.

Detects 'updated', 'modified', or 'last reviewed' dates on your page. Regularly updated content signals active maintenance to AI engines.

Counts and categorizes external links (academic, government, news, industry). Quality citations signal well-researched, trustworthy content.

Checks for links to About, Contact, Privacy Policy, and Terms pages. These standard trust pages signal a legitimate, trustworthy site.

Validates external links for relevance, quality, and availability. Broken or low-quality links erode the authority AI engines assign to your content.

Classifies your topic's freshness expectations and calculates staleness. Different topics (news vs evergreen) have different decay rates.

Assesses domain-level authority signals that AI engines use when deciding which sources to cite in generated responses.

Detects percentages, dollar amounts, multipliers, and year-stamped data. Specific statistics are among the most-cited content by AI engines.

Detects phrases like 'our research', 'we found', 'survey of', and custom charts. Original research gets cited far more than derivative content.

Scores how well you attribute statistics and factual claims to their sources. Properly cited content is more trustworthy to AI engines.

Detects definitional patterns ('X is Y', 'X refers to', 'X means'). Clear definitions are the most commonly extracted content by AI engines.

Detects 'vs' comparisons, pros/cons lists, and alternative analyses. Comparison content matches high-intent AI queries about choosing between options.

Checks robots.txt rules for AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended). Blocked bots can't index or cite your content.

Detects the presence of /llms.txt, an emerging standard that provides AI models with a structured description of your site and content.

Compares raw HTML vs rendered content to detect critical content hidden behind JavaScript. Most AI crawlers don't execute JS.

Measures TTFB, HTML size, and external resource count. Faster pages are crawled more efficiently by AI bots and prioritized in results.

Checks viewport meta tag, responsive CSS, and tap target sizes. Mobile-first indexing means AI engines see your mobile version first.

Detects /sitemap.xml and checks if your analyzed page is included. Sitemaps guide AI crawlers to discover and prioritize your content.

Validates your canonical tag to ensure AI engines know which version of a page is authoritative. Prevents duplicate content confusion.

Checks title tag and meta description for presence, length, keyword relevance, and uniqueness. Quality meta tags provide essential page context.

Detects hreflang attributes and language targeting. Proper i18n ensures AI engines serve the correct language version to users.

Assesses crawler-friendliness indicators including HTTPS, clean URL structure, redirect chains, and overall technical trustworthiness.

Detects conflicting or redundant canonical tags that send mixed signals to AI engines about which page version is authoritative.

Checks for consistent brand naming across your page (title, schema, content). Variations confuse AI engines about your brand identity.

Detects clear entity definition statements ('X is a Y that does Z'). Explicit self-definition helps AI engines categorize and cite your brand.

Counts reviews, testimonials, ratings, 'As seen in' logos, and social share signals. Social proof reinforces brand trust for AI engines.

Counts links to social profiles (LinkedIn, Twitter/X, YouTube, GitHub, etc.) and checks for sameAs schema. Cross-platform presence builds brand identity.

Queries ChatGPT with relevant prompts derived from your content and checks if your brand, domain, or URL is mentioned in responses.

Queries Perplexity AI with relevant prompts and checks if your content appears in its source citations. Perplexity explicitly links to sources.

Our AI overview checker tells you whether your URL appears in Google's AI Overviews for relevant queries. AI Overviews are replacing traditional featured snippets.

Not currently scored, pending redesign (T-150). This guide explains how AI citation rate compares against competitor URLs for the same queries, and what we will measure when the check returns.