AEO Knowledge Base
85 guides. 5 sections. Every signal that determines whether ChatGPT, Claude, and Perplexity cite your site - or skip it.
AEO is the practice of optimizing web content so AI answer engines can discover, parse, and cite it.
Foundations
How AEO works, scoring methodology, and the core ideas behind every guide below.
AEO Site Rank: How We Calculate Your 0-100 Rating
Your AEO Site Rank is a deterministic 0-100 score built from 48 criteria across five pillars: Answer Readiness...
Read guide →AEO Site Rank: How Benchmarks and Peer Comparison Work
Your AEO rank is your position among all audited domains, sorted by best score. We also rank you...
Read guide →AEO Page Rank: How Individual Pages Are Scored for Citation Readiness
AEO Page Rank is a 0-100 score measuring whether a specific page is structured for AI citation. It...
Read guide →Product & Offer Schema
Product and Offer schema audit checks that commerce pages include Product type with name, description, price, availability, and...
Read guide →Speakable Schema Markup
Speakable schema identifies content sections optimized for audio delivery by voice assistants. Adding Speakable markup to key paragraphs...
Read guide →Hreflang & Multilanguage Support
Hreflang audit checks that multilingual pages have correct hreflang tags with valid language-region codes, reciprocal references between versions,...
Read guide →ai.txt & TDM Policy
ai.txt is an emerging standard (like robots.txt for licensing) declaring whether AI systems may use your content for...
Read guide →Brand Mention Monitoring
Brand mention monitoring scans high-authority pages across the web for references to your brand and your competitors. Pages...
Read guide →AEO Scoring Criteria
The criteria that determine your AEO Rank.
Q&A Content Format: Write Like AI Reads
Use question-format headings (H2/H3) that mirror how real people ask AI assistants. Lead with a direct 1-2 sentence...
Read guide →FAQ Sections: 87 Questions Turned Our Score Into 88
Build a dedicated FAQ with 20+ real questions, detailed answers, and FAQPage JSON-LD schema. Use native HTML <details>/<summary>...
Read guide →Original Data: The Content AI Can't Find Anywhere Else
Publish proprietary data, first-hand analysis, and expert opinions that only you can produce. In our audits, sites with...
Read guide →Schema Coverage Ratio
Schema coverage ratio is the percentage of your indexed pages carrying relevant JSON-LD markup. Above 80% means you're...
Read guide →Canonical URL Strategy
The canonical audit checks every page for a rel="canonical" link pointing to the correct authoritative URL. Missing canonicals...
Read guide →Content Licensing Signals
The licensing audit checks for CreativeWork license properties in JSON-LD, meta tags indicating reuse policy, and copyright notices....
Read guide →Table & List Extractability
Table and list extractability measures whether your structured content uses semantic HTML -tables with thead/tbody/th, lists with ol/ul/li....
Read guide →Schema.org JSON-LD: The Scoreboard AI Actually Reads
Add JSON-LD script blocks to your pages with Schema.org types -Organization, FAQPage, Article, BreadcrumbList. In our audits, sites...
Read guide →Clean HTML: If Crawlers Can't See It, It Doesn't Exist
Your content must be in the raw HTML the server sends -not loaded by JavaScript after render. AI...
Read guide →Semantic HTML5: The Difference Between a Page and a Page AI Can Parse
Use semantic HTML5 elements: <main>, <article>, <section>, <nav>, <header>, <footer>, <time>, and <figure>. Maintain one H1 per page...
Read guide →Sitemap Completeness
Sitemap completeness compares the URLs in your sitemap.xml against actual pages found by crawling. Missing URLs, stale lastmod...
Read guide →Direct Answer Paragraphs: The First Sentence AI Steals
The first 1-2 sentences after every heading should directly answer the question that heading implies. AI engines extract...
Read guide →llms.txt -The File That Separates 63 from 34
llms.txt is a plain-text file at your domain root that hands AI assistants a cheat sheet about your...
Read guide →robots.txt for AI: Rolling Out the Red Carpet (or Slamming the Door)
Add explicit Allow rules in your robots.txt for AI crawlers: GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. Without these rules,...
Read guide →Internal Linking: The Web AI Uses to Map Your Expertise
Link your pages to each other strategically with topic hubs, "Related Articles" sections, breadcrumb navigation, and cross-references between...
Read guide →Content Freshness Signals
Content freshness measures whether pages include datePublished and dateModified in both JSON-LD and HTML time elements. Pages without...
Read guide →RSS Feed Presence & Quality
An RSS 2.0 or Atom feed at a discoverable URL lets AI indexing systems detect new and updated...
Read guide →Entity Authority: Why AI Cites Some Sites and Ignores Others
Make your business a verifiable entity with Organization schema, visible contact info, named authors with credentials, and sameAs...
Read guide →Fact Density Measurement
Fact density counts verifiable claims per 1,000 words -named statistics, specific numbers, dated references, attributable statements. In our...
Read guide →Definition Pattern Detection
Definition patterns are sentence structures like "[Term] is [definition]" or "[Term] refers to [explanation]" that AI systems extract...
Read guide →Content Publishing Velocity
Content velocity tracks new pages and substantial updates over a rolling 90-day window. Sites publishing at least weekly...
Read guide →Author & Expert Schema: The Byline AI Actually Checks
Add Person schema with name, jobTitle, sameAs links, and knowsAbout for every content author. Include visible author bios...
Read guide →Intelligence Report
AI-evaluated signals - does your content actually get cited?
Content Depth Score: What AI Engines Actually Want to Read
Content depth score feeds your page to an AI model and asks: "Does this cover the topic thoroughly...
Read guide →Citation-Ready Content Patterns: Writing Sentences AI Can Actually Use
Citation-ready patterns are specific content structures AI engines preferentially extract: attributable claims with sources, self-contained factual statements, comparative...
Read guide →Topic Authority Clustering: Why One Good Page Isn't Enough
Topic authority clustering maps your content into topic groups and evaluates whether you cover enough related subtopics to...
Read guide →Content Uniqueness Analysis: Do You Have Anything AI Doesn't Already Know?
Content uniqueness analysis uses AI to identify what percentage of your content provides information not readily available elsewhere....
Read guide →Author & Person Schema Depth: From Name String to Verified Expert
Author schema depth goes beyond checking if Person schema exists. It evaluates whether the markup includes enough detail...
Read guide →Wikidata & Knowledge Graph Presence: The External Trust Loop
Wikidata and Knowledge Graph presence means your business has a verified entry in public knowledge databases AI engines...
Read guide →Social Profile Verification: When sameAs Links Backfire
Social profile verification goes beyond checking sameAs URLs exist. It confirms linked profiles are active, contain consistent business...
Read guide →AI Hallucination Audit: What AI Engines Are Making Up About You
The AI hallucination audit asks multiple AI engines direct questions about your business and checks responses for accuracy....
Read guide →Live Citation Test: Does AI Actually Mention You?
The live citation test submits real user-style queries to ChatGPT, Claude, and Perplexity, then analyzes whether your domain...
Read guide →Cross-Engine Consistency Score: The 10-Point Gap That Changes Everything
Cross-engine consistency measures the variance in your scores, citation rates, and visibility across different AI engines. A gap...
Read guide →Original Data Pipeline: How AI Collects Proprietary Evidence for Every Article
The Original Data Pipeline is a five-stage system that automatically collects live web intelligence from news, academic, government,...
Read guide →Engine Optimization
Engine-specific playbooks for ChatGPT, Claude, Perplexity, and Google AI Overviews.
ChatGPT Conversational Query Matching -Why Keywords Don't Cut It
ChatGPT doesn't match keywords. It matches conversations. Your content needs to sound like one human explaining something to...
Read guide →ChatGPT Direct Answer Paragraphs -The Unit of Citation
ChatGPT pulls self-contained paragraphs -2-4 sentences that answer a question without needing anything else on the page. The...
Read guide →ChatGPT & Bing Indexation -The Gate You Didn't Know Existed
ChatGPT uses Bing as its retrieval backend. Not indexed by Bing? Invisible to ChatGPT. Period. Tidio (63) actively...
Read guide →ChatGPT Content Retrievability -Indexed Doesn't Mean Found
Retrievability measures whether ChatGPT actually finds your pages when relevant queries come in. Indexed by Bing? Necessary. Sufficient?...
Read guide →ChatGPT Q&A Distribution -How Many Questions Are You Missing?
Q&A distribution maps your question-answer content against questions ChatGPT users actually ask. Every gap -a question people ask...
Read guide →ChatGPT Recency Bias -Stale Content Disappears
ChatGPT inherits Bing's recency bias -recently published or updated content gets retrieved more often. Content with dateModified within...
Read guide →ChatGPT Comparison Table Extraction -Your Secret Weapon for "vs" Queries
ChatGPT extracts and restructures HTML comparison tables into its answers for "vs" queries. But here's what it actually...
Read guide →How Claude Scores Your llms.txt (It's Not Pass/Fail)
Claude grades llms.txt on four levels: Level 1 (exists with basic description), Level 2 (structured URLs for key...
Read guide →The ClaudeBot Directive: Two Lines That Change Your Score
Claude penalizes sites that don't explicitly mention ClaudeBot in robots.txt - even if GPTBot and PerplexityBot are welcomed....
Read guide →Claude's Compound Trust Multiplier for JSON-LD
Claude applies a compound trust multiplier to JSON-LD: 1 schema type is baseline, 2 types give a small...
Read guide →Entity Disambiguation: Why Claude Skips You for Competitors
Claude uses a disambiguation algorithm that cross-references Organization schema, sameAs links, address data, and domain signals to distinguish...
Read guide →Content Licensing: The Permission Signal Claude Checks Before Citing You
Claude evaluates content licensing signals before deciding how freely to cite you. A CreativeCommons license, TDM Reservation Protocol...
Read guide →Semantic HTML: The Signal Claude Reads That ChatGPT Ignores
Claude performs deeper semantic HTML analysis than any other engine. It evaluates heading hierarchy (H1-H2-H3 nesting without gaps),...
Read guide →Fact Blocks: The Content Pattern Claude Cites First
Claude preferentially cites content structured as fact blocks: a named claim, followed by evidence or source, followed by...
Read guide →AEO for Startups
AI visibility strategy for early-stage startups and accelerator alumni.
AEO for Startups: Why AI Visibility Matters Before Product-Market Fit
Startups that set up llms.txt, Organization schema, robots.txt AI directives, and a basic FAQ in their first week...
Read guide →The 5 Quick Wins That Move a Startup from 30 to 60
Five changes that consistently produce a 25-30 point score lift: (1) llms.txt at domain root, (2) Organization +...
Read guide →What 2,500 YC Startup Audits Reveal About AI Readiness
Across 2,500+ YC startups audited from 12 batches, the average AEO Site Rank is 38/100. Only 2% score...
Read guide →AEO vs SEO for Startups: What Early-Stage Founders Get Wrong
SEO requires domain authority built over months. AEO requires technical infrastructure built in hours. For an early-stage startup,...
Read guide →Your Series A Pitch Deck Needs an AEO Site Rank
Investors increasingly use AI assistants to research markets, evaluate competitors, and validate startup claims. A startup with strong...
Read guide →How Top YC Startups Get Discovered by AI Engines
The highest-scoring YC alumni share a playbook: comprehensive llms.txt, 4+ schema types with compound trust, extensive FAQ and...
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