Frequently Asked Questions

AEO is the practice of structuring your website so AI answer engines - ChatGPT, Perplexity, Google AI Overviews, Claude - can find, understand, and cite it. SEO gets you ranked in search results. AEO gets you into AI-generated answers. Different game, different rules.

SEO optimizes for page rankings. AEO optimizes for AI citations - being the source AI systems reference when answering questions. SEO runs on keywords and backlinks. AEO runs on structured data, clean HTML, entity authority, and Q&A content format. They're complementary, but AEO addresses the growing share of searches that never reach traditional results.

All the major ones: ChatGPT (OpenAI), Perplexity AI, Google AI Overviews, Claude (Anthropic), and Microsoft Copilot. Each engine has its own crawlers and citation patterns, but the 22 AEO criteria - organized across 4 dimensions (Content, Structure, Authority, Discovery) - improve visibility across all of them.

Over 60% of Google searches now end without a click. 40% of Gen Z use AI assistants instead of Google. When someone asks ChatGPT about your industry, you're either in the answer or you don't exist. AEO puts you in the conversation where purchase decisions are increasingly made.

An AI citation happens when an answer engine references your website as a source in its response. Perplexity answers a question about your industry and links to your site - that's a citation. Citations drive traffic and establish authority in AI-mediated discovery.

Not at all. Small businesses, local providers, niche e-commerce stores, solo consultants - they all benefit from AI visibility. We've seen smaller sites with well-structured content outperform larger competitors who haven't optimized for AI engines. Size matters less than structure.

Any industry where customers ask questions before buying. Healthcare, legal, financial services, SaaS, e-commerce, professional services, and education see strong results because people use AI assistants to research these topics before making decisions.

AI engines use web crawlers - GPTBot for ChatGPT, ClaudeBot for Claude - to index website content. When a user asks a question, the AI retrieves relevant indexed content, synthesizes an answer, and cites the original source. The better your content is structured for machine reading, the more likely it gets retrieved and cited.

AI-driven traffic is growing fast. Google AI Overviews appear on over 30% of search results. ChatGPT has over 200 million weekly active users. Perplexity processes hundreds of millions of queries monthly. The exact traffic attribution varies by industry, but the trend is undeniable - AI is becoming a primary discovery channel.

Yes. Siri, Alexa, and Google Assistant increasingly pull AI-generated answers from web content. The same structured data, Q&A formatting, and entity authority that drive AEO also make your content the go-to source for voice search answers.

Readiness is your technical foundation - whether your site has the right schema, llms.txt, clean HTML, and entity signals in place. Visibility is real-world performance - whether AI engines actually cite you when users ask relevant questions. You need readiness first, then visibility follows. Our audit measures readiness; our visibility analysis measures actual citation rates.

Yes. The 22 AEO criteria across all 4 dimensions (Content, Structure, Authority, Discovery) are language-agnostic. AI engines index content in all major languages. For multilingual sites, hreflang tags help AI crawlers serve the right language version. The same structural principles apply regardless of language.

The free audit analyzes your website across 22 criteria that determine AI visibility, including llms.txt, Schema.org structured data, Q&A content format, clean crawlable HTML, entity authority, robots.txt AI policy, FAQ sections, original data, content freshness, fact density, canonical URLs, and more. You get a score out of 100 with specific findings and actionable recommendations.

The 22 criteria are organized into 4 dimensions. CONTENT (7 criteria) - is your content worth citing? Q&A Content Format, FAQ Sections, Original Data, Content Freshness, Definition Patterns, Direct Answer Paragraphs, Fact Density. STRUCTURE (5) - can machines parse it? Schema.org Structured Data, Clean Crawlable HTML, Semantic HTML5, Sitemap Completeness, Schema Coverage. AUTHORITY (5) - should AI engines trust you? Entity Authority, Content Licensing, Author Schema, Canonical URL, Content Velocity. DISCOVERY (5) - can AI engines find you? llms.txt, robots.txt for AI Crawlers, Internal Linking, Sitemap Completeness, RSS Feed. Each is scored 0–10, weighted, and rolled up to a total out of 100.

Each criterion is scored 0–10 based on presence, quality, and completeness. The scores are weighted — Schema.org and Q&A Format carry 15% each, while robots.txt and Semantic HTML carry 5% each. The weighted total produces a score out of 100. Below 40 means major gaps. 40–60 means partial implementation. 60–80 is solid. Above 80 is excellent.

The automated crawl and analysis finishes in 2–3 minutes. You get results immediately — overall score, individual criterion scores, detailed findings, and prioritized recommendations. No waiting, no email required.

Yes. Enter any publicly accessible URL and get the same 10-point analysis. We use this constantly for benchmarking — it shows exactly where you can gain an advantage in AI visibility over competitors.

Visibility Analysis goes beyond the technical audit to test how your domain actually appears in AI answers. We run real queries against ChatGPT, Perplexity, Google AI Overviews, and Claude to measure citation rates, sentiment, and competitor mentions. This is your real-world AI visibility — not just readiness.

The audit evaluates your website's technical readiness — whether you've got the right infrastructure in place. The visibility analysis tests real-world results by querying actual AI engines and measuring citations, sentiment, and competitor mentions. The audit tells you what to fix. The visibility analysis tells you how you're actually performing.

A multi-engine audit runs the same domain through multiple AI engines independently — say, ChatGPT (GPT-5.2) and Claude (Opus 4.6). Each engine scores differently because they weight different content signals. HelpSquad scored 47 on ChatGPT but only 42 on Claude. The side-by-side comparison reveals blind spots a single-engine audit misses.

Each of the 22 criteria shows a score from 0–10 with a status indicator. Green (8–10) means strong. Yellow (5–7) means partial — room to improve. Red (0–4) means missing or broken. Click any criterion to see detailed findings and a link to the relevant Knowledge Base guide.

Each time we re-audit a domain, a new version is created. You can view any previous version and compare two side by side to see exactly which criteria changed. This is how you measure the impact of your AEO work over time.

Yes. After making improvements based on audit recommendations, request a fresh audit to see your updated score. Each re-audit creates a new version you can compare against previous ones to quantify progress.

It's a central page for any audited domain showing everything we know: per-engine score gauges, verdicts, visibility scores, score trends across versions, scorecard comparison, top opportunities, and key metrics. One URL, one unified view — instead of jumping between individual audit and visibility pages.

A score below 20 means most fundamental AEO infrastructure is missing - no llms.txt, no relevant schema, likely blocking AI crawlers, no FAQ content. The good news is the fastest gains come from this starting point. Adding llms.txt, basic Organization schema, and unblocking AI crawlers in robots.txt can double your score in a single day.

llms.txt is a plain-text file at your domain root — like robots.txt — that gives AI assistants a structured summary of your website. What your business does, key content areas, how to navigate your site. It helps AI systems understand your business without crawling every page. If you want AI visibility, you need one.

A one-line business description, your main services or products, key content areas with URLs, site structure and important pages, contact info, and any specialized terminology. Use markdown formatting with clear headings. Keep it factual and concise — think of it as a quick briefing document for an AI assistant.

llms.txt is the summary — your business and key pages in brief. llms-full.txt is the extended version with full service descriptions, complete URL inventory, detailed content summaries. AI systems check llms.txt first for a quick overview and llms-full.txt when they need deeper context.

The most impactful types: Organization (business identity), FAQPage (Q&A pairs), Article (author and publication info), BreadcrumbList (site hierarchy), and WebSite (site-level info). JSON-LD is the way to go. These help AI systems understand the structured meaning behind your text — not just the text itself.

Add a <script type="application/ld+json"> tag in your page HTML containing the schema data as JSON. Place it in the <head> or end of <body>. For Next.js, create a reusable JsonLd component. For WordPress, use Yoast SEO or Schema Pro. JSON-LD is preferred over microdata or RDFa because it's cleanly separated from HTML.

Use question-format headings (H2/H3) that mirror how people ask AI assistants. Lead with direct answers in the first 1–2 sentences, then add supporting detail. Include FAQ sections with proper schema. Make sure everything is server-side rendered — AI crawlers don't execute JavaScript.

Yes. GPTBot, ClaudeBot, and PerplexityBot all respect robots.txt directives. Block these crawlers and your content becomes invisible to their AI systems. Explicitly allowing them signals your content is available for citation. Check your robots.txt right now — we've seen sites unknowingly blocking AI bots.

The main ones: GPTBot (OpenAI/ChatGPT), ChatGPT-User (ChatGPT browsing), ClaudeBot (Anthropic/Claude), anthropic-ai (Anthropic general), PerplexityBot (Perplexity AI), and Google-Extended (Google AI Overviews/Gemini). Add explicit Allow rules for content pages and Disallow for admin and API routes.

SSR means your HTML content is generated on the server before being sent to the browser. Most AI crawlers don't execute JavaScript — they read raw HTML. If your content is rendered client-side, AI crawlers see empty pages. Use SSR, SSG, or pre-rendering to make sure your content is in the HTML source.

Critical. AI systems use heading hierarchy (H1 → H2 → H3) to understand content structure and topic relationships. One H1 per page — the main topic. H2s for major sections, H3s for subsections. Question-format headings like "What is AEO?" are especially effective because they match how users query AI assistants.

Internal links help AI systems map relationships between your content. Topic hubs, Related Articles sections, breadcrumbs, cross-references — they create a navigable content graph that AI crawlers follow. Strong internal linking distributes authority across pages and ensures important content is discoverable no matter where a crawler enters your site.

JSON-LD. It's cleanly separated from HTML — no inline attributes to maintain. It's easier to implement and debug. Google and most AI systems prefer it. Microdata and RDFa mix schema with HTML attributes, making them harder to maintain and more error-prone. JSON-LD goes in a single script tag and you're done.

Use Google's Rich Results Test or Schema Markup Validator to check syntax and required fields. Our audit also validates structured data as part of the Schema.org criterion. Common errors include missing required properties, wrong @type values, and orphaned schema blocks that reference entities not defined on the page.

Yes. No signup, no credit card. Enter any website URL and get a full 10-point analysis with scores, findings, and recommendations. We built this to help businesses understand their AI visibility gaps — the audit is the first step.

You get detailed findings and our Knowledge Base guides to implement improvements yourself. Each criterion has an in-depth guide with code examples and common mistakes. If you want hands-on help, we offer AEO Content Optimization services to deploy the changes for you.

Many improvements take a single day. Adding llms.txt, deploying Schema.org markup, updating robots.txt, fixing semantic HTML — all quick wins. Building FAQ sections and restructuring content for Q&A takes 1–2 weeks. Entity authority and original data are ongoing investments that compound over time.

Some improvements — writing FAQ content, adding author bios — require zero technical skills. Others — deploying JSON-LD schema, modifying robots.txt — require basic HTML knowledge or developer access. Our Knowledge Base guides include code examples for each criterion. For hands-on help, we offer professional services.

Yes. Every audit is versioned. You can see exactly how your score changed between audits — which criteria improved, which declined, and the overall trajectory. This is how you measure the impact of your AEO work.

Start with high-impact, low-effort wins: (1) Create an llms.txt file, (2) Add Organization JSON-LD to your homepage, (3) Update robots.txt to allow AI crawlers, (4) Add FAQPage schema to existing FAQ content. These four changes take under a day and typically produce noticeable improvements within weeks.

A score below 40 means several fundamentals are missing — like Crisp (34) and HelpCrunch (33) in our Customer Support benchmarks. Focus on: (1) llms.txt — quick to create, high impact, (2) Schema.org — Organization schema at minimum, (3) robots.txt — make sure you're not blocking AI bots, (4) Clean HTML — content must be in the page source, not behind JavaScript.

The audit is free. Self-implementation using our Knowledge Base guides costs nothing but your time. For professional AEO optimization, we build custom programs based on your industry, competitive landscape, and goals. Schedule a free consultation to discuss what makes sense for your business.

Yes. WordPress has excellent AEO support. Use Yoast SEO for schema markup, create llms.txt via FTP or a custom plugin, edit robots.txt through Yoast or directly, and build FAQ pages with Gutenberg blocks. Most WordPress themes already produce clean, crawlable HTML.

Yes, though Shopify requires some workarounds. Use apps like JSON-LD for SEO for schema markup, create llms.txt through the theme editor, modify robots.txt through Shopify admin, and build FAQ sections in product descriptions and dedicated pages. Shopify produces server-rendered HTML, which is good for AI crawlability.

Four things on day one: (1) Organization JSON-LD schema on every page, (2) a well-structured llms.txt at your domain root, (3) robots.txt explicitly allowing GPTBot, ClaudeBot, and PerplexityBot, (4) at least one FAQ page with FAQPage schema. This baseline takes a few hours and establishes the foundation everything else builds on.

AI systems evaluate structured data quality, content relevance, entity authority signals, content freshness, and how well the format matches the question. Sites with Schema.org markup, clear entity information, and Q&A-formatted content get cited far more often than sites with unstructured, anonymous content.

Depends on your audience. ChatGPT has the largest user base for general queries. Perplexity is growing fast among researchers and knowledge workers. Google AI Overviews affects anyone relying on Google search traffic. Claude dominates professional and enterprise contexts. The 22 AEO criteria across 4 dimensions improve visibility across all of them.

Technical changes — Schema.org, robots.txt — get picked up by AI crawlers within days to weeks. Content changes — FAQ pages, Q&A restructuring — take weeks to months to be fully indexed and reflected in AI answers. Unlike SEO, there's no established timeline. AI systems re-index on their own schedules.

No. They're complementary. SEO optimizes for search rankings and click-through traffic. AEO optimizes for AI citations and answer engine visibility. Many AEO improvements — structured data, semantic HTML, internal linking — also boost SEO. Implement both.

A visibility score measures how often and how prominently your domain appears in AI answers for relevant queries. We test this by running real queries against multiple AI engines, analyzing whether you're cited, what competitors show up alongside you, and what sentiment the AI expresses about your brand. It's real-world visibility — not just technical readiness.

Query variants are different phrasings of the same question. "Best live chat software," "top customer support tool," "live chat comparison 2025" — all variants of one topic. AI engines cite different sources for different phrasings. Testing multiple variants reveals gaps where you're visible for some queries but invisible for others.

AI engines typically cite 3–5 sources per answer. If competitors have better-structured content, stronger entity signals, or more FAQ pages, they get cited instead of you. Our competitor comparison shows exactly which competitors appear in AI answers for your target queries and what content signals give them the edge.

Threat analysis identifies competitors and trends that can erode your AI visibility. New competitors gaining citations in your space. Competitor content improvements outpacing yours. Emerging query patterns where you have no presence. Changes in AI engine behavior that affect your citation rate.

Yes. Each visibility analysis is versioned. You can track how citation rates, competitor positions, and query coverage shift over time. Regular monitoring catches visibility drops early, measures the impact of AEO improvements, and keeps you ahead of competitors who are also optimizing.

Each engine has its own training data, indexing system, and citation logic. ChatGPT prioritizes conversational relevance. Claude emphasizes structured data and entity clarity. Perplexity focuses on real-time web results. Google AI Overviews draws from its search index. A multi-engine audit reveals these differences so you can optimize across the board.

Zero-click search means the user gets their answer directly from AI without clicking through to any website. Over 60% of Google searches are now zero-click. AEO addresses this by making your brand the cited source in those AI answers - even when users never visit your site, they see your name, building awareness and trust.

Benchmarks show average and best AEO readiness scores across business sectors. We track Healthcare & Patient Advocacy, Customer Support & BPO, Music & Vinyl Retail, SaaS & Productivity, and AI & Marketing Technology. Each sector breaks down into categories. They help you understand where you stand relative to industry peers - not just in absolute terms.

It varies. In Customer Support & BPO, scores range from 33 (HelpCrunch) to 63 (Tidio), with the median around 45. Healthcare tends higher due to content-rich sites. Scoring above the industry median means you're ahead of most competitors. Top quartile means you've got a real competitive advantage in AI visibility.

Benchmarks come from real AEO audits of domains in each sector. We audit multiple companies per category using the same 22-criteria methodology across 4 dimensions and aggregate results. Each domain shows per-engine scores (ChatGPT, Claude, Gemini), best score, and overall visibility indicators. Benchmarks update as new audits are published.

Yes. Browse benchmarks by sector and category, then click any domain to view its full Domain Intelligence Dashboard - detailed scorecard, findings, recommendations. See exactly where competitors are strong and where they have gaps you can exploit.

Automatically, whenever new audits are published. Each audit includes a timestamp so you know how current the data is. Industries with more audited domains have more reliable benchmarks. We're continuously expanding coverage across sectors.

Low benchmarks usually mean most businesses in that sector haven't prioritized AEO yet. That's an opportunity, not a problem. Implementing AEO while competitors are still unaware gives you an early-mover advantage. Industries with low benchmarks see the fastest improvement from basic AEO work.

Yes. If your industry isn't represented, contact us. We need 4-5 domains per sector to establish meaningful benchmarks. We prioritize industries with strong demand for AI visibility.

It's a central page for any audited domain that pulls together everything: per-engine score gauges, multi-engine comparison, visibility analysis, score trends across versions, top opportunities, and key metrics. One URL you can share with stakeholders to show the complete AEO profile.

Compare your per-criterion scores against the sector average. If your Schema.org score is 3/10 but the sector average is 6/10, that criterion is a priority. Focus on closing the biggest gaps first - those represent the areas where competitors are already ahead and where improvement yields the most relative gain.

Use question-format headings (H2/H3) that match how people query AI assistants. Start each section with a direct answer in 1–2 sentences, then add supporting detail and data. Use bullet lists for enumerable items. This inverted-pyramid style makes it easy for AI systems to extract and cite your answer.

At least 20–30 substantive questions across your key topics. Quality over quantity — each answer needs 3–5 sentences minimum and genuine depth. Group FAQs by category with clear headings. Include FAQPage JSON-LD schema so AI engines parse the Q&A structure directly. Our FAQ has 131 items across 14 categories — that's the kind of coverage that gets cited.

Proprietary data, first-hand research, expert analysis with author credentials, unique case studies, perspectives you won't find elsewhere. AI engines value original sources because they add new information to the knowledge base. Rephrasing content from other sources adds zero value for AI citation.

Create content for humans first, then structure it for AI extraction. The best AEO content is genuinely useful to readers AND easy for AI to parse. That means clear headings, direct answers, proper schema, and author attribution. Don't create low-quality keyword-stuffed content — AI engines are smart enough to spot it.

Very. AI systems use author info as an authority signal — especially for YMYL topics like health, finance, and legal content. Include names, credentials, professional background, and links to profiles. Add Person schema markup. Anonymous content is at a serious disadvantage for AI citation.

Both — but updating existing high-performing content often yields faster results. Add Q&A headings, FAQ schema, author attribution, and internal links to existing pages. For new content, focus on topics where you have genuine expertise or original data. AI engines value freshness, so update publication dates when you substantially revise content.

Focus on the universal AEO criteria: structured data, clean HTML, entity clarity, Q&A formatting. These work across all engines. Multi-engine audits reveal engine-specific gaps — Claude weights structured data heavily while ChatGPT prioritizes conversational relevance. Address the common foundation first, then fine-tune for specific engines.

There's no magic number, but content must fully answer the question. Under 300 words rarely gets cited — too thin. Over 5,000 words can bury key answers. The sweet spot is thorough coverage with clear structure. Use headings to make long content navigable. Put answers in the first paragraph of each section.

AI engines prefer recently updated content, especially for time-sensitive queries. ChatGPT inherits Bing recency signals - content updated within 90 days gets a retrieval boost. Add dateModified to your Article schema, update publication dates on substantially revised pages, and maintain a regular publishing cadence to signal ongoing relevance.

Group questions by topic category with clear H2 headings per category. Each question should be an H3 with the answer immediately below. Lead every answer with a direct 1-2 sentence response, then elaborate. Add FAQPage JSON-LD schema matching every visible Q&A pair. Avoid hiding answers behind JavaScript accordions - AI crawlers need the HTML visible in the source.

Different training data, different evaluation criteria, different citation priorities. ChatGPT (GPT-5.2) focuses on conversational query matching and content retrievability. Claude (Opus 4.6) emphasizes structured data completeness and entity disambiguation. HelpSquad scored 47 on ChatGPT but only 42 on Claude — that 5-point gap reveals exactly which signals each engine cares about.

We currently audit with ChatGPT (GPT-5.2), Claude (Opus 4.6), and Gemini (2.5). Each engine evaluates the same 22 AEO criteria independently, producing separate scores, verdicts, and recommendations. The comparison dashboard shows all results side by side.

Start with the 22 AEO criteria that work across all engines. If you must pick one, choose by audience: ChatGPT for consumer-facing businesses, Claude for enterprise and professional contexts, Google AI Overviews if your traffic is Google-dependent. The multi-engine audit shows which engine gives you the biggest gap to close.

Per-engine scores with visual gauges and delta badges showing the gap between engines. Verdict comparison. A scorecard table showing how each criterion scored across engines. Merged opportunity recommendations with engine badges indicating which engine flagged each improvement.

Yes. Each engine produces its own findings and recommendations. On the comparison dashboard, opportunities are merged and tagged with engine badges — you can see which improvements are flagged by one engine versus all engines. Recommendations that appear across multiple engines are your highest priority.

Continuously, often without announcements. Major model updates — a new GPT version, a new Claude release — can shift citation patterns overnight. This is why regular re-auditing matters. A score from three months ago doesn't necessarily reflect current engine behavior.

Gemini 2.5 is Google's AI model powering Google AI Overviews — the AI summaries at the top of Google search results. It evaluates content for knowledge graph alignment, multimodal retrieval, and AI Overview inclusion. Since AI Overviews appear on over 30% of searches, Gemini optimization directly affects your Google search visibility.

Audit scores measure technical readiness — whether your content is structured for AI extraction. Visibility scores measure actual citation rates. Higher audit scores generally correlate with better visibility, but domain authority, content relevance, and competition also play a role. You need both metrics for the complete picture.

Custom AEO optimization programs tailored to your industry, competitive landscape, and visibility goals. Services range from foundational technical setup (llms.txt, Schema.org, robots.txt) to full content restructuring, multi-engine optimization, and ongoing visibility management. Schedule a free consultation to discuss what makes sense for your business.

Every business has different needs. We build custom programs based on factors like your current AEO score, number of competitors, content volume, and target AI engines. Schedule a free 30-minute consultation and we will walk through your specific situation and recommend an approach.

We offer both. AEO requires ongoing attention because AI engines update their models, competitors improve their content, and new query patterns emerge. But we also work on project-based engagements for specific, bounded needs. Let us know your situation during the consultation.

Schedule a free 30-minute consultation. We will review your current AI visibility, show you how competitors are performing, and outline a custom optimization roadmap. No commitment required.

Depends on your industry, competition, and current visibility. Businesses in competitive verticals often see measurable increases in AI-driven traffic and citations within 1-3 months. Our benchmark data shows most competitors in any given industry aren't optimizing for AI yet - that's a significant first-mover advantage for early adopters.

The Starter plan covers foundational AEO setup: llms.txt creation and deployment, Organization JSON-LD schema, robots.txt AI crawler configuration, and an initial FAQ section with FAQPage schema. These are the highest-impact, lowest-effort changes that establish your AI visibility baseline.

The Growth plan adds multi-engine audit coverage, content restructuring for Q&A extraction, expanded schema markup (Article, BreadcrumbList, FAQPage across all key pages), internal linking architecture improvements, and monthly re-audits to measure progress. It addresses the mid-weight criteria that separate average scores from strong ones.

The Scale plan includes everything in Growth plus the intelligent content pipeline - context-aware, humanized content production that continuously fills AI visibility gaps. It adds visibility analysis with competitor tracking, entity authority engineering, original data strategy, and ongoing optimization across all 22 criteria.

Yes. Agencies can white-label our audit infrastructure and run multi-domain AEO programs at scale. Volume pricing is available for agencies managing 10+ client domains. Contact us to discuss agency partnerships.

Foundational setup (Starter-level) takes 1-2 weeks. Full content restructuring (Growth) runs 4-8 weeks. Ongoing content pipeline (Scale) is a continuous engagement with monthly reporting. Most clients start with a bounded project and expand to ongoing once they see citation improvements in their visibility reports.

Schema coverage ratio measures what percentage of your indexed pages have relevant JSON-LD. A site with Organization schema on the homepage but no Article schema on blog posts has low coverage. The audit crawls all pages and calculates the ratio — above 80% gets full marks, below 40% scores poorly because most pages are invisible to structured data consumers.

Fact density counts verifiable claims per 1,000 words — named statistics, specific numbers, dated references, attributable statements. The audit uses NLP to identify factual assertions versus opinions or filler. Pages with fact density above 5 claims per 1,000 words are significantly more likely to be cited by AI engines. It's one of the strongest predictors of citation-worthiness.

Content velocity tracks how frequently you publish new or substantially updated content over a rolling 90-day window. The audit measures this through sitemap lastmod timestamps, RSS feed dates, and crawl-detected changes. Sites publishing at least weekly maintain stronger freshness signals than sites publishing monthly or less.

Conditional criteria are checks that only apply to certain site types. Product/Offer schema applies only to e-commerce. Speakable schema only to content-heavy sites. Hreflang only to multilingual sites. ai.txt/TDM policy only to sites with substantial original content. These 4 conditional criteria supplement the 10 universal technical audit criteria.

The audit compares URLs in your sitemap.xml against pages discovered by crawling. It checks for missing URLs, stale lastmod timestamps that don't reflect actual changes, incorrect priority values, and orphaned entries pointing to deleted pages. A complete, accurate sitemap ensures AI crawlers discover every page you want indexed.

Definition pattern detection identifies sentence structures AI systems extract for direct-answer responses. Patterns like "AEO is the practice of..." or "Content velocity refers to..." are structures AI engines pull when answering "What is..." queries. Pages with clear definition patterns appear in direct-answer results at significantly higher rates.

When multiple URLs serve the same content — with/without www, HTTP/HTTPS, trailing slashes — AI crawlers split signals across duplicates. The audit checks every page for a rel=canonical link pointing to the correct URL. Missing or conflicting canonicals confuse crawlers about which version to index, diluting your AI visibility.

The audit verifies your site has a discoverable RSS or Atom feed with proper metadata. It checks feed existence at common paths, a link tag in the HTML head, adequate item count, and pubDate per item. RSS feeds let AI indexing systems detect new content automatically without waiting for a full crawl.

AI engines frequently restructure HTML tables and lists into their answers — but only when the HTML is properly formed. The audit checks that tables use semantic thead, tbody, and th elements, that lists use proper ol/ul markup, and that data tables have clear column headers. Poorly formatted tables get skipped by AI extraction.

The audit looks for machine-readable licensing metadata: CreativeWork license properties in JSON-LD, meta tags indicating reuse policy, copyright notices, ai.txt declarations, and TDM Reservation Protocol headers. Clear licensing tells AI systems whether they can quote or reference your content in their answers.

The hallucination audit asks multiple AI engines direct questions about your business and checks their responses for accuracy. It catches four types of issues: facts AI invents about you, outdated cached info, competitor confusion where AI mixes you up with similar businesses, and missing information about key products or services AI should know but doesn't.

We submit real user-style queries to ChatGPT, Claude, and Perplexity, then analyze whether your domain appears in citations, which pages are referenced, whether quoted info is accurate, and how your citation rate compares to competitors for the same queries. This is real-world AI visibility — not a readiness checklist.

Content depth score uses AI evaluation to judge whether your pages cover topics with enough breadth and depth to be considered authoritative. A shallow page that skims a topic scores low even with perfect technical markup — because AI engines compare your depth against competing sources in real time.

Specific content structures AI engines prefer to extract: attributable claims with sources, self-contained factual statements, comparative assertions with evidence, definition-then-elaboration sequences. The intelligence report tests whether your content matches these patterns by running it through AI extraction algorithms.

Topic authority clustering maps your content into topic groups and evaluates whether you cover enough related subtopics to qualify as a real authority. A single page on a topic is weak. Multiple interconnected pages covering setup, comparison, pricing, and features — that's a cluster AI engines recognize as authoritative.

Cross-engine consistency measures the variance in your scores across ChatGPT, Claude, and other engines. A gap greater than 10 points — like Tidio's +14 Claude bonus or Crisp's +17 — signals your content is tuned for one engine but not another. The intelligence report uses this gap to trigger engine-specific recommendations.

Content uniqueness analysis uses AI to identify what percentage of your content provides genuinely novel information versus restating widely available knowledge. Pages that primarily restate common knowledge score low — AI engines already have that info from dozens of sources. They prioritize citing pages that add something new.

Author schema depth evaluates whether your Person markup includes enough detail for AI engines to verify the author as a real expert. Beyond just a name, deep schemas include sameAs links to LinkedIn, jobTitle, knowsAbout topics, and alumniOf institutions. Deeper schemas create stronger trust signals.

A verified Wikidata entry and Google Knowledge Panel mean your business entity exists in public knowledge databases that AI engines consult. Entities with Wikidata QIDs get higher trust scores because AI systems can cross-verify claims about your business against authoritative external sources — independent of your website.

It goes beyond checking that sameAs URLs are present — it confirms linked profiles actually exist, are active, and contain consistent business information. Dead social links or profiles with different business names damage trust rather than build it. AI engines follow these links to verify entity claims.

ChatGPT retrieves web content through Bing's search index. When ChatGPT browses the web to answer a question, it sends the query to Bing and processes what comes back. This makes Bing indexation a hard prerequisite — if Bing hasn't indexed your page, ChatGPT can't find it. Doesn't matter how well-structured your content is.

Submit your sitemap to Bing Webmaster Tools at bing.com/webmasters. Use the URL Inspection tool to check individual pages. You can also search site:yourdomain.com on Bing to see indexed pages. Our ChatGPT optimization audit checks Bing indexation automatically and flags pages missing from the index.

ChatGPT matches questions to web content using conversational NLP — not keyword matching. Content written in the same natural language patterns people use when talking to ChatGPT gets cited more often. Question-format headings, direct conversational answers, and natural phrasing outperform keyword-stuffed content every time.

ChatGPT inherits Bing's recency signals. Content with dateModified within the last 90 days gets a measurable retrieval boost. Stale content gradually drops out of ChatGPT retrieval even if it ranks well on Google. Regular content updates maintain ChatGPT visibility — it's that simple.

Self-contained 2–4 sentence blocks ChatGPT can extract and quote without needing surrounding context. The ideal paragraph: a direct answer, a supporting fact, a specific detail. Pages with 5+ such paragraphs get cited at significantly higher rates than pages that bury answers in long narratives.

ChatGPT frequently restructures HTML tables into comparison answers. Tables with proper thead/tbody/th structure, clear column headers, and factual data get extracted at high rates. Tables using CSS grid layouts, nested divs, or missing headers get skipped — ChatGPT can't reliably parse their structure.

Indexed by Bing means your page exists in the search index. Retrieved means ChatGPT actually selects it when answering relevant queries. Many indexed pages are never retrieved — they lack conversational relevance, have low Bing ranking, or don't contain extractable answer content. The retrievability test measures this gap.

Q&A distribution analysis maps your existing question-answer content against the questions ChatGPT users actually ask about your topic. Coverage gaps are missed citation opportunities. The analysis identifies high-volume questions you haven't answered and prioritizes new content based on query demand and competitive coverage.

Claude applies compounding trust to JSON-LD structured data. 1 schema type is baseline. 2 types give a small boost. 3 types cross a trust threshold. 4+ types create compounding trust. Sites with Organization plus FAQPage plus Article plus BreadcrumbList get exponentially higher Claude scores than sites with Organization alone. Tidio hit this threshold — that's part of why it scored 63.

Claude evaluates llms.txt on a four-level quality rubric: basic existence, structured URLs, detailed descriptions, and llms-full.txt link. No llms.txt means a trust penalty — Claude interprets it as a signal you haven't considered AI accessibility. Sites with high-quality llms.txt files get proportionally higher Claude scores.

Claude applies a specific trust penalty when ClaudeBot isn't explicitly mentioned in robots.txt — even if the site allows other crawlers like GPTBot. Explicitly allowing ClaudeBot signals willingness to be cited by Claude. Block ClaudeBot but allow GPTBot? Reduced Claude citation rates.

Claude cross-references Organization schema, sameAs links, address data, and domain registration signals to distinguish similarly-named businesses. Without clear entity signals, Claude will confuse you with competitors or refuse to cite you to avoid inaccuracy. HelpSquad's Claude score of 42 — 5 points below its ChatGPT score of 47 — was partly driven by thin entity signals.

Claude evaluates licensing to determine citation willingness. Clear CreativeCommons licenses, TDM Reservation Protocol headers, or ai.txt permissions explicitly granting citation rights increase citation rates. Ambiguous or missing licensing makes Claude more conservative about quoting content directly.

Claude performs deeper semantic HTML analysis than other engines. It evaluates strict H1→H2→H3 nesting, ARIA landmark roles, semantic sectioning elements (main, article, section, nav), and content-to-boilerplate ratio. Pages with clean semantic hierarchy get a measurable Claude scoring boost.

Claude preferentially cites content structured as fact blocks: a named statistic or claim, followed by its source, followed by context. A specific number, company attribution, date, market context. These match Claude's internal reasoning patterns and get extracted at higher rates than narrative prose.

Tidio got a +14 Claude bonus over what technical metrics alone would predict. Why? A 251-line llms.txt file. 4+ JSON-LD schema types triggering compound trust. Explicit ClaudeBot allow in robots.txt. Clean semantic HTML hierarchy. Claude's governance-first evaluation rewards sites that invest in machine-readable signals — and Tidio nailed all of them.

A context-aware content production pipeline that learns your domain, competitive landscape, and brand voice — then continuously produces citation-optimized content to fill your AI visibility gaps. It goes beyond auditing what’s missing to actually creating the content that gets you cited.

It starts with your AEO audit data, existing content, competitor analysis, and industry benchmarks. Every piece of content it produces and every citation signal it measures feeds back into the system. The more it works with your domain, the better it understands what your audience needs and what AI engines want to cite.

Two things. First, every piece is structured for AI citation from day one — proper schema, Q&A formatting, entity signals, fact density. Second, everything passes through our humanizer. No robotic phrasing, no filler, no telltale AI patterns. The result reads like it was written by your subject-matter expert, not a language model.

Our humanizer rewrites AI-generated drafts to eliminate machine-readable patterns — formulaic transitions, generic phrasing, repetitive structure. The output sounds natural and authoritative. This matters because AI engines are getting better at detecting and deprioritizing content that reads like it was generated by another AI.

It uses your audit gaps, competitive intelligence, query variant analysis, and citation performance data. If competitors are getting cited for topics where you have no content, that’s a priority. If existing content isn’t being retrieved, the pipeline identifies why and produces improved versions. Every decision is data-driven.

No. It augments your team by handling the volume of citation-optimized content that human writers can’t sustain alone. Your team focuses on strategy, original research, and thought leadership. The pipeline handles the structured, data-driven content that AI engines need to see at scale.

Audit → Produce → Measure → Refine. After content is published, the system tracks which pieces get cited, by which AI engines, for which queries. High-performing patterns are amplified. Underperforming content is analyzed and improved. Each cycle makes the next batch of content more effective.

Each piece of optimized content strengthens your domain’s overall authority signals. More citation-ready pages means more AI citations. More citations means higher trust. Higher trust means future content gets cited faster. The effect compounds — early investment in content quality pays increasing returns over time.