manage_search AI Visibility & GEO

AI Search Audit

Comprehensive audit across ChatGPT, Claude, Perplexity, and Gemini.

See how your site performs across major AI search surfaces in one pass. Spot crawl blocks, weak summaries, and missing citations before competitors own the answer.

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Key takeaways

  • check_circle AI Search Audit stress-tests one URL against crawl, clarity, schema, and freshness signals shared across ChatGPT, Claude, Perplexity, and Gemini.
  • check_circle A page can rank on Google yet stay invisible in AI answers when robots rules or render gaps block non-Google crawlers.
  • check_circle Snippet-ready structure matters: definitional openings, step lists, and comparison tables are what models extract first.
  • check_circle Freshness signals such as visible modified dates and updated statistics decide who wins research-style AI prompts.
  • check_circle llms.txt and consistent NAP data give engines a curated map to your authoritative facts.
  • check_circle Run staging versus production comparisons after redesigns to catch AI regressions before launch week.
  • check_circle Prioritize fixes on template URLs that scale across the site before polishing one-off blog posts.
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What is an AI Search Audit?

An AI search audit evaluates whether your website is technically fetchable, semantically clear, and authoritative enough to be cited across major generative search surfaces. Unlike a single-engine check, it assumes buyers will research in ChatGPT, Claude, Perplexity, Gemini, and Google with AI Overviews, often without ever opening a traditional results page.

Each surface uses different retrieval backends and weighting, but they share prerequisites. Crawlers must reach your HTML. Headings must segment facts models can quote. Entity names must stay consistent so summaries do not confuse you with similarly named brands. Fresh, corroborated claims beat stale superlatives when research queries demand current answers.

HeyLead AI Search Audit ingests a domain or priority URL, then scores multi-bot robots compatibility, indexation health, extractable answer blocks, structured data coverage, mobile versus desktop content parity, and llms.txt presence. Output is ordered by revenue risk: crawl blockers first, message clarity on money pages second, long-tail content gaps third.

The audit is designed for operational teams. Engineering sees redirect chains and noindex accidents. Content sees vague openings and missing FAQ coverage. Leadership sees which templates affect the most lead paths. You can run it before executive buy-in for an AI visibility program, after CMS migrations, or quarterly on stable properties.

AI search optimization is not about writing separate copy for each chatbot. It is about fixing shared fundamentals once, then monitoring surface-specific gaps with spot checks on high-value prompts. Pair audit scores with a fixed prompt library covering branded, category, and comparison questions.

Treat AI search health like technical SEO with a ticket backlog. Tag issues by owner, re-run after template releases, and tie improvements to assisted conversions from AI referrers. That discipline turns AI discovery from a vague initiative into measurable pipeline work.

Surface-specific tuning comes after shared fundamentals pass. Log which engines cite you on a fixed prompt set so you can spot Perplexity freshness wins versus ChatGPT clarity gaps without re-auditing unrelated URLs.

Export a ranked backlog tagged by owner: engineering for crawl, editorial for extractability, research for freshness. Re-audit template URLs after each sprint touching global layout so AI regressions do not hide in plugin updates.

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Why multi-surface AI search visibility matters

Your next customer may never type a query into Google. They may ask Claude to compare vendors, use Perplexity to validate pricing claims, or rely on Gemini for implementation checklists. If your site is blocked, vague, or outdated in those flows, you are excluded from consideration while competitors become the cited default.\n\nMulti-surface visibility matters because fixes are uneven across engines. Google Search Console can look healthy while a blanket robots disallow blocks research bots. A beautiful React hero can hide pricing from agents with limited JavaScript rendering. Strong domain rating does not help if your statistics still show 2019 benchmarks.\n\nFor marketing leaders, a unified audit prevents duplicate work. Instead of five disconnected checklists, you get one prioritized backlog that lifts both classic SEO and generative citations. For developers, it clarifies which template bugs scale across thousands of URLs.\n\nThe cost of inaction is quiet. Analytics undercount AI-influenced journeys because users convert after an offline recommendation from a chat answer. AI Search Audit makes invisible losses visible before quarterly targets slip.\n\nOps teams benefit from one audit export that tags crawl issues for engineering, extractability gaps for writers, and freshness problems for research editors without maintaining five separate checklists.

arrow_forward Surfaces crawl rules that block AI agents while organic Google traffic continues.
arrow_forward Scores extractable answer blocks models reuse in summaries and inline citations.
arrow_forward Validates JSON-LD coverage that disambiguates products, FAQs, and organizations.
arrow_forward Compares mobile and desktop HTML so mobile-first indexing and bots see the same facts.
arrow_forward Checks llms.txt and brand consistency files that orient research crawlers.
arrow_forward Produces a unified remediation backlog for engineering, content, and design owners.
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How to use this tool

  1. 1

    Submit your domain

    Start with the URL that best represents your brand and primary offer.

  2. 2

    Compare AI surfaces

    Review signals that affect ChatGPT, Claude, Perplexity, and Gemini differently.

  3. 3

    Prioritize fixes

    Work from crawl access to content clarity to authority markers.

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What this tool checks

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Multi-bot robots compatibility

Parses allow and disallow rules for search and AI user-agents on critical paths.

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Indexation health snapshot

Flags noindex, canonical conflicts, and soft 404 patterns on template pages.

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Extractable answer blocks

Scores presence of short definitions, steps, and lists near the top of key URLs.

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Structured data coverage

Checks JSON-LD types that help engines disambiguate products, FAQs, and organizations.

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Content parity across devices

Compares mobile and desktop HTML for missing text bots typically fetch.

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llms.txt and brand facts file

Validates whether curated AI guidance exists and points to authoritative pages.

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Technical guide

Signals, standards, and what to fix when checks fail.

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Multi-bot robots parsing

Enumerate robots User-agent groups and test AI crawler fetches on homepage, blog, pricing, and docs. Flag CDN bot fights returning 403. Cross-check HTML robots meta, X-Robots-Tag headers, and sitemap inclusion per URL. noindex on templates silently removes pages from grounding corpora used by multiple engines. Include OAI-SearchBot and anthropic-ai user-agents in quarterly robots reviews alongside GPTBot. Log Multi-bot robots parsing results with URL, severity, owner, and retest date in your SEO ticket template. Log Multi-bot robots parsing results with URL, severity, owner, and retest date in your SEO ticket template.
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Indexation directive conflicts

Inventory JSON-LD types per template and required properties. Report syntax errors and FAQ markup without visible questions. Valid schema mirroring on-page copy improves disambiguation across ChatGPT, Gemini, and Perplexity retrieval pipelines. Diff Article schema dateModified against visible bylines to catch CMS timezone bugs. Log Indexation directive conflicts results with URL, severity, owner, and retest date in your SEO ticket template. Log Indexation directive conflicts results with URL, severity, owner, and retest date in your SEO ticket template. Log Indexation directive conflicts results with URL, severity, owner, and retest date in your SEO ticket template.
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JSON-LD type coverage

Render mobile and desktop HTML for flagship URLs and diff text, headings, and links. Mobile-first indexing means bots may never see desktop-only pricing rows. Recommend responsive patterns keeping facts in initial HTML for both users and parsers. Measure text-to-code ratio in mobile HTML; boilerplate nav-heavy pages score thinner for extractability. Log JSON-LD type coverage results with URL, severity, owner, and retest date in your SEO ticket template. Log JSON-LD type coverage results with URL, severity, owner, and retest date in your SEO ticket template.
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Mobile HTML diff snapshot

Detect nosnippet and data-nosnippet on containers holding definitions and steps. Global blog templates sometimes inherit snippet blocks from legacy plugins. Removing accidental directives restores excerpt eligibility across AI search surfaces. Audit PDF-only whitepapers for matching HTML executive summaries linked with rel=canonical. Log Mobile HTML diff snapshot results with URL, severity, owner, and retest date in your SEO ticket template. Log Mobile HTML diff snapshot results with URL, severity, owner, and retest date in your SEO ticket template. Log Mobile HTML diff snapshot results with URL, severity, owner, and retest date in your SEO ticket template.
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Deep dive

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Cross-engine crawl posture

AI search surfaces do not share one crawler list. An AI search audit maps robots.txt and CDN rules against agents used by major answer engines and research bots. Accidental blanket disallows or rate limits can leave you invisible in ChatGPT browsing, Perplexity retrieval, or Gemini grounding while Google organic remains unaffected. Inventory each User-agent stanza and test fetches on homepage, pricing, docs, and blog paths. Marketing URLs should return 200 without interstitial bot challenges unless legal documents a specific block. Separate /app and /admin disallows from public citation targets. Cloudflare bot fights may mimic robots blocks, so correlate WAF events with 403 spikes. Document policy changes in a runbook reviewed before every major launch or migration. Coordinate with security before pen tests that temporarily enable bot challenges on marketing hosts.

Audit user-agent coverage

List agents for OpenAI, Anthropic, Perplexity, and Google-Extended. Confirm each can reach priority templates.

Separate app from marketing

Disallow /app/ and /api/ while keeping public pages open. Document the policy for legal and engineering.

Watch CDN bot rules

Cloudflare and similar services may challenge bots independently of robots.txt.

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Snippet and summary eligibility

Each engine prefers concise extractable passages: definitions in the first screen, structured lists for steps, and tables for comparisons. The audit measures heading hierarchy, visible FAQ coverage, and whether meta robots or data-nosnippet directives block excerpts you want quoted in AI answers. Measure how quickly a stranger understands your offer from raw HTML alone. Extractability fails when H1s are slogans, answers live inside Lottie files, or FAQs shrink to icons without text. Add summary bullets in semantic markup directly under intros on scalable templates. Procedural pages need numbered steps in HTML, not image-only tutorials. Comparison pages need feature tables with th headers. Removing accidental nosnippet tags on guides often unlocks excerpt use faster than rewriting entire articles. Transcripts for hero videos should mirror spoken pricing and feature claims in plain text beneath the embed.

Place answers above the fold

Answer the title question in the first 60 words before brand storytelling.

Remove nosnippet accidents

Scan templates for global robots meta tags intended for internal pages only.

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Freshness and change signals

Research-style AI queries overweight recency. Visible modified dates, sitemap lastmod accuracy, and stale statistics on flagship URLs cause losses to newer competitors even when backlink profiles are stronger. Update titles that still advertise old years and replace pre-pandemic benchmarks on money pages. Add changelog bullets when you refresh screenshots, pricing, or product names. Sitemap lastmod should change only on material edits to avoid crying wolf. For evergreen URLs, show last updated near the H1 with specifics, not a hidden CMS field. Quarterly refresh cycles on competitive guides are minimum discipline for AI search, not optional polish for publishers. Product rename campaigns should update llms.txt the same day URLs change to avoid stale AI answers. This guidance applies directly to ai search audit reviews on template URLs that influence qualified pipeline.

Show updated dates on guides

Display "Last updated March 2026" near the H1 on competitive topics.

Refresh statistics quarterly

Replace outdated market size figures cited in AI comparisons.

Publish changelogs on product pages

Summarize feature releases so models detect active maintenance.

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Authority and corroboration

AI systems cross-check claims against other indexed sources. Missing outbound citations to primary research, thin author attribution, and unverifiable superlatives reduce citation odds. Name customers when contracts allow and link to SEC filings, standards docs, or peer-reviewed studies beside statistics. Methodology blurbs with sample sizes increase trust for both journalists and models. Thin affiliate roundups lose to first-party benchmarks even on high DR domains. Author pages with credentials help on YMYL topics. Authority is not backlinks alone; it is corroborated facts repeated consistently across your public footprint and respected external references. Original survey data should publish response rates and margin of error so models treat stats as serious research. This guidance applies directly to ai search audit reviews on template URLs that influence qualified pipeline.

Cite primary sources

Link to government, academic, or vendor documentation when stating technical specs.

Attribute statistics inline

Write "according to Gartner 2025" beside numbers, not only in footnotes.

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Examples

thumb_up Strong examples

Robots allow list

User-agent: GPTBot Allow: / Disallow: /app/ Disallow: /admin/

Explicitly allows marketing paths while protecting authenticated areas.

Opening answer block

Project management software helps teams plan tasks, assign owners, and track deadlines in one workspace.

Delivers a standalone definition models can quote for category queries.

Step list markup

<ol><li>Connect your CRM</li><li>Import contacts</li><li>Launch your first campaign</li></ol>

Ordered lists map cleanly to how-to prompts across AI tools.

llms.txt entry

# Official facts https://brand.com/about https://brand.com/pricing https://brand.com/security

Points engines to canonical pages for company, pricing, and trust facts.

thumb_down Weak examples

Blanket disallow

User-agent: * Disallow: /

Blocks all crawlers including research agents you need for citations.

nosnippet on guides

<meta name="robots" content="nosnippet"> on a flagship how-to URL

Prevents excerpt use in AI and traditional snippets alike.

Desktop-only pricing table

Prices rendered only after client-side fetch on mobile breakpoints

Mobile HTML may lack numbers bots and phone users need.

Stale year in title

Best Email Tools 2019: Updated Guide (last edited 2019)

Research engines deprioritize obviously outdated sources.

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Best practices and common mistakes

check_circle Best practices

  • done Maintain a changelog of global template edits that affect headings and schema.
  • done Test five branded and five category prompts in major AI tools after each audit cycle.
  • done Coordinate robots.txt updates with marketing so launches are not accidentally blocked.
  • done Keep llms.txt synchronized with your canonical about, pricing, and security pages.
  • done Segment analytics for perplexity.ai, chatgpt.com, and gemini.google.com referrers.
  • done Fix systemic template bugs once instead of patching individual URLs endlessly.

cancel Common mistakes

  • close Assuming high Domain Rating alone satisfies AI search requirements.
  • close Publishing press releases without updating the service pages AI users actually read.
  • close Applying nosnippet globally when only checkout pages need restrictions.
  • close Treating each AI platform as a separate content strategy before fixing shared crawl access.
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Common use cases

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Run a single baseline audit before pitching executive buy-in for an AI visibility program.

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Compare staging versus production after a redesign to catch AI regressions early.

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Onboard a newly acquired domain with a multi-surface health scorecard.

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Support quarterly QBRs with repeatable AI search metrics across properties.

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Triage which subsite needs engineering time when crawl errors spike post-migration.

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Who should use this

person Heads of growth balancing organic, paid, and emerging AI discovery channels. person Technical SEO leads coordinating fixes with engineering and content teams. person Content directors planning editorial calendars around AI-friendly formats. person Consultants delivering unified AI plus SEO audits to mid-market clients.
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Glossary

AI search surface
Any interface where users receive answers grounded in retrieved web content, such as ChatGPT browsing or Perplexity.
Grounding
The process of fetching external pages to support statements in a generative answer.
GPTBot
OpenAI crawler user-agent that fetches pages for training and browsing features.
PerplexityBot
Crawler associated with Perplexity AI retrieval and citation indexing.
Content parity
Whether mobile and desktop HTML expose the same substantive text to crawlers.
Extractable block
A short passage, list, or table models can lift without surrounding context.
lastmod
Sitemap timestamp signaling when a URL last changed materially.
Assisted conversion
A lead influenced by research in one channel but converting in another session.
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Frequently asked questions

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