How do AI agents
see your site?

What this audit actually checks

1. llms.txt manifest, an optional, proposed convention (llmstxt.org) for a curated, machine-readable site index. Google states it does not use llms.txt for AI Overviews or AI Mode. Weight: 15%.
2. llms-full.txt corpus, an optional single-file full-text companion to llms.txt. Google does not use it either. Weight: 8%.
3. AI crawler allow-list in robots.txt, explicit Allow rules for GPTBot, ClaudeBot, PerplexityBot, Google-Extended, Applebot-Extended. Many sites block by default. Weight: 12%.
4. Schema.org structured data: JSON-LD presence, depth, and graph coverage (Organization, BlogPosting, FAQPage, BreadcrumbList, etc). Weight: 18%.
5. Open Graph + Twitter Card metadata, social-preview metadata completeness for X, LinkedIn, Slack, Discord previewers. Weight: 10%.
6. Speakable schema: SpeakableSpecification on long-form content. A Google beta for Google Assistant news read-aloud (US, English). It is not an AI Overviews input. Weight: 8%.
7. Heading hierarchy, single h1, structured h2/h3, no level-skips. AI agents use heading structure for content extraction. Weight: 7%.
8. XML sitemap, sitemap presence, URL count, lastmod tags. Weight: 7%.

Why most sites score under 60 in 2026

Most enterprise sites were built before AI agents were a citation surface. They still optimise for Google blue-link SERPs, meta titles, schema, sitemap. AI answer engines add their own gates: each runs its own crawler that robots.txt can block, and they reuse passages that answer a question on their own. Clean structured data on every page rather than only the homepage helps them identify the business behind the page. The gap between "blue-link ready" and "AI-citation ready" is where most visibility is lost in 2026.

What is AI visibility?

AI visibility is the measurable rate at which AI search engines: ChatGPT Search, Perplexity, Claude Web, Gemini, Google AI Overviews, Bing Copilot, discover, parse, and cite your domain when users ask brand-relevant or category-relevant questions. It is not the same as AI search ranking, which is the position your domain occupies inside a generated answer. AI visibility is the upstream signal: whether the engine can ingest your site at all, and whether the technical signals on the page make your content eligible for citation. Without visibility, ranking is impossible.

What is AI searchability?

AI searchability is the machine-readable layer underneath AI visibility: how easily an AI engine can crawl, parse, and reuse your site as a citation source without guessing. It is decided by concrete on-page artefacts, anllms.txt manifest, an AI crawler allow-list in robots.txt, JSON-LD on every page, a semantic heading hierarchy, a clean sitemap. The eight checks below are, in effect, an AI searchability score for any domain: they measure whether engines can read you, before any question of whether they choose to cite you.

How AI search visibility differs from Google SEO

Traditional Google SEO optimises for blue-link ranking: meta titles, internal links, page authority, backlinks. AI search visibility optimises for citation in generative answers, which adds its own requirements: AI crawler access in robots.txt, structured data depth, JSON-LD on every page rather than only the homepage, and passages that answer a question without the surrounding page. AI agents extract entities and citations differently than Googlebot ranks pages, which is why a site can rank well in Google but be invisible to ChatGPT or Perplexity. The two stacks overlap on schema and sitemap quality but diverge on crawler permissioning: GPTBot, ClaudeBot and PerplexityBot each obey their own robots.txt rules, while Google’s AI features use normal Googlebot crawling.

How to improve brand visibility in AI search engines

Short answer: the seven engineering moves below: publish anllms.txt manifest, allow AI crawlers in robots.txt, ship JSON-LD on every page, open each long-form section with a self-contained answer, keep a semantic heading hierarchy, maintain a clean XML sitemap withlastmod tags, and add a llms-full.txt content corpus. The two llms files are optional extras: Google states it does not use them for AI Overviews or AI Mode, which need only a page that is indexed and eligible for a snippet. For the Shopify-specific version of this playbook, see our guide to Shopify AI search optimisation for ChatGPT and Perplexity.

The full engineering checklist that moves the audit score most reliably in 2026:

1. Optional: publish an llms.txt manifest at the root of your domain with a curated, machine-readable index of canonical URLs grouped by intent. Google does not use it.
2. Allow GPTBot, ClaudeBot, PerplexityBot,Google-Extended, and Applebot-Extended inrobots.txt. A blocked crawler cannot read the site, and some sites block these without realising it.
3. Ship JSON-LD on every page: Organization withsameAs links to Crunchbase, LinkedIn, GitHub; BlogPostingon every article; FAQPage on long-form content; BreadcrumbListfor hierarchy.
4. Open each long-form section with a direct answer in its first sentence or two, so the passage still makes sense when quoted on its own.
5. Maintain a semantic heading hierarchy, single h1, structuredh2/h3 with no level-skips. AI agents use heading structure for content extraction.
6. Keep a clean XML sitemap with lastmod tags. Stale or missing sitemaps suppress recrawl frequency.
7. Optional: add a llms-full.txt single-file content corpus for tools that choose to read it. Google does not use it.

What strategies improve brand visibility in AI search engines?

Three strategy layers, in order of priority for engineering teams in 2026.

1. Technical-signal coverage, the seven on-page moves listed above cover the technical basics: AI crawler allow-list, JSON-LD, answer-first sections, headings and sitemap, plus the optional llms files. Without these, the rest of the strategy does not compound.
2. Entity authority, disambiguate your Organizationentity with sameAs links to Crunchbase, LinkedIn, GitHub, Wikipedia where applicable. AI engines weight cross-referenced entities much more heavily than orphaned ones. Pair this with consistent NAP (name, address, phone) in your structured data and footer.
3. Citation-eligible content depth, write the first 60 words of each long-form page as a self-contained answer to the page's primary question. AI engines extract that opening as a citation candidate. AddFAQPage JSON-LD with 4-6 concrete Q&A pairs per long-form piece, and use 60-120 word answers in the FAQ schema rather than terse one-liners.

The strategy compounds when all three layers are in place. Brands that ship only the technical signal stack improve their crawl-eligibility but still under-cite; brands that ship entity authority without the technical signals never get crawled in the first place. The audit on this page scores the first layer; the second and third layers are content + entity work that follows the audit fixes.

How to improve visibility in Google AI Overviews

Google AI Overviews, the generative summary panel at the top of Google search results, is built on Google’s normal Search systems. Google states there are no extra technical requirements: a page must be indexed and eligible to show a snippet, and no special markup or llms.txt file is needed. To improve visibility in AI Overviews specifically: make sure your key pages are indexed, structure long-form content as a series of self-contained answers, keep structured data consistent with the visible page, and make sure your Organization entity is disambiguated with Crunchbase and Wikipedia sameAs links where applicable.

Generative Engine Optimization (GEO) explained

Generative Engine Optimization (abbreviated GEO) is the umbrella term for optimising a site for generative AI search engines (ChatGPT Search, Perplexity, Claude Web, Gemini, Google AI Overviews and AI Mode, Bing Copilot, and newer entrants such as DeepSeek). GEO is distinct from traditional SEO in three respects: (1) it ranks citation eligibility not blue-link position, (2) it adds AI-specific checks, chiefly each engine’s crawler access, on top of the SEO stack, and (3) success is measured by whether the answer cites you, not by a rank. Most engineering teams adopt GEO by treating it as a superset of their existing SEO work, the structured-data and sitemap layers are reused, with new crawler-permissioning and manifest work layered on top.

Answer Engine Optimization (AEO)

Answer Engine Optimization (abbreviated AEO) is the citation-winning subset of GEO. While GEO is about being visible to AI engines at all, AEO is about being the source the engine quotes. The signals that move AEO most: FAQPage schema with concrete questions and 60-120 word answers, AI crawler allow-list, citation-friendly first-60-word leads on every long-form piece, and clean Organization entity references viasameAs.

LLM visibility for SaaS and ecommerce brands

LLM visibility measures citation rate and snippet quality across large-language-model answers, the share of relevant prompts where your domain appears in the generated response, and the prominence of that citation. It is the outcome layer on top of GEO/AEO inputs. For SaaS brands the prompts that matter are category and competitor comparison queries ("best AI visibility tool", "Rankscale vs SE Ranking"). For ecommerce brands they are product-discovery and review queries ("best running shoes for flat feet", "is Allbirds worth it"). Tracking LLM visibility means combining a technical-signal audit (run the tool above on a recurring schedule) with prompt-level brand tracking: querying ChatGPT, Perplexity, and Claude Web with a fixed prompt set weekly and logging which domains are cited. For a field report on what actually moves ecommerce citation rates, see AI search for ecommerce: what actually works.

How to track AI brand visibility over time

A weekly tracking process that engineering teams can actually maintain:

1. Run the AI visibility tool above on your own domain and the top 3-5 competitors in your category. Record the eight sub-scores in a tracking sheet.
2. Maintain a fixed prompt set of 20-30 category and brand queries. Each week, fire the same prompts at ChatGPT, Perplexity, and Claude Web. Log which domains are cited, in what position, with what snippet. AI search monitoring tools and LLM rank trackers automate this step; the manual version costs about 20 minutes a week.
3. Connect to Google Search Console and watch AI Overview impressions in the Performance report, a leading indicator of AI Overview eligibility.
4. Review monthly deltas on both axes. The technical-signal score moves first; the citation-rate score follows by 4-8 weeks. Use the technical-signal score as the early warning that a remediation has landed.
5. When the technical-signal score plateaus, the next gains come from content depth and entity authority (sameAs links, third-party citations of your domain) rather than from on-page fixes.

AI visibility platform vs AI visibility tool: what is the difference?

An AI visibility tool typically audits a single domain on demand and returns a snapshot score, like the free audit on this page. An AI visibility platform is the recurring-tracking layer on top: scheduled runs, historical trend data, alerts when scores move, and (in some platforms) prompt-level citation tracking. Most engineering teams start with a one-off audit, then graduate to a platform when they need to demonstrate trend improvement to executive stakeholders or to monitor competitor positioning continuously. The free audit on this page is positioned as the engineering-grade starting point: full technical signal coverage, no sign-up, and a remediation plan emailed for free.

What you do with the score

Each check returns a 0-10 sub-score and a specific remediation hint. Run the audit on your own domain and the top 3-5 competitors in your category, the deltas usually tell you exactly which signals are driving citation share. Most fixes are engineering tasks: a one-time generator script for llms.txt, a robots.txt update, JSON-LD blocks added to page templates. We can help if you want a fixed- scope quote, our Shopify SEO service covers the AI-visibility signal stack alongside technical SEO.