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Are you found by AI? asks buyer questions, saves the AI answers and counts how often they name each business. The public benchmarks now count saved answers directly: each business gets a percentage, the answers that name it divided by countable answers. The current snapshot, compiled on 29 September 2026, covers 271 businesses in Australia, 134 in the United States and 105 in the United Kingdom. See Benchmarks.

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What the data can tell you

The saved answers show which businesses appeared for the questions and engines checked. They do not establish the chance of being named for every possible buyer question, or prove that one website change caused a result. Different businesses have different questions, dates and answer counts, so the benchmarks are observed results, not a controlled comparison.
Earlier scoring research. Earlier articles compared weighted visibility and site-check scores. Those values were not percentages of answers and are not carried into the current benchmarks. Findings on this page that relied on those weighted scores have been removed. The original records remain preserved; current pages use counted answers or state that no comparable count is available.

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Independent mentions and consistent business facts

Two of the checks that guide the work sit partly or wholly off your website:
  • Your business facts. Make your name, services, locations and contact details clear and consistent. Inconsistent names, addresses or categories across directories make it harder for an engine to tell whether two mentions refer to the same business.
  • Independent mentions. Review the sources saved with the answers and identify relevant places where buyers could find you.
Your off-site presence (Wikidata and Knowledge Graph entity, Google Business Profile, brand mentions, search demand and referring domains) is scored as Footprint in the full report, beside AI Readiness rather than inside it. See AI Readiness and Footprint.
The free scan at areyoufoundbyai.com shows who the engines name instead of you. Pro also reports new pages on the web that mention your business.

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JavaScript-injected schema and AI crawlers

Schema markup is only as useful as the delivery method you use to put it on the page. Google’s crawler renders JavaScript. The AI fleet mostly does not: GPTBot, OAI-SearchBot, ClaudeBot and PerplexityBot fetch your raw HTML and move on without executing any scripts. If your schema is added to the page by a JavaScript tag (including Google Tag Manager, third-party “schema injection” tools or client-side frameworks), those crawlers see a page with no schema at all.
The Are you found by AI? scanner reads your site the way AI crawlers do, raw HTML first. If it reports “no schema” on a site whose SEO plugin shows schema, that disagreement is not a bug. It is the measurement. Source: areyoufoundbyai.com/research/schema-ai-crawlers.

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The comparison page opportunity

When a buyer is deciding between you and a competitor, they ask AI directly: “should I use X or Y?” or “what are the alternatives to X?”. The pages that rank for those searches are raw material for the answer. An analysis of 1,001 high-buy-intent searches across 119 B2B software brands shows a clear pattern. Who owns the top-ranked result, by search intent: Brands own their own “buy now” searches almost entirely. They have surrendered most of their “choose me over someone else” searches. Why this matters for AI visibility: when someone asks ChatGPT or Perplexity “should I use X or Y?”, the assistant’s search layer runs these same kinds of queries. Our scans keep the search queries engines return while answering, and comparison queries appear in them. The page that wins that search is source material for the answer about you. The opportunity: publish honest comparison pages on your own domain, one for every significant “[you] vs [rival]” pairing AI names against you. The free scan shows which rivals AI names instead of you, so you know which pages to build first.
The winning shape for a “vs” page: head-to-head comparison structure, honest both-ways analysis, and a superlative in the title (74% of winners use one). FAQ schema appears on just 7% of winning “vs” pages. Page shape beats raw authority here.
Method: source dataset assembled by Distribb (1,001 buyer-intent queries with the top-ranked pages, captured late 2025), analysed by Are you found by AI? in July 2026. These are Google search results, not AI answers, and the sample skews to B2B software. Prevalence is correlation, not causation. Source: areyoufoundbyai.com/research/high-intent-serps.

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The Microsoft GEO model factors

In January 2026, Microsoft Advertising published “From discovery to influence: a guide to AEO and GEO”, a platform playbook describing how an AI assistant chooses what to recommend. Its model describes three input layers and a reasoning phase. Here is how each factor maps to what the scan checks.
The assistant breaks your buyer’s question down and runs its own sub-searches before assembling an answer. Where an engine returns those searches, we keep them with the answer. Those are the pages you need to win.
AI weighs how recently your content was updated. Visible dates and dateModified signals are part of the site checks, with a fix when they are stale.
Machine-readable price and availability data (Product and Offer schema) is an explicit factor in Microsoft’s model. It is level 4 on the agent-readiness ladder.
The quotable-content signals from Citability: answer-shaped copy, concrete statistics, sourced claims and direct answers to buyer questions.
What the model already believes about you from its training data. Full reports include real model answers that name (or confuse) your brand.
AI Overviews and AI Mode issue live searches while composing answers. Whether bingbot and Googlebot can reach your latest content (including IndexNow submission) affects whether that real-time retrieval finds you.
What the crawler actually receives, not what a browser renders. JavaScript-shell detection and server-side schema checks show whether the crawler gets clean content or an empty page.
For local businesses, the buyer’s location matters. AI localises answers to city and region, so a national search ranking does not automatically become a local AI recommendation.
Feeds pushed directly to Merchant Center and platform consoles. The scanner does not measure these today. If you run a store, maintain your feeds. Microsoft’s own line is “completeness beats cleverness.”
The mapping to our measurements is ours; Microsoft has no affiliation with this analysis or product. Source: areyoufoundbyai.com/research/microsoft-geo-model.

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How to test a tactic against AI answers

Ask one question of every tactic: what changed in the saved answers after the work, with the questions and engines held as consistent as possible? A before-and-after comparison can help guide work, but it is not automatically a causal experiment. Models, sources and other parts of the business may change at the same time.