LLM Visibility: How to Check It and the Tools That Help

LLM visibility is how often and how accurately, AI platforms such as ChatGPT, Gemini, Microsoft Copilot, Perplexity and Google’s AI Overviews and AI Mode mention, cite or recommend your organisation when someone asks a question you should be part of the answer to. It sits alongside search rankings rather than replacing them and it is the measure most organisations investing in AI search optimisation for B2B companies want to see moving. Checking it properly takes structured manual testing, an audit of the signals AI systems rely on and tools that track answers over time.

Strong Google rankings do not guarantee a mention in an AI answer. Across B2B and professional services work, the organisations that get cited tend to have clear, consistent information about who they are and pages that answer buyer questions directly. The short version of the process is set out below.

• Build a fixed set of prompts that reflects how your buyers ask questions.
• Run them across ChatGPT, Gemini, Copilot, Perplexity and Google’s AI features, recording mentions, citations and competitors.
• Audit crawler access, structured data, entity consistency and content structure.
• Use LLM visibility tools to track the same prompts over time and benchmark competitors.
• Fix the gaps in order of impact, then test again against the same baseline.

Each step is covered below, along with the limits of what any checker can tell you. The aim is a repeatable process rather than a single snapshot.

What LLM Visibility Means

LLM visibility means appearing inside the AI answer itself, not only in a list of links beneath it. There are three levels to it and they carry different weight for a business trying to win enquiries.

A mention is when the AI names your organisation in its answer text. A citation is when it links to a page on your website as a source, which is common on platforms that search the web live. A recommendation is when the answer presents you as a suitable option for the buyer’s need, often in a shortlist alongside competitors and that is usually the outcome with the most commercial value.

How you appear matters as much as whether you appear, since an answer that describes your services inaccurately or places you in the wrong sector can do more harm than no mention at all. Priority Pixels measures visibility through AI Share of Voice, which compares how often a brand is referenced in AI answers against its competitors, as a companion metric to traditional rankings.

Google’s AI Overviews and AI Mode work differently from standalone assistants. Google’s guidance on AI features and your website says a page must be indexed and eligible to appear in Search with a snippet, with no additional technical requirements. That puts visibility there on the same foundations as organic SEO.

How to Check Your LLM Visibility Manually

A manual LLM visibility check is a structured test where you put the same set of prompts to each AI platform and record what comes back. It gives you a baseline that tools can extend but not replace, because you see the exact answer a buyer would see.

The prompt set is where most manual checks go wrong. Branded prompts show what the AI knows about you, but buyers rarely start there, so weight the set towards the problems you solve, phrased conversationally. A logistics software provider would learn more from “what should a growing distributor look for in a warehouse management system” than from a short keyword.

Answers vary from one run to the next, so run each prompt more than once and look at the pattern. A spreadsheet with one row per prompt, platform and date is enough to start, repeated monthly or quarterly. Our guide on optimising for ChatGPT, Gemini and Copilot covers how each platform sources its answers in more detail.

What an AI SEO Audit Should Check

An AI SEO audit checks whether AI platforms can reach your content, understand who you are and find answers worth citing. Your manual prompt testing tells it where to look first.

The checklist below covers the areas any AI SEO audit should include. It starts with access, since nothing else matters if crawlers cannot reach the page.

• Robots.txt, CDN and firewall settings for AI crawlers, confirmed in server logs.
• Indexing in Google and Bing, including verification in Bing Webmaster Tools.
• Organization, service and author structured data that matches the visible page content.
• Consistent business facts across your website, Google Business Profile, LinkedIn and directories.
• Pages that answer buyer questions directly under clear headings.
• Third party mentions on industry publications, review platforms and partner websites.
• Current AI answers about your brand, checked for accuracy.

Crawler access and consistent business facts come first, because they decide whether AI platforms can read and trust your information at all. New content makes more difference once those foundations are in place.

Crawler Access for ChatGPT, Gemini and Perplexity

Crawler access is the first technical check, since an AI platform cannot cite a page its search crawler is blocked from. Each provider publishes its own user agents, so a single blanket rule in robots.txt can switch off visibility you wanted to keep.

OpenAI’s documentation on its crawlers separates OAI-SearchBot, which surfaces websites in ChatGPT search, from GPTBot, which is used to train its foundation models. Sites opted out of OAI-SearchBot will not be shown in ChatGPT search answers, which is why many organisations allow it even when they block GPTBot.

Google’s list of common crawlers describes Google-Extended as a product token that controls whether crawled content is used to train future Gemini models and for grounding in Gemini Apps. Google states that it does not affect inclusion in Google Search, so blocking it will not remove you from AI Overviews.

Perplexity’s crawler documentation says PerplexityBot surfaces and links websites in Perplexity search results and is not used to crawl content for AI foundation models. Its separate Perplexity-User agent fetches pages when someone asks a question and generally ignores robots.txt.

Robots.txt is only half the check. Google’s guidance asks that crawling is allowed by any CDN or hosting infrastructure and bot protection on a CDN can block AI crawlers that robots.txt appears to allow. Server logs confirm which crawlers reach the website.

Structured Data, Entities and Third Party Mentions

Structured data and consistent entity information help AI systems understand who you are, what you do and where you operate. Consistent business facts across your website, Google Business Profile, LinkedIn and directories matter as much as markup, since conflicting versions give an AI platform no reliable fact to repeat.

Google’s guidance says you do not need new machine readable files, AI text files or special schema.org markup to appear in AI Overviews or AI Mode, though it does ask that structured data matches the visible text on the page. Organization markup with accurate sameAs links and properties such as knowsAbout still helps describe your business consistently and that consistency is what other platforms draw on.

The same reasoning applies to llms.txt, a proposed standard for a markdown file that gives AI agents a concise summary of a website. Thousands of sites publish one, according to the proposal, but Google has said AI text files are not needed for its AI features, so treat it as a tidy addition rather than a priority fix.

Third party mentions matter for a similar reason. The citations in your manual check show which outside sources each platform trusts for your category, such as industry publications, review platforms and partner directories and an audit should record whether you appear in them. Earning a place there is slower work than technical fixes, but it often separates the businesses that get recommended from those that are only mentioned.

LLM Visibility Tools and What They Measure

LLM visibility tools automate the manual check, running prompts at scale and reporting mentions, citations and competitor share over time. They fall into four broad categories, each measuring a different part of the picture.

Semrush and Ahrefs each offer tools covering the first two categories. The Semrush AI Visibility Toolkit benchmarks visibility and sentiment against competitors, tracks chosen prompts daily on platforms including ChatGPT and Google’s AI Mode and audits for technical issues that could block AI crawlers. Ahrefs Brand Radar runs prompts built from real search queries through AI Overviews, AI Mode, ChatGPT, Perplexity, Gemini and Copilot and reports how often a brand is mentioned or cited.

For first party data, Microsoft introduced, in public preview, an AI Performance report in Bing Webmaster Tools that shows how often your pages are cited across Copilot and AI summaries in Bing, with the grounding queries behind those citations. On the traffic side, GA4 now has an AI Assistant channel group that separates visits from assistants such as ChatGPT, Gemini and Claude from general referrals, so you can see which AI visits turn into enquiries.

In practice the two complement each other. Priority Pixels combines systematic manual testing with ongoing LLM performance tracking and analytics, reporting AI Share of Voice, citation rate per priority query cluster and the queries where competitors are picked instead.

Where LLM Visibility Checkers Fall Short

Every LLM visibility checker works from a sample, so its figures are estimates rather than a record of what every buyer sees. That changes how much weight a single number should carry, though the trends remain useful.

Prompt sampling is the first limit. There is no official equivalent of keyword search volume for AI prompts, so tools build their prompt sets from search data or from the prompts you supply and real buyers phrase questions in far more ways than any list can cover. A visibility score is only as representative as the prompts behind it.

Personalisation and variability are the second. The same prompt can produce different answers from one run to the next and answers can change with location, conversation history and memory settings, so a tool’s clean session may not match what a particular buyer sees. Model updates add a third layer, shifting answers without anything changing on your website.

Coverage differs too. No single tool covers every AI platform in the same depth and Google Search Console counts AI Overviews and AI Mode within the overall Web search figures in its Performance report. Trends across several weeks and more than one data source give a far more reliable picture than any single score.

Acting on What the Audit Finds

Findings only matter if they turn into a prioritised plan. The most useful order is access first, accuracy second, content third and authority last, since each stage depends on the one before it.

Access fixes are usually the quickest. Unblocking OAI-SearchBot, adjusting a firewall rule or verifying the website in Bing Webmaster Tools are small changes and server logs will confirm the crawlers are getting through. Accuracy fixes follow, correcting business facts at the source wherever an AI answer got them wrong.

Content work then targets prompts where competitors were recommended and you were not. That usually means pages that answer the question in the opening lines and show evidence of expertise, which is the territory generative engine optimisation covers. Authority building runs alongside it, earning mentions in the sources the platforms cited during your checks. For a wider view of how AI assistants are changing buyer journeys, our article on ChatGPT as a search engine sets out what is changing.

Test again against the same prompt set once changes have had time to be crawled and keep the baseline unchanged so that any movement means something. Priority Pixels reviews AI visibility with clients on a monthly call, covering citation movement, where new content is gaining ground and where the next round of work should focus.