AI SEO Audits: What to Check for LLM Visibility

Your website might rank on page one of Google, but that counts for very little if ChatGPT, Gemini or Copilot never mention your business. AI systems pull from different signals than traditional search engines, and most AI SEO audits still focus entirely on the wrong things. The result is businesses with strong organic rankings that are completely invisible in AI-generated responses.

We work with B2B companies and professional services firms who come to us puzzled by this exact problem. Their content ticks every traditional SEO box, yet when a potential client asks an AI assistant to recommend providers in their sector, they’re nowhere to be found. The gap between ranking well on Google and being cited by an LLM is growing wider, and the businesses that close it first will have a serious competitive advantage.

AI platforms are already shaping how people research products, compare vendors and make purchasing decisions. If your audit process hasn’t adapted to account for how these systems evaluate and select content, you’re working with an incomplete picture of your online visibility.

Why Traditional SEO Audits Aren’t Enough

A standard SEO audit checks page speed, mobile responsiveness, keyword density, meta tags and backlink profiles. Those factors still matter for Google rankings, but they tell you almost nothing about how an LLM evaluates your content. AI systems process information in fundamentally different ways to search engine crawlers, and the audit tools most agencies rely on were never designed to catch AI-specific gaps.

Traditional audit tools can’t assess whether your content is structured in a way that LLMs can extract and cite. They don’t check entity recognition, knowledge graph presence or whether your authority signals meet the threshold AI systems need before they’ll confidently recommend your business. You could score a perfect 100 on a Lighthouse audit and still be completely absent from AI-generated answers.

Search Engine Land’s research into LLM visibility found that the signals driving AI citations, including content freshness, brand narrative clarity and multimodal content, barely overlap with traditional ranking factors. An audit that only measures what Google cares about leaves the AI side of the picture entirely blank.

Structured data is a good example. A traditional audit might confirm that your site has basic schema markup, but it won’t assess whether that markup helps AI systems understand your organisation’s expertise, services and authority within your sector. The difference between having schema and having schema that works for AI is significant, and it’s one most audits overlook completely.

Here is what proper Organisation schema markup should look like when it’s built for AI comprehension. We’ve used a fictional law firm, Henderson Clarke Solicitors, to show how this works in practice:

{
  "@context": "https://schema.org",
  "@type": "LegalService",
  "name": "Henderson Clarke Solicitors",
  "description": "Commercial law firm specialising in corporate, employment and property law for UK businesses",
  "url": "https://hendersonclarke.co.uk",
  "logo": "https://hendersonclarke.co.uk/logo.png",
  "foundingDate": "2003",
  "areaServed": {
    "@type": "Country",
    "name": "United Kingdom"
  },
  "knowsAbout": [
    "Commercial Law",
    "Employment Law",
    "Property Law",
    "Corporate Mergers and Acquisitions",
    "Dispute Resolution"
  ],
  "hasOfferCatalog": {
    "@type": "OfferCatalog",
    "name": "Legal Services",
    "itemListElement": [
      {
        "@type": "Offer",
        "itemOffered": {
          "@type": "Service",
          "name": "Corporate Law",
          "description": "Mergers, acquisitions and company restructuring for SMEs"
        }
      },
      {
        "@type": "Offer",
        "itemOffered": {
          "@type": "Service",
          "name": "Employment Law",
          "description": "Contracts, tribunal representation and HR policy advice"
        }
      }
    ]
  },
  "sameAs": [
    "https://www.linkedin.com/company/henderson-clarke-solicitors",
    "https://www.google.com/maps?cid=12345678901234567"
  ]
}

The knowsAbout property is the part most businesses miss. It explicitly tells AI systems what topics your organisation has expertise in, which directly influences whether LLMs consider you a credible source on those subjects. Without it, AI platforms have to infer your areas of authority from your content alone, and they often guess wrong or don’t bother.

This is why an audit that stops at “schema markup present: yes” misses the point entirely. Having structured data isn’t enough on its own. What matters is whether that markup communicates the right signals to AI systems.

How ChatGPT, Gemini, Copilot and Perplexity Evaluate Content

One of the biggest mistakes in AI SEO auditing is treating all LLMs as though they work the same way. Each platform pulls from different sources, weighs authority signals differently and has its own method for deciding which businesses to mention. A proper audit needs to account for these differences.

This table breaks down what each major AI platform prioritises when deciding which content to surface and cite:

The key thing to take from this is that optimising for one platform doesn’t guarantee visibility on another. Each requires a slightly different focus:

• Gemini relies heavily on Google’s Knowledge Graph, so your schema.org markup and entity data need to be accurate and well-structured within Google’s ecosystem specifically. If your Google Business Profile is incomplete or your structured data is thin, Gemini is less likely to surface you.
• ChatGPT leans more on patterns across its training data. Consistent brand mentions on authoritative third-party sources carry more weight here than your own on-site markup.
• Perplexity searches the live web every time someone asks a question and cites sources directly. Your content needs to be current, crawlable and structured in a way that’s easy to extract from.
• Copilot pulls from Bing’s index, so businesses that have neglected Bing Webmaster Tools are potentially invisible on that platform regardless of how well they perform elsewhere. Many audits overlook this because most SEO strategies solely concentrate on Google.

AI systems favour content that demonstrates real-world experience and measurable impact over theoretical discussions or promotional material.

Before diving into the technical checks, it’s worth testing each platform directly. Ask ChatGPT, Gemini, Copilot and Perplexity questions about your industry. Ask for recommendations in your sector. Ask comparison questions. The results will tell you where you’re visible and where you’re not, broken down by platform.

How to Run an AI SEO Audit

A proper AI SEO audit covers ground that traditional tools don’t touch. The process starts broad and narrows down to specific, actionable findings that you can prioritise by impact.

Start With Entity Recognition

Search for your business name, your key people and your core services across ChatGPT, Gemini, Copilot and Perplexity. Record what each platform knows about you, what it gets wrong and what it misses entirely. This baseline tells you where you stand before any optimisation work begins.

Check Your Authority Signals For Consistency

AI systems need confidence before they’ll cite a business, and that confidence comes from consistent, verifiable information across multiple sources. Check whether your Google Business Profile, LinkedIn company page, industry directory listings and your own website all tell the same story about who you are and what you do. Contradictions between sources erode AI confidence in your brand. Even small inconsistencies, like a different founding year on your LinkedIn page versus your website or an outdated service description in a directory listing, can cause AI systems to downgrade or exclude you from responses.

How Extractable is Your Content?

AI systems extract information differently to how humans read a page. They favour clear heading hierarchies, direct answers to specific questions and content that can be pulled into a response without heavy rewriting. Audit your key pages for extractability: could an AI system pull a clean, accurate answer from your content, or would it struggle because your key points are buried in vague or overly promotional copy?

Make Sure AI Crawlers Can Assess Your Site

Review your robots.txt to confirm you’re not blocking OAI-SearchBot, GPTBot, Google-Extended, Bingbot or PerplexityBot. Check your server logs to see which AI crawlers have visited and how frequently. Google’s helpful content guidelines still apply here because content written primarily for human readers tends to perform better with AI systems as well.

Go Beyond Schema Validation

Tools like Google’s Rich Results Test will confirm whether your schema is technically valid, but they won’t tell you if it’s actually helping AI systems understand your business. Check for knowsAbout, hasOfferCatalog, author credentials markup and FAQ schema that gives AI systems ready-made question-and-answer pairs to work with.

What is LLMs.txt and why does it matter for AI visibility?

Most businesses have a robots.txt file that tells search engine crawlers what they can and can’t access. LLMs.txt is a newer concept that takes this a step further, giving AI systems a structured overview of your organisation and its content specifically designed for language model consumption.

The LLMs.txt specification defines a simple markdown-based format that sits at the root of your domain. It provides AI crawlers with a curated summary of who you are, what you offer and where to find your most important content. Without it, AI systems have to piece together an understanding of your business from whatever they happen to crawl, which often produces incomplete or inaccurate results.

Here’s a complete, working example of what an LLMs.txt file should contain, using the same fictional Henderson Clarke Solicitors firm from earlier:

# Henderson Clarke Solicitors

> Henderson Clarke is a UK commercial law firm specialising in
> corporate, employment and property law for businesses across
> England and Wales.

## About

Henderson Clarke Solicitors was founded in 2003 and provides
commercial legal services to SMEs, property developers and
growing businesses. The firm has offices in Bristol and London
with a team of 35 solicitors covering corporate transactions,
employment disputes, commercial property and litigation.

## Services

- [Corporate Law](https://hendersonclarke.co.uk/services/corporate-law/):
  Mergers, acquisitions, shareholder agreements and company
  restructuring for owner-managed businesses and SMEs
- [Employment Law](https://hendersonclarke.co.uk/services/employment-law/):
  Contracts, tribunal representation, HR policy drafting
  and TUPE transfers
- [Commercial Property](https://hendersonclarke.co.uk/services/commercial-property/):
  Leases, acquisitions, development agreements and
  landlord-tenant disputes
- [Dispute Resolution](https://hendersonclarke.co.uk/services/dispute-resolution/):
  Commercial litigation, mediation and arbitration

## Key Content

- [Guide to TUPE Transfers](https://hendersonclarke.co.uk/insights/tupe-transfer-guide/):
  Practical guide for employers managing staff transfers
- [Shareholder Agreement Essentials](https://hendersonclarke.co.uk/insights/shareholder-agreements/):
  What every business owner needs in their shareholder agreement
- [Commercial Lease Negotiations](https://hendersonclarke.co.uk/insights/commercial-lease-tips/):
  Key terms to negotiate before signing a commercial lease

## Sectors

- Owner-managed businesses and SMEs
- Property and construction
- Technology and professional services

## Contact

- Website: https://hendersonclarke.co.uk
- Location: Bristol and London, United Kingdom
- Email: enquiries@hendersonclarke.co.uk

The structure follows a deliberate hierarchy. The H1 heading and blockquote give AI systems a quick summary they can use for brief mentions. The sections beneath provide progressively more detail that platforms can draw from depending on the depth of response they’re generating. Every linked page gives AI crawlers a clear path to your most important content.

Place this file at yoursite.co.uk/llms.txt and reference it in your HTML head with <link rel="llms-txt" href="/llms.txt">. Your AI SEO audit should check whether this file exists, whether it accurately represents your current services and content and whether the links within it point to pages that are actually crawlable by AI bots.

Measuring AI Visibility and Tracking Progress

AI visibility metrics look nothing like the dashboards most businesses are used to. Organic traffic, keyword positions and click-through rates tell you about Google performance. For AI visibility, you need a different set of measurements entirely.

Brand mention tracking across AI platforms is the starting point. Run a consistent set of prompts across ChatGPT, Gemini, Copilot and Perplexity each month and record whether your business gets mentioned, how accurately it’s described and in what context. This manual process takes time, but it’s necessary. Automated tools for AI visibility tracking are improving, but most still can’t match the accuracy of querying each platform and evaluating the responses yourself.

The shift from tracking keyword rankings to tracking brand citations across AI platforms marks a meaningful change in how businesses need to think about measuring search performance. Traditional metrics still matter, but they’re no longer the full picture.

Crawl bot monitoring is something most businesses overlook entirely. Check your server logs for visits from OAI-SearchBot and GPTBot (OpenAI), Google-Extended (Gemini), Bingbot (Copilot) and PerplexityBot. If these crawlers aren’t visiting your site regularly, or if they’re being blocked by your robots.txt configuration, no amount of content optimisation will help because the AI systems simply can’t see your content.

Google Search Console won’t show you AI-specific data directly, but the crawl stats report can reveal whether Google-Extended is accessing your pages. For Bing, the equivalent data lives in Bing Webmaster Tools. Cross-reference this with your robots.txt to make sure you haven’t accidentally blocked any AI crawlers.

Citation quality matters as much as citation frequency. Being mentioned by an AI platform is only valuable if the mention is accurate, positive and contextually relevant. Track not just whether you appear, but what the AI says about you. Incorrect descriptions, outdated service listings or associations with the wrong industry sector all need flagging and correcting through your content and structured data.

Set up a quarterly review cadence at minimum. AI platforms update their models, retrain on new data and adjust their source preferences regularly. A visibility baseline from three months ago may already be outdated, and competitors who are actively optimising for AI will shift the landscape around you.

Building Long-Term AI Visibility

AI SEO auditing isn’t a one-off exercise. The platforms evolve constantly, new crawlers appear and the signals that influence AI citations shift as these systems become more sophisticated. Businesses that maintain strong AI visibility approach auditing as a continuous process, not as a project with a definitive end point.

Build AI visibility checks into your regular SEO workflow. Every time you publish new content, verify that it’s structured for AI extraction as well as human readability. Every time you update your services or team, check that your structured data and LLMs.txt file reflect the changes. Every quarter, run the full audit process to catch drift and spot new opportunities.

Competitive monitoring should be part of this routine too. Ask AI platforms about your competitors and see how they’re positioned. If a rival is getting mentioned consistently while you’re absent, investigate why. Often the answer lies in their structured data implementation, their third-party mention profile or simply the fact that their content answers questions more directly than yours does.

There’s a compounding effect to AI visibility work that’s worth understanding. Early authority signals feed into training data, which strengthens future citations, which generates more brand mentions, which reinforces authority further. Businesses that started building these signals early are now difficult to displace, not because they did anything particularly clever, but because they were consistent over time. That consistency is harder to replicate the later you start.

Priority Pixels runs AI SEO audits that test your visibility across every major AI platform. We identify the specific gaps holding you back and build an optimisation plan that delivers measurable improvements in how AI systems recognise, understand and recommend your business. If your current SEO audit doesn’t cover AI visibility, it’s only telling you half the story.