GEO Audit: How to Measure Your AI Visibility (Complete Guide + Scoring Grid)

Last week a SaaS founder sent me a message. He had typed his category into ChatGPT and his competitor showed up first in every single response. He wasn't mentioned anywhere. Eighteen months of SEO work, content, LinkedIn presence. And in AI engines, he was completely invisible.

What he was missing wasn't a better strategy. It was a clear starting point: knowing exactly where he stood. A GEO audit.

This guide gives you the complete method to run that audit yourself, understand your results, and prioritize the actions that will have the most impact on your LLM visibility.

Why run a GEO audit now and not in six months

In 2026, ChatGPT handles between 800 million and 1 billion queries per week. Perplexity surpasses 780 million monthly queries. According to Gartner, 25% of traditional search volume will migrate to AI assistants by end of 2026. These are no longer emerging trends. They are already the reality of the B2B buying journey.

A study by Princeton and IIT Delhi researchers, presented at KDD 2024, found that GEO methods such as adding statistics, quotations and cited sources raised content visibility in AI responses by up to 40%. Conductor's 2026 benchmarks, built on 3.3 billion sessions across 13,770 enterprise domains, put AI referrals at about 1% of website traffic, and describe that traffic as high-intent and more likely to convert than traditional channels.

In other words: fewer clicks, but far more qualified clicks. Brands that measure their AI visibility today and act on it are building a lead their competitors will take months to close.

The difference between a GEO audit and no audit isn't the quality of your strategy. It's the direction you're walking in.

What a GEO audit actually measures

A GEO audit is not an SEO audit with a few new tools. It's an analysis of your footprint in the information ecosystem that language models use to build their responses. It covers five distinct dimensions.

1. Presence: are you in the responses?

The first question is also the most fundamental. On queries in your category, does your brand appear in generated responses? This is your raw mention rate. A brand absent from all responses has an entity recognition problem. A brand present in 5% of responses has a volume problem. A brand present in 30% is in a solid starting position.

2. Position: where do you appear in the response?

Being mentioned third with the phrase "some users also find X useful" is fundamentally different from being cited first as the market reference. Models tend to name the default winner first, and that's the one the user remembers. Position within the response is almost as important as presence itself.

3. Sentiment: how are you described?

You can appear in 50% of responses and still lose customers if the description is poor. "A tool that may work for small teams on a tight budget" is a description that doesn't convert. "The reference for B2B marketing teams who want complete AI visibility" is a description that does. Sentiment isn't a detail: it's what stays in the user's head after reading the response.

4. Accuracy: is what AI says about you correct?

Models sometimes make errors about pricing, features, or your product's positioning. These errors usually come from contradictory sources or outdated information in their training data. An inaccurate description can be worse than no mention at all: it creates false expectations in the prospect's mind before they even visit your site.

5. Technical: is your content accessible to AI bots?

Everything else in the audit is pointless if your pages aren't accessible to AI crawlers. GPTBot, PerplexityBot, ClaudeBot: if these agents are blocked in your robots.txt or if your JavaScript prevents your content from rendering, you're invisible to RAG engines like Perplexity, regardless of how good your articles are.

The manual method: your GEO audit in 30 minutes

Before automating anything, run the audit manually once. It's the only way to truly understand what AI engines are saying about you, in what context, and with what tone. Here's how to do it.

Step 1: prepare your prompt list (5 minutes)

Build a list of 15 to 20 representative prompts. Cover three types of queries: broad category questions ("best AI visibility tracking tool"), specific use-case questions ("how to know if ChatGPT mentions my brand"), and comparison questions ("alternative to [main competitor]"). These are exactly the queries your prospects ask AI before making a purchase decision.

One important note: always use an incognito session. Models sometimes adapt their responses based on conversation history. You want the cold response, the one a stranger would see asking this question for the first time.

Step 2: test on at least 3 engines (15 minutes)

Test each prompt on ChatGPT (with web browsing enabled if possible), Perplexity, and Gemini. These three engines cover the majority of AI traffic in 2026. If you have time, add Claude.

For each response, note: is your brand mentioned (yes or no), in what position (1st, 2nd, 3rd or lower), with what sentiment (positive, neutral, mixed), with what exact description (copy the sentence literally), and which sources are cited if visible. Ten minutes of rigorous collection is worth more than an hour of intuition.

Step 3: check your technical signals (5 minutes)

Go to yoursite.com/robots.txt. Check that there's no Disallow rule blocking these agents: GPTBot (OpenAI), PerplexityBot, ClaudeBot (Anthropic), CCBot. If you use Cloudflare as a CDN, also check your bot protection settings: Cloudflare changed its default settings in 2025 to block AI bots, which silently made thousands of sites invisible to AI engines.

Then pick your most strategic page (homepage or main product page) and disable JavaScript in your browser. If key text content disappears, your AI bots can't see it either.

Step 4: audit your entity consistency (5 minutes)

Open your LinkedIn page, your G2 or Capterra profile if you have one, your Crunchbase profile, and your homepage. Read the first description sentence on each. Are you describing yourself the same way everywhere? Are you using the same keywords and the same positioning? If the answer is no, you're accumulating contradictory signals in LLM data. The model then has a blurry picture of what you are, and a model with a blurry picture doesn't recommend you.

The scoring grid: where do you actually stand?

Once you've collected your data, use this grid to turn your observations into an actionable score. Each dimension is scored out of 20 points, for a total of 100.

Dimension 1: Presence (20 points)

18 to 20 points: you appear in more than 50% of prompts on at least 2 engines. 12 to 17 points: you appear in 20 to 50% of prompts. 6 to 11 points: you appear in fewer than 20% of prompts. 0 to 5 points: you almost never or never appear.

Dimension 2: Position (20 points)

18 to 20 points: you're cited in first position in the majority of your mentions. 12 to 17 points: you're often in second or third position. 6 to 11 points: you rarely appear first, usually as an alternative. 0 to 5 points: when you appear, it's always at the bottom of a list or as a secondary mention.

Dimension 3: Sentiment (20 points)

18 to 20 points: descriptions are positive, precise, and match your target positioning. 12 to 17 points: descriptions are neutral and factual. 6 to 11 points: descriptions are vague, limiting, or mention weaknesses. 0 to 5 points: descriptions are incorrect, negative, or position you poorly relative to competitors.

Dimension 4: Accuracy (20 points)

18 to 20 points: all information cited about your brand is accurate. 12 to 17 points: a few minor inaccuracies on secondary details. 6 to 11 points: errors on important information like pricing, key features, or your target audience. 0 to 5 points: fundamentally incorrect information that creates false expectations.

Dimension 5: Technical (20 points)

18 to 20 points: all AI bots are allowed, content renders in static HTML, pages load under 2.5 seconds. 12 to 17 points: bots allowed but some performance or rendering issues. 6 to 11 points: some bots blocked or important content behind JavaScript. 0 to 5 points: major bots blocked or content inaccessible to AI crawlers.

What your score tells you

A score between 0 and 30 means you have a fundamental recognition problem. Models don't really know you. This isn't about fine-tuning: it's about basic presence. Before writing a single additional article, fix the technical layer (robots.txt, crawlability) and unify your description across all your platforms.

Between 31 and 50, you exist in the AI ecosystem but you're not a reference yet. Models mention you as one option among others, not the obvious choice. The main lever at this stage is content depth and independent citations: you need other sources talking about you consistently.

Between 51 and 70, your foundation is solid. You appear regularly with a decent description. The challenge now is dominance: becoming the first response on your most strategic prompts, improving your sentiment, and extending coverage to engines where you're still weak.

Above 71, you have a real competitive advantage in LLMs. Your priority is maintaining and extending it: new prompt categories, new geographic markets, continuous monitoring of each model's evolution.

The 3 priorities based on your score

After an audit, there are often too many possible actions. Here's how to focus your energy.

If your score is below 30: technical first

Check your robots.txt, fix AI bot blocks, and write one factual description of your product that you'll copy word for word across all your profiles: website, LinkedIn, G2, Capterra, Crunchbase. This consistency work takes 2 hours and its impact on RAG engines like Perplexity is measurable within weeks.

If your score is between 31 and 60: reference content

Identify the 3 prompts where you don't appear but should. For each, publish an article that answers that question better than anyone else: structured in self-contained sections, with sourced data, a FAQ at the bottom, and a JSON-LD FAQ schema. This type of content is directly citable by RAG engines.

If your score is above 61: independent citations and sentiment

At this level, your problem isn't structure or technique anymore. It's the depth of your footprint in the sources models consult. A guest post in an SEO newsletter read by 15,000 professionals, an authentic Reddit discussion in r/SaaS, G2 reviews that precisely describe what you do: every consistent mention in a credible source is one more vote for your AI visibility.

From one-time audit to continuous monitoring

The problem with manual audits, you've probably already figured out: they're snapshots. LLMs evolve. Perplexity updates its sources in real time. ChatGPT releases new training cycles. A score of 62 today could be 48 in two months if a competitor publishes 10 solid articles in your category and earns citations in industry newsletters while you're not watching.

Best practice is to redo a manual audit once per quarter on your most strategic prompts, and track your score continuously with a dedicated tool in between. Continuous tracking lets you see the impact of every action over time: you publish an article, earn a newsletter mention, fix your robots.txt. Two to four weeks later, you see the movement in your scores.

A GEO audit without follow-up is a photograph. A GEO audit with continuous monitoring is a video. You need both, but the video is what tells you whether you're actually moving forward.

This GEO audit gives you the foundation to know where you stand today. What separates brands that progress from those that don't isn't the quality of this initial audit. It's the ability to measure consistently, test actions, and see what moves. Vizible AI automatically tracks your presence in ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews and Google AI Mode every day, across all your category prompts, with the sentiment and position of each mention. That's exactly what you need to turn a one-time audit into a lasting competitive advantage.