How to Audit Your Brand's AI Search Visibility?

Here's the uncomfortable truth about AI search: 89% of brands are already appearing in AI-generated responses, but only 14% of marketers are tracking it. That gap, between appearing and knowing how you appear, is where brand narratives quietly go wrong.

An AI search visibility audit tells you whether you exist in the models your buyers use, how you're described when you do, and what's keeping you invisible when you don't. It's not the same as an SEO audit. It doesn't care about your keyword rankings or your domain authority score. It cares about whether ChatGPT, Perplexity, Gemini, and Google's AI Overviews reach for your brand when a relevant question is asked and what they say when they do.

This is that audit, step by step.

Step 1 — Establish Your Baseline: What Do the Models Actually Say About You?

Before you can fix anything, you need to know where you stand. The starting point is a structured prompt test: submit a set of category and comparison queries to the major AI platforms and log every response.

Start with these four platforms as a minimum:

For your prompt set, write 20–40 queries the way a real buyer would ask them, not keyword phrases, but actual questions. Mix three intent types:

• Category queries — "What are the best [product category] tools for [your audience]?"

• Comparison queries — "How does [your brand] compare to [competitor]?"

• Problem queries — "How do I solve [the problem your product addresses]?"
  

Run each query at least three times per platform. AI responses vary. A single run tells you nothing. Log every output in a spreadsheet with columns for: platform, query, brand mentioned (yes/no), position in response (primary / listed / absent), sentiment (positive / neutral / cautionary), and sources cited.

This spreadsheet is your baseline. Everything else in the audit builds on it.

Step 2 — Score What You Find

Raw responses aren't enough. You need a way to compare performance across platforms and track change over time. Use this scoring framework:

One thing that tends to surprise brands at this stage: 61.9% of brand mentions disagree across AI platforms. What ChatGPT says about your brand and what Perplexity says about it are often materially different. Treat each platform as a separate audience, not one homogenous channel.

Brands that appear in the top 3 AI responses receive 4.2x more brand searches within 30 days compared to brands that don't appear at all — so the position column in your log matters as much as the mention column.

Step 3 — Audit Your Source Footprint

When models mention your brand, they pull from somewhere. Identifying those sources is one of the most actionable parts of this audit because it shows you where your brand narrative is actually being built and it's usually not your website.

When a user asks an LLM about a brand, earned media accounts for 48% of citations versus only 23% from owned brand content. Your product pages and blog posts are not the primary inputs. Third-party coverage is.

Use Perplexity for this step. It shows its sources explicitly. Run your top 10 brand and category queries and note every URL cited. Then categorize what you find:

Look for gaps. If your brand appears in AI responses but no review platform content is being cited, that's a signal. If competitor brands are being cited from analyst reports and you aren't, that's a signal. The goal of this step is to understand which distribution channels are feeding the models that your buyers use.

Step 4 — Test Your Content for Citability

Your website might be well-optimised for Google and still be nearly invisible to AI models. The two systems reward different things. Brand search volume — not backlinks — is the strongest predictor of LLM citations, and content structure matters in ways that traditional SEO doesn't fully capture.

Take your five most important pages, homepage, key product pages, your best-performing blog post, and run each through these checks:

Entity clarity check: Does the opening section define what your brand is, who it's for, and what specific problem it solves, without superlatives or vague claims? A model needs to be able to extract a confident, accurate summary from the first 200 words. Research shows models extract 44% of citations from the first 30% of a page.

Lift-out sentence check: Read each page and ask: which sentences could be quoted accurately out of context? If a model extracts one sentence from your pricing page to answer a buyer's question, does that sentence make sense and create the right impression on its own?

Claim specificity check: Are your claims quantifiable? Quantitative claims get 40% higher citation rates than qualitative statements. "Reduces onboarding time by 60% for teams under 50 people" is citable. "Streamlines your workflow" is not.

Source citation check: Does your content link out to primary sources? Original research, named studies, credited experts? External citations increase citation probability by 300% compared to self-published content without references.

Run these checks across your five pages and flag every failure. These are your highest-leverage content fixes.

Step 5 — Check Technical Accessibility for AI Crawlers

A piece of content can be perfectly written and still be invisible to AI systems if the technical configuration blocks them. This is a quick check but an important one.

AI platforms use their own crawlers to retrieve content for retrieval-augmented generation (RAG). The major ones are GPTBot (OpenAI), ClaudeBot (Anthropic), and PerplexityBot. If your robots.txt file blocks these bots, you're opted out of AI search for those platforms entirely sometimes accidentally.

Check your robots.txt file for any rules that disallow AI crawlers. Then check whether your key pages are JavaScript-rendered: AI bots generally do not execute JavaScript, which means content that only loads client-side may not be readable to them at all.

Also check for schema markup. Pages with FAQ schema are 30% more likely to appear in rich AI results, and Article schema lifts citations by 22%. If your key pages have no structured data at all, that's a straightforward fix with measurable upside.

Step 6 — Benchmark Against Competitors

An audit in isolation only tells you half the story. Run the same prompt set you used in Step 1, but this time log competitor mentions alongside your own. You want to answer three questions:

1. Which competitors appear more frequently than you, and in which query types?

2. What sources are being cited when competitors are mentioned — and are those sources accessible to you?

3. How are competitors described versus how you are described?
   

The last question is often the most revealing. If a competitor is consistently described as "the most user-friendly option" and you're described as "feature-rich but complex," that's a positioning problem surfaced by AI responses, one that exists regardless of what your marketing copy says.

Build a simple comparison table from your logs like this:

Fill it in from your data. The gaps in this table become your prioritised action list.

Step 7 — Build Your Monitoring Cadence

A one-time audit is a snapshot. AI models update, competitor coverage shifts, and new third-party sources enter the citation pool. To stay ahead of that, you need a repeatable monitoring routine.

The minimum viable cadence for most brands:

For the weekly and monthly layers, tools like Otterly.ai, Profound, or Superlines can automate query logging across platforms. For the source audit layer, Perplexity's manual citation output is still the most transparent option.

The goal isn't to track everything, it's to detect directional movement early enough to act on it. A sudden drop in mention rate on comparison queries is worth investigating. A new competitor appearing in primary recommendation position across multiple platforms is worth understanding.

What to Do With Your Audit Results

The audit will typically surface one of three situations:

Not appearing at all. The priority is entity building, earning third-party mentions, getting listed in review platforms and analyst reports, and publishing content that gives models something factual to reference. See the GEO content brief framework for how to structure that content work.

Appearing but with weak or inaccurate framing. The priority is source correction, identifying which third-party content is feeding the inaccurate framing and either updating it (for owned content cited externally) or generating better content that displaces it.

Appearing well in some platforms but not others. Citation rates can vary by 615x between platforms like Grok and Claude for the same brand. Platform-specific gaps usually trace back to source preferences: ChatGPT skews toward Wikipedia and authoritative editorial sources; Perplexity and Google AI Overviews pull heavily from Reddit, YouTube, and community platforms. Fix the gap by building presence in the source types each platform prefers.

Get a free GEO Audit from ScribbleAI here.