How to Run an AI Search Audit for B2B Visibility

An AI search audit tells you whether your brand exists in the answers that ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot generate for the queries your buyers actually type. If you have never run one, the answer is probably “no, it doesn’t.” That gap between your organic SEO performance and your AI visibility is where pipeline disappears without you noticing.

A traditional SEO audit measures crawlability, indexation, keyword rankings, and backlink health. An AI search audit measures something different: whether large language models cite your brand, link to your pages, or even know you exist when a procurement engineer asks “best corrosion-resistant valve manufacturers in the Midwest.” Both matter. They are not interchangeable.

What an AI Search Audit Actually Covers

The scope breaks into three layers: platform coverage, query selection, and citation analysis.

Platform coverage means deciding which AI systems to include. At minimum, you should audit ChatGPT (GPT-4o with browsing), Perplexity, Google AI Overviews, Gemini, and Microsoft Copilot. Each AI platform retrieves and synthesizes information differently. Perplexity cites sources inline. ChatGPT pulls from training data plus live browsing. AI Overviews sit directly in Google search results, drawing from indexed pages Google already ranks. Gemini leans on Google’s knowledge graph. Testing a single platform gives you a fragment, not the picture.

Query selection is where most audits fail. You need to audit the keyword set your buyers actually use, not your vanity terms. For a wholesale distributor, that means queries like “who supplies ASTM A193 B7 threaded rod in bulk” or “industrial fastener distributor with VMI programs.” Pull your top 20 to 50 revenue-driving keywords. Add five to ten long-tail queries that match how engineers and procurement teams phrase questions in conversational AI search engines.

Citation analysis is the core output. For each query on each AI platform, you record: Does the AI mention your brand? Does it link to your site? Does it cite a competitor instead? Does it cite a third-party page (a directory, a forum post, a trade publication) that mentions you? This gives you a visibility metric per query per platform. We track these in a simple spreadsheet with columns for query, platform, brand mentioned (yes/no), URL cited, and competitor brands cited.

Running the Audit Step by Step

Start with your keyword list. Export your top organic keywords from Google Search Console or Ahrefs. Filter to the commercial-intent queries tied to revenue. If you sell industrial equipment, you want “hydraulic press manufacturer” and “custom stamping press quote,” not “what is a hydraulic press.”

Next, run each query manually across all five platforms. This is tedious. There is no shortcut that produces reliable data yet. Automated AI visibility tools exist (Otterly, Profound, and the AI search visibility checker we built), but manual verification catches nuance that automation misses, like whether the AI mentioned your brand in a negative context or lumped you in with companies you do not compete against.

Document every result. For each query, capture a screenshot and log the structured data. After 50 queries across five platforms, you will have 250 data points. That is enough to see patterns.

Score the results. We use a simple metric: citation rate. That is the percentage of query-platform combinations where your brand appears. If you show up in 15 out of 250 checks, your AI search visibility rate is 6%. Run the same scoring for your top three competitors. The delta tells you how far behind (or ahead) you are.

What the Audit Reveals

The patterns fall into predictable buckets for B2B companies.

The “invisible brand” pattern: your site ranks on page one of Google for a term, but no AI system mentions you. This means your content structure, entity markup, or authority signals are not formatted in ways that generative AI models can extract and cite. The fix is structural, not promotional. Schema and structured data for AI search close this gap faster than any other single optimization.

The “competitor-cited” pattern: AI systems mention your competitors by name for queries you should own. This usually traces back to their content being cited on the third-party sources that LLMs trust: Wikipedia, industry directories, trade publications, Reddit threads, and niche forums. Brand mention seeding is the playbook here.

The “fragmented citation” pattern: your brand appears on Perplexity but not ChatGPT, or on AI Overviews but nowhere else. This tells you which platforms your current content resonates with and which need specific optimization. Each LLM has different citation behavior, and the audit data shows exactly where to focus.

How This Differs from a Traditional SEO Audit

A technical SEO audit checks whether Google can crawl, render, and index your pages. An AI search audit checks whether AI-driven systems can extract, attribute, and recommend your content. The inputs overlap (clean site architecture, proper schema, topical authority), but the outputs are measured differently.

Traditional SEO gives you rankings, click-through rates, and organic sessions. AI search visibility gives you citation rates, brand mention frequency, and share of voice across AI platforms. You need both data sets to understand where your B2B pipeline is actually forming. Strong organic SEO is the foundation, but it does not guarantee AI visibility. We have seen sites ranking position one on Google for competitive B2B software terms that ChatGPT never mentions.

What to Do After the Audit

The audit produces a prioritized list of gaps. For most B2B companies, the first moves are:

• Add or fix JSON-LD schema (Organization, Product, FAQPage) across your core commercial pages
• Publish LLM-friendly content that directly answers the queries where you are invisible
• Seed brand mentions on the third-party sources your audit reveals AI systems cite most often
• Implement llms.txt to give crawlers a structured map of your most authoritative content

Re-run the audit quarterly. AI search is shifting fast, and the platforms update their retrieval and ranking logic on cycles that do not align with Google’s. A metric that looked solid in Q1 can erode by Q3 if a competitor starts publishing structured content in your category.

Frequently Asked Questions

How is an AI search audit different from a traditional SEO audit?

A traditional SEO audit evaluates technical health, indexation, and keyword rankings within Google. An AI search audit measures whether AI systems like ChatGPT, Perplexity, Gemini, and AI Overviews cite your brand when users ask questions relevant to your products or services. The data sources, the scoring metrics, and the optimization playbook are different.

How many keywords should I audit for AI visibility?

Between 20 and 50 for a first pass. Focus on your highest-revenue commercial queries and add a handful of conversational, long-tail questions that match how engineers and procurement teams use AI chatbots. Going broader than 50 in the first round adds effort without proportional insight.

Can I track traffic from AI Overviews in Google Search Console?

Google Search Console does not currently break out AI Overview clicks as a separate metric. You can infer impact by monitoring changes in click-through rate for queries where AI Overviews appear, but direct attribution requires supplementary tracking through UTM parameters on cited URLs or third-party tools.

Do I need AI visibility tools if my organic SEO is strong?

Yes. Organic rankings and AI search citation are not the same signal. A page can rank position one in Google and still be absent from ChatGPT and Perplexity responses. The audit data from our AI visibility checker consistently shows that gap, even for well-optimized B2B sites.