The Metric That Matters: How to Perform an AI Search Visibility Audit
If you search for your business on Google right now, you probably know exactly where you rank. You likely have a report sitting in your inbox detailing your position for every keyword.
But if a potential customer asks ChatGPT, “Who is the best service provider in Dallas?”, do you know if your name comes up?
This is the new frontier of search. It is no longer about ten blue links; it is about being the single, authoritative answer provided by an AI. To compete in this environment, you need to stop looking at SERPs (Search Engine Results Pages) and start performing an AI Search Visibility Audit.
What is an AI Visibility Audit?
Traditional SEO audits check your website’s speed, backlinks, and meta tags. An AI audit measures something entirely different: your “Share of Model.”
It tests whether Large Language Models (LLMs) like ChatGPT, Gemini, and Perplexity recognize your brand as a distinct “Entity” and—more importantly—whether they trust you enough to recommend you as a solution.
To determine if you are visible, you must test against three distinct types of AI behavior.
The 3-Engine Manual Audit
To manually audit your visibility, you cannot just search for your company name. AI models already know who you are if you specifically ask about “Sanbi.” The real test is unbranded queries—the generic questions your customers are actually asking.
- Bad Query: “What is Sanbi?”
- Good Query: “Best AI tools for checking brand visibility.”
Once you have your keywords, you need to open fresh instances of the three major engines and look for specific signals:
1. The Conversationalist (ChatGPT)
What to look for: Conversational recommendations. Does the AI list you in the top 3 options? Is the sentiment positive? ChatGPT acts like a consultant; if you aren’t in the recommendation list, you don’t exist to the user.
2. The Real-Time Mapper (Gemini)
What to look for: Real-time data and Map integration. Gemini pulls heavily from Google Maps and Google Flight/Shopping data. An audit here tests if your physical location and service area are correctly synchronized with your digital entity.
3. The Citation Engine (Perplexity)
What to look for: Sourced, citation-heavy answers. Perplexity acts like an academic researcher. It requires sources. If you appear here, check the citations—is the AI linking to your site, or is it linking to a third-party review of your site?
The “Personalization Trap”
There is a major flaw in doing this manually: Bias.
If you frequently visit your own website or search for your own brand, AI models (especially Google’s Gemini) learn your preferences. They will show you your business because they know you like it.
The Dangerous Illusion
You might see your brand pop up in ChatGPT and think you are winning, while a potential customer across town sees a completely different answer—one that recommends your competitor.
Unless you are running these audits in a pristine, depersonalized environment using API-level access, you are looking at a mirage.
The Solution: Automated, Unbiased Auditing
Running these queries manually every week is time-consuming and prone to error. You need data that reflects the market’s reality, not your browser history.
At Sanbi, we automated this process.
We run a 3-Engine Scan to generate a specialized AI Search Visibility Report. We bypass personalization to tell you exactly where you stand, which keywords you are failing, and how to fix your “Entity” status.
The shift from SEO to GEO (Generative Engine Optimization) is happening now. Don’t wait until your competitors own the AI answers.