Who Does AI Recommend in Your Category? How to Build Your Own Visibility Index

Software and consulting firms now publish AI visibility indexes for whole markets, such as CRM tools. The idea is simple. Ask AI engines a fixed set of buyer questions about a category, record which brands get named, and rank them by how often and how prominently they appear.

It is a market share report for a new kind of shelf. Instead of counting sales, it counts mentions in the answers buyers actually read.

Size and visibility do not move together. A large, well-funded brand can sit below a smaller rival because the smaller one has clearer pages, more third-party reviews, and consistent descriptions across the web.

AI engines do not care who spends the most on ads. They pull from sources they trust and repeat what those sources agree on. Brands with a consistent story get named. Brands with a muddled one get skipped.

A brand can rank first for one question and vanish on another. Best for small teams, cheapest option, easiest to set up, and best for a specific city are all different questions with different winners.

That is why a credible index locks its questions in advance and reruns the same ones each cycle. Change the questions and you cannot tell whether a brand improved or the test simply got easier.

ChatGPT, Gemini, and Perplexity pull from different sources and phrase things differently. One brand may lead in Perplexity because it gets cited often, while ChatGPT barely mentions it.

Averaging engines into one number hides that split. If you only see the blended score, you cannot tell which engine to work on.

You do not need a research team. Pick ten questions a real customer would ask about your service, and put your city in at least four of them. Run each through three AI engines. Log every business that gets named, in what position, and whether a source is cited.

Put it in a spreadsheet with businesses down the side and questions across the top. After one round you will see who owns your market in AI answers, and who is missing from it.

The useful part is the empty cells. If a competitor shows up for emergency and pricing questions and you do not, look at what their pages say that yours do not. Usually it is a direct answer, a specific number, or a mention on a site the engines trust.

Then check what the engines say about you when you do appear. Wrong hours, old services, or a mixed-up address are fixable, and fixing them is faster than building new content.

One snapshot tells you where you stand. Monthly snapshots tell you whether your work is paying off. AI answers shift as engines update and as competitors publish, so a result from three months ago may not hold.

Keep the question list identical between rounds. Add new questions in a separate tab so your trend line stays clean.

OMG automates this kind of tracking and scores each model separately instead of blending them. Starter is $99 a month, Managed is $499, and Pro is $649, and big markets or many locations can talk to us. A Managed plan is $499 a month, or $399 a month billed quarterly.

Managed publishes llms.txt and robots.txt updates and queues blog and FAQ drafts for your approval. Agencies get white-label support across multiple clients. Profound, Otterly, and Semrush's AI tools are alternatives if you mainly want reporting.

To see your own starting point, the free one-off audit at optimizemygeo.com takes a few minutes.