What does AI visibility tracking answer for you?
It answers a simple question. When a buyer asks an AI engine about your market, does your brand come up, and how? A normal rank tracker can't tell you that. AI answers are written replies, not a list of ranked links.
The term AI visibility tracking refers to running a fixed set of buyer questions through AI engines on a schedule and recording what each answer says about your brand.
Most teams want to know three things. Are we named at all? Are we named before or after our competitors? When we are named, is it in a good light, and with a link to our site?
AI visibility tracking gives you those answers for each engine and each prompt. It also shows how they move week by week. If the idea is new to you, our guide on how to measure AI visibility covers the thinking behind it.
How does the AI visibility tracker work?
It asks the engines your buyers' questions on a schedule and reads every answer.
- Build a prompt set. You list the questions buyers ask, such as "best kitchen fitter in Leeds". Each brand has its own prompt set.
- Choose the engines. Pick from ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews and Google AI Mode, depending on your plan. ChatGPT and Gemini can also be run as their apps show them.
- Set a schedule. Prompts run on the schedule you choose. That builds a steady record rather than one-off snapshots.
- Read each answer. Every answer is checked for a mention, where you first appear, sentiment, competitors named, whether you were recommended and which pages were cited.
- Score and compare. Results roll up into share of voice and a prominence score, split by engine, prompt and date.
- Fill the gaps. Prompt suggestions propose new questions where your coverage is thin, so the set grows with your market. AI search demand figures show roughly how often each question is asked, so you can put the busiest ones first.
Most answers are collected through each engine's API. The ChatGPT app and Gemini app options collect the answer as the app shows it, through a data provider, so you can compare the two views.
What do you see on screen?
You see one view per brand, with the headline numbers at the top and the detail underneath. The table sets out the main measures.
| Measure | What it tells you |
|---|---|
| Mention | Whether your brand appears in the answer at all |
| Position | Where in the answer your brand is first named |
| Share of voice | Your share of brand mentions, weighted 1/rank by position |
| Prominence score | A 0-100 score that folds in position, recommendation, citations and sentiment |
| Sentiment | The tone of what the answer says about you |
| Competitors | Every other brand named, and whether it was recommended |
| Cited pages | The web pages the engine used, where it shows them |
The Runs view shows each answer in full, so you can read exactly what an engine said. That matters, because a number on its own rarely tells you what to fix.
Filters let you look at one engine, one group of prompts or one date range. Each view can be exported to CSV, including every answer with the brands named and the order they were named in. If you run the same prompts in more than one country or region, you can split the results by market too. That is covered on local and multi-market AI visibility.
How accurate is AI visibility tracking?
It is accurate about what the engines said when we asked. It is also honest about what that can and cannot show. AI answers vary from one run to the next, even for the same question. Tracking is a sample of that, which is why a schedule beats a single check.
Consumer apps can differ a little from API answers. A person's chat history, their location and the model version in the app can all change what they see. API answers stay consistent over time, and the ChatGPT app and Gemini app options show how close the app answers are.
Gemini is asked with Google Search switched on, so it names the pages it used. Plain Claude is asked without web search, so it answers from what the model already knows. That shows how the model itself sees your brand. Claude with search, a separate engine, looks pages up first and names them, like the Claude app. The page on Perplexity, Gemini and Claude tracking explains the differences between engines.
No tool can promise that an AI engine will mention you. Tracking shows where you stand, what changed and which pages the engines lean on.
How is Microsoft Copilot covered?
Microsoft has no API for Copilot answers, so Copilot is covered through its citations. Bing Webmaster Tools has an AI Performance report that shows how often Copilot, Bing's AI summaries and some partner apps cited your pages. You export it and upload it to the Copilot screen, and each upload adds to the history.
The screen shows daily citations, the pages cited and the grounding queries, which are the searches Bing ran when it used your site. Each cited page shows which of your tracked engines also cite it. Grounding queries can be added to your prompts in one click, so you can see how every other engine answers the same question. There is no credit cost.
What happens when something changes?
Alerts tell you. There are five, and each person picks the ones they want in Settings.
- Visibility score drop. Your AI visibility score falls 5 points or more against a week earlier.
- A competitor passes you. A competitor is named in more answers than you over the last week, when it was not the week before.
- New wrong answer. Answer accuracy finds a new serious issue.
- Ranking drop. A tracked keyword falls 5 places or more, or drops out of the top 10.
- Low credit balance. Credits fall below 1,000.
Alerts show in the bell inside the app, and email is sent once a day at most, so it stays useful rather than noisy.
How is it different from rank tracking?
Rank tracking tells you where a page sits in Google for a keyword. AI visibility tracking tells you whether your brand is named in a written answer, and how. The two measure different things, and both are useful.
A page can rank well and still be left out of AI answers. The reverse happens too. A brand with modest rankings can be named often because directories, review sites and lists mention it. Keeping both in one login lets you see where they agree and where they don't. Daily Google positions, AI Overview checks and AI answers all sit side by side.
Why does AI visibility matter for search?
Buyers now ask AI engines for shortlists, comparisons and recommendations. If your brand is missing from those answers, you are missing from part of the buying process. Search Console clicks won't show you that gap. According to Pew Research Center, people clicked a normal search result in just 8% of visits when a Google AI summary appeared, against 15% when none did.
Tracking also helps explain odd results. A brand can be named for one question and missing for a very similar one. Tracking many prompts, rather than one, shows the pattern behind that. The cited pages then show which sources the engines trust in your topic, which is where AI citation tracking comes in.
Who uses AI visibility tracking?
In-house teams use it to report AI visibility next to organic search, and to spot problems early. Agencies run it across client accounts, with each client in its own workspace. You can read more on how it works for agencies.
A good place to start is a small set of real buyer questions. You can add more once you see where the gaps are. Every query uses credits at a published rate, and every credit spent is logged, so costs are easy to follow on the pricing page.
Where it fits
AI visibility tracking is the core of Axiom GEO. Competitor share of voice goes deeper on who else is named and why. AI answer accuracy checks look at whether what the engines say about you is true. Prompt and question research helps you find the questions worth tracking in the first place. Topic gaps looks at a whole topic and shows which parts the engines link to competitors and not to you.