How to Track Your Brand's Visibility in ChatGPT

If you are reading this because organic traffic has been sliding for months and nothing in Google Analytics explains it, this page is written for you. Not for your marketing team. For the person who has to explain the number.

Here is the thing that makes it maddening: when ChatGPT tells a buyer which three vendors to look at, and your company is not one of them, nothing happens in your analytics at all. No impression. No session. No referrer. No bounce. The moment that decided the deal left no trace on any dashboard you own. You cannot see the loss, so it looks like a mystery, and mysteries get blamed on the wrong things.

The activity happens inside the model. So you have to go and ask the model.

Do it yourself first, before you buy anything

You do not need a tool to find out where you stand. You need an afternoon. Every paid tracker in this category automates the loop below and adds a chart. Run it by hand once and you will understand exactly what you are buying, and you may find you do not need to buy it.

Step 1: write down the ten questions, in the buyer’s words

Not your keywords. The questions a person actually types before they have heard of you. If you sell warehouse inventory software, that is “best inventory system for a small warehouse”, not “inventory management solutions”.

Get them from real places: the first thing prospects ask on sales calls, your support inbox, the “people also ask” box on Google, and the questions in the subreddit where your buyers hang out. Ten is enough to see the pattern. Mix four kinds:

• Problem questions. “Why is my warehouse always short on stock?”
• Solution questions. “Best inventory software for small warehouses”
• Comparison questions. “Alternatives to [your biggest competitor]”
• Brand questions. “Is [your company] any good?”

The brand questions matter more than they look. If an engine cannot describe your company accurately when asked directly, it will never volunteer you unprompted.

Step 2: ask each question three times, in a clean session

Three times is the part everyone skips, and it is the part that makes the exercise worth doing. Ask the same engine the same question twice and you will often get different companies named. These systems sample; they are not lookup tables. One run tells you almost nothing. Three runs tell you whether a mention is real or luck.

Use a temporary or incognito chat every time. In ChatGPT use a temporary chat; in Claude start a new conversation; in Perplexity use an incognito thread. Otherwise the model leans on what you told it earlier in the session, and you end up measuring your own conversation instead of the world.

Do all four engines. They disagree more than people expect, and they cite completely different sources. Tracking only ChatGPT hides most of the picture.

Step 3: record five things per answer

A spreadsheet is fine. One row per question per engine per run:

Field	What to write down
Named	Yes or no. Were you mentioned at all?
Position	First, middle, or last. Being named fifth after four rivals is not a win.
Sentiment	Recommended, listed neutrally, or mentioned with a caveat.
Competitors	Every other company named, in order.
Sources	The URLs the engine cited or linked.

That last column is the one that pays for the whole exercise, and almost nobody fills it in. The engines are not inventing their shortlists. They are reading a small set of pages. In most categories the same fifteen to twenty URLs drive the majority of answers: a couple of listicles, a review site, some Reddit threads, a YouTube video. Those URLs are the actual battleground. You cannot get into the answer without getting onto that list, and you cannot find that list without writing down the citations.

Step 4: turn it into one number

Count the answers that named you, divide by the total number of answers. Ten questions, four engines, three runs is 120 answers. If you were named in 14 of them, that is roughly 12%. Do the same for your three biggest competitors.

That percentage is the number to watch. On its own it means little; moving over time against the same questions is what tells you whether anything you are doing works. Re-run the identical question set monthly. Change the questions and you have thrown away your baseline.

What our own numbers look like

We run this on ourselves, and we publish the result, including when it is bad.

Our most recent run: 12 buyer questions, sampled across ChatGPT, Claude, Perplexity and Google AI Overviews on 11 August 2026. We were named in 11% of answers. ChatGPT named us in zero out of twelve. Claude and Perplexity, two out of twelve each. Google AI Overviews blocked our sampling that day, so we have no reading for it and we are not going to estimate one.

The competitors the engines named instead of us, with the share of answers each appeared in: iPullRank 17%, Siege Media 17%, Profound 11%, Semrush 11%.

Sixteen days earlier the same twelve questions scored 12.8%. So our number went down over a period when we were actively publishing. We are telling you this because a company selling AI visibility that will not show you its own AI visibility is selling you a story. Ours is a work in progress, measured the same way we would measure yours.

The tools, and what they actually do

Once you have run the loop by hand, the tools make sense: they automate the sampling, store the history, and draw the chart. That is the product. None of them change your visibility; they measure it.

The field as of 2026, so you can judge any “best AEO tools” listicle you read against something:

• Otterly.AI is the cheapest credible entry, from around $29/month for a small number of tracked prompts across ChatGPT, Google AI Overviews, Perplexity and Copilot. Good for confirming what your manual run found.
• Profound starts around $99/month, with the realistic working tier nearer $399 and enterprise deployments quoted in the thousands. Broad engine coverage at the top end.
• Peec AI is commonly recommended for agencies running several brands, on transparent per-seat pricing.
• Scrunch AI leans toward the crawler-delivery side of the problem and is now part of Sitecore.
• AthenaHQ and Rankscale sit in the same measurement space, with Athena offering a free tier.
• Semrush AI Visibility Toolkit and Ahrefs Brand Radar are add-ons worth knowing about if you already pay for those suites, because they may cost you nothing extra.

Two honest caveats. Pricing in this category moves fast, so verify before you buy. And most of the ranked listicles you will find comparing these tools are published by one of the vendors in the list. This one is published by an agency, which is its own kind of bias, so weigh it accordingly. We do not sell a tracker.

What to do with what you find

The result usually lands in one of three places.

You are named, but late. You appear fifth, after competitors. The engines know you exist and do not rate you. Work on the sources: get onto the listicles and review pages that keep showing up in your citation column.

You are not named, and neither is anyone. The engines answer the question generically. This is the best case, because the category is unsettled and the definitive page has not been written yet. Write it.

You are not named, and three rivals always are. Look at what they have that you do not. In almost every case it is not better copy. It is that they appear on the third-party pages the engines are reading, and you do not.

One thing worth checking before any of this: whether the AI crawlers can reach your site at all. GPTBot, ClaudeBot, PerplexityBot and Google-Extended are named in robots.txt, and plenty of sites block them without realising. This is the best-documented lever in the whole field, and it costs nothing to check.

Start with the baseline

Do the manual run. It takes an afternoon and it will tell you more than a subscription will, because you will have read the answers rather than a chart of them.

If you would rather see the baseline first, run the free visibility check: give us your domain and a couple of competitors and we will send back your current share of answers and the sources the engines are citing in your category. We also publish an evidence register grading every claim we make in this field, including the ones that do not help us, a research library of the primary papers, and who we are if you want to know who is behind the numbers.