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Method

How to check what AI says about your brand, by hand

You can do a rough version of this yourself in an afternoon, for nothing. It will not survive a boardroom, and it is still more than most brands in this industry have ever done. Here is the method, and where it runs out.

12 August 2026 · 9 min read · Roo Wright

01Method

Almost every brand in this industry has a detailed view of its Google position and no view at all of what an assistant says when someone asks the same question. That gap is odd, because closing the crude version of it is nearly effortless.

What follows is the manual method. It takes an afternoon, costs nothing, and will tell you more than you currently know. It also has real limits, and we will be specific about those at the end rather than pretending otherwise.

Start with the questions, not with your brand

The instinct is to type your own brand name and see what comes back. Resist it. That answers a question almost nobody asks. Someone who already knows your name is not the customer you are worried about.

The questions that matter are the ones asked by people who do not know you yet, phrased the way people actually phrase things. Write twenty of them, spread across five shapes.

Question shapes to test
ShapeWhat it testsRoughly how it sounds
Open categoryWhether you are a default name at allWho are the good options for [category] in [market]
Attribute-ledWhether you own any specific qualityWhich [category] brands are best for [specific attribute]
AlternativesWhether you are in a competitor’s orbitWhat are alternatives to [competitor]
Head to headHow you are characterised when compared[Your brand] or [competitor], which is better for [use]
Buyer-stageWhether you appear in a considered shortlistWhat should I look for when choosing a [category] provider

Run them properly, which mostly means running them repeatedly

Ask each question at least three times, in a fresh session each time. This is the step people skip and it is the step that produces the actual insight.

A brand that appears in one answer out of three is in a completely different position from one that appears in all three, and both look identical if you only ask once. Volatility is information. A question where the names change every time is a question nobody has claimed, which makes it the cheapest ground available to you.

Do it on more than one assistant. ChatGPT, Claude, Perplexity and Gemini do not agree with each other, and Google AI Overviews behaves differently again because it sits on top of an existing index. Treating them as interchangeable will give you a confident and wrong picture.

Record five things, every time

A spreadsheet is fine. What matters is recording the same fields consistently, because the value compounds when you repeat this next month.

  • The exact question, word for word, because small changes in phrasing move answers.
  • The engine and the date.
  • Every brand named, in order. Order is not incidental. First mention carries disproportionate weight with a reader.
  • Whether you were named at all, which is the number you will end up reporting.
  • Any sources cited. This is the most valuable column and the one most people leave out.

That last column is where the manual exercise stops being a curiosity and starts being a plan. After twenty questions you will notice the same handful of domains appearing over and over. That set is what is actually deciding your category, and if you are not in it, you now know precisely what to work on.

Five ways people get this wrong

Asking about themselves. Covered above and worth repeating, because it is the most common mistake by a distance.

Asking once. One answer is an anecdote. You cannot distinguish a stable position from a coin flip.

Leading the question. Asking whether your brand is good is not a test. The model will generally be agreeable, and you will learn nothing except that it is polite.

Testing from one place. Answers differ by market, and for this industry they differ enormously. We wrote about why that happens separately.

Recording the verdict and not the reasoning. Knowing you were absent is mildly depressing. Knowing which three sources put your competitor there is actionable.

Where the manual method runs out

We would rather say this plainly than let you discover it in month three.

Sample size. One person asking twenty questions three times is sixty data points. That is enough to spot the obvious and not enough to be confident about anything subtle, or to defend a budget request.

Markets. You can only easily test from where you are. Testing properly across several markets by hand is slow and difficult to keep consistent.

Benchmarking. Being named in nine answers means nothing without knowing what typical looks like in your category. Absolute numbers are not a position.

Consistency over time. The value comes from repeating this identically. Manual processes drift, questions get reworded, someone leaves, and the comparison quietly stops being a comparison.

None of which is an argument against doing it. Do it. It is the cheapest way to find out whether you have a problem, and if the answer is no, you have saved yourself a purchase. If the answer is yes, you will have a much better conversation about what to do next.

Common questions

How many questions do I need?

Twenty is enough to learn something and few enough to finish. The value is in running each of them several times rather than running two hundred once, because a single answer tells you nothing about whether a position is stable.

Should I use a logged-in account or a fresh session?

A fresh session, every time. A logged-in assistant that has watched you research your own brand for months is not answering the question a stranger asks. Personalisation is the single fastest way to convince yourself you are doing better than you are.

How often should I repeat it?

Monthly is a reasonable rhythm for a manual check. Answers move as sources change and as models are updated, so a single snapshot ages quickly. What matters more than frequency is that you record it the same way each time.

02More from Insights
Mechanics

How an AI answer engine decides which brands to name

Models do not rank you and they do not reject you. They reach for whatever they can describe with confidence, and everything else is quietly left out. Here is what happens in between the question and the answer.

Markets

Why the same question returns different brands in different markets

Ask an assistant the same question from Toronto, London and Stockholm and you get three different shortlists. For iGaming that is not a curiosity, it is the whole measurement problem.

03Where this applies

Putting this to work.

The concept

What AI visibility means in iGaming

How answer engines decide which brands to name, and where it parts company with SEO.

For operators

AI visibility for casino, sportsbook and lottery brands

What AI assistants say when someone asks where to play, and who gets named instead of you.

For suppliers

AI visibility for iGaming suppliers and B2B vendors

Whether AI names you when an operator shortlists aggregators, studios, platforms or payments.

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