How to Find Which Competitors AI Recommends

01How do you find out which competitors AI recommends instead of you?

Run the questions your buyers ask through each engine yourself, in fresh sessions, and record four things per answer: the prompt, the engine, whether you appeared, and who appeared in your place. Then add a fifth column for the source the engine cited, because that is the column that turns a competitor name into something you can act on.

That is the whole method, and it is not a secret. The page currently ranking first for this question walks through a ten-minute version of exactly this test, with a prompt table and a results table. Another ranking page gives the same manual method and then tells you when to stop doing it by hand. A third spends four thousand words comparing the tools that will do it for you.

What none of them says is how you know the result is real. That is the part deciding whether the exercise is worth an afternoon or is theatre, and it is the part founders get wrong, because most of them answer this question by asking somebody else.

02Why doesn't a free AI visibility audit answer it?

Twice in six months, a builder posted the same offer to founder communities: drop your domain and I will show you which competitors the engines recommend instead of you. Both threads filled up with URLs, categories and positioning, one founder after another.

In neither thread did a single result get posted back in public. Dozens of domains went in, nothing came out that anyone else could read. The only substantive reply in the second thread was somebody plugging an unrelated tool.

That is the state of the market for this question. The ten-minute test article ends with a free audit delivered inside forty-eight hours. The tool roundups end with a free tier. That is a normal lead magnet, but it means the answer founders most often receive is a one-off number produced by someone else on prompts they never saw.

The problem is not accuracy. It is that the result is unfalsifiable. If an audit says three competitors are named and you are not, you cannot ask the useful follow-ups: which prompts, how many times each, on what day. Without those you cannot compare the number to next month, or to a second run of the same audit. You have a fact with no denominator.

03What has to be in the record for the answer to be usable?

Five fields. Product marketers running this at scale converge on the same short list, and the discipline is in keeping it short rather than in adding columns.

The fifth field is the one people skip and the one that pays. A competitor name on its own is a fact you can do nothing with. The cited source next to it tells you where the answer was actually assembled, and it is frequently somewhere you would not have guessed.

Read that as a method, not a complaint. Clicking through to the citations turned an unexplainable result into a specific one: the engine was not judging brands, it was reading a marketplace listing. The B2B equivalent is usually a review-site profile, a comparison page on somebody else’s domain, or an old forum thread. Each of those you can go and change. The competitor name you cannot.

04Why does checking your own brand name tell you nothing?

Because the engine knowing who you are and the engine recommending you are two different measurements, and only one affects revenue. Typing your company name into ChatGPT measures whether you exist in the index. It says nothing about the moment a buyer who has never heard of you asks which tool to use.

That is the branded and unbranded split from search, moved to a new surface. Brands win their own name by default and lose the category question, and a practitioner who scanned a large sample of B2B brands found that publishing more owned content did not close that gap on its own.

The second reason to write the prompts before you open any engine is that the engines do not agree with each other. An agency that tracked citation patterns across fifty B2B and consumer sites over a sixty-day window found that sites cited by one engine were often absent from another, and noted in the same write-up that outputs vary run to run and citation behaviour is not deterministic. Both caveats matter more than the findings around them.

Practically: run all three engines, keep the columns separate, and resist averaging them. A number that blends ChatGPT and Perplexity moves without telling you which lever moved it.

05Before you conclude you are invisible, are you reachable?

This is the cheapest step in the exercise and the one almost nobody does first. Absence has two very different causes that look identical from the outside. Either the engines can read your site and are choosing somebody else, or they cannot read it at all.

Accidentally is the word doing the work. Nobody sets out to block an AI crawler. It happens through a bot rule a security vendor shipped by default, a robots.txt written before these user agents existed, or a site that renders only in the browser. Your own server logs settle it in an afternoon, and we wrote up what AI crawler traffic looks like in access logs if you want to check yours.

06How many times do you have to run this?

More than once, which sounds obvious and is the step that gets silently dropped. People build the prompt matrix, run it across the engines, feel informed, and never open it again. Three weeks later the snapshot is describing a world that has moved.

Consistency as the metric is the sharpest reframing in any of these threads. It changes what you are looking for. You are not asking whether a competitor was named today, you are asking whether they hold that answer across repeats and across days. One vendor building in this space describes measuring exactly that.

For a founder doing this by hand, the honest minimum is smaller than that and still useful:

• Ten to twenty prompts, written once and frozen. Category questions, alternatives to your named competitors, and problem-first questions that never mention a product. If you would not recognise the phrasing from a sales call, it is your wording and not a buyer’s.
• Three repeats of each, in clean sessions. No chat history, no logged-in personalisation. One run tells you who won a coin toss.
• A weekly or fortnightly cadence. Direction of travel on the same questions is the signal. A precise number is available to nobody, including the vendors selling one.
• A dated file per run, never overwritten. The history is the asset, and you will want answers you never thought to save.

07When does the spreadsheet stop working?

Twenty prompts across three engines with three repeats is around one hundred and eighty answers to read and log every week. That is the wall, and product marketers who run this describe hitting it at roughly ten prompts before the manual version stops being realistic. What actually breaks first is not the running, it is the history: keeping last month’s answers in the same shape as this week’s, so a comparison means something.

The category that sells you out of that problem is real and crowded. Practitioners name the same handful.

Take that as a map, not a recommendation. What those threads never resolved is worth saying plainly: nobody compared the accuracy or pricing of any two of these tools against each other, and the roundups ranking for this question do not either. The one on this query lists six platforms with feature tables and no test of whether any two return the same result for the same brand. Until someone publishes that comparison, you are choosing a measurement instrument on trust, which is reason enough to keep your own small run going alongside whatever you buy.

There is a counter-position worth stating, because it cuts against our own category and it is held by serious people.

If that is your read of your market, the right response to this article is an hour a quarter, not a weekly process. The reason to do it at all is that the losses are invisible: a buyer who gets three competitor names and contacts one never shows up in your analytics as anything.

The last honest admission is the biggest one. Every thread behind this article stops at detection. Not one of them described what to do once you know you are losing a specific prompt, and the reason is that the fix is slow, indirect and specific to whichever source the engine happened to cite. Knowing who is recommended is the diagnostic. The treatment is a separate question, and we took a run at it in why ChatGPT recommends your competitors.

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