Is There a Tool That Tracks Whether AI Chatbots Are Recommending My Company? How to Set It Up

Yes. Tools that track whether AI chatbots recommend your company exist, and the category has a name: AI visibility or prompt monitoring. The mechanic is simple. The tool asks ChatGPT, Claude, Gemini, and Perplexity the questions your buyers actually ask, reads the answers, and records which companies get named.

The hard part is not finding a tool. It is setting one up so the numbers describe your real market instead of a sample of questions nobody types. Most of the monitoring setups we see fail on configuration, not software.

Here is the setup that produces usable data.

Step 1: Get a baseline snapshot before you build anything

Confirm you have a problem worth tracking. Open ChatGPT and type the exact sentence a buyer would type: "best [category] tool for [specific use case]." Watch who gets named. The usual result on a first run is three competitors and no mention of you.

If you want that run across all four engines at once, our free AI visibility check does it from a URL with no account. That single run is a diagnosis, not a program. It tells you whether to keep going.

Step 2: Choose questions a buyer would actually type

This is where most tracking setups fail. Teams load their prompt list with keywords ("AEO software") instead of questions ("what tool should I use to see if ChatGPT recommends my SaaS?"). Keywords belong in a search box. Buyers talk to assistants in sentences.

Build a starting pool of 20 to 30 questions across four kinds:

• Category shortlist questions: "what are the best tools for X," the ones that produce a named list.
• Comparison questions: "X vs Y for a 20-person team," where you either appear or do not.
• Problem-first questions: the pain described before the category is known, like "how do I tell if AI is sending me traffic."
• Objection questions: "is X worth the price," "what are the downsides of X."

A question only earns a slot if the answer is likely to name companies. A question that returns a definition teaches you nothing about your visibility. Cut it.

Step 3: Prefix every prompt with a buyer persona

The same question answered cold and answered as a persona produces different company lists. "What AEO tools should I use" and "I'm a Head of Growth at a 30-person B2B SaaS, what AEO tools should I use" are not the same query to a model.

So set a short persona preamble, role, company size, and location where location matters, and use the identical preamble on all four engines. Clark writes that preamble from your business profile and leaves the question text itself untouched, which is what makes a cross-engine comparison honest. Change the persona midstream and you have changed the experiment, not learned anything from it.

Step 4: Run all four engines on a fixed cadence

ChatGPT, Claude, Gemini, and Perplexity answer the same question differently, and they re-rank continuously. A monthly manual spot check cannot tell a real trend from a Tuesday. Daily runs, with each individual question coming back around roughly weekly, give you a line instead of a dot.

Clarity runs that rotation across all four engines every day on paid plans: 25 tracked questions on Growth, 50 on Scale.

Step 5: Log every company named, not just whether you appeared

Record four things per answer: whether you were mentioned, your position in the list when you were, every other company named, and the engine. That gives you mention rate, average position, and a competitor share-of-voice leaderboard built from real counts.

Skip composite "visibility scores." A number nobody can reconstruct is not evidence. The share of answers that mentioned you, and the list of the ones that did not, is. If a vendor leads a demo with a proprietary score and cannot show you the raw answer text underneath it, that is the moment to walk.

Step 6: Keep the full answers and their sources

Flagging a mention is the cheap part. Storing the complete answer text plus the source links behind it is what turns monitoring into strategy. Clarity keeps the full answer from each platform and up to 20 deduplicated source URLs, filterable by engine, so you can read exactly which third-party roundups and review pages keep feeding a competitor's mention. Those recurring URLs become a target list for off-site work. The archive is also your answer, six months later, when someone asks how you know any of this is real.

Step 7: Set movement alerts, then feed the gaps back into content

Dashboards go unread. Alerts do not. Trigger on a meaningful swing in mention rate or a two-place change in average position, and make the alert name the exact questions that moved and who took the ground.

Then close the loop. Every question where you are invisible becomes a content topic, and the post you publish gets measured against that same question wording afterward. With 71% of B2B decision-makers using AI chatbots during research, the questions you lose are the shortlist of work that matters.

The part tracking alone will never tell you

Mention rate is a leading indicator. It is not the result.

The run that matters looks like this: a buyer asks Perplexity which tool fits a 30-person team, your post gets cited, that person lands on the page, and weeks later the deal closes. Tracking shows you the citation. Only attribution ties the citation to the sale, and that is the number your board asks about.

So track first, because you cannot fix what you cannot see. Then insist on the second half. Everyone shows you rank. We show you revenue, and the diagnosis is free before you pay for anything.