What Does AI Actually Say About Your Brand? Start Monitoring It.

A summary tells you that LLM models think your product is "expensive." It doesn't tell you how often, in which model, or whether it is still true after the pricing page you rewrote last month.

That gap is why we built Monitor perceptions. You write one sentence you suspect AI is saying about your brand, or about a competitor. Genezio reads every AI answer it has already collected for you and returns two numbers: how many answers agree with the sentence, and how many disagree.

The problem with "AI says your brand is..."

Every GEO tool, ours included, can produce a perception summary. A model reads a sample of AI answers and writes back something like: "AI assistants describe Acme as reliable but expensive, with limited integrations."

That's useful the first time you read it. It stops being useful the moment you need to act on it.

Ask the follow-up questions a CMO will ask:

• Is "expensive" in 4% of answers or 40%?
• Is it a Gemini problem or a ChatGPT problem?
• We changed pricing on September 15. Did the claim drop after that?
• Is anyone saying the opposite?

A summary can't answer any of these. It compresses thousands of answers into a paragraph, and the compression throws away exactly the information you need: frequency, source, and direction.

There's a second, quieter problem. The claims that hurt you most are often not the frequent ones. One answer that says "hard to integrate" in the middle of a vendor comparison can cost more than ten answers that call you "reliable." A summary written for the average case will smooth that sentence out of existence.

Write the claim. Get a count.

The monitor starts with one sentence that can be true or false. For example:

• "Acme has the lowest fees."
• "Acme is hard to set up."
• "Competitor X has no free tier."

If you don't know where to start, the page suggests the six claims AI makes most often about your brand, taken from the statements Genezio has already extracted. One click turns a suggestion into a monitor.

Genezio then goes through the AI answers already collected for your brand, across the topics, scenarios and models you track. Each relevant paragraph gets one of three verdicts:

• Agrees: the paragraph states or clearly implies the claim.
• Disagrees: the paragraph says something that can't be true at the same time as the claim.
• Neither: same subject, but no position. A list of fees without a comparison. A sentence about a different company.

The result page shows two tiles: AI agrees and AI disagrees, each as a count of conversations and a share of the total. Below them, a breakdown by model, and a table of every paragraph behind the numbers. Click a row and you're in the original conversation, reading the full answer the AI gave.

That last part matters more than it sounds. When you bring a number to a meeting, someone will ask "show me." Here, you can.

What you do with the number

A perception count is only interesting when it moves, or when it refuses to.

Prove a content change worked. You rewrote the onboarding docs because AI kept calling your product "hard to set up." Start a monitor before the release, press Refresh three weeks after. If the "agrees" share fell, you have a sentence for the board: "We rewrote onboarding in March and the claim dropped in April." If it didn't fall, you've learned that the claim comes from somewhere you haven't touched yet, and the paragraph table shows you where.

Catch a reversal early. A claim you fixed can come back. Usually the cause is a new source that the models start to cite, such as a review, a forum thread, or a competitor's comparison page. A monitor shows this in weeks, not at the next quarterly review.

Track the claim you want to be true. Monitors are not only for damage control. If your positioning is "the option for regulated industries," monitor that sentence. The day AI starts to say it on its own, without your brand name in the prompt, is a day worth knowing about.

Watch a competitor's weakness. "Competitor X has no free tier" is a claim too. If AI starts disagreeing with it, they've changed something, or someone has written about it.

Compare models. The per-model breakdown often shows that a perception is not "AI's" view at all. It belongs to one model that reads one source the others ignore. That changes the fix entirely.

Precise counts, honest limits

A few things worth knowing before you start:

• One claim, one meaning. "Acme is expensive" and "Acme costs more than its competitors" are different monitors. Paraphrases are matched as new perceptions on purpose, because they are different claims.
• Filters are fixed per monitor. A monitor keeps the topics, models and date range it started with, so the numbers stay comparable from one refresh to the next. Want the same claim for a single topic? Create a second monitor.
• The first run on a large brand takes time. It reads every relevant answer in the date range, which can mean tens of thousands of paragraphs. You can close the page. The run continues in the background and the counts grow as it works.
• Refresh is manual for now. There is no automatic schedule yet. Press Refresh after a content release, or call the monitor from your own tooling (see below).

Start with the claim that worries you

Every marketing team has a sentence it hopes AI isn't saying. Write that sentence into the monitor first. Either the number is small and you can stop worrying, or it isn't, and you now have the exact paragraphs, the exact models, and a baseline to beat.

Open Monitor perceptions in your brand report to start. If you are not a Genezio customer yet, book a demo and we'll run your first claim with you.