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Guide

AI visibility monitoring, explained

Updated June 2026

When someone wants to know about your company, there is a growing chance they ask an AI assistant instead of a search engine. This guide explains what those assistants are saying about brands, why you usually never see it, how to check it yourself for free, and how to change what they say.

1. What is AI visibility monitoring?

AI visibility monitoring means regularly checking what AI assistants such as ChatGPT, Claude, Gemini, and Grok say when people ask them about a company, product, or person, and tracking how those answers change over time. It covers both visibility (do the models mention you at all, and for which questions?) and AI sentiment (is what they say positive, negative, or mixed?).

You may also see the related terms GEO (generative engine optimization) and AEO (answer engine optimization). Those describe the work of improving how you show up in AI answers. Monitoring is the measurement side: you cannot improve, or even notice a problem, without first seeing the answers.

This kind of monitoring is new, but the idea is familiar. Companies have watched their Google rankings and review sites for years. AI assistants are the newest place buyers form an opinion, and the only one where the answer is generated fresh for one person and cannot be looked up afterward.

2. Why it matters now

The companies that run search say it plainly. Google reports that its AI Overviews are used by more than a billion people, and it has started replacing the classic list of blue links with an AI-written answer. Pew Research Center found the share of U.S. adults who have used ChatGPT roughly doubled between 2023 and 2025, to about a third. Gartner expects traditional search volume to fall 25% by 2026 as people move to AI chatbots and virtual agents.

3. Where AI answers come from

To monitor AI answers well, it helps to know that an assistant can answer in two distinct ways, and the difference changes what you should measure.

From training data

Every model has a built-in picture of the world learned during training. That picture has a cutoff date, often many months in the past. When an assistant answers without searching, you are hearing its memory of you: old reviews and forum threads from before the cutoff. This is the answer people get when they ask a quick question and the model does not look anything up.

From live web search

Most major assistants can also search the web before answering, then cite the pages they read. These answers reflect what is published about you right now, and the citations tell you which sources the model trusts. This is closer to what a careful user sees today.

The gap between the two is the useful signal. If the live-web answer about you is warmer than the training-data answer, the internet has caught up with your improvements but the models' built-in view has not. If both repeat the same wrong claim, the source still exists and needs fixing. A good monitoring habit checks both lenses.

4. How to check what AI says about you, free

You can start today by hand. It takes about an hour per round:

  1. Write down the questions your audience actually asks. For example: "What is [company]?", "Is [product] any good?", "Best alternatives to [product]?", "Is [founder] credible?", "Should I buy from [brand]?"
  2. Ask each major assistant the same questions. ChatGPT, Claude, Gemini, and Grok each have their own view of you. Ask with web access on and, where the option exists, off.
  3. Record the answers verbatim in a spreadsheet, with the date, the model, and any sources cited.
  4. Score each answer as positive, neutral, or negative, and note recurring claims, both the flattering ones and the complaints.
  5. Repeat monthly and compare. The changes matter more than any single snapshot.

The honest limitations of the manual approach: it is a snapshot of a few questions on one day, scoring by hand is subjective and inconsistent across rounds, and most teams stop doing it after the second month. It also gets slow quickly if you track competitors or more than one name. But it is free, and one round will usually tell you whether you have a problem worth tracking.

5. How to improve what AI says about you

Models repeat what they read. Improving AI answers is mostly the unglamorous work of improving and refreshing what is published about you:

6. When to automate it

Manual checking breaks down when you need consistency: the same questions, asked the same way, across several models, scored the same way every time, on a schedule, with history you can chart. That is a job for software.

This guide is published by Saidly, which does exactly this. Saidly asks the AI models it covers (Claude, ChatGPT, Gemini, and Grok) about the names you track, on a schedule or on demand, with live web search on so the answers mirror what people actually see. Each report scores AI sentiment from 0 to 100 per model, pulls a representative quote from each model's answer, surfaces recurring themes and the sources the models cited, tracks the change since the last report, and recommends what to fix. Plans that include the model-only and compare lenses also show the gap between the models' built-in view and the live web. Reports land in your inbox and your dashboard.

Whether you use Saidly, another tool, or a spreadsheet, the core advice stands: pick your questions, measure on a schedule, fix the sources, and re-measure.

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