Is there a tool to monitor how AI chatbots describe your business?

Yes, and we use one every week. The more interesting question is what to do with it. Founders ask us this after a customer says "ChatGPT told me you do X" and X is wrong. Describing a business is a harder job for an engine than naming it, and the errors are rarely random. They come from specific pages. Here is our process for finding them.

What can go wrong in a description

We see the same five problems over and over:

Outdated facts. Old pricing, a retired product, a former address.
Wrong category. The engine files the business under an adjacent industry.
Borrowed identity. A similarly named company's details get mixed in.
Competitor framing. The description comes from a comparison page that positions you against a rival.
Thin description. Engines say very little because little exists to cite.

Each has a different fix, so the first job is to see the description clearly, across engines and across repeated runs.

Step one: build a description panel

We write prompts that ask engines to describe the business in the ways a buyer would:

"What does [brand] do?"
"Is [brand] good for [use case]?"
"[Brand] vs [competitor]"
"What are the downsides of [brand]?"
"Who are the best providers of [category]?"

We run them daily across engines, because single answers are unreliable. Then we read the language, not just the score. A description can be positive in tone and still wrong in substance.

Step two: define what counts

We set this up in the Brand Book of Promptwatch, which we run client programs on. Its analysis instructions decide what counts as a mention, and brand aliases catch variants and misspellings. It matters most when the brand name is a common word. Without it, the score drifts.

Step three: read sentiment and position

Promptwatch reports Visibility Score, Share of Voice and Position in Answer, plus a per-response Sentiment score from 1 to 100, with a weekly AI summary. We use the weekly summary as a prompt for the client call, and the per-response view to find the specific answers behind it. A heatmap compares two dimensions, such as models against topics, which shows quickly when one engine describes the business differently from the others. Real-UI monitoring covers ChatGPT (Search), Google AI Overviews, Google AI Mode, Copilot, Gemini, Perplexity, Amazon Alexa and Meta AI. Claude, DeepSeek, Mistral and Grok are tracked via API, which is worth stating to clients, because API answers can differ from what people see in the apps.

Step four: trace the description to its sources

This is the step most monitoring tools skip. In the citation analytics we look at which pages and domains the engine used for the wrong answer. Often there is an old directory entry, a third-party review with stale detail, or an out-of-date page on the client's own site. Offsite mentions, meaning pages that name the brand without a link, are included.

Step five: fix the source

Update your own pages first. A clear About page, accurate product and pricing pages, and consistent facts everywhere you publish.
Correct third-party listings and profiles you control.
Publish a page that directly answers the question the engine got wrong.
Check that AI crawlers can read the corrected page. Crawler logs, from the Professional plan upward, show whether bots such as GPTBot and OAI-SearchBot fetched it.

Then we wait. Engines refresh at different speeds. Expect weeks, and watch the weekly sentiment summary and the description wording.

Plan notes

Explore is free but limited to ChatGPT with 10 prompts. Essential ($95 a month) gives 50 prompts and all models. Sentiment tracking and citation analytics sit within the platform, while crawler logs begin at Professional ($245). Agency plans from $199 a month cover crawler logs and unlimited projects.

What monitoring cannot do

It cannot force an engine to rewrite its answer, and it cannot reveal a private conversation. Descriptions vary from run to run, so we report patterns across many runs. A single bad answer is an anecdote. The same wrong claim in run after run is a finding.

Start here

Write ten prompts that ask about your business the way a stranger would. Run them in two or three engines. Read every answer closely. If you find an error, trace it to a source before you change anything else. That one habit solves more description problems than any dashboard.