How to Monitor Your Brand Mentions in AI Answers
A method you can run by hand, the tool categories that automate it, and the four things monitoring genuinely cannot tell you no matter what you pay.
Pick a fixed prompt set a buyer would actually type, run it on a fixed schedule across the engines you care about, and log two things separately: whether you were named and whether a page was cited. Tools automate the cadence. None of them can measure every answer, and monitoring alone changes nothing.
Most people start monitoring the same way. They ask ChatGPT a question, see a competitor's name, screenshot it, and send it to someone in a panic.
That screenshot is not data. Run the same prompt again in a fresh chat and you may well get a different set of names. What you caught was one sample from a probabilistic system, and treating it as a verdict is how teams end up chasing noise for a quarter.
Monitoring is worth doing. It just has to be built like measurement rather than like a fire alarm.
The method, before any tool
You can run this by hand this afternoon. Most of the value is in the discipline, not the software.
1. Write a fixed prompt set
Ten to twenty prompts, phrased the way a buyer phrases them. Commercial intent beats brand curiosity. "Best conveyancing solicitor in Bristol" is a prompt. "Tell me about Acme Legal" is a mirror.
Cover three types: the category question ("best X for Y"), the comparison ("X vs Y for a small team"), and the problem question your product answers without naming a category at all.
Then freeze the list. A prompt set you keep improving produces a trend line that measures your editing, not your presence.
2. Run each prompt more than once
Same prompt, fresh chat, three or more times. Answers vary between runs on identical input. If you record one run per prompt you are recording coin flips.
3. Log two fields, not one
Was your brand named. Was one of your pages cited. These are different measurements and they move independently. You can be named with no link at all, and you can be cited as a source for an answer that recommends someone else.
Collapsing them into a single "visibility" number destroys the most actionable signal you have.
4. Log the competitors too
Record who else got named. A prompt where three competitors appear and you do not is a different problem from a prompt where nobody appears and the engine hedges.
5. Keep the cadence boring
Weekly or fortnightly, same day, same prompts. The point of monitoring is the second derivative: not where you stand, but which way it moved and when.
Old way, new way
The old way was rank tracking. One query, one number, one direction, and the number meant the same thing every day.
The new way is a distribution. Your result on a prompt is a probability, not a position, and the honest readout is "named in 7 of 12 runs this week, 4 of 12 last week." That is less satisfying and considerably more true.
Teams that try to force AI monitoring back into a rank-shaped number end up reporting confidence they do not have.
Where the tools fit
Doing the above by hand across four engines, twenty prompts, three runs each, weekly, is 240 chats a week. That is the honest reason the tool category exists.
Before describing anyone else, the damaging admission about us. Citedon is not a brand-monitoring dashboard. It is built around a single site's readiness and a citation measurement on inferred queries, and it does not do share-of-voice reporting across a large prompt portfolio, multiple markets, or multiple brands. If that is the job you are hiring for, a dedicated monitoring platform fits it better than we do, and we would rather say so than sell you the wrong shape.
The category, as checked on 2026-08-06 against each vendor's own published pages, with every price quoted on monthly billing. Feature sets and pricing in this space move fast, so treat everything below as true on that date and verify before you buy.
Profound runs structured prompts across AI platforms and tracks where and how a brand appears, including citations, sentiment, and competitive presence. Its published tiers scale by engine coverage: Starter at $99/month tracks ChatGPT only with 50 prompts, Growth at $399/month tracks 3 answer engines with 100 prompts, and Enterprise, at custom pricing, tracks up to 9 answer engines with SSO/SAML and SOC2. Its own page says the platform is "built for enterprise brands with a global footprint" (pricing).
Otterly.AI includes ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot on every tier, and sells Claude, Gemini, and Google AI Mode as paid add-ons on top. It prices by prompt volume: Lite at $29/month for 15 prompts, Standard at $189/month for 100, Premium at $489/month for 400 (pricing).
Peec AI is positioned for marketing teams, tracking brand performance and benchmarking competitors, with Starter at $95/month for 50 prompts, Pro at $245/month, and Advanced at $495/month, all tiers including unlimited users, daily tracking, and a choice of 3 models from ChatGPT, AI Mode, AI Overviews, Copilot, Perplexity, and Gemini (pricing).
Scrunch AI monitors brand presence in AI search and also analyzes and optimizes content, at $299/month Professional and $2,400/month Enterprise (site).
AthenaHQ covers a wider engine list including AI Mode and Grok, with a free Essential tier and Starter at $295/month (site).
Notice the shared unit. Almost everyone prices by tracked prompts, which tells you something real about the underlying cost: every prompt is an API call to every engine, repeated on a schedule. That is why a wider prompt set costs more, and why a cheap tier usually means a narrow sample.
The other thing to notice, verified across all five product pages on the same date: none of them applies machine-readable fixes to your website. They report. Applying is a separate job.
The four things monitoring cannot tell you
This is the part worth internalizing before you spend anything.
It cannot tell you why. The retrieval and ranking logic inside each engine is proprietary and changes without notice. A dashboard can show you that you dropped out of six prompts in a week. No dashboard has access to the reason, and any tool that presents a confident cause is inferring it.
It cannot measure everyone's answer. Results vary with the wording of the prompt, with what an assistant remembers about a specific user, with region, and with which surface someone opened. Every tool, including ours, is sampling. A number built from 100 prompts is a sample of an infinite prompt space.
It cannot separate "they could not read you" from "they read you and chose someone else." Those are the two failure modes and they need opposite responses. Monitoring reports the outcome. Only reading your page the way a machine does distinguishes the causes.
It cannot change anything. This is the one that gets skipped. A monitoring subscription is an instrument, and instruments do not move the thing they measure. If your product pages return a shell to a crawler, a weekly report will tell you accurately and indefinitely that you are not being mentioned.
What to do with what you find
Split your prompts into two piles.
The pile where nobody could read you is fixable. Crawlers blocked, content assembled by script, no structured data, no clear extractable answer near the top. That is input, it is yours, and it is checkable today.
The pile where you were read and passed over is a content and authority problem, not a technical one. More markup will not fix it. A better, more specific page might.
Most teams discover the first pile is larger than they expected, which is the useful part of doing the readiness check before buying a monitoring subscription.
Where to start
Before you build a prompt matrix or price a dashboard, find out whether the pages you would want cited can be read at all.
Run a free scan on your most important page to see how ChatGPT, Perplexity, Gemini, and Claude read it today, and which structural pieces are missing.