Kovmere vs Mention for Tracking Brand Sentiment in ChatGPT and Perplexity

Why is sentiment inside AI answers hard to track?

Conversations in ChatGPT and Perplexity are private. No listening tool can read a particular person's private conversation, so "sentiment in ChatGPT" is usually measured in one of two ways (a third, opt-in consumer panels such as Profound's, samples real conversations but is sold for prompt demand rather than per-brand tone):

• Upstream, by tracking the public sources assistants tend to use: reviews, articles, forum threads and social posts. If those turn negative, answers are likely to follow.
• Directly, by asking the engines questions yourself and reading or scoring the answers. That gives you the answer to your question, not to everyone's.

Mention works upstream. Kovmere works directly but leaves the judgement of tone to you. Neither approach on its own tells you everything a single signed-in user sees.

How do Mention and Kovmere compare?

What does Mention tell you about AI sentiment?

Mention's strength is the raw material. It watches a very wide range of sources, including review sites, and builds alerts with help from ChatGPT so you can catch the complaint thread or critical article that an assistant might later summarise. If a wave of negative reviews is building, Mention is the tool that sees it first.

What it does not do is show whether ChatGPT or Perplexity actually repeat that material, how they phrase it, or whether they still recommend you despite it. A negative article that no answer cites matters less than a neutral one that every answer leans on.

What does Kovmere tell you about how AI describes you?

Kovmere asks up to 20 questions for your market, up to 18 buyer questions that do not name you and up to 2 brand checks reported separately, three times on each engine. In the answers you can read how you are described, and see whether you are mentioned, recommended as an option and cited. The source ledger shows which pages the answers cite, so you can see when a negative review page or an outdated comparison keeps being cited for the questions where you are described poorly.

What it does not do is attach a sentiment score, read social media, or ask "what do you think of this brand?" style questions about reputation. Its questions are buying questions, so tone shows up in how you are framed when someone is choosing: as a leading option, an afterthought or not at all.

What should you look for when reading answers for tone?

Without a score, two signals matter most:

• Placement. Are you named first, in the middle of a list, or only under "other options"?
• Qualifiers attached only to you. Phrases such as "some users report", "can be expensive" or "mixed reviews" attached to your name but not to rivals'.

Our Kovmere vs Brand24 comparison lists the other things to look for. What is specific to Mention is the next step: once you know which review sites, news outlets and forums the answers cite when they qualify you, add exactly those sources to Mention's alerts, with your brand and product names as keywords, so you hear about new criticism on the pages that feed the answers before it spreads into them. Leave the thousands of sources no answer cites on a lower alert level, so the team's attention goes where it changes what buyers read.

How can you combine them for a reputation problem?

1. Find the cited sources first. Run Kovmere's questions and note which review pages, articles and threads the answers cite when they describe you.
2. Monitor those sources. Add the publications and review sites that matter to Mention's alerts, so you hear when new material appears there.
3. Respond where it counts. Correct or answer the pages that answers actually cite, rather than every mention online. Kovmere's fixes can include a profile update or a page that states the facts plainly, built from your own site.
4. Re-test. On Pro, the same frozen questions are asked again each week, so you can see whether the framing changes once the sources do.

Which tools score AI-answer sentiment directly?

If you need a sentiment figure for the answers themselves, look at trackers that score it. Brand24's AI visibility module reports visibility, share of voice and sentiment across several AI platforms, and Peec AI reports sentiment on a 0 to 100 scale alongside visibility and position. Treat any sentiment score as a summary, and read the answers behind it before acting, because a model judging tone can misread a balanced comparison as negative. We cover reporting tone to clients in how to report AI citation sentiment and repair timelines in how long it takes to reverse negative brand sentiment in AI answers.

Which should you choose?

• You run PR or social and need to catch negative coverage early: Mention.
• You need to know how assistants describe you to buyers and what to fix: a Kovmere Check, then Pro if you will act on weekly readings.
• You need a sentiment number for leadership: a tracker that scores answer sentiment, read alongside the answers.
• You have a live reputation issue: combine upstream listening with direct reading of the answers.

What do people ask about tracking AI sentiment?

Can Mention see what ChatGPT says about my brand?

No. Mention monitors public web, news, review and social sources. AI conversations are private, and Mention has no module that asks AI engines questions.

Does Kovmere give a sentiment score?

No. It shows the full answers, whether you are mentioned and recommended, and the cited pages. You judge the tone by reading them.

Is negative sentiment online always reflected in AI answers?

Not always. Answers draw on a selection of sources. A critical page that no answer cites may have little effect, while a single heavily cited review can shape many answers.

Which matters more, sentiment or being recommended?

For buyers who are choosing, being recommended usually matters more. A neutral description that puts you forward as an option is worth more than warm words in an answer that recommends a competitor.