What is AI reputation management?

The practice of tracking what AI assistants say about your brand, correcting what is wrong or outdated, and measuring whether the correction took hold. It matters because buyers increasingly take an AI answer as the final word.

What do I do if ChatGPT says something wrong about my brand?

First document it, then fix the sources. Models repeat what they read, so correcting your own pages and the third-party sources they cite is the reliable lever. LLM Pulse gives you the archived answer as evidence and tracks whether following runs drop the claim.

Can AI answers about my brand be changed?

Yes, over time. Answers follow the sources models trust, and updated or corrected sources flow into updated answers. The weekly tracking exists so you can verify the change happened instead of assuming it did.

How does sentiment scoring work?

Every brand mention in a tracked answer is analyzed and scored, then rolled up per model and over time. You see whether tone is improving, flat, or slipping, and you can open the underlying answers behind any score.

Do different AI models say different things about the same brand?

Routinely. They train and retrieve from different sources on different schedules, so an old controversy can persist in one model long after the others moved on. That is why sentiment is tracked per model.

Which models are covered?

Sentiment and reputation tracking run weekly across ChatGPT, Perplexity, Gemini, Google AI Overviews, and Google AI Mode on every plan, with Enterprise coverage reaching 14 models.

How much does AI reputation management cost?

Plans start at EUR 49 per month with a 14-day free trial, so you can see your current sentiment picture before committing.

For Brand and Communications Teams

Protect your brand reputation in AI answers

Buyers now ask AI about brands before they buy. Know what every model says about yours, catch wrong or outdated claims fast, and prove the fixes stuck.

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When AI repeats the wrong story, bring receipts

For comms teams answering to legal and leadership, every claim comes with the model, the date, and the full answer attached.

Reputation Analysis

Find the facet dragging your score down

An old controversy can sink one dimension of how AI describes you while product quality scores fine. Reputation breaks into scored dimensions set against your competitors, so comms works the specific weakness instead of a vague average, and sees where a rival is exposed too.

Isolate the dimension pulling the average down
See where a competitor is weaker than you
Track each dimension through a correction

Explore reputation analysis

Evidence Archive

Evidence legal can actually use

A customer's screenshot of a bad AI answer proves little. The archived response, with the model, the date, and your brand highlighted inside the claim, is a record comms and legal can act on when they correct the sources behind it.

The full answer, dated and attributed to a model
Claims documented before they spread
Hand the record to legal as it stands

Explore the responses archive

Annotations

Prove the correction stuck

You fixed the page, briefed the cited sources, and shipped the statement in March. A dated marker on the sentiment chart ties that work to the recovery that followed, which is the one slide a skeptical stakeholder accepts.

Mark the date each correction shipped
Tie the sentiment turn to your fix
Turn recoveries into a stakeholder slide

Explore annotations

Why Comms Teams Watch AI with LLM Pulse

Sentiment, claims, and evidence for every AI answer that mentions your brand.

Score Every Mention

Each brand mention gets a sentiment score, so you can trend how positively AI talks about you over time.

Compare Sentiment by Model

One model can repeat an old story the others dropped. Per-model sentiment shows exactly which assistant hurts you.

Catch Wrong and Outdated Claims

Reputation analysis flags negative, wrong, or stale statements AI makes about your brand, with the answer as evidence.

Keep the Receipts

Every answer is archived in full, so comms and legal can see the exact wording, model, and date of any claim.

Prove Corrections Stuck

After you publish corrections, weekly runs show whether each model picked them up or kept repeating the old line.

See Problems While Small

Weekly sentiment trends surface a turn in tone before it spreads across models and into buyer conversations.

How AI Reputation Protection Works

Four steps from first sentiment read to verified corrections.

1

Track the Brand Conversation

Your prompts run weekly across the models, and every mention of your brand gets a sentiment score.
2

Review Flagged Claims

Reputation analysis surfaces the negative, wrong, or outdated statements worth your attention, with the full answer attached.
3

Correct at the Source

Fix your own pages and work the third-party sources the models cite when they repeat the claim.
4

Verify Week After Week

Watch each model's answers and sentiment respond, and keep the archive as proof for stakeholders.

How Comms Teams Use It

Reputation jobs, from quiet quarters to active incidents.

Audit Before the Board Asks

Keep a standing sentiment report ready for the question leadership will eventually ask.

Handle an Active Incident

During an incident, track which models repeat the story and which have already moved on.

Arm Legal and Comms

Export archived answers as evidence when a model repeats a false claim about your products.

Show the Recovery

Chart sentiment after a fix and attach the annotation that marks when you shipped it.

"After noticing rising visits from various LLMs in GA4, we needed visibility into those black boxes. LLM Pulse gave us the clarity to understand our brand's evolution and shape new strategies to boost our exposure."

Victor Hernández

Casa Batlló

Frequently Asked Questions

Common questions about managing your brand's reputation in AI answers.

: The practice of tracking what AI assistants say about your brand, correcting what is wrong or outdated, and measuring whether the correction took hold. It matters because buyers increasingly take an AI answer as the final word.
: First document it, then fix the sources. Models repeat what they read, so correcting your own pages and the third-party sources they cite is the reliable lever. LLM Pulse gives you the archived answer as evidence and tracks whether following runs drop the claim.
: Yes, over time. Answers follow the sources models trust, and updated or corrected sources flow into updated answers. The weekly tracking exists so you can verify the change happened instead of assuming it did.
: Every brand mention in a tracked answer is analyzed and scored, then rolled up per model and over time. You see whether tone is improving, flat, or slipping, and you can open the underlying answers behind any score.
: Routinely. They train and retrieve from different sources on different schedules, so an old controversy can persist in one model long after the others moved on. That is why sentiment is tracked per model.
: Sentiment and reputation tracking run weekly across ChatGPT, Perplexity, Gemini, Google AI Overviews, and Google AI Mode on every plan, with Enterprise coverage reaching 14 models.
: Plans start at EUR 49 per month with a 14-day free trial, so you can see your current sentiment picture before committing.