Read your AI reputation now
Run a free audit to see how ChatGPT, Perplexity, Claude, Gemini and Grok describe your brand — including sentiment and the sources behind it. 60 seconds, no card.
Your brand already has a reputation inside AI answers. When a buyer asks ChatGPT 'is [your company] any good?', it produces a confident paragraph — and most companies have never read what it says. That paragraph is shaping deals you'll never know you lost.
AI brand monitoring is reading that paragraph on a schedule, catching it when it turns negative or wrong, and tracing it back to the source you can fix.
What AI brand monitoring actually tracks
- Presence — does the AI mention you at all for your category's key questions?
- Sentiment — is the framing positive, neutral, or negative?
- Accuracy — are the facts it states about you (pricing, features, positioning) correct?
- Sources — which pages is the model pulling from to form its view?
- Competitor framing — how does it describe your rivals relative to you?
Why sentiment beats raw mentions
A mention count tells you the AI knows you exist. Sentiment tells you whether that's helping or hurting. Being described as 'a budget option with limited support' on every engine is worse than not being mentioned — it pre-loses the deal.
The negative-citation trap
If an engine says 'Brand X is known for poor onboarding, according to recent reviews,' that framing repeats across thousands of answers. Find the source review or thread driving it, address the underlying issue, then refresh the content so the model re-learns.
How the monitoring loop works
- 01Define the prompts — the real questions buyers ask about your brand and category.
- 02Run them across engines on a schedule — ChatGPT, Perplexity, Claude, Gemini, Grok.
- 03Score each answer — presence, sentiment, accuracy, and the cited sources.
- 04Alert on change — a sentiment drop or a competitor overtaking you should ping you the same day.
- 05Trace and fix — follow the citation to the source, correct it, and earn corroboration.
- 06Re-measure — confirm the framing actually shifted on the next run.
A real scenario
A B2B tool noticed Perplexity consistently called their support 'slow' — sourced from a year-old Reddit thread. They resolved the original complaint publicly, published a current support-SLA page with structured data, and seeded two fresh, accurate data points. Within a month the 'slow support' framing dropped out of Perplexity's answers. They only knew to act because they were monitoring sentiment, not just mentions.
Setting it up with Stackwise Rank
Stackwise Rank runs your brand prompts across all five engines, scores sentiment alongside citation and mention rate, surfaces the exact sources behind each framing, and alerts you when sentiment shifts or a competitor gains ground. The trend view lets you prove a reputation fix actually landed, rather than hoping it did.
Common mistakes
- Counting mentions while ignoring whether they're positive or negative.
- Checking once, manually, then never again — AI answers drift constantly.
- Reacting to a bad framing without finding the source feeding it.
- Monitoring only ChatGPT while Perplexity quietly tells a worse story.
FAQ
What is AI brand monitoring?
The practice of regularly checking how AI answer engines describe and cite your brand, so you can catch negative framing, missed mentions, and competitor gains early.
Why does AI sentiment matter?
AI engines summarize and frame your brand for buyers. A negative or inaccurate framing repeated across answers shapes purchase decisions before a prospect ever visits your site.
Can I fix what AI says about me?
Often yes — by correcting the source content the model pulls from, earning fresh corroboration, and improving the structured facts on your own site.