How to Check What AI Says About Your Brand

How Do You Know What AI Says About Your Brand?

Your potential customers are asking ChatGPT about your product before they visit your website. They are using Google’s AI Mode to compare you to competitors. They are turning to Perplexity to help make final buying decisions.

What those AI platforms say about your brand matters more than most marketers realize.

Recent survey data from Semrush shows the scale of this shift. Fifty-seven percent of U.S. consumers now use AI to narrow down product choices. Fifty-three percent use it to compare products they are already considering. Fifty percent use it to help make final purchasing decisions.

These AI-generated descriptions influence whether buyers consider your product at all.

What AI Sentiment Analysis Reveals About Your Brand

AI sentiment analysis measures how favorably AI platforms describe your brand and whether those descriptions are accurate.

Think about it this way. An AI platform might describe a project management tool as a “good fit for SMBs and mid-market teams” with “strong automations.” In the same response, it might claim the tool is “not always the best choice for enterprises” and offers “less depth and rigor” than specialized enterprise tools.

These descriptions shape buyer perception. Some are based on facts. Others are hallucinated by the AI model. Either way, they influence how potential customers think about your company before they ever reach your website.

Conducting AI sentiment analysis helps you identify the negative narratives or inaccuracies surrounding your brand so you can fix misconceptions and improve how AI platforms describe you.

Why Negative AI Descriptions Cost You Customers

A military surplus retailer learned this lesson the expensive way. ChatGPT was repeatedly describing the company’s sleeping bags as “outdated technology” because the AI associated the word “surplus” with obsolete equipment rather than authentic military-grade gear.

Jordan Brannon, President at Coalition Technologies, worked with the client to fix the problem. “We had to refresh messaging on the about page, homepage, and social profiles to steer those responses back toward accuracy.”

The results were significant. AI referral traffic increased by 429 percent. Conversions from AI traffic jumped by 547 percent.

Those numbers show how much influence AI platforms have over consideration and conversions.

How AI Sentiment Analysis Works Across Two Channels

To understand AI sentiment, you need to look at both AI-generated answers and the sources that influence them.

AI platforms build their descriptions of brands using information from across the web. Your website provides some of that information. Third-party reviews, media coverage, forums, comparison articles, and social media discussions provide the rest.

Analyzing AI answers helps you understand how those platforms currently describe your brand. Monitoring sentiment across the broader web helps you understand what may be shaping those perceptions.

Together, these two views provide a complete picture of how AI platforms perceive your company and where inaccurate or negative narratives may originate.

Testing AI Platforms Manually to Find Brand Mentions

You can start by manually testing prompts across AI platforms.

Ask questions related to your category, competitors, ideal use cases, and brand. If you sell project management software, you might ask about the best project management software for remote teams.

Reviewing those responses helps you understand whether your brand appears in relevant AI answers, which competitors appear alongside you, and what strengths, weaknesses, and narratives AI systems associate with your company.

Manual analysis becomes difficult to maintain across hundreds or thousands of prompts. That is where platforms like Semrush become useful.

Measuring AI Sentiment at Scale with Analytics Tools

Semrush’s AI Visibility Toolkit includes reports to help marketers measure how AI platforms describe their brand.

The Brand Performance report shows how AI platforms currently position your brand relative to competitors. The “Share of Voice vs. Sentiment” chart gives you a high-level view of how often you are mentioned and how positively you are mentioned relative to rivals.

The “Overall Sentiment” section breaks mentions into favorable or general.

The Perception report helps you understand what factors are driving positive and negative sentiment. The “Favorable Sentiment Over Time” chart monitors how AI platforms’ perception of you shifts over time.

These reports refresh weekly across ChatGPT, Google AI Mode, Gemini, and Perplexity.

Tracking Sentiment in Third-Party Web Mentions

AI platforms describe brands based on their training data and information from across the web.

That means in addition to your website, third-party reviews, media coverage, forums, comparison articles, and social media discussions all influence how AI platforms describe your company over time.

This is where monitoring brand mentions becomes critical for AI sentiment analysis.

AI Mentions monitors your brand mentions across reviews, forums, social media, and news sites, analyzing sentiment in real-time. This helps you spot emerging reputation issues early and identify negative narratives before they become more widespread and start to influence your brand sentiment across AI answers.

Monitoring brand mentions helps you understand how brand perception is changing and catch problematic third-party narratives before they become embedded in AI platform responses.

Finding the Sources That Drive AI Sentiment

Before you improve AI sentiment, you need to understand which sources are shaping it.

Use the Narrative Drivers report to see which pages AI platforms rely on when describing your brand. Some will be owned properties, such as your homepage and product pages. Others will be third-party sources, including review sites, media coverage, and forums.

Pay particular attention to sources associated with negative sentiment or inaccurate descriptions.

Filter for low sentiment scores to identify pages that contribute to negative or misleading AI narratives.

For owned pages, look for outdated claims, vague positioning, weak product explanations, missing context, and ambiguous wording.

For third-party sources, look for recurring criticisms or complaints, inaccurate descriptions of your products or services, outdated information, comparisons that position competitors more favorably, and misunderstandings that repeatedly appear across multiple sources.

These issues influence how AI platforms describe your brand. Once you identify the sources driving those narratives, you prioritize which pages, platforms, and messages need attention first.

Strengthening Your Owned Content to Improve AI Perception

Some of the narratives AI platforms associate with your brand come directly from your website.

If important details are missing, unclear, or outdated, AI platforms fill those gaps using assumptions or information from third-party sources.

Focus on making it easier for AI platforms to accurately understand what your company does, who it serves, and what makes it different.

To strengthen owned content, clearly and consistently explain what your product or service does. Define who it is for and which use cases it supports. Add missing context that might otherwise be filled by assumptions. Replace vague marketing language with specific descriptions. Keep product information, features, and positioning up to date. Ensure important information appears on the pages AI platforms are most likely to cite.

Zbyněk Fridrich’s team followed this approach with WorkLounge, a coworking space that was having AI misrepresent some of their services. They rewrote roughly 90 pages to better explain details such as 24/7 access, quiet zone availability, phone booths, and membership benefits.

Over the following months, the brand’s favorable AI sentiment percentage increased from 67 to 82, while AI visibility and referral traffic also improved.

When AI platforms cannot confirm specific details about your products or services, a precise set of updates to key details improves favorable AI sentiment.

Improving Your Third-Party Presence to Shape AI Narratives

The narratives that AI platforms associate with your brand do not just come from your website. Third-party platforms like review sites, forums, comparison sites, and other sources also influence how AI platforms describe your company.

You have limited control over those conversations, but you influence them by improving how your brand is represented across the web.

That involves updating third-party business profiles and listings, responding to reviews and customer feedback, correcting inaccurate information where possible, contributing expert insights to industry publications, earning coverage from trusted media outlets, and participating in relevant industry communities and forums.

AI Mentions helps you identify which specific queries trigger competitor citations instead of yours, revealing exact content gaps to fix. It tracks which product features and use cases AI models do not understand about your offering due to insufficient training data. It tests whether content updates improve AI mention frequency before you invest in full-scale production.

The goal is to make sure customers and AI platforms encounter accurate and up-to-date information wherever they learn about your brand.

Over time, having consistent third-party narratives reinforces the positioning and messages you want AI platforms to associate with your company.

Common Causes of Negative AI Sentiment

Negative AI sentiment typically stems from outdated messaging, unclear positioning, negative third-party reviews, inaccurate forum discussions, and missing or incomplete information on owned pages.

When your website lacks clear information, AI platforms rely on assumptions or third-party sources when describing your brand. Those assumptions are not always accurate.

Improving AI Sentiment Without Controlling Third-Party Sources

You improve AI sentiment without controlling third-party sources by making your own content more accurate and complete.

Updating owned pages, correcting missing or unclear information, and ensuring key product and brand details are consistently reflected across trusted third-party sources influence the narratives AI platforms rely on over time.

You cannot directly control what third-party websites say about your brand. You do control how clearly and completely you explain what your product does, who it serves, and what makes it different.

Understanding the Difference Between Traditional and AI Sentiment Analysis

Traditional sentiment analysis measures what people say about your brand across the web. AI sentiment analysis measures what AI platforms say about your brand.

Both matter. Traditional sentiment analysis helps you understand public perception. AI sentiment analysis helps you understand how that perception translates into the answers AI platforms give potential customers.

The distinction matters because AI platforms filter, summarize, and sometimes misinterpret the information they find. What people say about your brand and what AI platforms repeat about your brand are not always the same thing.

Taking Control of Your AI Brand Perception

AI platforms are shaping how buyers first encounter your brand. Those initial impressions influence whether potential customers consider your product, how they compare you to competitors, and whether they convert.

AI Mentions serves as an early warning system for problematic narratives, showing which queries trigger competitor recommendations instead of yours and revealing knowledge gaps in your content that prevent AI citation eligibility. The tool prioritizes content creation based on high-value queries where you are currently invisible but could rank with proper knowledge coverage. If you are losing deals to AI-recommended competitors or wondering why your brand does not appear in ChatGPT research sessions, explore AI Mentions to diagnose citation gaps instead of just tracking vanity metrics.