AI tools like ChatGPT, Google AI Overviews, and Perplexity are now a first stop for product research and brand comparisons. When those answers get your brand wrong, most people won't question it — they'll just move on.
This guide shows you how to find what AI is saying about your brand, why errors happen, and how to fix them.
How can I tell what AI is saying about my brand?
To find out what AI is saying about your brand, you need to monitor multiple AI platforms systematically — manual spot-checks aren't reliable enough to catch the full picture.
AI tools like ChatGPT, Google AI Overviews, and Perplexity don't all return the same answers, and responses shift as models update. A one-time search tells you what one platform said once. It won't surface patterns, track changes, or catch errors across product lines.
Semrush's AI Visibility Toolkit monitors how your brand appears across AI platforms at scale — tracking mentions, sentiment, topic associations, and how responses change over time — without requiring you to manually query each platform.
How do I check what ChatGPT, Google AI Overviews, and Perplexity say about my brand?
To check what ChatGPT, Google AI Overviews, and Perplexity say about your brand, use a tool with a large database of LLM prompts to give you an accurate picture of your brand’s perception.
Semrush's AI Visibility Toolkit has a database with 213 million prompts to help you accurately track prompts. After inputting your domain, scroll to the “Your Performing Topics” section.
Use the topic dropdown to read the AI responses from different AI systems.
How can I audit AI answers across multiple prompts and platforms?
You can audit AI answers across multiple prompts and platforms by tracking what AI returns for a range of brand, product, and category queries — not just your brand name — across multiple LLMs over time.
The AI Visibility Toolkit runs this audit automatically — testing varied prompt types and logging responses across platforms.
How can I trace where AI got incorrect information about my brand?
You can trace where AI got incorrect information about your brand by identifying which third-party sources — reviews, forums, aggregators, or news articles — are feeding the wrong details into AI responses.
AI systems learn from the content they're trained on, and in some cases, retrieve from the live web. If AI is describing your brand incorrectly, a source somewhere is likely the reason why.
The Narrative Drivers tool helps identify which sources are driving your brand narrative in AI responses, so you can pinpoint where the misinformation is coming from and prioritize fixes.
Once in the tool, click “Citations | Branded.” Then click the number in the “Answers” column.
Clicking the number opens a list of answers from each site with the sources at the bottom of each answer.
Review these answers for any incorrect information, and ask sources to update any inaccurate information.
How can I see which attributes AI associates with my brand?
You can see which attributes AI associates with your brand by analyzing how AI platforms describe you across a range of prompts — not just whether you're mentioned, but how you're characterized.
AI might associate your brand with a product you discontinued, a price point you've changed, or a category you've moved away from. Those associations shape how customers perceive you before they ever visit your site.
Semrush’s Perception tool highlights how AI perceives your brand. Scroll to the “AI Feature Descriptions” section to see what phrases AI uses to describe your brand.
For example, AI describes the below eyeglass company as having a free home-try on program. If this wasn’t true for this brand, they’d want to explore the answers and sources, and fix the incorrect information.
How can I tell if AI mentions my products, not just my brand?
You can tell if AI mentions your specific products by tracking product-level queries separately from brand-level ones.
AI might recognize your brand name while having little to say about individual products. That gap matters: a customer asking "what does [Product Name] do?" or "is [Product Name] worth it?" needs a different answer than one asking about your company broadly.
Use the Visibility tool to search for specific product names to see if and how your brand appears. The “Mentioned” tab means a third-party source mentions your brand. Check these prompts to make sure the information is correct.
Note which sources appear most often — then prioritize getting mentions on those same platforms for products with low or no visibility. If AI references G2 when discussing one product, that's your signal to drive G2 reviews for the products that aren't showing up.
What types of brand misinformation appear in AI answers?
The types of brand misinformation that appear in AI answers range from outdated facts and wrong pricing to negative reviews from unhappy customers.
Most errors aren't random — they trace back to specific sources AI has weighted heavily, whether that's an old press release, a review site with stale data, or a competitor comparison page.
What are the most common types of misinformation AI produces about brands?
The most common types of misinformation AI produces about brands include outdated information, fabricated details, competitive misattribution, and missing products.
- Outdated information: Discontinued products, old pricing, or deprecated features described as current
- Fabricated details: Founding dates, employee counts, or features that don't exist
- Competitive misattribution: A competitor's product, feature, or positioning attached to your brand, often sourced from comparison articles
- Missing products: AI recognizes your brand but doesn't surface specific products where customers are searching
For example, Perplexity pulls together outdated information about products this marketer no longer sells:
Why do AI tools get products, pricing, or positioning wrong?
AI tools get products, pricing, or positioning wrong because they generate answers based on statistical patterns in their training data — not by verifying facts against a live, authoritative source.
When training data contains conflicting, outdated, or incomplete information about your brand, the model fills the gaps with whatever is most statistically plausible. If a model receives three different answers to the question "What does Company X do?" from five different sources, a hallucination is practically inevitable.
Pricing can also be vulnerable as it changes frequently but lives on in old blog posts, comparison pages, and review sites long after it's been updated. And those pages often outrank your own pricing page in the sources AI draws from.
Why does AI confuse brands, competitors, or categories?
AI confuses brands, competitors, or categories because it learns associations from the web — and the web frequently groups competing brands together in comparison articles, listicles, and review roundups.
When multiple brands appear together repeatedly in the same context, AI systems build associations between them. For example, a feature mentioned in a "[Brand A] vs. [Brand B]" article can end up attributed to the wrong company.
Smaller or newer brands are especially exposed. Lesser-known brands with website authority or inconsistent online data are particularly vulnerable because the model has little reliable information to draw upon.
So, work on growing your authority by building backlinks, optimizing your content for AI, and launching a digital PR campaign.
Where do AI tools get information about my brand?
AI tools get information about your brand from third-party sources, datasets, or your own website.
Understanding where AI sources its information is the first step to correcting it.
What sources do AI systems use for brand-related answers?
The sources AI systems use for brand-related answers include third-party review sites, forums, news articles, industry directories, comparison pages, and social media — weighted by how frequently and consistently a claim appears across those sources.
Your official website is one input among many. If a review site, Reddit thread, or competitor comparison page makes a claim about your brand more often or more prominently than your own content does, AI is likely to reflect that claim in its answers.
Common sources that shape AI brand answers:
- Review platforms (G2, Trustpilot, Capterra)
- Forums and communities (Reddit, Quora)
- News and press coverage
- Industry directories and aggregators
- Competitor comparison and "best of" listicles
- Social media profiles and posts
Use Visibility Overview’s “Topic & Sources” report to see which domains mention your brand. Click to “Cited Sources”and open the dropdown for a domain. Then, click “View full response” to read the full response including the sources used to generate the response.
Why does AI trust third-party sources more than official websites?
AI trusts third-party sources more than official websites because official content is perceived as promotional, while third-party content is perceived as independent, and therefore more credible.
Your pricing page says your product is the best value. A G2 review, a Reddit thread, and a TechRadar comparison article say something more neutral — and AI systems give more weight to independent sources over a single self-reported claim. The more sources that agree on a detail, the more likely AI is to treat it as fact.
This is why a single outdated review or a stale comparison article can override accurate information on your own site.
How do forums, reviews, and aggregators shape AI answers about brands?
Forums, reviews, and aggregators shape AI answers about brands by acting as high-volume, high-frequency signals that AI systems treat as representative of real user opinion.
A single Reddit thread with 200 upvotes discussing an old pricing model can carry more weight than your updated pricing page. A G2 review from two years ago describing a deprecated feature can persist in AI answers long after you've shipped a replacement.
But this is also an opportunity. The same sources that spread misinformation can be used to correct it. Identifying which forums and review platforms AI is pulling from for your brand — and actively managing your presence there — is one of the most direct ways to influence what AI says about you.
Why is AI getting my brand information wrong?
AI is getting your brand information wrong because its answers reflect the quality, consistency, and recency of what's been written about you across the web — not just what you say about yourself.
If third-party sources conflict with your official content, are more numerous, or haven't been updated to reflect changes in your business, AI will likely get it wrong.
Why does AI get facts wrong about brands?
AI gets facts wrong about brands because the sources it draws from may contain inaccuracies.
Most AI systems combine two inputs: a base of training data with a cutoff date and live web retrieval that pulls current sources at the time of a query. Both can introduce errors.
Training data reflects whatever was published before the cutoff. If your brand was misrepresented in enough articles, forums, or reviews, the model absorbed those inaccuracies. Live retrieval helps with recency, but carries its own risk: the pages being pulled may be outdated, low-quality, or simply wrong.
Why can outdated or incorrect information persist in AI answers?
Outdated or incorrect information persists in AI answers because AI models aren't updated in real time. Once a claim is embedded in training data — or continues to appear on high-authority third-party pages — it keeps surfacing in responses even after you've corrected it on your own site. Removing the wrong information from the web, not just updating your own pages, is what drives change faster than updating information on your own site alone.
What reputation signals shape how AI describes a brand?
Some reputation signals that shape how AI describes a brand include entity identity, evidence and citations, and technical credibility.
- Entity identity: Organization schema on your homepage, consistent NAP (name, address, and phone number) data across directories, linked social profiles, and Google Knowledge Graph
- Evidence and citations: Press mentions, reviews, and citations from authoritative publications
- Technical credibility: Site speed, security, and accessibility signals that tell AI your site is a trustworthy source
For example, if we ask Perplexity about the worst coffee brands, it lists different brands based on negative reviews.
Stay on top of negative mentions with a tool like Media Monitoring. Media Monitoring compiles mentions across the web from inputted keywords.
You can filter these mentions by sentiment, letting you quickly view any negative mentions that you need to address.
How can I fix incorrect information about my brand in AI answers?
You can fix incorrect information about your brand in AI answers by making sure it’s consistent across online sources like third-party content and directories, and taking steps to fix anything that’s incorrect.
How do I correct AI answers about my brand?
You can correct AI answers about your brand by working backwards from the error: identify what's wrong, find where AI is sourcing it, and update or replace that source.
Start by reviewing the “Key Sentiment Drivers" section in the Perception tool to identify weak areas. Weak areas are areas with low sentiment, which could be due to incorrect information. Click the thought-bubble icon to view the sources that are contributing to lower sentiment.
Once you know which pages contain incorrect information, contact the publisher to request a correction.
How do I fix outdated information about my brand in AI answers?
You can fix outdated information about your brand in AI answers by updating the pages — both owned and third-party — that are still publishing the old details.
Start with the sources AI is referencing. If a review site lists your old pricing, request an update or leave an owner response with current information. If an old press release is being cited but references deprecated products, see whether it can be updated or replaced with a current version.
AI reflects what the web currently says — keeping third-party sources current is as important as updating content on your site.
What should I update on my website first?
The first things to update on your website are the pages most likely to be crawled and extracted by AI systems like your homepage, about page, product or service pages, and any FAQ content.
- Homepage: Ensure your brand description, category, and core value proposition are accurate and explicitly stated
- Product and service pages: Update pricing, features, and use cases; remove or redirect pages for discontinued products
- About page: Confirm founding details, leadership, and company description are current
- FAQ content: Structure answers in plain language. AI systems extract FAQ-type content for direct answers.
- Schema markup: Add or update Organization schema (a type of structured data) so AI systems can verify your identity, location, and key attributes
The Questions tool gives recommendations for strategic opportunities so you know exactly what to fix first.
How do I report incorrect information in ChatGPT, Google AI Overviews, and other AI platforms?
You can report incorrect information in ChatGPT, Google AI Overviews, and other AI platforms using the native feedback tools each platform provides — though these corrections are slow and not guaranteed.
- ChatGPT: Use the thumbs down icon to open the report submission box
- Google AI Overviews: Use the thumbs down icon at the bottom of the overview and then select “Report a problem”
- Perplexity: Use the thumbs down icon or “...” to access the “Report” link
Treat platform reporting as a supplementary step, not a primary fix. These channels have no guaranteed turnaround and no confirmation that a correction will be made. Fixing the underlying sources is what reliably changes AI output.
How do I make sure AI uses official sources instead of third-party content?
You can make AI more likely to use official sources by strengthening the trust signals that tell AI systems your site is the authoritative reference for your brand.
Here are some tips:
- Publish clear, factual, jargon-free descriptions of your products and company — the easier your content is to extract, the more likely AI is to pull from it
- Build authoritative third-party mentions through press coverage, industry publications, and review platforms — AI favors brands that are vouched for by credible external sources
- Keep your content updated with explicit dates so AI systems can assess recency
The goal is to make your official content the most consistent, credible, and extractable version of your brand story across the web.
Remember that it’s still worthwhile to receive brand mentions even from third-party sources. All positive mentions help build brand visibility.
How do I know if AI answers about my brand are improving?
You can tell if AI answers about your brand are improving by tracking changes in how your brand is described across platforms over time — not just whether you're mentioned, but whether the descriptions are accurate.
How can I track changes in AI-generated brand and product descriptions?
You can track changes in AI-generated brand and product descriptions by monitoring how AI platforms describe your brand across a consistent set of prompts over time.
The AI Visibility Toolkit tracks brand descriptions, sentiment, and topic associations across AI platforms automatically — so you can see when answers shift, which attributes are gaining traction, and where errors persist after corrections have been made.
For example, you can review how your sentiment and mentions in different feature categories shift over time in the Perception tool.
How do I measure accuracy versus frequency in AI brand mentions?
You can measure accuracy versus frequency in AI brand mentions by tracking both metrics separately — because appearing often in AI answers means nothing if the descriptions are wrong.
Frequency tells you how often your brand surfaces. Accuracy tells you whether what AI says reflects reality. A brand mentioned frequently but described incorrectly has a bigger problem than one mentioned rarely but described well.
Use the Brand Performance tool to see what categories you appear in most frequently. Make sure the categories are accurate to your business, and fix any that aren’t.
How long does it take for corrections to appear in AI responses?
Corrections to AI responses can take weeks to months to appear, depending on the platform, how frequently it updates, and how widely the corrected information has spread across the web.
Models with real-time web retrieval like Perplexity may reflect corrections faster than models that rely primarily on training data. The more sources that publish the corrected information, the faster AI systems are likely to reflect it.
Consistent monitoring through the AI SEO Toolkit is the only reliable way to know when corrections have taken hold.
Monitor your AI visibility and sentiment
AI misinformation about your brand won't fix itself. But it's also not out of your control.
You now know where AI gets its information, why errors happen, and what to do about them. The next step is simple: find out what AI is actually saying about you and start controlling your brand narrative.