--- description: AI hallucinations cost businesses $67.4B in 2024. Wrong pricing, fake features, and fabricated limits are silently killing your pipeline. title: AI Is Showing Wrong Info About Your Product: How to Fix It image: https://www.mersel.ai/blog-covers/Maintenance-rafiki.svg --- [Introducing Cite:Your AI content agent for inbound leads.Your AI content agent.See how](/cite) Platform [Cite - Content engineYour dedicated website section that brings leads](/cite)[AI visibility analyticsSee which AI platforms visit your site and mention your brand](/platform/visibility-analytics)[Agent-optimized pagesShow AI a version of your site built to get recommended](/platform/ai-optimized-pages) [Blog](/blog)[Pricing](/#plan)[About](/about)[Contact Us](/contact) Language [English](/en/blog/what-happens-when-ai-gets-product-information-wrong)[繁體中文](/zh-TW/blog/what-happens-when-ai-gets-product-information-wrong) [Home](/)[Blog](/blog)AI Is Showing Wrong Info About Your Product: How to Fix It 18 min read # AI Is Showing Wrong Info About Your Product: How to Fix It ![Mersel AI Team](/_next/image?url=%2Fworks%2Fjoseph-headshot.webp&w=96&q=75) Mersel AI Team March 18, 2026 Book a Free Call On this page [Key Takeaways](#key-takeaways)[Why AI Gets Your Product Information Wrong](#why-ai-gets-your-product-information-wrong)[The Real Business Impact: A Risk Impact Matrix](#the-real-business-impact-a-risk-impact-matrix)[The 5-Step Correction Playbook](#the-5-step-correction-playbook)[Step 1: Run a Prompt Audit Across All Major AI Platforms](#step-1-run-a-prompt-audit-across-all-major-ai-platforms)[Step 2: Trace and Neutralize the Source Data](#step-2-trace-and-neutralize-the-source-data)[Step 3: Deploy AI-Native Infrastructure (The Technical Foundation)](#step-3-deploy-ai-native-infrastructure-the-technical-foundation)[Step 4: Execute the Content Patch](#step-4-execute-the-content-patch)[Step 5: Build a Continuous Feedback Loop](#step-5-build-a-continuous-feedback-loop)[When DIY Fails: The Execution Gap](#when-diy-fails-the-execution-gap)[The Managed Path: How Done-for-You GEO Handles AI Misinformation](#the-managed-path-how-done-for-you-geo-handles-ai-misinformation)[FAQ](#faq)[Sources](#sources)[Ready to Protect Your Brand?](#ready-to-protect-your-brand)[Related Reading](#related-reading) ## What Happens When AI Gets Your Product Information Wrong When AI gets your product information wrong, buyers quietly disqualify you based on facts that were never true. A ChatGPT response quoting your price at three times the actual rate, or claiming you lack an integration you launched last quarter, will end an evaluation before you ever get a chance to respond. This is not a hypothetical edge case — audits across 50 brands found that 72% had at least one factual error in AI-generated responses, with an average of 3.4 errors per brand, according to research by Metricus App. The stakes are unusually high right now because buyers have shifted their research behavior faster than most marketing teams have adapted. According to Bain & Company, 85% of B2B buyers already have a vendor shortlist before they speak to a single sales rep, and that shortlist is increasingly formed in AI conversations. If an AI tells a buyer you're too expensive, don't support their use case, or lack a feature your competitor has, that buyer is gone. You'll never know why. This article breaks down why AI misinformation happens, what the business impact looks like with real examples, and the specific steps to fix it, including a Risk Impact Matrix and Correction Playbook you can use immediately. ![](/blog-covers/Maintenance-rafiki.svg) ## Key Takeaways * AI hallucinations and factual errors cost global businesses an estimated $67.4 billion in 2024, according to Four Dots research. * 72% of brands have at least one factual error in AI-generated responses, most commonly incorrect pricing (41% of brands) and outdated features (34%), per Metricus App audits. * Wrong pricing is the single most dangerous error type: AI quoting too high a price causes buyers to falsely disqualify a brand without ever visiting the website. * Traditional SEO tactics (backlinks, keyword density) do not fix AI hallucinations. Correction requires an AI-native infrastructure layer: schema markup, `llms.txt`, and server-rendered HTML. * Wells Fargo improved AI Overview accuracy from 43% to 91% after deploying advanced Schema Markup with Entity Linking, per Schema App case study data. * Monitoring dashboards diagnose the problem but do not solve it. Execution at both the content and infrastructure layer is required to correct what AI engines say about your brand. ## Why AI Gets Your Product Information Wrong AI language models are probabilistic text engines, not factual databases. They do not retrieve your pricing page and read it the way a human would. Instead, they synthesize patterns across everything they were trained on: review sites, competitor comparison blogs, archived press releases, Reddit threads from two years ago, and occasionally your own website if the content is accessible to AI crawlers. When those external sources contradict your current reality, the AI does not know to resolve the conflict. It picks the version that appears most frequently across its training data. That version is often outdated, partial, or simply wrong. Several specific failure modes drive the majority of errors. **JavaScript-rendered pricing pages.** Many SaaS brands build pricing pages using React or Vue without server-side rendering. AI crawlers like GPTBot and PerplexityBot often cannot execute JavaScript, which means they cannot read your actual pricing at all. Deprived of the primary source, the model will estimate your price from a G2 review or a competitor comparison article, as documented by Metricus App in their brand audit research. **Weak entity resolution.** AI systems map information to entities. If your brand entity is weakly defined, the model may blend your product's attributes with a competitor's, misattributing features or claiming parity where none exists. **Stale third-party data dominating training.** Your website may be accurate, but a 2023 TrustRadius review saying you lack enterprise SSO carries more weight in the model's training data simply because that review has more inbound links. The AI cites the review, not your current feature page. **Content gap.** If you have not published a structured, factual answer to "How much does \[Brand\] cost?" or "\[Brand\] vs \[Competitor\] feature comparison," the AI fills that gap with whatever it can find. And what it finds is rarely flattering or accurate. "Marketers often try to correct an AI by arguing with the chatbot or filing a support ticket, but LLMs don't have editorial teams or brand accuracy request forms," notes the AIBoost research team. "The AI's output is a reflection of the brand's fragmented data ecosystem. The only way to fix the output is to fix the underlying data sources." ## The Real Business Impact: A Risk Impact Matrix Understanding where to focus correction efforts requires mapping error types by how frequently they occur and how severely they damage pipeline. The matrix below draws on audit data from Metricus App's study of 50 brands across eight AI platforms. AI Hallucination Risk Impact MatrixLOW PIPELINE IMPACTHIGH PIPELINE IMPACTLOW FREQUENCYHIGH FREQUENCYWrongPricing41% of brandsOutdatedFeatures34% of brandsWrongComps28% of brandsFakeLimits19% of brandsCritical / Fix FirstHigh / Fix NextTargeted Fix RequiredMonitor _The matrix maps four hallucination types by frequency of occurrence and pipeline impact severity, based on Metricus App audit data across 50 brands. Wrong pricing sits in the critical quadrant: it occurs in 41% of brands and directly causes buyer disqualification before any sales conversation begins. Fabricated limitations are lower frequency but high impact because they create false ICP mismatches that are nearly impossible to overcome._ Here is what each error type means in practice for a Head of Marketing. | Error Type | Frequency | What AI Says | Actual Pipeline Effect | | ---------------------- | ------------- | ---------------------------------------------------------- | --------------------------------------------------------------- | | Wrong Pricing | 41% of brands | "Plans start at $299/month" (actual: $49) | Buyers disqualify on budget before visiting your site | | Outdated Features | 34% of brands | "Does not include \[feature you launched Q3\]" | Buyers assume product gap, shortlist competitor | | Wrong Comparisons | 28% of brands | Attributes competitor's unique feature to them exclusively | Loses head-to-head evaluations to falsely differentiated rivals | | Fabricated Limitations | 19% of brands | "Only suitable for enterprise companies" | Eliminates mid-market pipeline that should be converting | The Air Canada case illustrates that the legal exposure is real, not theoretical. A Canadian civil tribunal ruled in 2024 that Air Canada was financially liable for a customer service chatbot that hallucinated a bereavement fare policy, forcing compensation for a discount that never existed. The ruling established that AI-generated misinformation carries the same legal weight as official company statements, according to SCET Berkeley's analysis of the case. ## The 5-Step Correction Playbook This sequence follows a deliberate logic. You cannot patch content you have not audited, and you cannot make infrastructure changes meaningful without knowing which specific claims are wrong. Each step depends on completing the one before it. ### Step 1: Run a Prompt Audit Across All Major AI Platforms Start by querying the exact conversational prompts your buyers use during evaluation. Do not use traditional keyword research tools here. Use the actual questions buyers ask AI at the moment they are shortlisting vendors: "What does \[Brand\] cost?", "Does \[Brand\] integrate with \[Tool\]?", "\[Brand\] vs \[Competitor\]: which is better for \[use case\]?" Query these prompts in clean browser sessions across ChatGPT, Perplexity, Claude, and Google AI Overviews. Document every error. Categorize by type: pricing, feature omission, wrong comparison, fabricated limitation. For Perplexity and Copilot specifically, note which sources they cite. This tells you where the hallucination originates. ### Step 2: Trace and Neutralize the Source Data Once you have identified the error type and source, your goal is to reduce the influence of the bad source while amplifying the correct primary source. If the AI is pulling from a 2023 G2 review that says you lack mobile support, you cannot delete that review. But you can overwhelm it with fresh, authoritative, structured data from your own domain. Update your official third-party profiles (G2, Capterra, Trustpilot) with current accurate information. Outdated review site data is one of the most common hallucination sources, as documented in Metricus App's audit methodology. ### Step 3: Deploy AI-Native Infrastructure (The Technical Foundation) Once you know what is wrong, build the technical layer that establishes your brand's ground truth for AI crawlers. This is where most teams stop and where the problem persists. **Create an `llms.txt` file** at your root domain (`https://yourdomain.com/llms.txt`). Write it in plain Markdown. Include a factual company summary, exact current pricing tiers, and direct links to Markdown versions of your critical product and pricing pages. Think of it as the `robots.txt` equivalent for AI inclusion rather than exclusion, as explained in Yotpo's guide to the emerging standard. **Implement deep Schema markup** using JSON-LD. Basic SEO plugins are not sufficient. Deploy `Organization` schema with `sameAs` links connecting your domain to LinkedIn, Crunchbase, and official review profiles. Use `Product` and `Offer` schema to define pricing tiers in machine-readable format. Use `FAQPage` schema on any page that addresses common buyer questions. This creates what Schema App's enterprise documentation calls a "closed verification loop" that prevents AI from relying on stale third-party data. **Audit your `robots.txt` file** to confirm you are not accidentally blocking AI crawlers like GPTBot or PerplexityBot from your pricing and product pages. This is a surprisingly common configuration error. **Render pricing in server-side HTML.** If your pricing page is built with a JavaScript framework, ensure the content is available in server-rendered HTML. If it is not, add the pricing data explicitly to your `Offer` schema markup. This addresses the JavaScript rendering gap directly. ### Step 4: Execute the Content Patch With the technical foundation in place, deploy what TrySteakhouse's GEO research calls the "Hallucination-Patch Workflow": treat each confirmed AI error as a software bug and write a targeted content patch to correct it. A content patch is a highly structured article or FAQ page built specifically around the hallucinated claim. If AI is stating your product is enterprise-only, the patch title might be "\[Brand\] for Growing Teams: Plans, Pricing, and Features for Companies Under 500." If AI is quoting wrong pricing, the patch is a current, comprehensive pricing breakdown with comparison tables. Format matters here. According to Search Engine Land's guide to fixing AI hallucinations, the direct factual answer must appear in the first 50 words of the page. Use HTML tables for feature comparisons because LLMs parse structured table data efficiently. Avoid marketing adjectives. AI systems favor "boring but clear" explanations over promotional language. Understanding [generative engine optimization](/blog/what-is-generative-engine-optimization-geo) will help you structure these patches for maximum citation probability, because the formatting rules that make content AI-citable are distinct from traditional SEO writing conventions. ### Step 5: Build a Continuous Feedback Loop After publishing patches, connect Google Search Console, GA4, and AI referral traffic data to track whether the corrections are taking effect. Monitor which content is driving AI-referred inbound. Re-query the original error prompts weekly for four to six weeks to confirm the hallucination has cleared. Update patches based on empirical performance signals, not assumptions. This is the step that transforms a one-time fix into a compounding system. Patches that earn citations get refined. Gaps that emerge get addressed with new content. **Why this sequence works:** You cannot write effective correction content without knowing precisely what AI is saying (Step 1) and where it learned it (Step 2). Infrastructure changes (Step 3) without content patches leave AI crawlers with clean access but nothing structured to read. Content patches (Step 4) without infrastructure mean crawlers may never properly index the correction. The feedback loop (Step 5) is what prevents the problem from recurring as AI models update. For a deeper tactical breakdown of how to update specific AI engine records about your brand, see our guide on [how to correct outdated or wrong brand information in ChatGPT](/blog/how-to-correct-outdated-wrong-brand-information-chatgpt). ## When DIY Fails: The Execution Gap The five steps above are clearly defined. So why do most marketing teams fail to complete them? The answer is resourcing, not understanding. Step 3 alone requires an engineer familiar with JSON-LD schema, server-side rendering configurations, and AI crawler behavior. Most engineering teams have a six-month sprint backlog and no familiarity with GEO-specific technical requirements. Content teams capable of writing structured, citation-optimized patches are different from blog writers: they need to understand how LLMs parse tables, what makes content "answer-shaped," and how to write for AI extraction rather than keyword density. The WE Communications and USC Annenberg Center research found that 64% of communications professionals are worried about AI amplifying false narratives, yet 36% have already experienced direct misinformation. The gap between recognizing the problem and having the capacity to solve it is where most organizations stall. Monitoring tools make this worse, not better. Platforms like Profound, AthenaHQ, and Scrunch are genuinely useful for measuring the size of the problem. But they are dashboards. They show a Head of Marketing exactly where ChatGPT is hallucinating their pricing, then leave the execution to an already-overloaded internal team. The implicit assumption that a brand has the internal capacity to act on dashboard insights leads to expensive software that nobody acts on. A common workaround, attempting to manually correct AI errors by filing support tickets or prompting the chatbot, does not work. As AIBoost's research team notes, LLMs do not have editorial teams or brand accuracy request forms. The correction must happen at the data layer, not the conversation layer. ## The Managed Path: How Done-for-You GEO Handles AI Misinformation For teams without the engineering or content bandwidth to run this correction playbook internally, a fully managed GEO service closes the execution gap. Mersel AI's approach addresses hallucinations at both layers simultaneously. On the infrastructure side, Mersel deploys the full AI-native layer behind your existing site: `llms.txt` configuration, JSON-LD schema for `Organization`, `Product`, `Offer`, and `FAQPage`, entity definitions, and crawler access configuration. Human visitors see nothing different. No engineering resources from your team are required. On the content side, Mersel builds correction patches from your buyers' actual evaluation prompts, not keyword guesses, and delivers them directly to your CMS. The feedback loop is connected to GSC, GA4, and AI referral data, so each piece gets updated based on what is actually earning citations and driving qualified inbound. This is the model that closed the gap for a Series A fintech startup that went from 2.4% to 12.9% AI visibility in 92 days, with 20% of demo requests influenced by AI search. The compounding effect matters: teams that start this process earlier accumulate citation signal faster, and the gap between them and a competitor who starts six months later accelerates over time. Mersel AI is a done-for-you managed service, not a self-serve dashboard. Teams that need real-time prompt monitoring with direct UI access will find self-serve platforms like Profound or AthenaHQ more suitable for that specific need. For a broader view of the tools and services in this space, the [generative engine optimization software landscape](/blog/generative-engine-optimization-software) covers the full market from monitoring platforms to managed execution services. You can also review [how to protect your brand from hallucinations in AI answers](/blog/how-to-protect-brand-from-hallucinations-ai-answers) for a complementary tactical framework focused specifically on the protection side of this problem. ## FAQ **How common are AI hallucinations about brand pricing and features?** According to Metricus App's audit of 50 brands across eight AI platforms, 72% of brands had at least one factual error in AI-generated responses, with an average of 3.4 errors per brand. Incorrect pricing was the most common error, appearing in 41% of brands audited. Outdated feature claims appeared in 34% of brands. **Can I submit a correction request to ChatGPT or Perplexity to fix wrong information?** No. AI language models do not have editorial teams or brand accuracy request forms. As documented by AIBoost, the AI's output reflects its training data and real-time retrieval sources. The only effective correction path is fixing the underlying data: updating your site's schema markup, deploying an `llms.txt` file, publishing structured correction content, and ensuring AI crawlers can access accurate HTML-rendered pages. **What is the fastest way to correct a specific AI hallucination about my product?** The fastest path combines two actions: add explicit `Product` and `Offer` schema markup to your pricing page so AI crawlers can read structured data, and publish a targeted content patch addressing the exact hallucinated claim. According to Search Engine Land's hallucination fix guide, place the direct factual answer within the first 50 words of the patch and use HTML tables for feature comparisons. Wells Fargo improved AI Overview accuracy from 43% to 91% after deploying advanced Schema Markup, per Schema App's case study. **Does traditional SEO fix AI hallucinations about my brand?** Not directly. BrightEdge research indicates that 60% of Perplexity citations overlap with Google's top 10 results, so strong SEO rankings help, but keyword optimization, backlinks, and meta tags do not address the root causes of AI hallucinations. Fixing hallucinations requires machine-readable ground truth data: `llms.txt`, structured schema, and server-rendered HTML for dynamic content like pricing pages. **How long does it take for AI engines to stop repeating a hallucination after I fix the source data?** Timeline varies by platform and how frequently the AI's retrieval index refreshes. Industry observations suggest initial corrections appear within two to eight weeks for platforms using real-time retrieval augmented generation (like Perplexity), while base model training data changes on longer cycles. Publishing a structured content patch and implementing schema markup simultaneously gives the fastest visible correction because it improves both the crawlable content and the structured data AI engines parse directly. ## Sources 1. [Four Dots — Business Impact of AI Hallucinations: Rates and Ranks](https://fourdots.com/business-impact-of-ai-hallucinations-rates-and-ranks) 2. [Suprmind — AI Hallucination Statistics & Research Report 2026](https://suprmind.ai/hub/insights/ai-hallucination-statistics-research-report-2026/) 3. [Metricus App — AI Hallucinations: The 4-Step Brand Fix](https://metricusapp.com/blog/ai-hallucinations-brand-fix/) 4. [SaleSpeak — AI Hallucinating Your Pricing?](https://salespeak.ai/aeo-news/ai-hallucinating-your-pricing) 5. [Yotpo — What Is LLMs.txt & Should You Use It?](https://www.yotpo.com/blog/what-is-llms-txt/) 6. [Search Engine Land — How to Identify and Fix AI Hallucinations About Your Brand](https://searchengineland.com/guide/fix-your-brands-ai-hallucinations) 7. [WE Communications & USC Annenberg — Communicators at Critical Moment as Generative AI Redefines Brand Reputation](https://www.wecommunications.com/news/we-communications-and-usc-annenberg-report-finds-communicators-at-critical-moment-as-generative-ai-redefines-brand-reputation) 8. [Forbes — GenAI Search's Impact on Brand Reputation and How to Control It](https://www.forbes.com/councils/forbescommunicationscouncil/2025/03/10/genai-searchs-impact-on-brand-reputation-and-how-to-control-it/) 9. [AIBoost — Dealing With AI Hallucinations About Your Brand](https://aiboost.co.uk/dealing-with-ai-hallucinations-about-your-brand/) 10. [SCET Berkeley — Why Hallucinations Matter: Misinformation, Brand Safety, and Cybersecurity in the Age of Generative AI](https://scet.berkeley.edu/why-hallucinations-matter-misinformation-brand-safety-and-cybersecurity-in-the-age-ofgenerative-ai/) 11. [Schema App — How Wells Fargo Used Schema Markup to Solve AI Search Hallucinations](https://www.schemaapp.com/customer-stories/how-wells-fargo-used-schema-markup-to-solve-ai-search-hallucinations/) 12. [Schema App — What 2025 Revealed About AI Search and the Future of Schema Markup](https://www.schemaapp.com/schema-markup/what-2025-revealed-about-ai-search-and-the-future-of-schema-markup/) 13. [TrySteakhouse — The Hallucination-Patch Workflow](https://blog.trysteakhouse.com/blog/hallucination-patch-workflow-treating-generative-errors-content-bug-reports) 14. [Intuition Labs — AI Hallucinations in Business: Causes, Costs, and Prevention](https://intuitionlabs.ai/articles/ai-hallucinations-business-causes-prevention) 15. [The Ambitions Agency — llms.txt for GEO: What It Is, Why It Matters, and a Copy-Paste Example](https://theambitionsagency.com/llms-txt-for-geo/) ## Ready to Protect Your Brand? AI misinformation is not a theoretical risk. It is actively shaping your buyers' shortlists right now, in conversations you cannot see. The correction playbook above gives you the framework. If your team does not have the bandwidth to execute it, we can run the entire program for you. [Book a call to see how your brand appears in AI answers today](/contact) ## Related Reading * [My Brand Is Being Cited by AI but the Sentiment Is Negative: What to Do](/blog/my-brand-cited-by-ai-sentiment-negative-what-to-do) * [What Is an AI Bot Crawler?](/blog/what-is-an-ai-bot-crawler) * [Should I Block or Allow AI Bots Like GPTBot and ClaudeBot?](/blog/should-i-block-allow-ai-bots-gptbot-claudebot) ```json {"@context":"https://schema.org","@graph":[{"@type":"BlogPosting","headline":"AI Is Showing Wrong Info About Your Product: How to Fix It","description":"AI hallucinations cost businesses $67.4B in 2024. 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