Discovering that ChatGPT, Claude, or Perplexity is spreading wrong information about your brand can feel like watching a rumor spread in real-time—except this rumor reaches millions of users daily. Whether AI models are confusing your company with a competitor, stating outdated product information, or attributing features you don't offer, these inaccuracies can erode trust and send potential customers in the wrong direction.
The challenge is that you can't simply call up an AI and ask for a correction. These models learn from vast datasets of web content, and fixing misinformation requires a strategic approach to content creation, distribution, and monitoring.
This guide walks you through the exact steps to identify where AI is getting your brand wrong, create corrective content that AI models will learn from, and establish ongoing monitoring so you catch future inaccuracies before they spread. By the end, you'll have a systematic process for taking control of how AI represents your brand.
Step 1: Audit How AI Models Currently Describe Your Brand
Before you can fix anything, you need to know exactly what's broken. Start by systematically testing how different AI platforms describe your brand when users ask about you.
Open ChatGPT, Claude, Perplexity, Gemini, and Microsoft Copilot in separate browser tabs. Test each platform with a variety of prompts that potential customers might actually use. Don't just ask "What is [Your Company]?" Try questions like "What features does [Your Company] offer?", "How does [Your Company] compare to [Competitor]?", and "What are the pricing options for [Your Company]?"
As responses come in, document every inaccuracy you find. Create a spreadsheet with columns for the AI platform, the prompt you used, the incorrect information provided, and what the correct information should be. This becomes your correction roadmap.
Pay special attention to these common error categories. Feature misattribution happens when AI claims you offer capabilities you don't, or misses key features you do provide. Outdated information appears when AI references old pricing, discontinued products, or former company details. Competitor confusion occurs when AI blends your brand with similar companies or incorrectly positions you in the market. Fabricated claims are the most dangerous—instances where AI invents details about your company that have no basis in reality.
Take screenshots of every problematic response and save the exact prompts you used. You'll reference these later when creating corrective content, and they serve as baseline measurements to track whether your fixes are working. For a deeper dive into testing methodologies, learn how to track ChatGPT responses about your brand systematically.
Categorize each error by severity. High-severity issues directly impact purchase decisions or misrepresent core offerings. Medium-severity problems create confusion but don't immediately harm conversions. Low-severity inaccuracies are minor details that should be corrected but aren't urgent. This prioritization helps you tackle the most damaging misinformation first.
Step 2: Identify the Source of Misinformation
AI models don't make up information randomly—they synthesize from web content they've been trained on. Your next step is detective work: figure out where these errors originated.
Start with your own website. This might sound counterintuitive, but many brands discover that AI is accurately repeating outdated information from their own neglected pages. Check your about page, product documentation, old blog posts, and press release archives. If you changed your pricing model two years ago but forgot to update a comparison chart buried on page seven of your site, AI may have learned from that outdated chart.
Next, search Google for your brand name and review the top 20-30 results. Look specifically for third-party articles, competitor comparison pages, review sites, and user forums. These are prime sources where AI models learn about brands. When you find articles containing the same inaccuracies AI is repeating, you've likely identified a training source.
Pay special attention to high-authority sites. A single error on a major industry publication or well-linked review site can carry more weight in AI training than dozens of correct mentions on smaller sites. Note which authoritative sources contain errors—you'll need to address these specifically. Understanding brand reputation in AI search helps you prioritize which sources matter most.
Understanding that AI models synthesize from multiple sources is crucial. Rarely is there one single culprit spreading misinformation. More commonly, AI has encountered conflicting information across dozens of sources and synthesized a response that doesn't match any single source perfectly. This is why comprehensive correction across multiple channels matters more than fixing one article.
Step 3: Create Authoritative Corrective Content
Now that you know what's wrong and where it likely came from, it's time to create content that teaches AI models the correct information about your brand.
Start by publishing clear, factual content on your own website that directly addresses each inaccuracy you documented. If AI consistently gets your pricing wrong, create a comprehensive pricing page with detailed breakdowns. If AI confuses your features with a competitor's, build a features page that explicitly states what you do and don't offer.
Structure this content for both human readers and AI comprehension. Use clear headings, bullet points for feature lists, and direct language. Avoid marketing fluff—AI models learn better from straightforward factual statements than from promotional copy filled with adjectives. Mastering prompt engineering for brand visibility can help you understand how AI interprets different content structures.
Implement structured data and schema markup on these pages. Schema.org provides specific markup types for organizations, products, and services that help AI understand the relationships between different pieces of information about your brand. When you mark up your founding date, headquarters location, product names, and key features with proper schema, you're essentially providing AI models with a structured data feed about your brand.
Create an FAQ page that answers the exact questions AI is getting wrong. If users ask AI "Does [Your Company] integrate with Salesforce?" and AI incorrectly says no, your FAQ should include that exact question with a clear, detailed answer. This targeted approach helps AI models find authoritative answers to the specific queries that are currently producing errors.
Ensure absolute consistency across all owned properties. Your main website, help documentation, blog, press release page, and any other official channels should tell the exact same story about your brand. Conflicting information across your own properties confuses AI training and makes corrections take longer to stick.
Update your about page, product pages, and any historical content that might contain outdated information. Don't just add new correct content—actively remove or update old content that contradicts your current reality. AI models can't distinguish between your 2023 pricing page and your 2026 pricing page unless you make it clear which is current.
Step 4: Amplify Correct Information Across the Web
Correcting your own website is necessary but not sufficient. AI models learn from the entire web, so you need correct information appearing on third-party sites as well.
Identify high-authority sites that currently mention your brand with inaccuracies. Reach out to these sites with polite correction requests. Most reputable publications will update factual errors when you provide correct information and evidence. Be specific in your request—don't just say "this is wrong," provide the exact correction and link to authoritative sources on your site.
For sites that published comparison articles or reviews based on outdated information, offer to provide updated details. Many content teams appreciate when companies help them keep their content current, especially if you frame it as helping their readers get accurate information.
Publish guest content on industry sites and publications where your target audience reads. This serves double duty: it gets correct information about your brand onto authoritative domains, and it builds your expertise in your industry. When you contribute thoughtful articles to respected publications, you create new training data for AI models that accurately represents your brand. Explore the best ways to get mentioned by AI for more amplification strategies.
Update your profiles on business directories and information databases. Wikipedia, Crunchbase, G2, Capterra, and industry-specific directories are often sources AI models reference. Ensure your Wikipedia page (if you have one) contains current, well-cited information. Update your Crunchbase profile with current funding, employee count, and product details. Claim and update your profiles on review sites.
Build backlinks to your corrective content. The more authoritative sites that link to your accurate product pages, pricing information, and feature documentation, the more weight those pages carry in AI training. Focus on earning links from industry publications, partner sites, and relevant business directories. This approach directly impacts your brand authority in LLM responses.
Step 5: Implement an llms.txt File for Direct AI Communication
Beyond traditional SEO and content strategies, you can communicate directly with AI crawlers using an emerging standard called llms.txt.
Think of llms.txt as a robots.txt file for AI models—a simple text file that sits in your website's root directory and provides structured information specifically for AI systems to reference. While not all AI models currently use this standard, adoption is growing as the need for authoritative brand information becomes more recognized.
Create a file named llms.txt and place it at yourdomain.com/llms.txt. In this file, include key facts about your brand in a clear, structured format. Start with basics: your company's official name, founding date, headquarters location, and a one-sentence description of what you do.
List your core products or services with brief, factual descriptions. Include current pricing tiers if applicable. Specify key features and capabilities using straightforward language. This isn't the place for marketing copy—think of it as writing a fact sheet for a journalist.
Crucially, include a section on what you're NOT. If AI commonly confuses you with competitors or attributes features you don't offer, explicitly state these distinctions. For example: "We do not offer mobile apps" or "We are not affiliated with [Similar Company Name]." This negative information helps AI models avoid making incorrect associations.
Keep your llms.txt file updated as your brand evolves. When you launch new products, update pricing, or make significant changes, update this file. Think of it as a living document that provides AI models with your current brand truth.
While llms.txt is still emerging and not universally adopted, implementing it costs nothing and positions you ahead of the curve. As more AI systems recognize this standard, you'll already have accurate information ready for them to reference. This is one of the most effective strategies for improving brand mentions in AI responses.
Step 6: Set Up Ongoing AI Visibility Monitoring
Fixing current inaccuracies is just the beginning. New misinformation can emerge as AI models retrain, as new content about your brand appears online, or as your company evolves. Ongoing monitoring ensures you catch problems early.
Establish a regular testing cadence. Monthly is a good starting point for most brands—test the same set of prompts across major AI platforms and document the responses. This creates a timeline showing whether your corrections are taking effect and alerts you to new inaccuracies that appear.
Create a standardized testing protocol. Use the same prompts each month so you're comparing apples to apples. Your prompt set should cover your brand name, key products, main features, pricing, and common comparison queries. Save these prompts in a document you can reference each testing cycle. Consider implementing real-time brand monitoring across LLMs for continuous visibility.
Consider using AI brand visibility tracking tools that automate this monitoring process. Manual testing is valuable for understanding nuances, but automated tracking lets you monitor more prompts, more platforms, and more frequently without the time investment. These tools can alert you when sentiment changes or new inaccuracies appear.
Track not just whether information is correct, but how AI models position your brand. Are they mentioning you in relevant contexts? When users ask about your industry or use case, does your brand come up? Are you being compared to appropriate competitors or being lumped in with irrelevant alternatives? This broader visibility tracking helps you understand your AI presence beyond just factual accuracy.
Document your progress over time. Create a simple tracking sheet showing what percentage of test prompts produce accurate responses each month. This data helps you demonstrate ROI on your AI correction efforts and identify which types of misinformation are proving hardest to fix. Understanding brand sentiment in AI responses adds another dimension to your tracking.
Taking Control of Your AI Narrative
Fixing how AI mentions your brand isn't a one-time project—it's an ongoing discipline that becomes part of your content and SEO strategy. The brands that take control of their AI narrative now will have a significant advantage as AI-powered search becomes the primary way people discover and evaluate companies.
Start with your thorough audit across multiple AI platforms. Document every inaccuracy, categorize by severity, and trace the likely sources. Then systematically publish corrective content on your own properties, ensuring consistency and implementing proper structured data. Amplify that correct information by updating third-party sites, earning coverage on authoritative publications, and building backlinks to your accurate content.
Implement technical solutions like llms.txt that communicate directly with AI systems. Even if adoption is still growing, you're positioning yourself ahead of competitors who haven't considered this channel yet.
Most importantly, establish monitoring so you catch new issues before they become entrenched. Regular testing, whether manual or automated, ensures your corrections are working and alerts you to emerging problems.
Here's your action checklist: Test five or more AI platforms with brand-related queries and document all inaccuracies. Audit your own website and identify third-party sources spreading misinformation. Publish comprehensive corrective content with proper schema markup on your website. Update business directories, review sites, and reach out to publications for corrections. Create and publish your llms.txt file with key brand facts. Set up a monthly monitoring cadence to track AI responses over time.
The landscape of search is fundamentally changing. Traditional SEO focused on ranking in the top ten results. AI visibility requires ensuring that when someone asks an AI about your industry, your use case, or your brand specifically, they get accurate, compelling information. Stop guessing how AI models like ChatGPT and Claude talk about your brand—get visibility into every mention, track content opportunities, and automate your path to organic traffic growth. Start tracking your AI visibility today and see exactly where your brand appears across top AI platforms.
