AI assistants are telling potential customers things about your brand: and some of what they say may be wrong. Wrong pricing, wrong product capabilities, wrong founding story, wrong use cases. For a potential customer using an AI assistant as part of their evaluation process, inaccurate AI descriptions can directly prevent the consideration and purchase that should have happened.
Fixing incorrect AI information about your brand is not a one-step process and it is not instantaneous, but it is methodical. This guide covers how to identify what AI is saying wrong, diagnose why it is saying it, and systematically correct the content signals that determine what AI assistants learn and say about your brand.
What you will learn:
- How to systematically identify incorrect AI descriptions of your brand
- The root causes of AI brand inaccuracy and how to address each one
- How to use entities.txt to anchor authoritative brand information
- Which content changes correct AI brand descriptions fastest
- How to measure whether your corrections are taking effect
Step 1: Audit What AI Platforms Are Currently Saying About Your Brand
Before you can fix incorrect information, you need to know what is being said. Run a systematic brand accuracy audit across the major AI platforms.
For each platform (ChatGPT, Perplexity, Claude, Google AI Overviews/Gemini, Grok, Microsoft Copilot), run the following queries and record the responses:
Identity queries:
- "What is [your brand name]?"
- "What does [your brand] do?"
- "Who founded [your brand]?"
- "When was [your brand] founded?"
Product queries:
- "What are the features of [your product]?"
- "How much does [your product] cost?"
- "Who uses [your product]?"
- "What is [your product] best for?"
Comparison queries:
- "How does [your brand] compare to [Competitor A]?"
- "[Your category] tools comparison" (check if your brand appears and how it is described)
Record every factual claim AI platforms make about your brand. Create a simple spreadsheet with columns for: platform, query, AI response summary, and accuracy assessment (correct/incorrect/partially correct).
By the end of this audit, you have a clear inventory of what AI is saying about your brand and where the inaccuracies are concentrated.
Step 2: Diagnose the Source of Inaccuracies
Incorrect AI descriptions come from specific sources. Identifying the source helps you prioritize the fix.
Source 1: Outdated Web Content
AI platforms with real-time retrieval (Perplexity, SearchGPT) are citing content that was accurate when it was written but is no longer current: old pricing pages, outdated product descriptions, press coverage that described a version of your product that no longer exists.
Fix: Update your website content so the most authoritative descriptions of your brand, product, and pricing are accurate and current. Ensure your pricing page, About page, and product description pages reflect current reality. Add dateModified to updated pages and submit them for re-crawling through Google Search Console and Bing Webmaster Tools.
Source 2: Third-Party Sources with Wrong Information
Review sites (G2, Capterra, Trustpilot), competitor comparisons, and press coverage may contain inaccurate descriptions that AI platforms cite. A TechCrunch article from 2021 that described your product incorrectly may still be in AI training data.
Fix: Identify the specific third-party sources containing the wrong information. For review platforms, update your company's official profile. For press coverage, reach out to publications to request corrections where the errors are factual and clearly wrong. For unavoidable old content that cannot be corrected, create fresh, authoritative counter-content that dominates the same queries.
Source 3: Training Data From Before Your Product Evolution
AI models trained on historical data may describe your brand as it was 2 to 3 years ago, before product pivots, pricing changes, or major feature additions.
Fix: Training data corrections are not directly addressable, but their effect can be mitigated by dominating the retrieval layer with current, authoritative content. Platforms with real-time retrieval update their outputs faster. For GPT-4-class models on fixed training data, the correction is slower and depends on model updates.
Source 4: Sparse Training Data Leading to Hallucination
If your brand has limited web presence, AI models may generate plausible-sounding but inaccurate descriptions by interpolating from similar brands or categories.
Fix: Increase the volume and quality of accurate content about your brand across authoritative web sources. More accurate information about your brand in AI training data reduces the likelihood of hallucinated descriptions.
Step 3: Fix Your Owned Content Signals
Your website is the most controllable source of brand information for AI systems. Ensuring it accurately represents your brand across every key page is the foundation of AI brand accuracy.
Key pages to audit and update:
Homepage: The homepage description of what your company does should be specific, accurate, and current. AI systems often use homepage content as the primary source for brand identity descriptions. Rewrite your homepage headline and description to be factually precise and clearly distinguishable from competitors.
About page: Include your founding date, founding story, team composition, company size, and precise description of what you do and who you serve. Add Organization schema with accurate foundingDate, description, employee count, and sameAs references to authoritative external profiles.
Product/pricing page: Current, accurate pricing with clear tier descriptions. AI systems frequently cite outdated pricing. Ensure your pricing page has dateModified in its schema reflecting the last time pricing was verified.
Team/founder page: Named founders and team members with accurate role descriptions. AI hallucinations about founders often come from sparse or no authoritative team page content.
Step 4: Deploy entities.txt
Entities.txt is a machine-readable file at your domain root that defines your brand's authoritative identity in a structured format. AI crawlers that read it use it as a direct source for brand attribute resolution.
Example entities.txt structure:
# Entity: YourCompanyName
type: Company
name: Your Company Inc.
legal_name: Your Company Incorporated
founded: 2020
founders: Jane Doe, John Smith
headquarters: San Francisco, CA, United States
website: https://yourdomain.com
products: [Your Product Name] - AI search optimization platform for B2B companies
description: [Your Company] provides AI Engine Optimization auditing and citation monitoring tools for B2B marketing teams. Founded in 2020, headquartered in San Francisco.
categories: AEO Software, AI Search Optimization, Marketing Technology
pricing_model: Subscription, SaaS
target_audience: B2B marketing teams, SEO agencies
The Entities.txt Guide for Reducing AI Hallucination and the Robots.txt to Entities.txt Evolution guide cover the full implementation.
After deploying entities.txt, submit it for crawling and verify that AI platforms with entities.txt support can access it.
Step 5: Create Definitive Brand Description Content
Create a piece of content specifically designed to be the authoritative source AI systems reference when asked about your brand. This can be:
- A dedicated "About [Your Brand]" page that functions as a comprehensive, factual brand description
- A press page or media kit with accurate, current company facts
- A blog post titled "What is [Your Brand]: How [Your Product] Works" that directly addresses the queries AI platforms receive about you
This content should:
- Use your exact brand name in H1 and early H2 headings
- State key facts directly: founding date, product description, pricing range, target audience
- Be structured with direct answer sentences under question-form headings
- Include FAQPage schema with the specific questions AI platforms are getting wrong
Step 6: Monitor the Corrections
Run your brand accuracy audit monthly for 3 to 6 months after making corrections. Track whether specific incorrect claims are disappearing from AI responses and whether corrected information is being adopted.
Platforms with real-time retrieval (Perplexity, Grok, SearchGPT) typically update within 2 to 6 weeks of content corrections. Platforms using fixed training data update only on model release cycles, which may be months.
Track the trend: the percentage of your brand audit queries that return fully accurate descriptions should increase month over month as your corrections propagate through the retrieval layer.
For ongoing brand mention monitoring across AI platforms, the AEO Monitoring and Tracking Guide covers how to set up systematic citation probe workflows that include brand accuracy checks alongside standard citation rate tracking.
