You asked ChatGPT about your company and the answer made you wince. The category was wrong. The description was outdated. The terminology was off. The whole thing read like it was written about a different company.
You're not alone. 59.8% of AI brand misrepresentation stems from wrong categorization alone. And most brands discover the problem by accident — a board member, a prospect, or a competitor mentions it.
Why AI Gets Your Brand Description Wrong
There are three root causes. Identifying yours determines the fix.
Root Cause 1: Outdated training data
LLMs are trained on historical data. If you pivoted your positioning 6 months ago, AI may still be working from your old messaging. Your website changed, but the training data didn't catch up. This is especially common for companies that've gone through rebrands, pivots, or significant product evolution.
Root Cause 2: Conflicting signals
Your website says you're a "developer platform." Your G2 profile says "project management tool." Your founder's LinkedIn says "AI infrastructure." Your press coverage from 2023 calls you a "startup studio." AI synthesizes all of these into... a mess. The more contradictory signals exist, the more hedged and vague AI's description becomes.
Root Cause 3: Missing structured data
AI models look for structured, machine-readable signals to form confident descriptions. If you don't have schema markup, an llms.txt file, or well-structured documentation — AI has to guess based on unstructured content. And guessing produces errors.
Step-by-Step: Correcting Your AI Brand Description
Step 1: Audit what AI currently says
Open ChatGPT, Claude, Gemini, and Perplexity. Ask each: "What does [your company] do?" and "What category is [your company] in?" Document every inaccuracy. Then check the AI Brand Directory for your full profile.
Step 2: Identify the root cause
Compare what AI says against your actual positioning. Is the problem:
- Time-based? (AI describes who you WERE, not who you ARE)
- Signal-based? (different sources say different things)
- Data-based? (AI simply doesn't have enough structured information)
Step 3: Fix your primary source — your website
- Update your homepage H1, meta description, and about page to clearly state your category, audience, and value proposition
- Use specific language, not marketing abstractions
- Add Organization schema markup with your correct category, description, and founding date
- If you've pivoted, add a "What we do" section that explicitly addresses the old vs. new positioning
Step 4: Create or update your llms.txt file
This is a machine-readable file at yourdomain.com/llms.txt that tells AI models exactly who you are. Include: company name, category, value proposition, target audience, key features, competitive differentiation, and founding information. Keep it factual and specific. Setup guide →
Step 5: Align your Brand Vault
Your Brand Vault is the comprehensive ground truth about your company — every fact, claim, and data point that AI should know. Update it across all tabs: identity, value proposition, audience, features, pricing, and competitors.
Step 6: Fix the secondary sources
- Update G2, Capterra, and TrustRadius profiles with current positioning
- Update your LinkedIn company page description
- Update your Crunchbase profile
- Check your Wikipedia page (if you have one) for accuracy
- Review and update any directory listings
Step 7: Create correction content
Publish a blog post or landing page that explicitly addresses the misconception: "What [Company] Actually Does" or "[Company]: From [Old Category] to [New Category]." This gives AI a clear, recent signal to override outdated training data.
Step 8: Monitor and iterate
Run weekly AI brand audits to track whether corrections are propagating. Different models update at different speeds — Perplexity (real-time search) picks up changes fastest, while ChatGPT's parametric knowledge updates more slowly.
How Long Do Corrections Take?
| Model | Timeline | Notes |
|---|---|---|
| Perplexity | Days to weeks | Uses real-time search |
| Gemini | 1-3 weeks | Frequent crawling |
| Claude | 2-6 weeks | Periodic training updates |
| ChatGPT | 2-8 weeks | Browse mode picks up changes faster than parametric updates |
Structured data changes (schema, llms.txt) propagate fastest. Unstructured content changes (blog posts, about pages) take longer.
The Correction Toolkit — What You Need
- AI Brand Directory — diagnose current state
- Brand Vault — maintain your ground truth
- llms.txt file — machine-readable brand identity
- Schema markup — structured data for search and AI
- AI Content Grader — check if your content is AI-readable
Frequently Asked Questions
Can I contact OpenAI or Anthropic to correct what their models say about my company?
There's no direct correction mechanism like Google's "suggest an edit." The way to correct AI is to fix the source material AI learns from — your website, structured data, reviews, and documentation. AI models update their knowledge over time as they encounter better signal.
Why does each AI model say something different about my company?
Each model has different training data, different cutoff dates, and different retrieval strategies. ChatGPT relies heavily on parametric knowledge (what it learned during training), while Perplexity searches the web in real-time. This is why correcting your public information ecosystem matters more than trying to fix any single model.
What if my company has the same name as another company?
Entity disambiguation is a real challenge. The fix is adding enough specific, structured signals (schema markup with your founding date, location, and category) that AI can distinguish you. Your llms.txt file is especially important for this — it gives AI an authoritative self-description to resolve ambiguity.
I pivoted recently and AI still describes my old positioning. What's the fastest fix?
Create an llms.txt file with your current positioning, update your schema markup, publish a blog post explicitly addressing the evolution (e.g., "From [Old] to [New]: Why We Pivoted"), and update your G2/Capterra profiles. Perplexity should pick up the changes within days; other models within weeks.