When ChatGPT gets your brand wrong: the complete fix playbook

When a potential customer asks ChatGPT about your industry, your brand might come up. If the information is wrong — outdated pricing, a competitor listed as the market leader, a product feature you discontinued years ago — you have a problem you probably do not know about yet.

This playbook covers exactly what to do: how to find the inaccuracies, where to fix the sources, and how to track whether the corrections actually take effect.

Step 1: Find out what AI models are actually saying

Before you can fix anything, you need to know what is broken.

The manual approach

Open ChatGPT, Claude, Gemini, and Perplexity. Ask each one the same questions your customers would ask:

• "What does [your brand] do?"
• "How does [your brand] compare to [competitor]?"
• "What are the best [your category] tools?"
• "Is [your brand] reliable?"

Record every response. Compare the claims against your actual facts — pricing, features, founding date, product descriptions, team size, anything that can be verified.

What to look for

Not all inaccuracies are equally damaging. Prioritise by business impact:

1. Pricing errors — a wrong price directly affects purchase decisions
2. Competitor comparisons — being described as inferior or as an alternative to the wrong competitor shapes perception
3. Feature descriptions — outdated or incorrect feature lists create mismatched expectations
4. Category misplacement — being described in the wrong category means you are invisible to the right buyers
5. Factual errors — wrong founding date, headquarters, team size, or product history undermine credibility

The systematic approach

Manual checks are useful for a first look, but they do not scale. AI models update their responses over time, and a claim that was accurate last month might not be accurate today.

AIVIS automates this process: it queries multiple AI models on a schedule, extracts individual claims from each response, and scores them against your verified brand facts. When something changes or a new inaccuracy appears, you see it immediately instead of discovering it weeks later from a confused customer.

Step 2: Trace the inaccuracy to its source

AI models do not invent information from nothing. Every claim has a source — usually a combination of web pages, structured data, and training corpora. Finding the source is the only way to fix the problem permanently.

Common sources of AI brand misinformation

The source-fixing priority

Fix these in order — the first items have the highest impact on AI training data:

1. Your website — this is the one source you fully control. Fix it first.
2. Structured data — schema markup is explicitly designed for machine consumption. Models and search engines prioritise it.
3. Wikipedia and Wikidata — high authority, frequently used in training data.
4. Directory profiles — G2, Crunchbase, LinkedIn company page. Consistency across these signals reinforces accuracy.
5. Review sites — respond to incorrect reviews with factual corrections. You cannot edit reviews, but your responses are part of the public record.

Step 3: Fix the sources you control

Your website

• Update every page that contains outdated information. Check pricing, features, team, about, and product pages.
• Add or correct Organization schema markup with your current description, founding date, and social links.
• Add Product schema with accurate pricing and feature descriptions.
• Make sure your homepage states clearly, in plain text, what your company does, who it serves, and what makes it different. AI models extract from page content — if the information is not on the page, it cannot be found.
• Check that your robots.txt does not block the pages you want AI models to access.

Structured data

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Your Brand",
  "url": "https://yourbrand.com",
  "description": "One sentence that accurately describes what you do.",
  "foundingDate": "2020-01-15",
  "sameAs": [
    "https://linkedin.com/company/yourbrand",
    "https://x.com/yourbrand"
  ]
}

This structured data is read by AI models, search engines, and knowledge graphs. Keep it current and consistent with your website copy.

Directory and social profiles

Go through every profile your brand has — LinkedIn, X, Crunchbase, G2, Capterra, AlternativeTo, Product Hunt — and ensure they all say the same thing. The same company description, the same category, the same key facts.

Inconsistency is worse than absence. If LinkedIn says you are a "marketing analytics platform" and G2 says you are a "brand monitoring tool," AI models have conflicting signals and may surface either one — or blend them into something neither accurate nor useful.

Step 4: Use each AI provider's feedback channels

Every major AI provider has a mechanism for reporting inaccuracies. None of them guarantee corrections, but submitting reports creates a record and may influence future updates.

OpenAI (ChatGPT)

• Use the thumbs-down button on any inaccurate response and describe the error
• Submit detailed corrections through OpenAI's help center
• For business-critical errors, contact OpenAI's business team if you have an enterprise relationship

Google (Gemini)

• Use the feedback button directly on inaccurate Gemini responses
• For Google Search AI Overviews, use the "Report" button
• Submit feedback through Google's feedback forms
• Fix your Google Business Profile if applicable — this feeds into Google's knowledge graph

Anthropic (Claude)

• Use the thumbs-down feedback on Claude responses
• Submit detailed corrections through Anthropic's support channels

Perplexity

• Flag inaccurate responses using the feedback button
• Perplexity uses live web results, so fixing the source pages often produces the fastest corrections

Important context on feedback

Submitting feedback is worth doing, but it is not a fix. None of these providers offer a guaranteed correction timeline. The most reliable path to accurate AI responses is fixing the upstream sources — your website, structured data, and public profiles — because all models draw from the same public web.

Step 5: Monitor whether corrections take effect

This is where most businesses stop — and where the problem compounds. You fixed the source, submitted feedback, and assumed the job was done. But AI models update on their own schedules, and a correction that worked for one model might not have reached another.

What to track

• Did the specific inaccuracy disappear from each model's responses?
• Did new inaccuracies appear while you were fixing old ones?
• Are the models now using your corrected information, or are they still pulling from cached or older sources?
• Has your competitor's information changed in a way that affects comparisons?

Manual tracking

You can track this manually by running the same queries weekly and comparing results. This works for a small number of queries across a small number of models, but it becomes impractical quickly.

Automated tracking

AIVIS handles this by running scheduled scans across multiple AI models, comparing each response against your verified facts, and flagging changes. When a correction takes effect — or when a new inaccuracy appears — you see it in your scan history with a timestamp and the specific claim that changed.

The point is not which tool you use. The point is that monitoring must be ongoing. AI models are not static. They update, retrain, and change their responses. A brand that was accurately described last month may be inaccurately described today.

Step 6: Build your evidence trail

When AI gets your brand wrong, you need more than a fix — you need proof. An evidence trail serves three purposes:

1. Internal accountability — when a team member asks "do we have an AI accuracy problem?", you have data instead of anecdotes
2. Correction requests — when submitting feedback to AI providers, timestamped evidence of persistent inaccuracy is more compelling than a single complaint
3. Trend analysis — tracking inaccuracies over time reveals whether your corrections are working, whether new problems are emerging, and which models are the most problematic

For each inaccuracy you discover, record:

• The date you discovered it
• Which AI model produced it
• The exact response text
• What the correct information is
• The source you traced it to
• What you did to fix it
• Whether the correction has taken effect

Common mistakes to avoid

Trying to optimise AI responses directly. AI models are not search engines. There is no equivalent of SEO for AI responses. The only reliable path is fixing the source data that models are trained on.

Ignoring models you do not use. Your customers use all of them. An inaccuracy in Gemini matters even if your team only uses ChatGPT.

Fixing once and forgetting. AI models retrain and update. A correction that worked three months ago may have been overwritten by newer training data. Monitoring must be continuous.

Focusing only on your own brand. What AI models say about your competitors in relation to you matters just as much. If a model describes a competitor as the leader in your category, that affects you whether your own information is accurate or not.

Assuming AI inaccuracies are rare. In AIVIS scans across hundreds of brands, the majority of AI responses contain at least one claim that does not match the brand's verified facts. The question is not whether AI is getting your brand wrong — it is how wrong, and whether you know about it.

The timeline

Realistic expectations for each step:

The initial audit and source fixes are a one-time investment. Monitoring is not. AI models change continuously, and the only way to stay accurate is to keep watching.