ChatGPT keeps giving wrong info about my company - has anyone successfully gotten corrections?

I deal with this constantly. Here’s the reality you need to understand:

Why corrections are so hard:

1. Training data is static - ChatGPT (and similar models) learned from data up to a cutoff date. Once trained, that knowledge is baked in until the next training cycle.

2. Feedback isn’t instant - Your thumbs-down and feedback ARE collected, but they inform future training, not immediate fixes.

3. No “edit” button exists - Unlike Google, there’s no Search Console equivalent where you can request content changes.

The actual correction timeline:

What actually works:

1. Fix sources, not AI - Update Wikipedia, news articles, your own site
2. Create counter-content - Publish authoritative, accurate content everywhere
3. Build volume - AI will encounter more correct info if you create more of it
4. Target real-time platforms - Perplexity reflects source changes faster

The 4-month timeline you’re on is unfortunately normal. Keep pushing, but adjust expectations.

I’ve handled several AI misinformation crises. The lawsuit mention is particularly tricky - here’s how to address it:

The legal mention problem:

AI systems treat lawsuit mentions as significant (because they often are). Even dismissed cases stick because:

• Multiple news articles covered the filing
• Court records are public
• AI doesn’t distinguish “dismissed” from “lost”

What worked for a client in similar situation:

1. Published a detailed “case closure” announcement
2. Got law firm to publish case summary on their site
3. Sought industry publication coverage of dismissal
4. Updated LinkedIn and professional profiles

The key insight: You need the DISMISSAL to be as prominent as the original filing. If there are 10 articles about the lawsuit and 1 about the dismissal, AI gives more weight to the lawsuit.

Create as much authoritative content about the positive resolution as exists about the original case.

Experienced Wikipedia editor here. A few things about the Wikipedia angle:

Wikipedia matters A LOT for AI:

• It’s heavily weighted in training data
• Both ChatGPT and Claude reference it frequently
• Getting Wikipedia right can cascade to AI systems

But be careful:

• Don’t edit your own company’s page directly (conflict of interest)
• Follow Wikipedia’s sourcing requirements strictly
• Propose changes on the Talk page
• Use reliable secondary sources

Common mistakes:

• Adding promotional content (gets reverted)
• Citing only company materials (not considered reliable)
• Making changes without discussion
• Being too aggressive

What works:

• Get independent media coverage first
• Use that coverage as Wikipedia sources
• Propose changes through proper channels
• Be patient and follow the process

A well-sourced Wikipedia correction propagates to AI faster than almost anything else.

We went through something similar. Here’s what the CEO exit rumor specifically required:

Our situation: AI kept saying our CEO “stepped down” when she just took a brief medical leave and returned.

What fixed it:

1. CEO published personal blog post clarifying timeline
2. Company issued “CEO Year in Review” press release
3. Got CEO interviewed on industry podcast
4. Updated executive team page with clear “since 2019” language
5. Added CEO to recent conference speaker rosters

The pattern: AI needs RECENT signals that someone is current. Old news about “changes” plus silence reads as departure.

For your CEO:

• Get them visible doing current CEO things
• Conference appearances, interviews, bylined articles
• Make “CEO [Name]” appear in recent, authoritative content

AI will eventually learn that your CEO is… still your CEO.

Before you can fix problems, you need to know exactly what AI is saying. Systematic monitoring is essential:

What to track:

• Exact wording AI uses about your company
• Which platforms have which inaccuracies
• What prompts trigger the wrong information
• Whether corrections are taking effect over time

Why monitoring matters:

• Different platforms have different info (fix each separately)
• Some prompts get accurate answers, others don’t
• You can measure whether your correction efforts work
• Catch new inaccuracies before they spread

Our approach: We use Am I Cited to monitor brand mentions across platforms. When we see inaccurate responses, we:

1. Document the exact inaccuracy
2. Identify the likely source
3. Fix the source
4. Track whether the AI response changes

Without systematic monitoring, you’re fixing problems blindly.

Legal options when AI gets it very wrong:

When legal action might help:

• Defamatory statements (false and damaging)
• Clear factual inaccuracies affecting business
• GDPR violations (in applicable jurisdictions)

The reality:

• Most AI companies have terms limiting liability
• Suing over training data is untested legally
• Cases like NYT vs. OpenAI are still pending
• Legal action is slow and expensive

More practical legal approach:

• Demand letters to platforms with specific inaccuracies
• DMCA notices if your content is being misrepresented
• Document everything for potential future action
• Work with platform legal teams directly for severe cases

GDPR angle (if applicable): Right-to-be-forgotten requests have been attempted. Results mixed, but worth exploring for EU-related content.

For most companies, fixing sources beats legal action. But document everything in case legal becomes necessary.

Smaller company perspective - we couldn’t afford the full PR blitz, so we focused on what we could control:

Low-budget correction strategy:

1. Owned media first

   • Updated every page on our site
   • Created detailed “About” section
   • Added FAQs addressing common misconceptions

2. Free platforms

   • LinkedIn company page (detailed, current)
   • Crunchbase profile (often cited by AI)
   • Google Business Profile
   • Industry directories

3. Content creation

   • Blog posts with current facts
   • Case studies mentioning current team
   • Customer testimonials with dates

4. Relationship leverage

   • Asked partners to update their descriptions of us
   • Got customers to mention us in their content
   • Reached out to industry analysts

Total cost: Time, mostly. But we saw improvements in Perplexity within 2 months.

This thread has been incredibly helpful. Here’s our action plan:

Immediate (this week):

1. Set up systematic AI monitoring across platforms
2. Document all current inaccuracies by platform
3. Create internal “Fact Check” page to share with investors
4. Update all owned properties with current, prominent information

Short-term (next 30 days):

1. Major press release with correct facts on all key issues
2. Get CEO visible - podcast interviews, conference appearances
3. Publish case closure announcement for the lawsuit
4. Work with Wikipedia through proper channels

Ongoing:

1. Continue monitoring to track if corrections take effect
2. Build volume of correct content over time
3. Target Perplexity first (faster updates)
4. Prepare investors proactively for AI inaccuracies

Key insight: Can’t directly edit AI, but can flood the internet with correct information until AI catches up. And manage expectations of people who matter in the meantime.

Thanks everyone. This is a long game but at least now we have a strategy.