AI keeps getting facts wrong about our company - what's the actual process to correct this?

I deal with this daily. Here’s the reality: You cannot directly correct AI systems. You have to fix the ecosystem that AI learns from.

Step 1: Identify the Error Source

AI errors come from three places:

1. Outdated training data - Old articles, old version of your site
2. Third-party misinformation - Incorrect articles, old reviews, wrong directories
3. Hallucination - AI made it up based on partial information

For each error, investigate: Where did this likely come from?

Step 2: Prioritize High-Authority Corrections

Fix information on platforms AI trusts most:

1. Wikidata - Structured data many AI systems use
2. Wikipedia - If you have an article
3. Crunchbase - Company information source
4. LinkedIn - Professional profiles
5. Google Business Profile - Location and basic info
6. Your website - Clear, prominent corrections

Step 3: Create “Correction Content”

Publish content that explicitly addresses the error:

• “Updated: Our 2026 Pricing Guide”
• “Company Facts: [Your Company] Founded in 2018”
• Blog post announcing the feature they claim you don’t have

Step 4: Build Fresh Mentions

New mentions on authoritative sites create new training signals:

• Press coverage with correct information
• Industry publication mentions
• New reviews with accurate details

Timeline: Expect 4-12 weeks for changes to propagate. Some systems update faster than others.

The competitor confusion problem is solvable.

Our situation: We’re “TechFlow” - competitor is “FlowTech.” AI constantly mixed us up.

Solution:

1. Explicit differentiation content Created a page: “TechFlow vs FlowTech: Different Companies”

   • Clear statement we’re separate entities
   • Different founding dates, locations, products
   • Unique identifiers for each

2. Entity-rich content Every major page now includes:

   • Full company name with context
   • Founding information
   • Headquarters location
   • Founder names

3. Wikidata separation Ensured we have separate, accurate Wikidata entries.

4. sameAs schema Connected our entity to verified profiles:

   • LinkedIn company page
   • Crunchbase profile
   • Official social accounts

Result: Confusion dropped from 40% to under 5% in 8 weeks.

The key is making our unique identity unmistakable across the web.

Fixed our pricing misinformation. Here’s what worked:

The problem: AI was citing our 2022 pricing. We raised prices in 2023.

The solution:

1. Updated pricing page prominently

   • Added “Pricing effective January 2024”
   • Clear “Last updated: [date]”
   • dateModified in schema

2. Published pricing announcement Blog post: “2024 Pricing Update”

   • Explained changes
   • Included actual numbers
   • Linked from multiple places

3. Updated third-party sources

   • G2 and Capterra profiles
   • Industry directories
   • Partner websites

4. Fresh mentions

   • Got pricing mentioned in two industry articles
   • Posted accurate pricing in relevant Reddit threads

Timeline:

• Week 2: Perplexity started citing correct pricing
• Week 6: ChatGPT mostly correct
• Week 10: Claude updated

The fresher and more authoritative your pricing content, the faster it corrects.

Let me save you time: Direct feedback to AI companies rarely works.

Our experience:

• Submitted 15 corrections to ChatGPT feedback - 0 responses
• Used Claude’s feedback mechanism - no visible changes
• Perplexity feedback form - no response

Why this doesn’t work:

• Volume of feedback is massive
• No dedicated correction team
• They can’t manually override training data
• Individual corrections don’t scale

What works instead: Fix the web, not the AI.

AI companies can’t/won’t manually correct your specific issues. But they WILL incorporate corrected source material in future training and indexing.

Your energy is better spent:

• Updating source content
• Building new authoritative mentions
• Creating fresh, accurate content

It’s frustrating, but it’s the reality.

Track your correction progress systematically:

Set up correction monitoring:

1. Document the errors

   • Exact error statement
   • Which platforms show it
   • Screenshot with date

2. Create test prompts

   • Design prompts that trigger the error
   • “What year was [company] founded?”
   • “Does [company] offer [feature]?”
   • “What is [company] pricing?”

3. Weekly testing

   • Run prompts across all platforms
   • Document: error still present? Partial correction? Fixed?

4. Track correction timeline

   Error	Correction Started	Perplexity Fixed	ChatGPT Fixed	Claude Fixed
   Founding year	Jan 1	Jan 15	Feb 10	Feb 5

5. Identify what worked

   • Which corrections were fastest?
   • What content drove the change?
   • Replicate for other errors

Tools: Am I Cited can automate some of this tracking, but manual testing ensures you catch specific errors.

Wikidata is underrated for AI corrections.

Why Wikidata matters:

• Structured data source for many AI systems
• Powers knowledge panels
• Easily editable (with proper citations)
• Changes propagate to multiple systems

How to fix Wikidata:

1. Find your entity Search for your company name

2. Audit current data

   • Founding date (P571)
   • Headquarters location (P159)
   • Official website (P856)
   • Industry (P452)
   • Key people (P169, P112)

3. Edit with citations

   • You need reliable sources
   • News articles, official documents
   • Wikipedia is not a source for Wikidata

4. Add missing properties

   • The more complete, the better
   • Include products, subsidiaries, etc.

Our correction: Fixed founding date in Wikidata with press release citation. Saw Google Knowledge Panel update in 1 week. AI systems started showing correct date within 4 weeks.

Wikidata is often the fastest lever for factual corrections.

Press releases help when done right:

Effective correction press releases:

Structure them to be AI-friendly:

• Lead with the fact in first sentence
• Include specific data points
• Repeat key facts multiple times

Example opening: “[Company Name], the [descriptor] platform founded in 2018, today announced…”

Not: “Building on years of innovation, the team at…”

Distribution strategy:

• Major wire services (appears on many sites)
• Industry-specific outlets
• Local news for headquarters location
• Trade publications

Why this works:

• Creates fresh, authoritative content
• Distributed across many domains
• Includes correct information prominently
• Provides citable source for AI

We issued one press release specifically to correct our founding date narrative. It appeared on 50+ sites. AI started citing the correct date within 5 weeks.

How we fixed “doesn’t support feature X” misinformation:

The problem: AI said we don’t have API access. We’ve had it for 18 months.

Investigation: AI was citing our own old documentation from before the feature launched. Plus competitor comparison articles that were outdated.

The fix:

1. Product page overhaul

   • API feature prominently displayed
   • “API Access” in page title
   • Screenshots of API in action

2. Documentation update

   • Clear API documentation
   • “Available since [date]”
   • Rich examples and use cases

3. Content campaign

   • Blog: “Getting Started with [Product] API”
   • Case study: “How [Customer] Uses Our API”
   • Integration guides with popular tools

4. Third-party updates

   • Updated G2/Capterra feature lists
   • Reached out to comparison sites for updates
   • Posted in relevant Reddit threads about our API

Timeline: Week 4: Perplexity corrected Week 7: Claude corrected Week 10: ChatGPT mostly corrected

The key was overwhelming the old information with new, authoritative, feature-specific content.

Framework for systematic error correction:

1. Error Inventory List every factual error you’ve found:

• Error statement
• Platforms affected
• Business impact (high/medium/low)
• Likely source

2. Prioritization Fix highest-impact errors first:

• Customer-facing misinformation
• Pricing/feature errors
• Competitive confusion

3. Correction Action Matrix

4. Timeline Tracking Document when correction started and when each platform fixes.

5. Prevention

• Consistent entity information everywhere
• Regular audits of AI accuracy
• Quick response to new errors

Treat this as ongoing maintenance, not one-time project.

This thread is incredibly helpful. Here’s our correction action plan:

Immediate (This Week):

1. Audit and fix Wikidata entry
2. Update Crunchbase profile
3. Fix Google Business Profile
4. Set up monitoring for test prompts

Week 2-3:

1. Investigate error sources
2. Update website with correction content
3. Issue press release with correct founding date
4. Create differentiation page for competitor confusion

Month 2:

1. Feature-specific content campaign
2. Updated pricing page with dateModified schema
3. Third-party profile updates
4. Industry publication outreach

Ongoing:

1. Weekly monitoring of test prompts
2. Track correction progress by platform
3. Quick response to new errors

Key learnings:

• Can’t fix AI directly - fix the web
• Wikidata is high-impact, fast lever
• Source identification is crucial
• Patience required - 4-12 weeks typical

Thank you all. This gives us a real action plan instead of just frustration.