AI keeps making stuff up about our company - how do we prevent hallucinations?

Your situation is common for startups. Here’s why and how to fix it:

Why AI is wrong about you:

The fundamental issue:

AI doesn’t “know” facts. It predicts what words should come next based on patterns. When it lacks reliable data about you, it generates plausible-sounding fiction.

The solution framework:

You can’t “train” ChatGPT directly, but you can:

1. Become the dominant source - Make your correct info the most available and authoritative
2. Create consistency - Same info everywhere, zero conflicts
3. Add structured data - Give AI explicit, machine-readable facts
4. Build verification chains - Link to external validators

For your specific false claims:

Entity consistency is critical for reducing hallucinations:

The problem:

Inconsistency confuses AI. If your founding date is different across sources, AI has to guess.

Consistency audit checklist:

Common inconsistency sources:

1. Old press mentions - Article from 2022 with outdated info
2. Auto-generated profiles - Sites that scrape and get it wrong
3. Employee LinkedIn - Team members have conflicting company info
4. Data aggregators - ZoomInfo, Apollo, etc. with old data

Fix priority:

1. Your website (you control)
2. LinkedIn company page (you control)
3. Crunchbase (you can edit)
4. Google Business Profile (you control)
5. Employee LinkedIn (ask team to align)
6. Third-party directories (contact for corrections)
7. Data aggregators (usually have correction processes)

Schema markup for consistency:

{
  "@type": "Organization",
  "name": "Your Company",
  "foundingDate": "2021-03-15",
  "address": {
    "@type": "PostalAddress",
    "addressLocality": "Austin",
    "addressRegion": "TX"
  },
  "numberOfEmployees": {
    "@type": "QuantitativeValue",
    "value": 12
  }
}

This explicitly tells AI systems: “These are the facts.”

I went through this exact situation. Here’s what worked:

Our timeline:

• Month 0: Discovered wild hallucinations
• Month 1: Fixed all controllable sources
• Month 2: Schema markup, press release
• Month 3: Perplexity started getting it right
• Month 4: Google AI Overviews improved
• Month 6: ChatGPT still occasionally wrong but better

What moved the needle most:

1. Crunchbase Pro - Seriously, pay for it. AI systems heavily reference Crunchbase for company data.

2. LinkedIn completeness - Every field filled, founder profiles linked, company description explicit.

3. Organization schema - On homepage with all key facts explicit.

4. Press release - Distributed on major wire with correct company facts. Creates authoritative external source.

5. Wikipedia attempt - We weren’t notable enough for Wikipedia, but we created a Wikidata entry (lower bar, still helps).

What didn’t work:

• Reporting to OpenAI (no mechanism really)
• Just updating our website alone
• Hoping it would fix itself

Cost:

• Crunchbase Pro: $300/year
• Press release distribution: $400
• Everything else: Time

ROI:

One investor told us they almost passed because “ChatGPT said you raised Series A and your cap table looked different.” Avoiding that confusion is worth the investment.

Technical approaches to AI data correction:

For RAG-based systems (Perplexity, Google AI):

These pull from live web. Fix your indexed content:

1. Ensure your site is crawlable
2. Update robots.txt to allow AI crawlers
3. Create authoritative pages for each fact type
4. Build backlinks to your authoritative pages

For ChatGPT/Claude (training-based):

Harder to influence. Strategies:

1. Create widely-cited content with correct info
2. Get correct info into sources they likely trained on (Wikipedia, major publications)
3. Hope training updates incorporate new data

llms.txt implementation:

Create a machine-readable summary:

# llms.txt for [Company]
Name: [Exact Company Name]
Founded: 2021
Headquarters: Austin, Texas
Employees: 12
Funding: Bootstrapped (no external funding)
Founder: [Name]
Website: https://yourcompany.com
About: [One sentence description]

Put at yourcompany.com/llms.txt

Monitoring setup:

Query each platform monthly:

• “What year was [Company] founded?”
• “Where is [Company] headquartered?”
• “How many employees does [Company] have?”
• “Has [Company] raised funding?”

Track changes over time to measure improvement.

Ongoing monitoring and correction process:

Monthly audit template:

When you find errors:

1. Document (screenshot with date)
2. Identify likely source of bad data
3. Fix or outrank the source
4. Wait 4-6 weeks
5. Re-test

Automated monitoring:

Am I Cited and similar tools can:

• Track brand mentions across AI platforms
• Alert on changes
• Compare to competitors
• Historical tracking

Quarterly review:

• Overall accuracy score
• Trend direction
• Remaining problem areas
• Strategy adjustment

Annual:

• Comprehensive fact audit
• Update all properties
• Refresh press coverage
• Schema markup review

This is exactly what I needed. Here’s my action plan:

Week 1: Audit and Document

• Test all AI platforms with key questions
• Document current state (screenshots)
• Identify all sources of incorrect info

Week 2: Fix Controllable Sources

• Website About page - explicit facts
• LinkedIn company page - complete all fields
• Employee LinkedIn - ask team to align
• Organization schema - implement with all facts

Week 3: External Sources

• Crunchbase Pro - upgrade and update
• Google Business Profile - verify and complete
• Create llms.txt file
• Audit and fix any third-party directories

Week 4: Authority Building

• Press release with company facts
• Wikidata entry (if eligible)
• Industry directory listings

Ongoing:

• Monthly AI platform testing
• Document improvement over time
• Continuous source monitoring

Key metrics:

• Number of incorrect facts per platform
• Time to correction
• Consistency score across sources

Investment:

• Crunchbase Pro: $300/year
• Press release: ~$400
• Time: ~20 hours total

Expected timeline:

• Perplexity: 2-4 weeks
• Google AI: 4-8 weeks
• ChatGPT: Unknown, ongoing

Key insight:

Can’t “correct” AI directly. Must become the most authoritative, consistent source so AI naturally gravitates to correct information.

Thanks everyone - finally have a concrete path forward!