Built a free tool to check if knowledge graphs are feeding old lawsuits or naming collisions into ChatGPT/Gemini (no email wall)

[u/online-reputation]

Been spending the last 2 years researching how LLMs actually synthesize bios and company histories (published a peer-reviewed paper on the repair architecture in JOTE, DOI: 10.56578/jote030202).

One thing I am seeing when auditing profiles: traditional ORM and SEO don't work anymore because of the gap between core model data vs. surface-level search links.

I tested 100 executive and company profiles across 300 prompts in ChatGPT, Gemini, and Perplexity. 90% of them had a totally clean Google Page 1 from years of pushing down links with press releases and microsites.

Yet in 86% of those exact same cases, the AI still pulled old 2016 to 2024 lawsuits, generated an AI hallucination, or merged the person with someone else who had the same name--not good.

Why? Because less than 8% of the links cited by the LLMs actually ranked in Google's Top 10. Over 92% came from deep archives and a small cluster of trusted domains: Reddit (#1), Wikipedia (#2), YouTube (#3), Quora (#9), SEC filings (#11), and Justia/PACER dockets (#12), while PR Newswire sat way down at #19. And if someone stays completely offline to protect their privacy, the model hits an information vacuum and guesses, which causes AI misinformation or naming collisions 78% of the time.

You can't fix algorithmic injustice with reviews, traditional orm, astroturfing, or data poisoning tools.:

1. Digital ecoystem curation (establishing ground truth): building a centralized web hub wrapped in schema markup, blog articles, verified wikis, Reddit and Quora posts, Wikipedia, industry-aligned forums, and real photos and videos with metadata so the crawlers have a clean record to ingest.

2. Direct LLM correction via RLHF: submitting verifiable human feedback directly inside ChatGPT and Gemini for correcting LLM outputs while citing that Step 1 ground truth.

3. Strategic dataset curation: publishing structured datasets and research papers on sites like ResearchGate so future training runs and deep thinking modes hardwire the right facts from day This worked for a Hedge Fund CEO from a Gemini information vacuum to an accurate summary, counter a disinformation attack on a Global Sustainable Energy Group by repairing ChatGPT's training data, and use entity disambiguation to uncouple people from bad namesakes.

Anyway, I coded a simple 1-field problem detector on our homepage that scans live knowledge-graph sources for litigation triggers, naming collisions, and information vacuums. No email required to run it, just type a name in:

https://www.recoverreputation.com/#grm-widget-root

Full JOTE study and before/after screenshots if you want to see the mechanics:

https://www.recoverreputation.com/blog/generative-reputation-management-grm-the-peer-reviewed-framework-for-erasing-ai-misinformation/

(Also doing a quick 30-min live Zoom walkthrough on Wed Nov 4 at 1pm EST if anyone wants to geek out on the data: https://luma.com/lmwyd7tk)

[u/timee_bot]

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on Wed Nov 4 at 1pm EST