How Do I Correct Wrong Company History in AI Answers?
Wrong company history in AI answers — a misnamed founder, an incorrect founding year, a merged-away parent company — almost always traces to conflicting entity records across the web. Fix the canonical fact on your own About page, correct the structured databases engines trust (Wikidata, Wikipedia, Crunchbase), align third-party listings, then re-test the prompts. AI reflects the consensus of its sources; change the consensus.
Why AI engines get company facts wrong
Large language models don't store a single verified fact about your company. They reconstruct it from patterns across training data and, for grounded answers, from retrieved pages. When an outdated 2018 press release says one founder and your current site says another, the model resolves the conflict statistically — often favoring the version that appears in more places or in higher-authority sources like Wikipedia. The GEO research by Aggarwal et al. (2024) showed that citations and authoritative sourcing drive what generative engines surface; the same mechanics decide which "fact" wins.
The correction protocol
Work from the most authoritative record outward, because engines weight structured, cross-referenced sources most heavily.
| Step | Source | Why it matters |
|---|---|---|
| 1 | Your About page | Add a plain-language founding statement and Organization schema with foundingDate and founder |
| 2 | Wikidata | Structured, machine-readable; feeds Google's Knowledge Graph and many models |
| 3 | Wikipedia | High-trust; cite a reliable secondary source for every change |
| 4 | Crunchbase, LinkedIn, Bloomberg | Correct the same field everywhere — engines detect consensus |
| 5 | Historical press coverage | Request corrections or add a dated clarification on your newsroom |
Consistency is the signal. If every high-authority record agrees, the model has no competing version to hallucinate.
Make your About page canonical
Publish an unambiguous, quotable sentence: "Acme was founded in 2019 by Jane Doe and John Roe." Wrap it in schema.org/Organization markup exposing foundingDate, founder, and sameAs links to your Wikidata, LinkedIn, and Crunchbase profiles. Those sameAs links tell engines these records describe the same entity, so corrections propagate as one coherent identity rather than as disconnected mentions.
Re-test and monitor
Run the exact prompts that produced the error ("Who founded Acme?", "When was Acme founded?") across ChatGPT, Perplexity, Gemini, and Claude weekly. Retrieval-grounded engines correct fastest once your pages are re-crawled; facts memorized in model weights only shift at the next training cut. Continuous citation tracking tells you which engines still repeat the old version and which source they're pulling it from, so you know where to push next.
Frequently asked questions
- Why does AI cite the wrong founding year?
- Models blend multiple sources — an old press release, a Crunchbase field, and a Wikipedia edit may disagree. The engine surfaces whichever record appears most often or most authoritatively, not the truth.
- How long until corrections show up?
- Retrieval-based answers (ChatGPT Search, Perplexity, Gemini grounding) can update within days of a re-crawl. Facts baked into a model's training data only change at the next model release, which can be months.
Keep exploring
See how AI engines talk about your brand — track mentions across ChatGPT, Perplexity, Claude, Gemini and 5 more. Start with Menra