The source of truth isn't the source you control
Ask ChatGPT or Perplexity when your company was founded, how much you've raised, or what you actually sell, and the answer often traces back to a place your marketing team never touches. Not your homepage. Not your beautifully rewritten About page. Wikipedia and Wikidata.
These reference sites are structured, machine-readable, and heavily weighted in the data that shaped many foundational models. A single Wikidata property, one clean field that says founded: 2014, gets ingested with a confidence your marketing copy will never earn. Your site can say 2013 in ten places. The model still repeats 2014, because it learned that fact from a source it treats as canonical.
Here's the uncomfortable part. Most companies have never audited their own Wikipedia or Wikidata entry against reality. The founding date is off by a year. The funding figure stopped at Series B two rounds ago. A whole product line launched last year and the entry doesn't mention it. That error sits there, lightly watched, quietly outvoting everything you publish.
One field, six wrong answers
6 of 7
In this illustrative dashboard, six of seven assistants repeated a Series B funding figure pulled from Wikidata, even though the company closed a Series C and updated its own site months earlier. In this same illustrative dashboard, the marketing site scored 100% accurate. It didn't matter, because nothing was pointing the models back to it.
Why marketing teams miss it, and where Crescive fits
The reason this rots for years is ownership. Nobody's job description includes checking a wiki. Comms owns the press page. Web owns the CMS. Nobody owns the structured facts sitting on third-party reference sites, so they drift while everyone assumes the website is the last word. It isn't.
Crescive treats high-authority reference sources like Wikipedia and Wikidata as first-class citation inputs, not an afterthought. When an assistant repeats a stale founding date or an old funding number, Crescive traces the answer back to the source feeding it and flags whether a reference site, not your own copy, is the culprit. Then it routes the fix through the same evidence-first process as any other citation gap: here's the wrong fact, here's the authoritative correction, here's the source that proves it, under Crescive's automated criteria, approve before anything ships.
This matters because you don't fix Wikipedia by editing it like a landing page. Bad edits get reverted, and thin sourcing gets challenged. You need the correction, the citation that supports it, and a human sign-off. That's exactly the workflow, so the fix survives and the models eventually relearn the right answer.
How to audit and fix your reference-source facts
- Ask three assistants the boring factual questions: founding year, total funding, headquarters, and current product lineup. Note every answer, right or wrong.
- For each wrong answer, find the source. Search your Wikidata entity and Wikipedia article and compare each field against reality. The stale fact is usually sitting there in plain structured form.
- Gather the proof before you edit anything. A press release, an SEC or Companies House filing, a funding announcement, or a reputable news article that states the correct fact with a date.
- Update Wikidata first for structured properties like founding date and headquarters, citing your source in the reference field. Machines read this directly.
- Correct the Wikipedia article with the same citations, following its sourcing rules so the edit isn't reverted. Add the missing product line if it's notable and independently covered.
- Re-run the same assistant questions on a schedule. Model knowledge lags, so track when the corrected fact starts appearing instead of assuming the edit was instant.
- Assign an owner. If no single person is accountable for these facts, they'll drift again within a year.
Key takeaways
- For foundational company facts, AI assistants often weight structured reference sources like Wikidata above your own website, no matter how current your copy is.
- A stale founding date or outdated funding figure on Wikipedia can outlast years of correct marketing copy because nobody on the team is assigned to check it.
- Fixing it means tracing wrong answers back to their source, correcting the reference site with real citations, and confirming the model relearns the fact over time.
FAQ
Why do AI assistants trust Wikipedia over my company website?
Wikipedia and Wikidata are structured, machine-readable, and heavily represented in the training data behind many models. Their facts are treated as canonical, so a founding date or funding figure listed there is often weighted more heavily than the same fact on your marketing site. If those entries are outdated, assistants can repeat the wrong information even when your website is completely accurate.
How does Crescive fix stale facts on reference sites?
Crescive tracks whether high-authority reference sources like Wikipedia and Wikidata are feeding outdated facts into AI answers about your brand. When it finds a stale fact driving wrong answers, it traces the answer to its source and routes a correction through an evidence-first process: the incorrect fact, the authoritative citation that supports the fix, and a human approval gate before anything is published.