If ChatGPT, Perplexity, or Gemini is recommending your competitors when it should be recommending you — or worse, saying something wrong about your brand — you have a problem that traditional SEO does not solve. You have an entity problem. And entity problems compound: every week the wrong information stays in the model’s training corpus and live retrieval index is a week your prospects are being given inaccurate answers about you by the system they trust most.

This piece is the diagnostic and the repair sequence. Read it once, then run it.

How do I know if AI is saying something wrong about my brand?

Open ChatGPT, Perplexity, and Gemini in three tabs. Type these five prompts, swapping in your brand name and category:

1. Tell me about [Brand Name].
2. What does [Brand Name] do?
3. Who founded [Brand Name] and when?
4. Where is [Brand Name] based?
5. What is [Brand Name] best known for?

Copy every answer into a single document. Now mark each fact as correct, stale, conflated (mixed up with a different brand), or fabricated. If more than one fact across the three models lands in the last three buckets, you have a measurable inaccuracy problem.

The most common findings, in order of frequency:

• Stale facts. Outdated headcount, old funding amounts, a former CEO listed as current, a discontinued product described as flagship.
• Conflation. AI confuses your brand with a similarly-named company in an adjacent space. Often the other company has a stronger Wikipedia presence.
• Fabrication. The model invents a fact that is not in any source. Usually a “founded year” or “headquarters city” filled in confidently from the wrong reference.
• Wrong recommendation. When asked “best [category] tool”, the model lists three competitors and omits you — even though your category fit is closer.

What causes AI to get it wrong?

AI brand inaccuracies have three root causes. Every fix sequence has to identify which one is in play before any work begins.

1. Outdated content on your own site

The model crawled your site months ago and indexed an old “About” page, a stale press kit, or a homepage that has since been rewritten. Your current content is right; the indexed version is not. This is the easiest cause to fix.

2. Conflicting entity signals across sources

Your LinkedIn says one thing, your Crunchbase profile says another, a directory listing says a third, your homepage says a fourth. The model picked the most “authoritative” of those (often Wikipedia or Wikidata) and ran with it — even if that source is wrong. This is the most common cause and the most underdiagnosed.

3. Third-party sources citing inaccurate information

A press article from 2022 cited an outdated stat and got picked up. A directory scraped your old data and republished it. A Reddit thread mentioned you wrongly and the model treats it as a signal. This is the hardest cause to fix because you do not own the source.

The fix sequence — in order of impact

Do not skip steps. Each one is sequenced because the next step depends on the previous one being clean.

Step 1 — Audit all entity references for consistency

Pull your brand’s data from every public source AI models weight heavily:

• Your own site: footer, About page, contact page, schema markup
• Wikipedia (if present)
• Wikidata (the structured layer behind Wikipedia)
• LinkedIn company page
• Crunchbase
• Google Business Profile
• Industry directories (top 5 in your category)
• Press kit and recent press releases

Put every fact into a spreadsheet: founded year, headquarters, headcount band, funding total, founder names, current CEO, primary product, primary market. The columns where the values disagree are exactly where AI will hallucinate from. Mark every disagreement. This audit alone is 80% of the fix work.

Step 2 — Update your own content to be unambiguous and current

On your site, deploy:

• Organization schema in JSON-LD on every page, with sameAs references pointing to the canonical sources you control: Wikipedia (if present), LinkedIn, Crunchbase, Wikidata. The sameAs array is what tells AI models “these references all describe the same entity.”
• A clean, dated About page with founded year, current headquarters, current leadership, and current product description. Use ISO 8601 dates. State facts in declarative sentences AI can extract.
• A lastReviewed date in your Article schema for any content older than 12 months that contains facts AI is citing wrongly.

Citable deploys this layer in week one of every engagement. It is the floor.

Step 3 — Add explicit correction schema where applicable

For brands with structured corrections to publish (a rebrand, an acquisition, a CEO change, a HQ move), Corrections and OnlineBusiness schema with explicit dissolutionDate and successor properties give AI models a clean signal to update. This is rarely used but extremely effective when the situation calls for it.

Step 4 — Pursue third-party source corrections

This is the slowest step. Three sub-tactics:

1. Wikipedia and Wikidata. If your brand has an entry, add it to your watchlist. Update outdated facts with cited sources. If your brand has no entry but is notable enough, get one written — but do not author it yourself; that is a conflict-of-interest violation.
2. Press articles with wrong facts. Email the publication’s corrections desk. Most major outlets will issue corrections for factual errors with a 24–72 hour turnaround. Smaller publications may not respond.
3. Directory listings. Most B2B directories (G2, Capterra, Crunchbase) have explicit “claim this profile” workflows. Claim, verify, and overwrite.

For aggregator and scraper sites that republish wrong data, removal requests are slow and sometimes impossible. Focus your energy on the upstream sources first; downstream copies tend to follow.

How long does it take to actually change AI answers?

Honest answer: 30 to 90 days, depending on the model.

• Perplexity refreshes its retrieval index frequently. Changes to your own site and to high-authority sources show up within 2 to 6 weeks.
• ChatGPT’s web browsing component picks up live updates quickly, but the underlying model’s parametric knowledge only updates with each new model release. Some inaccuracies will persist until the next training cut-off.
• Google AI Overviews follow Google’s index. If your fix appears in the regular search results, the AI Overview will start reflecting it — typically within 4 to 8 weeks.
• Gemini behaves similarly to AI Overviews on retrieval, with model parametric updates on a slower cycle.

The fastest way to know if your fix is working is to re-run the original five prompts every two weeks and log the deltas. The Citable AI Visibility Audit does this monthly and reports the trajectory.

What this is worth

Inaccurate AI brand mentions are not a vanity problem. Every prospect who asks ChatGPT about your category and gets the wrong answer is a deal you do not know you lost. Conservatively, in a B2B category where 30% of buyers consult AI before a discovery call, a 12-week fix sequence on entity inaccuracies pays for itself on the first deal that closes correctly because the AI gave the right answer.

If you have already opened ChatGPT, Perplexity, and Gemini and seen something wrong, you do not need more diagnosis. You need the fix sequence. Get an AI Visibility Audit — we run the full prompt set, document every inaccuracy, map the source causing it, and give you a 90-day repair plan. 1,200 EUR. Five business days.