How to Fix AI Getting Your Brand Wrong (Stale Pricing, Wrong Features)

Because LLMs don't have a database of facts about you — they generate answers by predicting likely text from training data and, when browsing is on, from whatever pages they retrieve in that moment. Neither path guarantees your current truth.

Three root causes do most of the damage:

• Stale training data. A base model has a knowledge cutoff. Claude's training data, for example, runs to a fixed cutoff date, so anything you changed after that simply doesn't exist to the un-browsing model. It will happily quote last year's pricing.
• Bad retrieval, repeated loudly. When a model browses, it pulls from a handful of sources. If an outdated review, a competitor's comparison page, or an old press release ranks well, that becomes 'truth.' One Columbia Tow Center study found generative search tools gave incorrect answers more than 60% of the time when asked about news content.
• Hallucination, plain and simple. Even good models invent specifics. Reported hallucination rates cluster around 10–20% for leading models and spike far higher on open-ended questions — exactly the kind people ask about brands.

The upshot: you're not arguing with a mind. You're editing a corpus.

It depends on whether the platform retrieves live or relies on baked-in memory. Retrieval-based engines update in weeks; base-model knowledge updates on training cycles you don't control — months, sometimes a major version.

Split your targets into two buckets and stop expecting them to behave alike:

• Live-retrieval (fast lane): Perplexity crawls the web continuously; Google AI Overviews and ChatGPT's web-search mode pull from a live index. Correct the underlying source and these can reflect it in roughly two to eight weeks.
• Baked-in memory (slow lane): Ask ChatGPT or Claude with browsing off and you're querying frozen training data. Your correction only lands when that data is re-scraped and the model is retrained.

This is why a single 'it's still wrong' test in one chatbot tells you almost nothing. You have to check each engine, in its retrieval mode, and track over time. (AEOeye's free audit runs the same brand prompts across ChatGPT, Perplexity, Google AI, Claude and Gemini at once, so you can see which engine is wrong and which source it's leaning on — that's the part that turns guessing into fixing.)

On a page you control, stated plainly, dated, and machine-readable — then mirrored on the third-party sources the models trust most. A correction buried in a blog post nobody links to won't move anything.

Priority order, highest leverage first:

1. Your own canonical page (pricing page, product page, or a dedicated facts/FAQ page). This is the source you fully control.
2. Wikipedia and Wikidata. Wikidata feeds Google's Knowledge Graph and is queried directly by Copilot, Alexa, Siri and other AI systems to resolve brand identity. Wikidata has no notability bar, so most brands can create an entry today. Wikipedia accounts for 7.8% of all citations in ChatGPT's browsing mode — disproportionate influence for one site.
3. High-authority third-party listings — your Crunchbase, G2, LinkedIn, app-store listing, and the review sites that rank for your name. Models cross-reference these.
4. Recent, well-distributed content that repeats the correct fact in plain language, so retrieval has fresh, consistent signal to grab.

Consistency across these is the whole game. When every source agrees, the model answers with confidence. When they conflict, it guesses — and guesses are where wrong pricing comes from.

Add structured data — specifically Organization, Product, and Offer schema — so AI crawlers read your facts as machine-readable data instead of inferring them from prose. This is the closest thing to handing the model a fact sheet.

For wrong pricing, the fix is concrete: put explicit Product and Offer schema on your pricing page with the current price, priceCurrency, and priceValidUntil. For wrong identity facts (founding date, CEO, what you actually do), use Organization schema with name, foundingDate, founder, sameAs (linking to your Wikidata, LinkedIn, Crunchbase), and description.

Why it works: schema removes ambiguity. The model doesn't have to parse a marketing sentence to guess your price — it reads a field. Pair that with an FAQPage schema block that asks and answers the exact wrong question ('How much does X cost?') with the right answer in the first sentence. Validate everything with Google's Rich Results Test and reference the schema.org Organization spec for valid properties. This is table stakes for AEO/GEO, not an advanced tactic.

Lead with the answer, name the wrong claim explicitly, and date it. AI retrieval rewards pages that state the fact in the first 50 words and match the phrasing of the question being asked.

Do this:

• Front-load the fact. First sentence: 'As of June 2026, [Product] Pro costs $49/month.' Don't make the crawler hunt.
• Name and bury the old claim. Add a line like 'Some older sources list $29/month — that pricing ended in 2025.' This gives the model a disambiguation signal so it can resolve the conflict in your favor instead of averaging the two.
• Match real questions. Use the exact phrasing people (and AI) use: 'Does [Brand] still offer [dead feature]?' → 'No. [Feature] was discontinued in March 2026. Here's the current alternative…'
• Date the page visibly and update the dateModified in your schema. Freshness is a ranking signal in the fast lane.

Keep it plain. The flowery brand-voice paragraph is for humans; the crawler wants the unambiguous statement. Write both, but make sure the unambiguous one comes first.

Check all five major engines — ChatGPT, Perplexity, Google AI, Claude, Gemini — separately, because they cite wildly different sources and a fix in one rarely means a fix in all. Re-check every two to four weeks after you ship a correction.

The fragmentation is real: only about 11% of domains are cited by both ChatGPT and Perplexity, and Perplexity averages 7.42 citations per response versus ChatGPT's 3.86. So the source you need to fix for Perplexity may be invisible to ChatGPT entirely.

Practical monitoring routine:

• Ask each engine the same 3–5 brand questions (pricing, features, 'what is [Brand]', 'is [Brand] legit').
• Note the wrong answer and the source it cited — the citation tells you exactly which page to fix next.
• Run it again after each fix lands, on a calendar reminder.

Doing this by hand across five tools is tedious and easy to drop. A free AEOeye audit automates the sweep and surfaces the cited source per engine, which is the data point that actually directs your next move.

Go to the source directly: request a correction or removal, and if that fails, drown it out with stronger, fresher, more authoritative signals. You can't edit a competitor's comparison page, but you can outrank and out-cite it.

Your options, in order:

• Wikipedia/Wikidata: edit it yourself following their sourcing rules, or flag the error on the article's Talk page with a citation. This propagates widely because so many systems read it.
• Review/listing sites (G2, Crunchbase, app stores): most have a 'claim this profile' or 'suggest an edit' flow. Claim it and fix the facts.
• Press/blog errors: email the author or outlet with the correction and a link to your canonical source. Many will update.
• Can't get it changed? Publish more authoritative, more recent content with the right fact and earn links to it. When the weight of fresh, consistent sources tips the model's way, the stale outlier stops winning retrieval. This is slower but it's how you win the long game when you don't own the page.