Consistent Brand Facts: Fixing Contradictions on Your Own Site

AI answers come from two places. One is what the model learned in training, frozen at a cutoff date. The other is what it retrieves from the web when it answers, and retrieval fires at the engine's discretion, query by query.

Both paths can pick up a contradiction you created. Say you run a hypothetical project management tool. Your pricing page says the Team plan is $29 per user. Your footer still says "plans from $25". A comparison page from last year says $24. An engine that retrieves two of those pages now holds two different prices from the same source, and nothing tells it which one is current.

The training path is worse. If the old price sat on your site when the model's snapshot was taken, the model may repeat it with no web access at all, and no edit you make today reaches that answer until a later model ships.

Be clear about the strength of this claim. Contradictions on your own site are a plausible mechanism behind complaints like "ChatGPT gets our pricing wrong". Nobody has published a measured effect of fixing them. What makes the fix worth doing anyway is that it is cheap, fully in your control, and removes a cause you can actually see. For a wider look at the question, see whether mistakes on your own site cause AI errors, and for how engines behave when sources disagree, read what AI does with conflicting information.

Sort the work by who owns each stage before you start.

In your control

• What your pages say. Every value on every page you publish, including footers, PDFs, old landing pages and structured data.
• Whether crawlers can read it. Server-side rendering, per-bot robots.txt rules, and whether your CDN or firewall lets AI crawlers through.
• Whether you suppress it by accident. noindex, nosnippet, max-snippet:0 and data-nosnippet keep content out of AI answers on Google and Bing.
• How specific your statements are. A plain sentence with the number in it, on a page that can be fetched.

Not in your control

• The training snapshot. What the model learned before its cutoff stays until the vendor ships a new model.
• Whether retrieval happens. Engines decide per query whether to search at all.
• The queries engines actually run. OpenAI says ChatGPT search typically rewrites a prompt into one or more targeted queries, so you can't see the real input.
• Which retrieved pages get used. Position in the model's context matters, and no publisher can set it.

Partly in your control

Third-party pages (directories, review sites, old press coverage) carry your facts too. You can't edit them, but you can ask. When engines cite other sites instead of yours, being named correctly on those sites is the route into the answer. The evidence shows an association there, not a promised effect. The playbook for asking sites to fix wrong facts covers that outreach.

An inventory is a spreadsheet with one row per fact and one column per place that fact appears. The goal is to see every value side by side, so a contradiction is obvious at a glance. Start with the definition of brand facts if your team hasn't agreed on what counts.

1. List the facts buyers ask about. Price per plan, plan limits, free tier or trial terms, founding year, headquarters, founders or owner, product names, integrations, certifications, supported countries.
2. List every surface. Pricing page, About page, homepage, footer, product pages, help center, comparison pages, press kit, PDFs, structured data blocks, social bios, and each language version of the site.
3. Record the exact wording. Copy the sentence, not your memory of it. "From $25" and "$29 per user" look different in a cell, and that's the point.
4. Mark the source of truth. For each fact, name the owner (finance for prices, legal for ownership) and the canonical value.
5. Flag every mismatch. Any cell that differs from the canonical value is a fix. Any cell that is vague when it could be specific is a candidate fix.
6. Find the pages you forgot. Search your own site for old prices, old product names and old plan names. Retired landing pages that still return a 200 status are the usual culprits.

Where contradictions usually hide

Translated pages deserve their own pass. The guide to language versions and conflicting facts covers that case. If you'd rather start with a quick scan than a spreadsheet, the free check for contradicting facts on your own site flags candidates to review.

Once the inventory shows a mismatch, fix it in two moves: settle the value, then say it plainly.

Settling the value is a business decision, not a writing one. Get the owner to confirm the current price, the correct founding year, the legal entity name. Write it into a single internal reference, such as a brand fact sheet, so the next page update copies from one place.

Then state the fact as a specific sentence on a page that can be fetched. "The Team plan costs $29 per user per month, billed annually" is a sentence an engine can quote. "Flexible pricing for teams of every size" gives it nothing. Stating a specific fact (a price, a number, a one-line definition) is the only on-page practice with a positive experimental signal. The 252,000-trial factorial study "What Gets Cited" (arXiv:2605.25517) found that including explicit price information helped consistently.

Keep the claim in proportion. That study ran on a synthetic corpus with pages already placed in the model's context. It tells you a clear fact is usable once retrieved. It does not tell you a clear fact gets your page retrieved.

Three rules keep the fix clean:

• Same wording everywhere. If the pricing page says "$29 per user per month", the footer should not paraphrase it as "under $30".
• Fewer places, not more. A footer that links to pricing is safer than a footer that repeats a price you'll forget to update.
• Structured data must match the copy. If you already have markup, check it carries the same values. This is about removing a contradiction. Markup is not a lever for getting cited, and Ahrefs' matched test found AI Overview citations fell 4.6% after pages added schema.

A corrected fact only counts if the crawler that matters can read it. Three checks cover most failures.

Rendering

Vercel's crawler study found that ChatGPT and Claude crawlers fetch JavaScript files but don't execute them, so they can't read client-side rendered content. Independent re-testing as of June 2026 found the same for the main AI crawlers, with Googlebot and AppleBot as the exceptions. If your price loads from an API after the page renders, many AI crawlers may never see it. Fetch the page with JavaScript disabled and confirm the fact is in the raw HTML.

Crawler access

robots.txt rules are per bot. Blocking GPTBot stops training use. OAI-SearchBot controls ChatGPT search, and OpenAI says each setting is independent. A CDN or firewall can also turn away a crawler that robots.txt allows. The 99Visibility AI Crawler Checker requests a page as each AI crawler and shows which ones get blocked.

Accidental suppression

Google documents noindex, nosnippet and max-snippet as controls over how content shows in its results, including AI features. Bing says content marked data-nosnippet stays indexed but is excluded from snippets and AI summaries. A template that wraps your pricing table in data-nosnippet to hide a cookie banner hides the price too.

Work from the facts buyers ask about most, and from the gates that block everything else.

1. Check access first. Confirm your pricing and About pages return the facts in raw HTML and that retrieval crawlers are allowed in robots.txt and not blocked at the firewall. A perfect fact behind a 403 does nothing.
2. Inventory pricing. It changes most often and costs most when wrong. The playbook for fixing AI pricing errors goes deeper.
3. Inventory identity facts. Founding year, HQ, ownership, legal name, founders.
4. Fix the canonical pages. Pricing and About pages get the specific sentence for each fact.
5. Clean the secondary surfaces. Footer, help center, PDFs, structured data, translated pages.
6. Retire stale pages. Redirect or update old campaign and comparison pages that still carry outdated values.
7. Then look outward. Note which third-party sites repeat the old value and ask them to correct it.
8. Re-check what AI says. Ask the engines the buyer questions again. The free checker on this site shows a snapshot of how engines answer up to three of those questions. Remember that retrieval-off answers won't change until a newer model ships.

• Fixing the pricing page and stopping. The contradiction usually lives on the page you didn't touch: the footer, an old PDF, a comparison page.
• Creating an AI-only fact page. A page written for crawlers that says something different from what visitors see is cloaking. Hidden text and instructions aimed at AI crawlers are adversarial content. Research on search agents shows manipulated content can change what they endorse, which is exactly why engines and platforms treat it as abuse. Don't do it.
• Bumping dates to look fresh. Changing dateModified without changing the fact proves nothing and no engine documents rewarding it. Update the date when the content changes.
• Adding markup and calling it fixed. Schema, FAQ schema and llms.txt are not levers for AI answers. Google says structured data isn't required for its generative AI features. Fix the copy.
• Vague replacements. Swapping a wrong number for "competitive pricing" removes the error and the quotable fact at the same time.
• Expecting an instant change. Retrieval answers can update once engines recrawl. Training-based answers can lag by a full model generation. Nothing you do guarantees when, or whether, a given answer changes.

Which brand facts should I check first? Start with pricing and plan limits, because they change most often and buyers ask about them directly. Next, check identity facts: founding year, headquarters, ownership, legal name and founders. Then product names and integrations. Prioritise any fact you've changed in the last two years, since that's where an old value is most likely still sitting on a forgotten page.

How long until AI answers reflect a corrected fact? There is no published timeline. Answers built from live retrieval can change once the engine recrawls your page, which depends on the engine. Answers built from training data stay frozen at the model's knowledge cutoff until the vendor ships a new model. Because engines decide per query whether to search, you may see the old and new value for a while.

Does adding structured data fix brand fact errors? Not on its own. Google says structured data isn't required for its generative AI features, and Ahrefs' matched test found AI Overview citations fell 4.6% after pages added schema. If you already have markup, make sure its values match your page copy, so it doesn't become another contradiction. Existing markup does no harm, but it is not a lever for getting cited.

What if a third-party site has my facts wrong? Correct your own site first, so there is one clear, fetchable source stating the right value. Then contact the third-party site and ask for a correction, pointing to the page where the fact is stated. When engines cite other sites instead of yours, being named correctly there is associated with appearing in the answer, though no one can promise the effect.

Should I repeat key facts on many pages so AI sees them? No. Repetition multiplies the places a fact can go stale. State each fact fully on its canonical page, such as pricing or About, and link to it from elsewhere. Where a fact must appear twice, use identical wording. Fewer, consistent statements are easier to keep correct than many paraphrased ones.