ChatGPT Is Wrong About Your Company: How to Fix It

You cannot edit what ChatGPT says about your company, because no correction path reaches a model's weights. What you can change are the three inputs a wrong answer is assembled from: the pages an engine fetches while it answers, the third-party sources it trusts about you, and the entity record that tells it which company you are. Correcting an AI answer is an evidence operation with a lag measured in weeks, not a support ticket with a resolution time. This piece is the protocol, including the parts that do not work.

The short version, before the mechanics: find which input carries the error, fix what you own inside a week, open the earned work on what you do not own, then measure the answer across many runs. The cost of leaving it alone is a shortlist you never see forming. G2 found AI chatbots are now the single largest influence on B2B shortlists, 51% of software buyers now begin research inside an AI chatbot, and one-third of buyers purchased from a vendor they had never heard of before the chatbot named it.

Why does ChatGPT repeat something about your company that stopped being true?

Because the answer is assembled from inputs that age at different speeds, and only one of them is yours. OpenAI describes 3 primary source classes behind its foundation models, and everything learned that way is frozen until the next training run. For current questions the engine adds live retrieval: ChatGPT search runs one or more targeted queries and reads what comes back.

Retrieval does not read the whole web. Seer's analysis of 500 citations found 87% of SearchGPT citations matched Bing's top results, and an AirOps study of 548,534 pages mapped which page traits correlate with being pulled into an answer. A correction on a page nothing links to and nothing ranks for is one the engine will usually miss.

The third input is other people's writing about you. Profound found 57% of AI citations point to sources brands do not control, so most of the material behind an answer about your company was written by someone with no obligation to keep it current. Old pricing, a retired product name, a founder who left: it stays live until the source changes.

Which kind of wrong is it: stale, sourced, inferred, or confused?

Name the mode before you spend anything: the four modes need different work, and three of them ignore your blog. The IAB's AI-era measurement guidance separates Presence, Prominence, Portrayal, and Persuasion, and a wrong fact is a Portrayal problem: you are visible, and what is being said is inaccurate.

1. Stale: the fact changed and the record did not

You raised prices, retired a plan, or shipped a feature, and the pages carrying the old version are still the most retrievable thing about it. This is the cheapest mode to fix. If your own site still carries the superseded claim anywhere, start there before blaming a model.

2. Sourced: a third party published it wrong

A directory listing, a review profile, or a years-old news story carries the error, and the engine is reporting its source faithfully. Google's guidance for review content lists 14 recommendations for what makes third-party coverage substantive, which doubles as a checklist when you ask a publisher to update your profile.

3. Inferred: the model asserts an attribute you never claimed

Engines appear to decompose a buyer question into attribute-specific retrievals, and category entities get matched to attributes they sit near. A recognized entity gets pulled into answers about compliance or pricing tiers it has never written a line about. A 2026 analysis of brand dynamics in LLM recommendation systems shows how sticky and unevenly distributed those associations are. Nothing on your site grounds the claim, so the model fills the space with inference.

4. Confused: the model has you mixed up with someone else

A shared name, three variants of your legal entity, or a different self-description on every page leaves the engine with an ambiguous string where an entity should be. Entity-oriented retrieval research across 443 configurations shows how much retrieval quality depends on that resolution. Correcting a fact will not help until the engine agrees whose fact it is.

What can you actually change, and what is out of reach?

Draw the line honestly, because half the correction advice in circulation quietly promises the impossible half. You own your pages. You can ask the sources that carry the error to update it, and the publisher decides. You have no access at all to what a model absorbed in training.

The first row has a prerequisite most teams skip. Most AI crawlers do not execute JavaScript: crawler research Vercel ran with MERJ documented that these agents read raw HTML, and Cloudflare's agent-readiness work across the 200,000 most visited domains found large parts of the web effectively illegible to them. A correction rendered client-side is one most of these agents never read. OpenAI documents four relevant user agents, and Cloudflare measured one AI crawler's share of requests climbing from 2.2% to 7.7%, so the fetch is happening whether or not your page answers it.

How do you run the correction protocol?

Six steps, in order. Proving the error repeats comes before spending a dollar fixing it.

1. Establish that the error is a pattern. Run the question that produces it at least ten times, across days and two engines, recording the answer and every source cited. The SparkToro research found AI engines are highly inconsistent when naming brands, Search Engine Land's write-up of a separate study found recommendation lists rarely repeat exactly, and a 2026 variance-components study found run-to-run noise large enough to swamp real differences in small samples.

2. Trace the claim to its source. Open every citation in the answers you captured. The error resolves to a page you own, a page someone else owns, or no cited page at all, and that fork decides everything downstream.

3. Purge the superseded claim from your own property first. Old landing pages, gated PDFs, and press releases outrank your correction more often than teams expect.

4. Publish a correction that is retrievable and dated: server-rendered, linked from a page with traffic, stating the current fact in plain language near the top.

5. Request the third-party fix in writing, with proof attached, starting with sources that actually appeared in your captured citations.

6. Re-measure on a schedule. Ronald Sielinski's "Quantifying Uncertainty in AI Visibility" makes the point with confidence intervals: a single post-fix observation cannot tell you whether anything changed.

One corrective, because this is where correction budgets die: rewriting pages into AI-friendly phrasing does little on its own. C-SEO Bench (NeurIPS 2025), which tested conversational-SEO methods across two tasks and six domains, found only 3 of 54 unilateral conditions produced statistically significant citation-rank gains. Some persuasion language moves the number the wrong way: scarcity and exclusivity framing measurably reduces how often a model recommends a product. And serving corrected text to bots while showing people something else is cloaking under Google's spam policy.

How do you write a correction page an engine will actually use?

State the corrected fact in one self-contained sentence near the top, name the claim it supersedes, date it, and attach the proof. Engines elevate extractable evidence: the GEO study (Aggarwal et al., KDD 2024) found that adding statistics, quotations, and citations lifted citation visibility by roughly 30 to 40% in its benchmark, while its own error bars showed keyword stuffing did nothing.

Write the sentence so it survives being lifted out of the page, because that is how it will travel. A line naming the current price and the month it took effect beats a paragraph explaining that pricing has evolved. Hedged phrasing gives a model nothing to prefer over the stale source.

Substantiate the claim. The FTC's advertising guidance expects a claim to be backed before it is made, and that discipline is also what makes a claim usable to a retrieval system. The W3C provenance vocabulary models any record as Entity, Activity, and Agent, a useful template for a correction page: name what changed and when, then name who says so.

How long does a correction take, and how will you know it worked?

Expect weeks for the inputs you own and months for the ones you do not, with no guarantee at any point. Retrieval-fed answers can shift as soon as an engine re-crawls a changed page, publisher corrections move at the publisher's pace, and training associations move only when a lab ships a new model.

Knowing it worked is a measurement question. The pre-fix base rate you recorded in step one is the only thing a post-fix run can be compared against. Semrush's AI Visibility Index, built on 126 million AI search prompts, sets the scale: five fresh answers are an anecdote.

Decide in advance what counts as success. The wrong claim disappearing from eight runs in ten is a win, and its survival in the other two is expected.

How does Trovance help you correct what AI says about your company?

Trovance turns the one-off capture in step one into a standing record. You define the tracked questions your buyers ask, including the ones that surface the wrong claim, and the system observes them repeatedly across engines, preserving each answer run with the citations that produced it. An answer snapshot keeps what was said, when, and which sources carried it, so the disputed sentence never has to be hunted down by hand again.

That record is what makes the four modes distinguishable. When the cited sources are all yours, the error is on your property and the fix is on your desk. When the answer is built from third-party pages that never mention you, it is a sourcing problem. When answers describe a different company, or attach attributes no cited page supports, you can diagnose entity confusion or inference, and answer coverage across your tracked questions shows how far the misstatement has spread.

Correcting the record then becomes work with a queue. Your Brand Core holds the claims you are entitled to make and the proof behind each one, so the corrected fact has a dated canonical home. Each recommended action names the asset the evidence points at: the correction page, the benchmark that proves the number, the comparison page a displaced source will never write for you. Drafts are produced from approved claims, and a person approves everything before it publishes.

What Trovance will not promise is that a model stops repeating the wrong fact. Nothing reaches the training weights, no vendor controls what an engine says, and no single visibility score certifies that a correction landed. Trovance is building toward tighter attribution between a published correction and the answers that follow, and stays explicit about that gap. What it does today is verify: the next analysis cycle reruns the same questions and compares new snapshots against the ones taken before you shipped, so you learn whether the answer moved.

What should you do this week?

Pick the single wrong claim that costs you most in an evaluation and run only that one to ground. Capture ten answers across two engines, save every cited source, and read the citations before writing anything. If the error traces to your own pages, you can be finished in days. If it traces to a publisher, send the correction request this week and accept a longer clock.

Fix the mechanics while you wait, because they gate everything downstream: confirm your key pages render server-side and that the corrected fact appears as a plain sentence a model can lift. One name and one description should describe you everywhere. Profound's 7.5 million-conversation sample shows how much commercial conversation now happens inside chat, and OpenAI reporting 800 million users as of April 2025 is why a stale sentence is expensive.

Be skeptical of anyone selling a guaranteed correction. The measurement variance alone forbids the promise. If you want the capture, the citation trail, and the re-measurement running without an afternoon of manual prompting, start a free Trovance analysis on the questions that surface the wrong claim.

See what the engines are saying

• The 10-minute audit of what ChatGPT tells buyers about you: capture the wrong claim with its citations.

• Where ChatGPT gets information about your business: how the sentence got written.

• How to track AI citations: turning one-off captures into a comparable record.

• Why your business isn't showing up in ChatGPT: absence needs a different fix.

Fix the record

• A named entity is not a retrieval signal: recognized is not the same as retrievable.

• A relevant page can still miss the proof: what makes a correction usable.

• AI crawlers don't run your JavaScript: the prerequisite for anything you publish.

• How to measure AI search visibility without one score: telling whether the answer moved.

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