Why does AI say wrong things about my company?

Because it reproduces the most retrievable, corroborated version of a fact from its training data and the web, and that version can be stale, contradictory, or dominated by a wrong source. The model is not lying; it is summarizing what is available. The fix is to make the accurate version more available and better corroborated than the wrong one.

How do I correct AI misinformation about my brand?

Fix the source, not the sentence. Publish and date the correct fact on an authoritative page you control, update every profile and listing showing the old fact, and earn corroborating mentions on independent sources so the weight of evidence shifts. You cannot delete a dominant wrong page, so out-corroborate it, then re-check the assistants' answers over time.

How do I fix an outdated fact AI keeps repeating?

First log the exact prompt, assistant, and likely source. Publish the current fact on a clearly dated page, update listings and profiles that still show the old version, and get the correction onto a reputable independent source or two. Then re-ask the same prompt on a schedule. Models refresh on their own cadence, so expect direction over subsequent refreshes, not an instant change.

Can I force an AI assistant to update a fact about my brand?

No. You cannot edit an assistant's answer directly, and no vendor guarantees when a corrected fact will appear, because models refresh training and indexes on their own schedule. What works is making the accurate, dated, corroborated version dominant across the sources the model reads, then measuring the answer repeatedly until the wrong claim recedes.

What AI gets wrong about your brand and how to fix it

Guide · AI Visibility · 5 min read · last verified 2026-07-25

Reviewed before publication Editorial board Independent commercial review

In shortWhy AI states false facts about your brand: stale data, wrong-source dominance, entity confusion. How to diagnose, correct at the source, and measure the fix.

When an assistant states something false about your company — an outdated price, a product you discontinued, the wrong founder, a competitor's feature credited to you — it is almost always repeating a stale or dominant source, not inventing a lie. That distinction matters, because it tells you where the fix lives: in the sources the model reads, not in an argument with the model. You cannot edit an AI's answer directly, but you can change what it has to draw from.

This piece explains why assistants get brand facts wrong, how to diagnose the specific cause, and how to correct it — with the honest caveat that there is no instant fix and the only proof is measuring the answer over time.

Why does AI say wrong things about your company?

Because it is summarizing the web and its training data, and both can be out of date, contradictory, or dominated by a source that is wrong about you. An assistant does not "know" facts; it reproduces the most retrievable, corroborated version of them. If the most available version is stale, the model repeats the stale version confidently.

The fix, therefore, is never to demand a correction from the model. It is to make the accurate version more available, more consistent, and better corroborated than the wrong one — and then to wait for the model's view to catch up.

The three usual causes: stale data, wrong-source dominance, entity confusion

Most brand errors trace to one of three roots:

Stale training or index data — the model learned a fact that was true once (an old price, a former CEO, a discontinued plan) and has not been updated.
Wrong-source dominance — one loud, highly cited source says something inaccurate, and its authority outweighs your correct-but-quieter page.
Entity confusion — the model conflates you with a similarly named company, product, or person, and blends facts across the two.

Diagnosis matters because the remedy differs. Stale data needs fresh, dated corroboration; source dominance needs you to win on the pages that source dominates; entity confusion needs disambiguation.

First, capture exactly what's wrong, and where

Before fixing anything, document the error precisely: which assistant, which prompt, the exact wrong claim, and — where the tool shows it — which source the model leaned on. Vague reports ("ChatGPT says weird stuff about us") cannot be fixed; a logged instance ("for 'who founded [company]', Perplexity names the wrong person and cites an old profile") can.

Capture it across assistants, because they read different source mixes and one may be wrong while another is right. That spread is itself a clue to which source is driving the error.

Fix the source, not just the sentence

Once you know the cause, correct it at the source layer. For stale facts, publish and prominently date the current fact on an authoritative page you control, and get the correction reflected wherever the old fact still lives — your profiles, listings, and any third-party pages you can influence. According to the Princeton GEO study (2024), sourced and quotable content earns more visibility — citations about +40%, statistics about +37% — so a well-attributed correction is more likely to be picked up than a bare assertion.

For wrong-source dominance, you usually cannot delete the offending page, so out-corroborate it: get the accurate fact onto several independent, reputable sources so the weight of evidence shifts. One correct page rarely beats a dominant wrong one; a chorus can.

Correct entity confusion with consistent, corroborated facts

Entity confusion is fixed by disambiguation. Use your full, exact name consistently, pair it with distinguishing facts (category, location, founding year), and make sure structured data and reference pages clearly separate you from the entity you are confused with. The more consistently the correct associations appear across sources, the easier it is for a model to resolve you as a distinct entity.

This is slow, cumulative work — you are retraining a statistical association, not flipping a switch — but it is the durable fix. Consistent entity signals are also what protect you from future confusion, not just the current instance.

Why there's no instant fix, and what to expect

Be honest with yourself and your stakeholders: you cannot force an assistant to update on your timeline. Models refresh their training and indexes on their own cadence, and no vendor guarantees when a corrected fact will appear. What you can expect is that consistent, corroborated, dated corrections tend to propagate over subsequent refreshes — direction, not a date.

Avoid two traps: do not fabricate impressive-sounding statistics to make your correction "stick" (a false number can itself become the next error a model repeats), and do not assume one edit worked. Assume nothing until you have re-checked the answer.

A worked example of a correction loop

Suppose an assistant lists an old, higher price for your entry plan. The loop looks like this: log the wrong answer and its likely source; publish the current price on a clearly dated pricing page and update every listing and profile that still shows the old figure; earn a mention of the current price on an independent source or two; then re-ask the same prompt across assistants over the following weeks. If the wrong price recedes, the fix is working; if not, you have not yet displaced the dominant source and need more corroboration.

Measuring whether the correction stuck

The correction is a hypothesis until the answer changes, so the last step is measurement, on repeat. Re-ask the exact prompts that produced the error, on a schedule, and track whether the wrong claim fades and the right one appears. This is the loop Magrios runs: it records what assistants currently say about you for a locked set of brand questions, flags the inaccuracies and the sources behind them, and re-scans the same prompts after you act so you can confirm the wrong answer is actually receding rather than assume it. Fixing a brand error is not a one-time edit; it is a measured campaign against a stale consensus.