TL;DR
Being described wrongly by an AI assistant is a different problem from not being mentioned, and it has four causes: outdated information, fabricated claims, unhelpful competitor bracketing, and criticism that is accurate. You cannot submit a correction to a model. You correct the sources it reads, which works for the first three. For the fourth, the only real fix is the underlying problem.
Key takeaways
Check what is said, not just whether you are named. Most brands monitoring AI visibility track presence and never read how they are described.
Outdated information is the most common problem, not hostility. Old pricing, discontinued features, a founder who left, a category you have moved on from.
There is no correction channel. No mechanism exists to tell a model it is wrong about you. You update the sources and wait for retrieval to catch up.
Who you are bracketed with shapes perception. Being listed beside the budget options positions you, whatever your own page says.
Fabricated claims are a real risk. Models produce confident, plausible, incorrect statements about products, including features you do not have.
If the criticism is accurate, content will not fix it. Campaigns to dilute true negative sentiment are suppression, and they fail against the thing that generated the criticism.
Table of contents
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Four ways to be described wrongly
Most AI visibility work tracks whether you were named. Being named and described badly is a separate problem, and it has four distinct causes with four different fixes.
Related reading: How AI Visibility Platforms Help Improve Your GEO Strategy
Cause | What it looks like | Where it comes from | Can you fix it? |
|---|---|---|---|
Outdated information | Old pricing, a discontinued feature, a former founder, a category you left | Your own stale pages and old third-party records | Yes, and relatively quickly |
Fabricated claims | A feature you do not have, a limit that is not real, an integration you never built | Model generation, sometimes from a confused source | Partly, over time |
Unhelpful bracketing | Listed beside the budget options when you are not one | Comparison pages and roundups that group you | Partly, by earning better placements |
Accurate criticism | A genuine weakness, repeatedly mentioned | Real customer experience, reported honestly | Not by content |
Why the distinction matters
Each needs a different response, and applying the wrong one wastes a quarter.
Outdated information responds to updating sources. Fabrication responds to publishing clear, authoritative, structured facts. Bracketing responds to earning placement in better comparisons. Accurate criticism responds to fixing the thing being criticised, and to nothing else.
Teams that treat all four as a reputation problem to be solved with content campaigns succeed at the first three by accident and fail at the fourth expensively.
The one people get wrong most
The fourth. There is a whole category of advice about deploying content to dilute negative sentiment or counter negative narratives.
If the criticism is inaccurate, that advice is fine and it is really about correcting a false claim. If the criticism is accurate, dilution is an attempt to drown out true information about your product, and it does not work, because the customers generating the criticism keep generating it.
Before you start
Read what is actually said about you. Not a sentiment score, the sentences. Ten minutes with three assistants gives you more than a quarter of dashboards, and it tells you which of the four problems you have.
The next section covers how to do that properly.
How to find out
Twenty minutes, no budget, and more useful than any sentiment dashboard.
The questions to ask
In ChatGPT, Gemini and Perplexity, in a fresh session each time:
Question | What it reveals |
|---|---|
What is [your company]? | Your category and basic facts |
What does [your company] do? | How your offering is characterised |
Who is [your company] for? | Whether your audience is understood |
What are the downsides of [your company]? | The criticism, stated plainly |
How much does [your company] cost? | Whether pricing is current |
[Your company] vs [main competitor] | How you are positioned against them |
Who are alternatives to [your company]? | Who you are bracketed with |
Is [your company] still in business? | Occasionally alarming |
That fourth question is the one most teams never ask and the one that produces the most useful answer. Ask it directly and read the whole response.
What to record
Copy the answers verbatim into a document. Not a summary, the text. Then mark each claim:
Mark | Meaning |
|---|---|
Correct | Accurate and current |
Stale | Was true once |
False | Never true |
Fair | Accurate criticism |
Unfair | Criticism based on something stale or false |
That classification sorts your findings into the four causes and tells you which playbook applies.
Repeat it properly
Answers vary between runs, so ask each question at least three times. A claim appearing once may be noise; a claim appearing in all three runs is established in the sources.
Run the whole exercise monthly and keep the documents. The trend in how you are described matters more than any single reading.
Who should do this
Someone who knows the product well enough to spot a subtly wrong claim. A marketer who joined last month will miss that the integration mentioned was deprecated two years ago.
This is also why sentiment tooling is a poor substitute. A classifier marks a sentence positive or negative. A person who knows the product notices that the sentence is positive and wrong.
Outdated information
The most common cause by a wide margin, and the most fixable. Models are not hostile to you; they are reading something you published in 2023.
What goes stale
Fact | Where the old version lives |
|---|---|
Pricing | Old comparison articles, review platform profiles, your own archived pages |
Feature set | Documentation you never deleted, third-party reviews |
Leadership | Press coverage, Crunchbase, your own team page |
Category | Your positioning changed; the web did not |
Integrations | Partner directories listing deprecated connections |
Headcount and funding | Profiles nobody updated after the last round |
Limits and tiers | Review articles describing a plan structure you replaced |
Why it persists
Old pages stay indexed. A comparison article from 2024 describing your pricing is still there, still readable, and still being retrieved. Your current pricing page competes with it rather than replacing it.
This is why stale information outlives the thing it describes, often by years.
The fix, in order
Update your own pages first. Any page of yours carrying an old fact is the easiest correction and the one you fully control.
Delete or redirect genuinely obsolete pages. An old pricing page left live for sentimental reasons is actively working against you.
Date your current facts visibly. Pricing as of October 2026 gives a retrieval system a reason to prefer your version over an undated older one.
Update every third-party profile you control. Review platforms, directories, partner listings, Crunchbase.
Contact publishers of significant outdated articles. Many will update a factual error if you point to it politely, especially pricing.
Publish a current, structured facts page. Clear, dated, easy to parse, covering the facts that keep being got wrong.
How long it takes
Your own pages can affect answers within weeks once re-crawled. Third-party sources take longer and depend on when those pages are re-read. Articles you cannot get updated may persist indefinitely, which is why your own clearly dated version matters.
The preventive habit
When you change pricing, discontinue a feature or rebrand, add a step to the process: update the external profiles too. That list exists in the entity consistency work and it takes an hour.
Most stale-information problems are created by changes that were never propagated outward, and the propagation is cheap if it happens at the time.
Fabricated claims
Sometimes an assistant states something about your product that was never true anywhere. A feature you do not offer, a limit that does not exist, an integration you never built.
Why it happens
Models generate plausible text. If every competitor in your category has a particular integration, a model answering a question about your category may attribute it to you, because it is a reasonable completion rather than a retrieved fact.
It can also come from a genuinely confused source: an article that conflated you with a similarly named company, or a review that described the wrong product.
Why it matters more than it sounds
A false positive is worse than a false negative. A prospect told you have a feature you lack will discover that in a trial, and the discovery is a broken expectation rather than a missing one.
In regulated categories it is worse again, since a fabricated capability claim attached to your brand can create problems beyond disappointment.
What you can do
Action | Effect |
|---|---|
Publish an explicit, structured feature list | Gives retrieval something definitive to prefer |
State what you do not do, plainly | Unusually effective, because few companies publish it |
Date your capability pages | Helps a system prefer your version |
Find and correct the confused source | If the fabrication traces to one article, that is the fix |
Use clear, distinct product naming | Reduces conflation with similarly named products |
The non-fit statement
A page or section saying who you are not for and what you do not do is the most underused asset here.
It is directly retrievable, it is the sort of specific claim that gets quoted, and it addresses fabrication at the source by giving a system an explicit negative to work from rather than leaving a gap to fill.
It also builds credibility with human readers, who are used to pages that claim everything.
What you cannot do
There is no mechanism to submit a correction. Some providers offer feedback options and they are not a reliable route, and nothing edits a model's training.
What works is making the correct information clearly available, well structured and dated, so that retrieval prefers it. That is slower than a correction form and it is the only route that exists.
Who you are bracketed with
The most overlooked of the four. Which companies you are listed alongside positions you more effectively than anything on your own website.
Why it matters
If an assistant answers alternatives to [market leader] with three budget tools and you, you have been positioned as a budget tool. If it lists you beside two premium options, you are a premium option.
That placement happens in the answer, before a prospect reads a word you wrote, and it frames everything after.
Where bracketing comes from
Source | Influence |
|---|---|
Comparison and roundup articles | The strongest. These literally group companies |
Review platform categories | You are in a category someone assigned you to |
Directory taxonomies | Same |
Your own comparison pages | You choose who you compare yourself against |
Analyst groupings | Influential where they exist |
What you can influence
Your own comparisons. If you only publish comparisons against cheaper tools, you have told every retrieval system which set you belong to. Compare against the companies you want to be bracketed with, honestly.
Review platform categories. Check which category you sit in and whether a more accurate one exists. This is often a support ticket.
Roundup inclusion. Being added to the right roundups is editorial outreach: a genuine pitch to the author of a relevant list, on the merits. Slow and legitimate.
Your own positioning language. State explicitly who you compete with and who you do not, in plain words. Systems read it.
What you cannot do
Remove yourself from a comparison someone else wrote, or buy your way into a better bracket. Paying for inclusion in a roundup is a paid placement, and presenting it as editorial raises the disclosure problems covered elsewhere in this cluster.
The practical check
Ask three assistants for alternatives to your main competitor, and separately for the best options in your category. Record who appears beside you.
If the set is consistently wrong, that is a positioning finding worth taking to whoever owns product marketing. It is often more actionable than a visibility score, because it tells you how the market is being described rather than just whether you are in it.
When the criticism is accurate
Sometimes an assistant says your onboarding is difficult, your pricing is opaque or your support is slow, and it is right.
This is the case where most advice in this category goes wrong, so it is worth being direct.
What you will be told to do
Deploy content campaigns to dilute negative mentions. Publish authoritative material that counters the narrative. Shift the sentiment in your favour.
If the criticism is inaccurate, that advice is reasonable and it is really about correcting a false claim.
If the criticism is accurate, it is an attempt to drown out true information about your product, and it does not work.
Why it does not work
The criticism exists because customers experienced something and said so. Those customers keep existing and keep saying so. Publishing ten articles about how simple your onboarding is does not change the onboarding, and the reviews continue to accumulate on the other side.
Retrieval systems weight independent sources heavily for exactly this reason. Your own content asserting the opposite of what customers report is the configuration most likely to be discounted.
It is also expensive, and the money spent on the campaign would have paid for some of the fix.
The honest sequence
Verify the criticism. Read the sources. Is it true, partly true, or true of a version you have since changed?
If true, route it to whoever owns it. Product, support, pricing. This is a product finding that arrived through a marketing channel, and it is valuable precisely because of that.
If partly true, address the real part and correct the rest. Both halves, honestly.
If it describes something you fixed, that is the stale information problem, and you treat it as such with dated, clear evidence of the change.
Only then, communicate. Once the thing is genuinely better, saying so is accurate rather than spin, and the evidence accumulates on your side.
The reframe worth making internally
AI assistants summarising customer sentiment are, in effect, running a continuous research panel on your product and telling you the result in plain language.
Ask what the downsides of your product are, read the answer, and take it to a product review. That is a better use of the finding than a content brief, and it is the one thing in this article that can change the answer permanently.
The exception
If criticism is genuinely unfair, from a competitor-run source, a review farm or a single aggrieved account amplified beyond its weight, that is a different situation and worth addressing through the platform's own processes and through accurate counter-evidence.
The test is simple and uncomfortable: would your own support team recognise the complaint? If yes, it is not unfair.
Why you cannot correct a model
This is the structural fact that shapes everything else here, and it is worth stating plainly because a great deal of marketing implies otherwise.
There is no correction channel
No form, no portal, no support queue where you report that an assistant is wrong about your company and have it fixed.
Some providers offer general feedback mechanisms. They are not a correction service, they carry no commitment, and they are not a route you can plan around.
You cannot edit training data
Training corpora are assembled by model providers on their own schedule. No third party writes to them, and no service injects your brand into them, whatever the marketing says.
This matters because it determines what the realistic lever is. When a model's description of you improves after you change something, that is almost always retrieval reading your updated sources, not the model having learned anything new about you.
What you actually influence
Lever | Effect |
|---|---|
Your own pages | Direct. Update, date and structure them |
Your profiles on third-party platforms | Direct, where you control them |
Publishers of inaccurate articles | Indirect. Many will correct a factual error if asked |
Review platforms | Indirect, through genuine customer activity |
The underlying reality | The only lever for accurate criticism |
The implication for timelines
Corrections propagate at the speed of retrieval, which depends on when your updated pages are re-crawled and when third-party sources change.
Your own pages can show up in answers within weeks. Third-party corrections take longer. Articles you cannot get changed may persist indefinitely, which is why your own clearly dated authoritative version is the durable defence.
How to hold this
Treat AI descriptions of your brand as a downstream reflection of what the web says about you. That framing produces the right actions: fix the sources, make the correct information unmistakable, and address the real issues that generate accurate criticism.
It also rules out the wrong actions, which mostly involve paying someone to change a model's mind.
The correction playbook
Four causes, four responses. Match them properly and the work is tractable.
By cause
Cause | First action | Owner | Realistic timeline |
|---|---|---|---|
Stale | Update or retire your own pages carrying the old fact | Marketing ops | Weeks |
False | Publish explicit, dated, structured facts including non-fit statements | Content | Weeks to months |
Bracketing | Fix review platform categories; publish honest comparisons against the right set | Product marketing | Months |
Accurate criticism | Route to product, support or pricing | Whoever owns the issue | Quarters |
The general sequence
Audit. Twenty minutes, three assistants, eight questions, answers copied verbatim.
Classify every claim as correct, stale, false, fair or unfair.
Fix your own sources first. Fastest, free, entirely in your control.
Then third-party sources you control. Profiles, directories, review platform listings.
Then publishers. Polite, specific, factual correction requests on significant articles.
Route the accurate criticism internally. It is a product finding, not a content brief.
Re-audit monthly and keep the records, so you can see what moved.
The one page worth building
A current, dated, well-structured facts page: what you are, who you are for, who you are not for, what you do and do not do, current pricing structure, and the things people most often get wrong, stated plainly.
It is directly retrievable, it is the sort of specific content that gets quoted, and it gives every correction effort a canonical thing to point at.
Unusually, it also serves human readers well, because prospects are looking for exactly those facts and rarely find them stated so directly.
What to measure
Not a sentiment score. Track the specific claims.
Track | How |
|---|---|
Number of stale claims still appearing | Count them in the monthly audit |
Number of false claims still appearing | Same |
Whether the bracketing set has changed | Record who appears beside you |
Whether accurate criticism has been addressed | A product question, not a marketing one |
That is four countable numbers from a twenty-minute monthly exercise, and they are more actionable than any dashboard metric in this category.
What sentiment scores are worth
Every tool in this category offers one. They are useful as a flag and poor as a metric, and it is worth understanding why before you report one to anyone senior.
What a sentiment score is
A classifier reads the sentence mentioning your brand and labels it positive, neutral or negative. That is the whole mechanism.
Where it fails
Problem | Example |
|---|---|
Accurate but negative reads the same as false and negative | Our onboarding genuinely is complex versus a competitor's claim that it is |
Positive and wrong scores well | A glowing description of a feature you do not have |
Context is lost | Good for small teams is positive, and may be positioning you out of enterprise deals |
Comparative framing confuses it | Cheaper than X but less capable depends entirely on the reader's priorities |
Small samples swing wildly | Answers vary between runs, so a monthly score moves on noise |
That first row is the serious one. A score cannot tell you whether to fix your content or your product, and that is the only question that matters here.
How to use it properly
As a trigger to read, not as a number to report.
Do | Do not |
|---|---|
Use a drop as a prompt to read the actual sentences | Report the score as a KPI |
Track the specific claims instead | Set a target for the score |
Read a sample of answers monthly yourself | Trust a score you cannot trace to retrievable text |
The rule
Any sentiment score you cannot click through to the source sentence is decoration. When evaluating tools, ask to see the text behind a score. A vendor who cannot show you is selling a number.
What to report instead
The four counts from the previous section: stale claims, false claims, bracketing set, and whether accurate criticism has been routed to an owner.
Those are concrete, they map to actions, and they survive someone asking how you calculated them, which a sentiment score generally does not.
Six mistakes
1. Tracking presence and never reading the description. Most monitoring records whether you were named. Being named and described as the cheap option with poor support is a worse outcome than being absent, and no presence metric reveals it.
2. Treating accurate criticism as a content problem. If customers genuinely find your onboarding hard, publishing articles about how simple it is does not change the onboarding, and the reviews keep accumulating. Route it to whoever owns the issue.
3. Assuming negative means unfair. Read the sources before responding. A significant share of what looks like a reputation problem is an accurate report of something you can fix.
4. Leaving old pages live. A 2024 pricing page kept for sentimental or SEO reasons is actively competing with your current one. Update, redirect or delete it.
5. Reporting a sentiment score. It cannot distinguish accurate criticism from false criticism, which is the only distinction that matters. Report the specific claims instead.
6. Believing a service can correct the model. No correction channel exists and nothing edits training data. What works is fixing the sources, which you can mostly do yourself.
The pattern
Four of six come from treating this as reputation management rather than source management. The sources are the lever, and most of them are yours.
The one that costs most
Mistake two, because it converts a solvable product problem into an unsolvable marketing one, and it spends a quarter's budget doing so.
Ask three assistants what the downsides of your product are. Read the answer. If your support team would recognise the complaint, you have a product finding that arrived through a marketing channel, and that is the most valuable thing this whole exercise produces.
FAQs
How do I find out what AI says about my brand?
Ask ChatGPT, Gemini and Perplexity eight questions in fresh sessions: what you are, what you do, who you are for, your downsides, your pricing, how you compare to a competitor, your alternatives, and whether you are still trading. Copy the answers verbatim and repeat three times each.
Can I ask an AI company to correct information about my brand?
No. There is no correction channel. Some providers offer general feedback mechanisms, which carry no commitment and are not a route you can plan around. You correct the sources instead, which works for most problems.
Why does an AI have outdated information about us?
Because old pages stay indexed. A 2024 comparison article describing your old pricing is still readable and still retrievable, and it competes with your current page rather than being replaced by it. Update your own pages, date them, and retire genuinely obsolete ones.
What if an AI states something about our product that is simply false?
Publish explicit, dated, structured facts, including what you do not do. Non-fit statements are unusually effective because few companies publish them, and they give a retrieval system a definite negative rather than a gap to fill with a plausible guess.
How do we change which competitors we are listed alongside?
Check your review platform categories, publish honest comparisons against the set you want to be grouped with, and pitch for inclusion in relevant roundups on merit. You cannot remove yourself from someone else's comparison, and buying placement raises disclosure problems.
Should we run a campaign to counter negative sentiment?
Only if the criticism is inaccurate, in which case it is really a correction. If it is accurate, content cannot fix it: the customers generating the criticism keep generating it, and independent sources are weighted heavily. Route it to product instead.
Are sentiment scores useful?
As a flag to go and read the actual sentences, yes. As a metric, poor. A score cannot distinguish accurate criticism from false criticism, which is the only distinction that determines what you should do. Track specific claims instead.
How often should we check?
Monthly, with the records kept. The trend in how you are described matters more than any single reading, and answers vary between runs, so ask each question at least three times before concluding anything.
Fixing what AI says about you
Spend twenty minutes this week asking three assistants eight questions about your company, and copy the answers down. Then mark every claim as correct, stale, false, fair or unfair.
That classification is the whole method. It sorts a vague sense that something is wrong into four specific problems with four specific owners.
The four responses
Stale: update and date your own pages, retire obsolete ones, then fix third-party profiles. Weeks.
False: publish explicit, dated, structured facts including what you do not do. Weeks to months.
Bracketing: fix review categories, compare against the right set, pitch for better roundups. Months.
Accurate criticism: route it to product, support or pricing. Quarters, and it is the only fix that exists.
The thing to resist
Campaigns to dilute negative sentiment. If the criticism is false, correct it. If it is true, the customers generating it will keep generating it, and the money would have part-funded the actual fix.
If producing the corrective content is the constraint
The facts page, the non-fit statements, the honest comparisons and the dated capability pages are all real writing, and producing them consistently is where most teams stall. Blazly SEO covers research, planning, drafting and publishing from about $39 a month.
The factual review stays with you, and on this particular job it matters more than usual: a page built to correct inaccuracies cannot itself contain any.
Summary
Being described wrongly is a different problem from being absent, and it has four causes. Outdated information is the most common and the most fixable, and it persists because old pages stay indexed and compete with your current ones. Fabricated claims need explicit, dated facts and clear non-fit statements. Competitor bracketing is shaped by comparison pages and review categories more than by your own positioning. And where the criticism is accurate, no amount of content fixes it, because the customers producing it keep producing it. There is no channel for correcting a model and nothing edits training data, so the lever is always the sources, most of which you control. Check monthly, record the specific claims rather than a sentiment score, and treat the downsides question as the product research it actually is.