Who fixes wrong AI answers about your company?
The short answer
When ChatGPT, Gemini, Perplexity or Google’s AI Overviews state something wrong about a company, four kinds of provider get called: traditional online reputation management (ORM) firms, GEO and AI-search specialists, law firms, and, for simpler cases, nobody at all, because an in-house team can do it. They differ less in ambition than in mechanism. Some correct the sources an engine reads; some monitor and report; some pursue the publisher legally; some still run a search-suppression playbook that predates AI answers entirely.
We have written separately about how to fix AI misinformation about your brand and what to do when ChatGPT gets your company wrong. This piece answers the adjacent question buyers actually ask: who does this work, and how do you tell them apart?
The provider landscape, compared
| Provider type | What they actually do | Best suited to |
|---|---|---|
| Traditional ORM firms | Search-result management, positive-content campaigns, monitoring; several now market AI-answer services | Broad reputation programmes where search results and reviews matter as much as AI answers |
| GEO / AI-search specialists | Trace which sources feed an answer, correct or outrank them, strengthen machine-readable owned pages, re-measure per engine | A specific wrong answer, or ongoing accuracy in AI engines as a maintained capability |
| Law firms | Defamation claims, right of reply, publisher corrections and takedowns through legal channels | Wrong answers rooted in defamatory or unlawful coverage |
| DIY / in-house | Engine feedback buttons, publisher correction requests, authoritative pages on your own site | Straightforward factual errors with identifiable, cooperative sources |
Traditional online reputation management firms
The established names come from the search era. Status Labs, founded in Austin in 2012 with offices in the US and Europe, is a full-service ORM agency built around search-result management and positive-content campaigns, and now markets services addressing AI answers and hallucinations alongside that core. ReputationDefender, founded in 2006 and now part of Gen Digital (the Norton parent), offers more productised reputation and privacy services, strongest for individuals, executives and smaller businesses cleaning up search results and personal data exposure.
Both are competent at what they were built for. The open question to put to any ORM firm is how much of the engagement is genuinely source-level correction for AI engines, and how much is the search-era playbook relabelled.
GEO and AI-search specialists
A newer category works the mechanics directly: identify which pages each engine retrieves and cites for the query in question, correct or outrank those sources, make the accurate version machine-readable on owned properties, then re-measure per engine. This is closer to technical SEO and structured-data work than to publicity, which is why it tends to sit with specialists rather than generalist agencies. It suits organisations that want a specific answer fixed and kept fixed, rather than a broad reputation campaign.
Law firms
Where the wrong AI answer originates in coverage that is defamatory or otherwise unlawful, the source problem is a legal problem. Defamation counsel can pursue corrections, right of reply or removal at the publisher, and because AI engines lean on the published record, the answer usually shifts once that record changes. Legal routes are slow and poorly suited to the common case of merely outdated or conflated material, but for the genuinely defamatory case they reach sources nobody else can touch.
Doing it yourself
Plenty of cases need no vendor. The major engines carry feedback controls on individual answers; publishers correct verifiable factual errors more often than people expect; and a clear, consistent, well-structured statement of the facts on your own site is the single highest-leverage asset in the whole exercise. The DIY route runs out of road when the error repeats across engines, lives in sources you cannot edit, or touches a matter sensitive enough that documentation and discretion start to matter.
How the fix actually works
Whoever does the work, the durable version follows the same sequence. First, diagnosis: run the failing prompts across each engine and identify which sources the answers draw on, since ChatGPT, Gemini, Perplexity and AI Overviews each retrieve differently and can fail for different reasons. Second, source correction: fix the pages that feed the error, through publisher corrections, updates to directories and reference entries, or by making stronger pages the easier ones to retrieve. Third, the owned layer: publish the accurate facts prominently, consistently and in machine-readable form, with structured data, so every engine has an authoritative version to reach for. Fourth, re-measurement: run the same prompts again, per engine, against the baseline, and keep watching, because models update on their own schedules.
Note what is absent: suppression. The ORM tactic of burying a result under positive content worked when the battlefield was ten ranked links. AI assistants synthesise from retrieved sources, so a page pushed to position twelve can still be cited, and volume-produced content increasingly reads to the engines as manipulation. The suppression-era playbook does not translate directly, and no honest provider will guarantee removal of a source or a specific answer. The engines are not theirs to command.
How to choose
Three questions do most of the sorting. Does the provider measure per engine, before and after, with the actual prompts and answers, rather than a generic sentiment dashboard? Do they fix sources or just monitor, that is, will they do the correction, structured-data and outranking work themselves, or hand you a report of what is wrong? And for sensitive matters, how do they handle discretion: working under NDA, behind existing counsel or a lead communications firm, without adding their own name to the story. Be wary of anyone promising guaranteed removal or a fixed timescale; the mechanics above make both impossible to promise honestly.
Where Morris McLane fits
Morris McLane does the execution-layer version of this work. We diagnose which sources each engine is citing for the failing prompts, correct the machine-readable record, on owned pages, structured data and the third-party sources engines actually pull from, and re-measure per engine against a documented baseline. We usually work behind a lead communications firm or counsel, under NDA, so the client’s existing advisers stay the single point of contact. The work sits within our reputation management and AI search visibility services. If an engine is getting your company, or your client’s, wrong, start here.
Frequently asked questions
Who fixes wrong AI answers about a company?
Four kinds of provider handle it: traditional online reputation management firms such as Status Labs and ReputationDefender, which have extended search-focused services towards AI answers; GEO and AI-search specialists, which correct the source pages engines retrieve and cite; law firms, where the wrong answer originates in defamatory coverage; and in-house teams doing it themselves through publisher corrections and stronger owned pages. Many organisations combine two of these, typically a strategy lead plus a technical execution partner.
Can a traditional ORM firm fix what ChatGPT says about my company?
Partly. The established firms are strong on the search layer, and several now market AI-answer services. But the suppression playbook built for Google's ten blue links does not translate directly: AI assistants synthesise from retrieved sources rather than ranking pages, so pushing a result to page two does not stop an engine citing it. Ask any ORM firm whether they correct the citing sources themselves and measure answers per engine, before and after.
Do I need a lawyer to fix a wrong AI answer?
Only when the wrong answer originates in coverage that is genuinely defamatory or unlawful, rather than merely outdated or thin. In those cases defamation counsel can pursue corrections, right of reply or removal at the publishing source, and the AI answer typically shifts once the underlying record changes. For the more common case, stale or conflated but lawful material, legal routes achieve little and source-level correction is the practical path.
Can you fix wrong AI answers about your company yourself?
Often, for straightforward errors. Use the feedback controls in ChatGPT, Gemini and Perplexity, request corrections from the publishers an answer cites, and publish the accurate facts clearly on your own site with consistent structured data. The limits are time and stubbornness: errors that repeat across several engines, sit in sources you cannot edit, or touch sensitive matters usually justify specialist help.
How do you choose a provider to fix AI answers?
Three tests separate the field. First, measurement: a credible provider baselines what each engine says before work starts and re-measures afterwards, per engine, rather than reporting generic sentiment scores. Second, mechanism: ask whether they correct and outrank the citing sources or simply monitor the answers. Third, discretion: for sensitive matters, ask how they work under NDA and whether they can operate behind your existing advisers rather than alongside them.
