AI Reputation Management: When AI Describes Your Brand Wrong

By Cory Maki · Jun 11, 2026

A buyer asks ChatGPT to describe your company. It calls you a project management tool when you are an analytics platform. It quotes a price you retired eighteen months ago. It attributes a competitor's feature to you. The buyer never tells you this happened. They simply form an impression, and either book a call carrying wrong expectations or quietly move on.

This is the new shape of reputation risk. AI now summarizes your brand for buyers before they ever visit your website, so stale pricing, wrong positioning, or a confused product category quietly shape demand. The scale is not theoretical: ChatGPT has crossed 800 million weekly users, and Google's AI Overviews reach roughly 1.5 billion people a month. When the answer is wrong, most people will not question it — they will just move on.

You cannot call OpenAI and ask them to fix it

The first instinct after a customer flags a wrong AI answer is to find the off switch. There isn't one. You cannot contact OpenAI, Google, or Anthropic and request that a specific wrong fact about your brand be corrected, because the models do not work that way. AI systems do not store verified facts in an editable database — they generate responses by pattern-matching across training data and retrieved web sources.

That architecture is the whole problem. A large language model predicts useful text from training data and context, while retrieval-augmented generation adds a live search step before the answer is written. If the retrieved sources are outdated, thin, contradictory, or dominated by third-party summaries, the final answer still sounds confident while being wrong. The model is not lying about your brand. It is making its best guess from noisy data, and serving that guess to every buyer who asks.

Native feedback channels exist — the thumbs-down in ChatGPT, the feedback icon in Perplexity — and you should use them for serious cases. But these corrections are slow and not guaranteed. The reliable path is to correct the public sources that AI systems retrieve or learn from.

The errors are bigger than annoying, and they rarely stay on one platform

This is not a cosmetic problem. One audit found that 72% of brands have factual errors in AI responses, and 35% of brands report that inaccurate AI answers have already damaged their reputation. In one documented case, hallucinated product specs caused a 25% spike in returns.

Worse, the errors travel. When a pricing error appears on ChatGPT, the same error exists on at least two other AI platforms about 60% of the time. So a wrong answer is rarely a single-platform incident you can patch in one place — it is a pattern rooted in your shared evidence base across the web.

The brands most exposed are predictable. Companies with multiple product lines, frequent pricing changes, or recent rebrands have the highest hallucination rates, and brands with limited web presence outside their own domain see higher fabrication rates because AI fills the gaps with plausible-sounding invented detail. If your footprint is thin, the model improvises. If your footprint is contradictory, the model picks the wrong version.

Why a repositioning haunts you for months

Consider a SaaS company that repositioned from project management to AI workflow automation. The homepage changed overnight. But the old comparison pages, help articles, software directories, and partner descriptions still called it a task tracker. In that situation, the AI describes the company using the old category, because the outdated phrase remains more common across the web than the new positioning. This is why repositioning without a source cleanup creates months of AI confusion. The model reflects the consensus of your evidence graph, not your latest hero section.

Why "the AI got it wrong" is usually your legal problem too

Founders sometimes assume the platform owns the mistake. Courts have started to disagree. In Moffatt v. Air Canada, a British Columbia tribunal held the airline liable for misinformation its own chatbot gave a customer about bereavement fares, and rejected the airline's argument that the chatbot was a separate legal entity responsible for its own actions. The tribunal's reasoning was blunt: a company is responsible for all the information on its site, whether it comes from a static page or a chatbot.

That case involved a bot the company deployed, not a third-party model describing it. But the direction of travel is clear — where AI output touches a commercial relationship, regulators and courts increasingly look to the business, not the machine, to ensure accuracy. Treat your AI representation as something you are accountable for, not something that happens to you.

The correction process: audit, source repair, reinforcement

The fix is not to panic-edit your homepage. Without knowing exactly what AI gets wrong, on which platforms, and from which sources, you are guessing — and the fix for a pricing error sourced from a stale G2 listing is completely different from the fix for a fabricated detail with no source at all. This is the discipline behind my ARC Method: Audit, Reddit and Reputation, Citability.

Audit first. Query ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews with the questions buyers actually ask — "What does [company] do?", "What are the best tools for [use case]?", "How much does [company] cost?" Record every response with model name and date. Spot-checks aren't enough; responses shift as models update, and each platform may carry different errors. One reason this matters: brands in the top quartile for web mentions earn over 10x more AI Overview mentions than the next quartile, so missing entirely is its own failure mode.

Repair the canonical evidence. Update official pages, schema markup, sitemaps, and the high-ranking third-party descriptions feeding the error. Removing or correcting wrong information on external sites — reviews, directories, old comparison posts — drives change faster than updating your own site alone, because a claim embedded across high-authority third-party pages keeps surfacing even after you fix your own copy. Prioritize pages that are already indexed, cited, or ranking for your brand. They influence retrieval far more than a brand-new page with no authority. If models can't even crawl your current truth, start with why ChatGPT can't see your website.

Reinforce the corrected narrative. Align your brand description, category, and pricing across your homepage, About page, product pages, FAQs, and every external profile — G2, Capterra, LinkedIn, Crunchbase, press. Repeat the same entity facts in machine-readable form, because AI corrects a narrative when the broader evidence becomes consistent, crawlable, and repeated across trusted sources — not because you assert it is wrong. Structure matters as much as substance; see how to make your content citable by AI. And because Reddit and Wikipedia remain among the most-cited domains in AI answers, Reddit's role in AI citations is not optional cleanup — it is core reputation surface.

Set expectations: weeks to months, then monitor forever

Corrected brand information typically takes weeks to months to appear consistently, depending on crawl frequency, source authority, retrieval behavior, and how widely the error is spread across third-party sites. Search-connected answers can update quickly; model-level knowledge lags. Track share of voice and answer accuracy monthly, and expand into new query clusters only once your core brand description stabilizes.

This is the part founders underestimate. AI representation is not a one-time fix — it is an alignment process, the same test → improve → monitor loop you already know from SEO, now with the model in the loop. If you want the structured version of that loop, I lay it out in the 90-Day GEO Roadmap for SaaS founders, and the full framework is in my book, Reddit, AI Overviews & GEO.

The brands that win here are not doing anything revolutionary. They are doing reputation management with a sharper understanding of which sources AI consumes — asking "will the model find this, understand it, and repeat it correctly?" before they publish anything at all.