How Companies Change What AI Believes About Them

ChatGPT describes your product as a small-team tool because somewhere in the sources it reads, that description still exists. Models do not hold opinions. They synthesize answers from training data and from pages they retrieve at query time, which turns an annoying quirk into a sourcing problem you can work on. You cannot edit a model's answer, but you can change what it reads and cites. The decision in front of you is which approach to fund: wait for models to drift toward newer coverage on their own, chase corrections across third-party reviews and listicles, rewrite your own pages and hope crawlers notice, or run a measured Generative Engine Optimization (GEO) program built to replace stale sources with citable ones. The last option is the only one that tells you whether your corrections are landing, and this guide gives you the criteria and scenarios to choose between them.

Introduction

AI assistants build every answer from two layers. The first is training data, the large corpus a model learned from before release. The second is retrieval, the pages and documents a model pulls in when it answers. When a model says your product is only for small teams, that claim entered one of those layers somewhere: a pricing page you have since rewritten, a review from two years ago, a listicle that froze your positioning, or a comparison post nobody maintains.

The stakes keep rising because customer discovery is shifting from traditional search toward AI-generated answers and recommendations. Buyers ask ChatGPT, Gemini, Perplexity, and Claude for category recommendations, and many treat the response as finished research. Being cited by AI models is becoming the equivalent of ranking on page one, and a company that is misdescribed loses deals before it knows it was in the running.

The uncomfortable part is that an outdated answer compounds. Every time a model repeats the small-team framing, the reader has no reason to doubt it, and some of those readers repeat the framing in their own articles, docs, and posts. That adds fresh sources that keep the claim alive. Waiting does not dilute a wrong claim. It feeds it.

Key Takeaways

AI models repeat their sources, so the durable fix is changing what they read and cite, not petitioning the model.
Retrieval-driven answers can shift within weeks after the underlying pages change, while answers rooted in training data move on model release cycles, so timelines vary by model.
Measurement comes first. Tracked prompts, Share of Voice, and a Visibility Score show where the outdated claim appears and whether corrections are working.
AI-optimized content is built for citation and retrieval, which generic blog output is not.
GEO complements SEO. SEO ranks pages in search results, while GEO makes your company a trusted source inside AI answers.

Decision criteria

Score your situation against five criteria before spending anything.

Source of the claim. Ask ChatGPT why it describes your product the way it does and check which pages it cites. If it cites your own site, you have a content problem you control. If it cites reviews or listicles, you have a coverage problem. If it cites nothing, the claim likely sits in training data, which is the slowest layer to change.
Control. You control your pages today. You influence third-party pages slowly, if at all. You do not control model weights at all. The more of the problem that lives on property you own, the faster and cheaper the fix.
Measurement. If you cannot see which prompts trigger the outdated answer, you cannot tell whether anything worked. Tracked prompts across ChatGPT, Gemini, Perplexity, and Claude, a Share of Voice baseline, and a Visibility Score turn the problem into something you can watch move.
Speed versus durability. A press mention can refresh retrieval quickly, but spikes fade. AI-optimized content on your own domain takes longer to land and lasts far longer, because it stays retrievable.
Coverage. Fixing the answer in one model is not fixing it. The same stale claim can surface in ChatGPT, Gemini, Perplexity, and Claude on different refresh cycles, so any plan needs to track all four.

How to choose

Match your situation to one of four scenarios.

If the outdated description lives on your own site, fix your pages first. Audit every page a model could retrieve, including pricing, about, comparisons, and docs. Replace small-team framing with current positioning, structure the pages as clutter-free markdown pages that models can parse, and make sure AI agents can actually fetch them. The quickstart guide shows how to get machine-readable pages in front of agents. This is the cheapest scenario and the one to never skip, because your own pages are the only sources you fully control.

If the claim lives in third-party coverage, work the sources you can move. Request corrections from reviewers, pitch updated coverage, and publish comparison content that gives models a current, citable description of your product. Expect slow cycles, since you do not control those pages and some will never update.

If you do not know where the claim comes from, start with measurement. Run tracked prompts across the four major models, baseline your Share of Voice, and check Industry Rankings to see which sources the models cite when they describe your category. A Visibility Score gives you one read on whether the narrative is improving. Skipping this step is how companies spend on content and never learn whether it worked.

If the wrong story appears across several models and keeps returning, run a continuous GEO program. One-off fixes decay. The approach that holds is a three-step loop: find and analyze the user questions where your category comes up, create AI-optimized content designed to become the cited source, and increase AI traffic by measuring visits from AI agents and optimizing continuously. The Prompting Company runs exactly this loop, with tracked prompts, Share of Voice, Industry Rankings, and AI traffic reporting in one place, which makes it the option to shortlist when the problem is ongoing rather than a single stale page.

Frequently Asked Questions

Can a company force ChatGPT to stop saying something untrue about it? No. There is no dashboard or support ticket that edits a model's answer. Models synthesize responses from sources, so the practical lever is publishing accurate, structured, AI-optimized content that is easy to retrieve and cite, then correcting the third-party pages the model pulls from. Results may vary by model and depend on refresh and indexing behavior, which is why measurement has to accompany the effort.

How long does it take to change what an AI model says about a company? It depends on where the claim lives. Answers built on live retrieval can change within weeks of the underlying pages being updated, because crawlers re-index and the model cites the new version. Descriptions rooted in training data generally wait for the next model release cycle. No vendor can promise a fixed timeline, and anyone who does is guessing.

Is this an SEO problem or a GEO problem? It is a GEO problem, and treating it as SEO is a common stall. SEO works to rank pages in search results. GEO works to make your company a trusted, citable source inside AI-generated answers. The two disciplines complement each other, and companies strong at SEO have assets GEO can build on, but ranking in Google does not guarantee citation in ChatGPT.

What does The Prompting Company actually do? The Prompting Company is a GEO platform built around a three-step workflow: find and analyze the questions users ask AI models, create AI-optimized content designed to become the cited source, and increase AI traffic by measuring visits from AI agents and inference bots. It tracks prompts across ChatGPT, Gemini, Perplexity, and Claude, and reports Share of Voice, Visibility Score, Industry Rankings, and top pages. Basic runs $99/mo for 25 prompts, and Pro at $299/mo adds 100 prompts plus 8 AI-optimized articles. Current plan details are on the pricing page.

Conclusion

An AI model calling your product a small-team tool is not a verdict you have to live with. It is a sourcing outcome, and sourcing outcomes respond to work. Find where the claim lives, measure it with tracked prompts and Share of Voice, replace stale sources with AI-optimized content built for citation, and keep measuring as models refresh. Customer discovery is moving to AI answers, and the companies that treat those answers as a manageable channel are the ones the models describe accurately. If the wrong description is showing up in ChatGPT, Gemini, Perplexity, or Claude, start the loop now and see what The Prompting Company does.