Have you asked ChatGPT about yourself yet? If not, you should. Millions of people are using ChatGPT to research businesses, evaluate professionals, and make purchasing decisions. What it says about you, or whether it says anything at all, is shaping perceptions right now.
Open ChatGPT and type your name or your business name. Ask it to describe you. Ask it what you are known for. Ask it to compare you to your competitors. The answers may surprise you, and understanding why ChatGPT responds the way it does is the first step toward influencing those responses.
Where ChatGPT gets its information
ChatGPT is trained on a massive dataset of text from the internet, including websites, news articles, books, forums, and social media. It has a knowledge cutoff date, which means it does not know about things that happened after its training data was collected. It synthesizes what it has learned into conversational responses, but it does not search the web in real time (unless using specific browsing features).
This is fundamentally different from how Perplexity works, which searches the web live for every query. With ChatGPT, the information is baked into the model. If positive, authoritative content about you existed on the web when the training data was collected, ChatGPT likely knows about it. If it did not, ChatGPT may have limited or inaccurate information, or it may not know about you at all.
Why ChatGPT might get things wrong
ChatGPT can confidently present information that is incomplete, outdated, or outright incorrect. This is a characteristic of how large language models work. They generate the most statistically likely response based on patterns in their training data, which means they can produce plausible-sounding statements that are factually wrong.
For businesses, this can be a real problem. ChatGPT might associate you with the wrong industry, attribute achievements to someone else, or miss your most important differentiators entirely. It might surface negative information that has since been resolved or present outdated details about your services.
What you can do about it
You cannot directly edit what ChatGPT says. But you can influence it by shaping the information that future training data will draw from. This is where AI optimization intersects with traditional reputation management and SEO.
Start by building a strong, authoritative web presence. Your website should clearly describe who you are, what you do, and what you are known for. Use specific, factual language rather than vague marketing copy. The more clear, consistent information that exists about you across reputable sources, the more likely AI models are to get it right.
Third-party coverage is critical. Press articles, industry publications, Wikipedia mentions, and authoritative directory listings all contribute to the dataset that AI models train on. One mention in a respected outlet carries more weight than dozens of self-published blog posts.
I wrote about this concept in detail on HackerNoon: if your products are not AI searchable, you are already losing. The businesses that invest in building citable, authoritative content now position themselves to be represented accurately by AI systems in the future.
ChatGPT vs. other AI models
ChatGPT is not the only AI system people are using to research businesses. Claude, built by Anthropic, handles reputation queries differently, with more caution and more explicit statements about uncertainty. Perplexity searches the web live and cites its sources directly. Google AI Overviews pull from Google's own index.
Each platform has its own tendencies, and what one says about you may differ from what another says. A complete AI visibility strategy accounts for all of them.
Monitoring your AI presence
Make it a habit to periodically check what AI systems say about you. Ask ChatGPT, Claude, and Perplexity about your business. Note what they get right, what they get wrong, and what they leave out. This gives you a roadmap for where to invest your content and PR efforts.
Pay attention to how AI systems answer comparison queries too. "Who are the best [your industry] in [your city]?" is a query that more and more people are asking AI rather than Google. If your competitors appear in those answers and you do not, that is a visibility gap you need to close.
Take control of your AI narrative
The businesses and professionals who proactively manage their AI presence will have a significant advantage over those who discover too late that AI is saying the wrong things about them. This is still early. The playing field is not yet crowded. But it will be.
If you want to understand what AI systems are saying about you and take concrete steps to shape that narrative, our AI search optimization services are built for exactly this. We audit your AI presence across all major platforms and build a strategy to improve it. Start the conversation below.
Related resources
- What does Claude say about you?, Check your presence in another major AI model
- How to appear in AI search results, Strategies for all AI platforms
- Google AI Overviews guide, Optimize for Google's AI search
- AI search optimization services, We manage your AI presence
Research and context behind this guide
Public adoption of AI tools for research and decision-making has accelerated fast. A study from Pew Research on US public and expert AI views shows that a majority of American adults have used a generative AI tool. This is a mainstream audience. When someone types your name into ChatGPT, they join a growing behavior pattern that shapes purchasing and hiring decisions.
On the technical side, the gap between how AI systems synthesize information and how traditional search indexes it is well documented in recent information retrieval literature. Preprints catalogued at arXiv's Information Retrieval section show ongoing research into how large language models weight source authority, recency, and repetition when forming responses. The practical takeaway: consistent, factually precise coverage across multiple independent domains outperforms any single high-traffic page. Meanwhile, Google Search Central's AI features guidance confirms that structured, authoritative on-page content improves how AI-powered summaries represent a business, a principle that extends to how ChatGPT itself was trained on crawled web data.
The privacy dimension is real too. The FTC's privacy and security guidance for businesses increasingly covers AI-generated profiles and inaccurate data representations, and the International Association of Privacy Professionals has published frameworks for understanding when AI-generated descriptions of individuals may trigger data accuracy obligations under state and federal law. If ChatGPT is producing materially false statements about you or your company, it may carry regulatory weight depending on your industry and location.
What this looks like in practice
Medical practices often find that ChatGPT credits them with procedures they do not perform or lists physicians who have left the group. The fix is straightforward. We help clinics publish updated, structured content on their own sites and secure coverage in health journalism outlets. Within the following training cycles, AI responses begin to reflect the current roster and correct specialties.
Software founders frequently discover that ChatGPT miscategorizes their products. This confusion often traces back to early press coverage that used loose language. Because a single authoritative mention can outweigh a company's own website copy in training weight, the wrong framing sticks. Earning accurate, detailed write-ups in industry publications and updating structured data helps shift the model's description to reflect the correct positioning.
Commercial contractors with thin online presences often find that ChatGPT says almost nothing about their firms, defaulting to generic regional competitors when asked for recommendations. Steady content and citation work changes this. Securing features in trade publications and consistent profiles in local business journals helps firms appear by name in relevant queries, with accurate descriptions of project scale and geographic focus.
By the numbers
AI chatbot adoption has moved faster than almost any consumer technology in history. A Pew Research survey on how the U.S. public views artificial intelligence shows that a growing share of adults report using AI tools to look up information about products, services, and local businesses. These are real people forming real opinions based on what an AI tells them, and that audience expands every quarter.
The accuracy problem is documented. Researchers publishing on arXiv's information retrieval preprint server show that large language models produce factual errors at rates that vary sharply by how well-represented a topic is in training corpora. Entities with thin or inconsistent web footprints see higher error rates than entities with dense, cross-referenced coverage. If your public web presence is sparse, your odds of experiencing these errors go up considerably.
Media behavior is shifting in parallel. The Reuters Institute for the Study of Journalism reports that adults increasingly use AI chatbots to get news or background information. That behavioral shift matters for reputation because it changes where people form their first impression of you. A first impression is increasingly a paragraph generated by a model that cannot cite its sources in real time or flag when it works from stale data. The window between someone asking ChatGPT about your business and forming a judgment is seconds.
These trends tell a consistent story. Chatbot usage is mainstream. Hallucination rates are significant. The migration away from link-based search toward conversational AI is accelerating. If you treat AI reputation as a future concern, you are already behind the curve. The corrective steps, publishing clear authoritative content, earning third-party citations, and maintaining consistent entity signals across the web, strengthen your overall digital presence regardless of which AI model is asked about you next.
Another client situation
Attorneys often find that ChatGPT describes them as general practitioners with no noted specialization, even if they have spent years building a focused practice. The problem frequently stems from website copy that leads with broad phrases instead of specific, factual language about case history and concentration. ChatGPT pattern-matches to generic attorney language instead of actual positioning. Rewriting primary pages to lead with concrete facts, securing bylined articles in legal publications, and listing accurate specialty descriptions in authoritative directories solves this. Following these updates, AI models are far more likely to correctly identify specific practice areas.