“How do I get my company to show up in ChatGPT?” is fast becoming one of the most common marketing questions of the decade. The honest answer is that no one can guarantee a placement — these are probabilistic systems, not a directory you can pay to join. But there is a great deal you can do to improve your odds, and it starts with understanding how the answer is built.
First, understand what the model is doing
When someone asks an assistant to recommend a business, the model draws on two things: what it absorbed during training, and — for tools with live retrieval — what it can pull from the web in that moment. Your job is to make sure that in both cases, there is accurate, positive, easy-to-parse information about you to draw on.
1. Make your own website unambiguous
This is the foundation. Your homepage and key pages should state, in plain language, exactly what you do, who you serve and where. Avoid clever taglines that hide the substance. A model that reads “we spark joy through experiences” learns nothing; a model that reads “we are a boutique wedding-photography studio serving Lyon and the Rhône-Alpes region” learns everything it needs to recommend you for the right query.
2. Add structured data
Machine-readable markup (such as schema.org structured data for your organization, products and reviews) helps systems understand your business without guessing. It is invisible to human visitors but a clear signal to machines about your name, category, location and offerings.
3. Build consistent third-party presence
Models weigh corroboration. If several independent, credible sources describe you the same way, the model becomes more confident in mentioning you. That means accurate listings, genuine customer reviews, coverage in industry publications, and a consistent name and description everywhere you appear. Inconsistency — a different company name here, an old address there — makes a model hesitant.
4. Publish genuinely useful content
Content that clearly answers real questions in your field does double duty: it helps customers, and it gives models well-structured material that associates your brand with expertise in your category. You are reading an example of this right now.
5. Fix what the model gets wrong
Often the problem isn’t absence — it’s inaccuracy. A model might describe an old product line, an outdated price, or confuse you with a similarly named company. The only way to catch this is to ask the questions and read the answers regularly, then correct the underlying sources the model is likely leaning on.
The part most people skip: measurement
Every step above is guesswork unless you measure the result. You need to know, before and after your changes, how the major assistants actually respond when asked about your category. That means running the real questions against the real models on a schedule and tracking whether your mention rate and the quality of your description improve.
That measurement loop is exactly what Promptly Seen was built for: it runs your category’s questions against leading AI models and shows you how you are described and recommended over time — so the work you put in has a scoreboard, not a shrug.