Ask ChatGPT for a project management tool and it names five. Ask it who builds custom software in Nijmegen and it names five. Your brand is in that list or it is not, and the list is short. What puts a brand on it is ordinary work: say clearly who you serve, get described the same way in the places a model reads, and make your own pages easy to lift from. Here are five moves in the order I would do them, each with a way to check whether it worked.
1. Say who you are for, in one sentence
A model recommends a brand when it can tell which question that brand belongs in. Most homepages make that impossible. “A powerful platform for modern teams” fits every question and therefore none of them. “Project management for small remote engineering teams” fits one, and gets named as soon as it is asked.
Write that sentence the way your buyer would describe their own situation, then put it in the first hundred words of your homepage, in your meta description, and in the opening line of your About page. If you serve two segments, write two sentences and give each its own page. A model learns two clear sentences easily. Guessing which half of a vague one applies to whom is what defeats it.
Check: open a fresh chat, paste your homepage URL, and ask “who is this for, and when would you recommend it?” If the answer hedges, the page hedges.
2. Get other people to describe you the same way
What third parties write about you carries more weight than what you say about yourself, because a model reads it as the general view. Reviews, category roundups, comparisons, trade directories, forum threads and industry press all feed that view.
Pick the places your buyers already check: a Capterra listing, a Trustpilot profile, your trade association's member directory, a roundup in an industry title. Claim your entry in each one and make it open with the same sentence from step one.
Check: ask an assistant what people say about you, by name, and watch which phrasing comes back. That is your public description, whoever wrote it.
3. Use that sentence everywhere a crawler can reach
When your homepage, your listings and your press kit each describe you slightly differently, a model averages the versions and hedges. This is the dullest of the five and the cheapest to do: pick the sentence, then walk every surface that describes you.
- Your homepage, your About page and your meta description.
- Every directory and review profile you claimed in step two.
- Your LinkedIn bio and other social profiles, which get quoted back more often than people expect.
- Your press kit, your standard email boilerplate, and any partner page that describes you on someone else's site.
Check: search your brand name and read the first ten descriptions of you that are not your own site. Count how many match.
4. Make your best pages easy to quote
A model reaches for text it can lift cleanly: a direct answer near the top, headings that say what follows them, and a page it is allowed to fetch. Four habits cover most of it.
- Answer the question in the first sentence, then explain. A page that warms up for three paragraphs loses to one that does not.
- Add structured data so a crawler can read what the page is about.
- Keep an llms.txt: a plain text file at your domain root that sums up what you do and points at your key pages, the way robots.txt points at your sitemap.
- Confirm the assistants' crawlers are allowed to fetch you. Blocking GPTBot or ClaudeBot in robots.txt drops you out of every answer that would have retrieved you.
That last one catches more businesses than it should. A rule added years ago to keep scrapers off a staging site is still there, and nobody re-reads robots.txt.
5. Measure before and after
Every move above is a hypothesis. Look at how the models describe you now, change one thing, wait, and look again.
A workable method by hand: write down ten to twenty questions the way your buyer would ask them, using their words for the problem. Put each one to ChatGPT, Claude and Gemini. Record which brands get named and in what order. Repeat in three weeks. A single snapshot tells you little, because the same question asked twice returns different brands. The trend over several weeks is the signal.
Two things will distort the result if you let them. A model that cannot search the web answers from memory, so a change you shipped last week cannot possibly appear in it. And a run that half failed looks like a drop in visibility when it is a drop in measurement. Note which model answered, and how many of your questions actually produced an answer, every single time.
That is the part we automated with Brandtrace: the same questions, on the models your buyers use, on a schedule, with the competitors and the sources named alongside. See what it finds for your domain.
Expect the first two steps to show up within a month or two. Steps three and four arrive later, as pages get re-crawled and re-read. The measurement is what tells you which one did the work.