How to get ChatGPT to recommend your brand

First, the honest caveat: nobody can guarantee ChatGPT will recommend you. Answers vary between runs, models update, and the ranking logic is not public. What you can do is stack the observable odds in your favour. Everything below is based on how these systems visibly behave, not on secret tricks.

1. Find out where you stand today

Before changing anything, measure. Take the ten questions a real buyer would ask - "best [category] for [audience]", "alternatives to [competitor]", "[your brand] vs [competitor]" - and run them through the providers' APIs multiple times. Record your mention rate, your position when mentioned, and every source the answers cite. This baseline is what makes every later change testable instead of vibes.

Write the questions from the buyer's vocabulary, not yours. "Best CRM for a five-person agency" is what someone actually types; "enterprise-grade customer relationship management platform" is what your homepage says. If your prompt list reads like your own marketing copy, you're measuring how well you describe yourself, not whether buyers find you.

2. Win the sources the answers already cite

When ChatGPT searches the web before answering, it leans on a small set of pages per question: comparison posts, review aggregators, community threads, industry lists. Those citations are a literal to-do list. Get reviewed on the aggregators that keep appearing. Pitch the authors of the comparison posts that omit you. Answer the recurring questions in the communities that get cited. This is classic digital PR, aimed with unusual precision.

The precision is the point: instead of guessing which twenty publications matter for your category, your own sampled answers tell you exactly which five to seven pages keep coming up. Rank them by how often they're cited across your question set and work down the list in that order - that's a better prioritization signal than any generic "top publications in your industry" list a PR agency will sell you.

3. Make your own site quotable

• State plainly what you are, who you're for, and what you cost. Models synthesize; give them clean sentences to synthesize from.
• Publish an honest comparison page. If you don't describe how you differ from competitors, a third party will do it for you, less favourably.
• Keep pricing public and current. "Contact us" pages give an assistant nothing to say when someone asks about cost.
• Use structured data (Organization, Product, FAQ schema) so crawlers parse your facts unambiguously.

A minimal FAQ schema block is a few minutes of work and directly answers the shape of question buyers ask an assistant:

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "How much does [Product] cost?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "[Product] starts at $29/month for up to 5 users, billed monthly or annually."
    }
  }]
}

4. Don't block the crawlers

Check your robots.txt. GPTBot and OAI-SearchBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot, and Google-Extended all identify themselves. Blocking them while wanting AI visibility is asking to be recommended by systems you've locked out. Also make sure your key pages render without JavaScript - many AI crawlers read plain HTML and never execute your client-side bundle, so anything that only appears after a fetch is invisible to them.

This is worth a direct check, not an assumption - bot-protection tools and CDN abuse filters block these crawlers by accident far more often than robots.txt does on purpose. We cover the exact syntax and the common accidental-block causes in a dedicated post on how AI crawlers work.

5. Feed the durable record

Search grounding decides today's answers; training data decides the model's baseline instincts. Wikipedia (where you legitimately qualify), Wikidata, GitHub, established review platforms, and long-lived industry publications all persist into future training runs. Mentions there compound in a way your own blog cannot - a blog post you publish this week can influence an answer next week; a Wikipedia mention can influence answers for years, across every future model that trains on a snapshot of the public web.

6. Re-measure on a schedule

Run the same question set weekly or monthly with the same method. A single scan tells you where you stand; a series tells you whether anything you did worked, and alerts you when a model update quietly drops you from answers you used to win. Treat it like uptime monitoring for your brand's presence in AI.

Mistakes that quietly cancel all of this out

• Changing the question wording between measurements. A different prompt is a different experiment - you can't tell if your mention rate moved because of your work or because you asked a different question.
• Optimizing for one provider only. A page written purely to please one assistant's search behavior often does nothing for the others; track providers separately and expect the gains to arrive unevenly.
• Publishing a comparison page and never updating it. A stale comparison page is worse than none - it gets cited with numbers that are now wrong, and you don't control the correction.
• Treating one good run as proof it worked. Wait for the next full sample before declaring victory; a single favorable answer is exactly the noise this whole approach exists to filter out.