How Do I Get My App Recommended by ChatGPT?

To get your app recommended by ChatGPT, give AI assistants clear, quotable facts they can find and trust: a plain-language description of what you do and who it is for, a machine-readable pricing page, an llms.txt file, and at least one independent page that describes you in the third person. AI recommends what it can read and cite confidently.

How do ChatGPT, Claude, and Perplexity actually decide what to recommend?

They recommend the apps they can find, understand, and quote without guessing. When someone asks an AI assistant for a tool like yours, the model either pulls from what it learned during training or searches the live web, then summarizes the few sources it trusts most. To be one of those sources, your app needs pages that state plainly what you do, who it is for, and what you cost.

Two things matter here. First, can the assistant retrieve a page about you at all. Second, once it has the page, is the relevant fact written so cleanly that the model can lift it word for word. A page that buries "we help solo founders schedule social posts" under three paragraphs of vibe is a page the model skips. A sentence that states it directly is a sentence it can quote.

• ▸It can find you: your site is crawlable, your pages load without JavaScript hoops, and the facts live in plain text, not only inside an image or a video.
• ▸It can understand you: one clear sentence says what the app does and who it serves, near the top, in language a stranger would use.
• ▸It can trust you: the same facts show up in more than one place, ideally including a page you do not control, so the claim looks corroborated rather than self-asserted.

What is the difference between SEO and answer engine optimization (AEO)?

SEO aims to win a click from a list of blue links. AEO, also called answer engine optimization or GEO, aims to get your facts quoted inside the answer itself, often with no click at all. Classic SEO optimizes for ranking. AEO optimizes for being the source an AI assistant cites when it writes its reply.

The practical shift is small but real. Old SEO rewarded pages that targeted a keyword and kept people on-site. AEO rewards pages that answer a specific question so cleanly that a model can extract the answer and attribute it to you. You still want Google to find your site. You just also want the answer box, the Perplexity citation, and the ChatGPT recommendation. For the SEO half that still matters, see SEO basics for makers who would rather build.

Why does clear, quotable, self-contained content get cited more?

Because an AI assistant has to stand behind whatever it quotes, and it picks the passages it is most confident about. A self-contained sentence ("Acme converts Figma files to clean React components, free for up to 10 files a month") needs no surrounding context to make sense. A sentence that says "our tool makes it easy" tells the model nothing it can repeat.

Here is the blind spot, and it is the hard part: you cannot read your own homepage the way a model does. You already know what your app is, so vague copy reads as fine to you. A first-time reader, human or machine, sees a fog. The fix is to write down the boring, specific facts you assume everyone already knows.

• ▸One-line definition: what it is, in plain words, no metaphor.
• ▸Audience: who it is for, named specifically.
• ▸The job it does: the concrete problem it solves, with an example.
• ▸Price: the actual numbers, not "affordable" or "flexible".
• ▸Proof: what makes the claim credible, stated honestly.

What is llms.txt and a machine-readable pricing file, and why do they help?

An llms.txt is a small plain-text file at the root of your site that hands AI tools a clean map of your most important pages and facts. A machine-readable pricing file (often pricing.md) does the same for your plans and prices. Both remove guesswork: instead of scraping your styled marketing page, a model reads a tidy summary you wrote on purpose.

They help because they shrink the gap between what you mean and what a model parses. Your homepage might wrap the price in a slider, a tooltip, and an animation. A plain Markdown file just says it. We use both on this site, and you can see the shape of them at /llms.txt and /pricing.md. For the how-to, read what is llms.txt and does my app need one and how to make your pricing readable by AI.

# pricing.md (excerpt)

## Starter
- Price: $0 (free)
- Includes: 10 exports a month, 1 project

## Pro
- Price: $12/month
- Includes: unlimited exports, 5 projects, priority support

That block is dull on purpose. Dull is exactly what a machine can read without misreading. When an agent or assistant needs to tell a user what you cost, it can quote those lines verbatim and get it right.

How does an independent, published review give AI a clean page to quote?

An outside review gives AI a third-party page that describes your app in the third person, which reads as more credible than your own marketing. When a model weighs whether to recommend you, a sentence on a page you do not own ("saasreview found Acme's onboarding clear and its checkout reliable") carries more weight than the same claim on your homepage, because it looks corroborated rather than self-asserted.

This is also how you borrow authority before you have any. A brand-new app has no user base to point to, but it can still earn one clean, quotable, outside page. That page becomes a citation an assistant can lean on. We go deeper on this in how reviews help AI find and recommend you and, for the trust side, how a brand-new app earns trust with zero users.

What honest things can you do this week to become more citable?

You can do most of the high-value work in an afternoon, and the cost of skipping it adds up: every week without these is a week an assistant can recommend a clearer competitor instead of you. Here is a realistic order.

1. 1.Rewrite your homepage hero into one plain sentence: what it does, who it is for. Read it to someone who has never seen the app and watch their face.
2. 2.Put your real prices in plain text on the page, with numbers, not adjectives.
3. 3.Add a pricing.md and an llms.txt at your site root pointing to your key pages.
4. 4.Write a short FAQ in real question-and-answer form, since that maps directly to how people ask AI tools.
5. 5.Get one clean, independent page that describes your app in the third person, so a model has something to cite besides you.

What does not work, and why does it hurt?

Keyword stuffing, hype words, and walls of vague copy actively lower your odds. Modern models are tuned to prefer clear, specific, attributable writing, so a page that repeats "best AI scheduling tool" twelve times reads as noise, not signal. The model has nothing concrete to quote, so it quotes someone clearer.

• ▸Keyword stuffing: dilutes the real facts and signals low quality.
• ▸Hype words like "revolutionary" or "seamless": they carry no extractable meaning, so they get ignored.
• ▸Facts hidden in images or video: a model cannot quote a screenshot of your pricing.
• ▸Inconsistent claims across pages: if your price differs between two pages, a model lowers its confidence and may skip you entirely.

The throughline is simple. Write for a sharp, literal reader who will repeat your words to someone else and has to be right. That reader is the AI assistant, and the human asking it. For the persona view of all this, see our guide for AI-built apps, or get a clean outside page started with a quick review.