How to Get Your Startup Recommended by ChatGPT and Other AI

A founder showed me her analytics last month, confused. Signups were up, but a growing slice of new users could not say how they found her. When she started asking them directly, the same answer kept coming back: "I asked ChatGPT for the best tool to do X, and yours came up." She had not done anything to make that happen. She had gotten lucky. Her competitor, who showed up in the same answer more often, had not.

This is the shift underway in how people find software. Buyers used to type a query, scan a page of blue links, and click. More and more, they ask an AI tool a question and read the synthesized answer, and the products named in that answer get the consideration. The discipline of getting named there has a name, generative engine optimization, and this post is about how it actually works, without the snake oil that has already attached to it.

Why AI recommendations are the new front page

For a decade, the goal of search marketing was to rank on the first page of Google. The first page still matters, but a layer has formed on top of it. When someone asks ChatGPT, Claude, or Perplexity "what is the best tool for X," or when Google shows an AI Overview above the links, the buyer often acts on that answer without scrolling at all.

That changes the target. You are no longer only competing for a ranked position. You are competing to be one of the few products a model names when it summarizes your category. Being on page one is necessary but no longer sufficient, because the model might cite three sources and your competitor's comparison page is one of them while your homepage is not.

The good news for a small team is that this layer is less saturated than classic SEO and rewards clarity over budget. The model is trying to give a correct, useful answer. If your product is the right answer and the web says so clearly, you have a real shot, even against bigger incumbents whose web presence is bloated and vague.

How models actually choose what to recommend

To optimize for it you need a rough mental model of how the decision gets made. Two inputs matter.

The first is training knowledge. The model learned about the world, including your category, from a large slice of the web frozen at training time. Products that were described widely and consistently before that cutoff are part of what the model "knows." You cannot edit this directly, and it is slow to change, but broad lasting coverage of your product feeds it over time.

The second, and the one you can move quickly, is retrieval. Many AI tools now search the live web at answer time and synthesize from what they find. Perplexity does this by default, ChatGPT does it when it decides a query needs fresh information, and Google's AI Overviews are built on live results. For these, the question becomes simple: when the model searches your category right now, does it find clear, credible pages that name and describe your product? If yes, you can be cited within days. If no, you are invisible regardless of how good the product is.

Both inputs reward the same thing: the web describing you clearly, consistently, and in enough credible places that a model has material to quote.

What actually gets you cited

Here is the work, in rough order of leverage.

Describe yourself precisely on your own site. The model needs a clean, unambiguous statement of what you do, who it is for, and how it differs. Vague hero copy that could describe ten products gives a model nothing specific to repeat. Name the problem and the mechanism in plain words.

Be quotable. Models lift clear sentences. A page with a crisp definition, a direct comparison, or a specific claim gives the model a sentence to use; a page of adjectives does not. Write the sentence you would want the model to say about you, and put it on the page.

Earn third-party mentions. This is the GEO-specific multiplier. Comparison articles, review sites, directories, forum threads, and other people's blog posts are exactly what a retrieving model pulls from when it answers "best X tool." One credible comparison page that includes you can do more for AI recommendations than five posts on your own domain.

Keep your facts consistent everywhere. If your pricing, positioning, and category name differ across your site, your directory listings, and review pages, the model gets conflicting signals and hedges. Consistency is a ranking factor for trust, human and machine alike.

Make your pages crawlable and structured. Clean HTML, fast pages, and schema markup help models parse and trust your content. This is the same hygiene that helps SEO, which is the theme: GEO is mostly SEO done well, aimed slightly differently.

Lever	Helps SEO	Helps GEO	How fast
Precise on-site description	Yes	Strongly	Days
Quotable claims and definitions	Some	Strongly	Days
Third-party mentions and comparisons	Yes	Strongly	Weeks
Consistent facts across the web	Some	Strongly	Ongoing
Crawlable, structured pages	Yes	Yes	Once

The honest limits

Be skeptical of anyone who promises to "get you into ChatGPT" for a fee. You cannot buy a slot in a model's organic recommendations, and attempts to game models with keyword-stuffed junk tend to age badly as the tools get better at detecting it. The durable strategy is the boring one: be genuinely the right answer, and make sure the credible web says so.

It is also not instant for the deepest layer. Live retrieval can pick up a new page in days, but shifting what a model "knows" by default takes broad coverage accumulating over months. Treat GEO like SEO: a compounding investment, not a switch. The teams that show up in answers next year are the ones building clear, well-cited presence now.

And it does not replace classic search. People still click links, AI Overviews sit on top of real results, and the same content that ranks tends to be the content models cite. GEO and SEO are one effort with two payoffs. If you want the wider context for where this fits among all your channels, how to use AI for marketing maps the full landscape, and if you are a builder who would rather not run any of this by hand, marketing for technical founders covers the minimal cadence.

Where Revnu fits

Getting cited by AI is exactly the kind of work that is simple to describe and tedious to sustain: publish clear pages, target what buyers ask, keep facts consistent, earn mentions, repeat. Revnu runs that loop for you. It writes content tuned for both Google and the LLMs that recommend your space, keeps your positioning consistent, and learns which angles get picked up, all with your approval before anything ships. The same shared learning loop that powers its other channels applies here, which is the core idea behind an AI growth agent: what works in one place informs the next. You can see the channels it covers on the features page.

Where this leaves you

Your buyers are already asking AI what to use, and the answer is decided by what the credible web says about your category right now. You cannot pay your way into the answer, but you can earn it: describe yourself precisely, write quotable claims, earn third-party mentions, keep your facts consistent, and keep your pages clean. It is SEO with a slightly different aim, it compounds over months, and it rewards the clear over the loud. Start writing the sentence you want the model to say about you, and then make the web say it too.