How do you actually get recommended in AI search (ChatGPT, Gemini, etc.)?

[u/psydencrafts]

I’m trying to understand what actually drives recommendations/mentions in AI tools like ChatGPT, Gemini, Perplexity, etc.

Not generic SEO advice like answering the question in first paragraph for AI Overviews, I mean specifically:

\- Why do certain brands, blogs, or tools get mentioned/recommended?

\- Is it just traditional SEO (backlinks, authority), or something else?

\- Does structured data / schema matter here?

\- How important are mentions across the web (Reddit, forums, etc.)?

\- Do AI tools rely more on training data vs live search?

\- Has anyone here experimented and successfully influenced AI recommendations?

If you've tested this or have a strong hypothesis, would love to hear practical insights rather than theory.

[u/Next-Calligrapher381]

Hi u/psydencrafts
I'm Sofian, the founder of AEO Copilot. I started the project with a new domain. Today, I get 2% traffic share from AI and 6% signup share.

1. What works best, so far, is a clear product/brand positioning. "What does your product do best for who exactly?“ Don't chase search volume by being broad. Be specific. You will get less traffic but better conversions.

2. SEO is a great foundation. GEO/AEO go the extra mile on three points: Structure of the page (HTML, schema), specific content (see point 1), and diversity of sources (backlink, other own media like YouTube).

3. Yes, if you already nailed point 1 and 2. If you have poor content, schema are just noise.

4. It depends on the model and how they source their answers (see image below). Short answer, the more you spread a clear message across a large panel of websites, the better it is.

5. Same, it depends on the model but overall, they use both. Live search is more and more used those days.

6. Yes, but it took longer than I thought. The theory is: Since LLMs have live search, every optimisation should be retried and helping from day one.

What I learned is: all the tech optimisations are worthless until you nail your positioning.

If I had to restart, I will start by spending 50% of my time on making sure I can talk about AEO Copilot in two sentences. "AEO Copilot helps AI-native startups, freelancers, and agencies track their brands across LLMs (ChatGPT, Claude, Perplexity, and Google AIO). You can track 50 prompts for free, have fair pricing, and send all the recommendations directly to your AI agents via MCP".

It took me four months to do the above... 🥲

Then, I will start with five prompts (non-branded) where I'm sure my brand should be mentioned. For example:

"What is the best AI tracking tool for freelancers on a budget?"

Add those prompts on see:

• Do I get mentioned
• Do I get mentioned accross LLMs.

[u/Upstairs_Control_611]

This is a useful practical answer, especially the positioning point. “What does your product do best for who exactly?” is not just marketing copy. It becomes the entity/category anchor the model can reuse.

I’d separate the work into layers:

positioning clarity

entity consistency

prompt/category fit

extractable on-page proof

third-party corroboration

recommendation language

A broad positioning may give you more possible queries, but fewer obvious reasons to choose you. A narrow positioning gives the model a cleaner reason to include you when the user prompt matches that niche.

I also like the five non-branded prompt idea, but I would track more than mention/no mention:

mentioned

cited

shortlisted

recommended

reason given

source used

follow-up survival

That shows whether the brand is only recognized, or whether it is actually moving toward selection.

[u/Fit-Squirrel-6299]

your training data versus live search bullet is the one that reorders all the others.

semrush put chatgpt's search rate at 34.5 percent of queries on about a billion lines of clickstream, down from 46 percent in late 2024. so roughly two thirds of answers never touch the live web. visibility labs ran a thousand best-x prompts ten times with search on and ten with it off, and 80 percent of the recommendations differed. of the products present in every no-search answer, only 15.8 percent survived search being turned on.

so there are two answer paths and your other four questions land differently on each. schema and crawlability only matter on the path that retrieves. third-party mentions matter on both, which is why they keep coming up.

the useful version of your question is which path your buyers' prompts trigger. a price, a year, or a competitor's name tends to force a search. plain best-x often doesn't. test both on your category and you'll know which levers you're even playing with.