How to Get Recommended by Every AI (ChatGPT, Claude, Gemini, Grok & More)

Your customers are no longer asking Google. They're asking AI: *"What's the best [your category]?"* — and the assistant answers with one or two business names. If you're not one of them, you don't exist for that buyer. This is the single biggest shift in discovery since search itself, and almost no one is optimized for it yet.

The good news: you don't have to optimize for each AI separately. Get the fundamentals right and you get recommended across *all* of them — ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek, Mistral, Copilot, and the assistants coming next.

Why one playbook works for every AI

Every major assistant draws from the same well. The live-search ones (Perplexity, ChatGPT search, Gemini, Copilot) retrieve from the public web in real time. The rest are *trained* on that same web plus a handful of trusted sources. So the signals that make one model recommend you are, overwhelmingly, the signals that make all of them recommend you.

There are three of them.

1. Be an unmistakable entity

AI has to understand *who you are* before it can recommend you. Reduce the ambiguity:

• Publish an llms.txt at your domain root — a clean, plain-English summary of what you do, for whom, and your key pages.
• Add Organization + FAQ schema (JSON-LD) so models can resolve you as a real business and lift quotable answers.
• Make your name and details consistent everywhere — site, profiles, directories. Conflicting signals confuse the model.
• For local businesses, a complete Google Business Profile and LocalBusiness schema matter for "near me" AI answers.

2. Earn citation density on the sources every model trusts

This is the biggest lever, and the one most businesses miss. Models recommend what is *recommended a lot* across sources they trust:

• Review platforms: G2, Capterra, Trustpilot, and the leading directory for your niche.
• Community: genuine, helpful presence in Reddit threads and forums where buyers ask for recommendations.
• Editorial: "best [category]" roundups, comparison articles, and press mentions.
• Reference: a Wikipedia page (if you qualify), and consistent mentions across the open web.

Brands cited across 5+ of these trusted sources get named dramatically more often in AI answers. This is earned, not bought — and it compounds over weeks and months.

3. Own the quotable, answer-style content

Give the models something clean to lift:

• Clear "best [category] for [use case]" pages and "[competitor] alternatives" pages — exactly what AI pulls vendor lists from.
• FAQ blocks that answer buying-intent questions in one quotable paragraph each.
• Content that's current; stale pages get passed over.

What about Alexa, Siri, and international AIs?

Voice and ambient assistants (Alexa+, Apple Intelligence/Siri) and international models (DeepSeek, Mistral/Le Chat, Baidu's Ernie, Yandex's Alice) increasingly use the same retrieval-and-trust mechanics. The same three fundamentals carry across borders and surfaces. Where a market has its own dominant review sites or directories, add those to step 2 — but the playbook is the same.

The honest timeline

Getting recommended by AI is a compounding game, not a switch. Entity and structured-data fixes are fast (days). Citation density builds over weeks to months. The businesses that start now own the answer before their competitors realize the game changed.

Don't guess — measure it

You can't improve what you can't see. The fastest way to know where you stand is to check whether each AI actually names you today, across every engine, and see exactly which gaps to fix.

That's what we built Cited to do: run the real buying-intent prompts across ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek, Mistral and Copilot, score whether you get recommended, show who's named instead of you, and generate the exact fixes (llms.txt, schema, the sources to get listed on) — then monitor daily as your visibility climbs.

Run a free scan at trycited.io and see if AI recommends your business — then make it.