You get recommended by AI when it can read your site, match you to buyer questions, quote your answers, verify your claims and send a ready buyer to a site built to convert. That is five jobs, and most businesses skip at least four of them. Many small businesses are never recommended even once.
What does it take to get recommended by AI?
It takes a system, not a trick, because AI engines recommend the businesses they can confidently describe.
The Match Protocol™ is a five-layer framework developed by Clint Rogers. Each layer moves one stage of the Match Protocol Score™. The benchmark shows where most owners are stuck: the average Found score was 80, the average Trusted score was 87, and the average Recommended score was 15. AI can see you. It just is not choosing you.
Here are the five steps in order.
Step 1: Can AI read your website?
The first layer, Machine-Readable Architecture, makes your site legible to machines.
That means schema markup in JSON-LD, which is code that labels your business facts. It means an llms.txt file, a plain file that summarizes your site for AI models. It means semantic HTML, which uses proper headings and structure. It means removing JavaScript bloat that hides content from crawlers, and confirming AI crawlers are allowed in. In the benchmark, 88% of businesses had incomplete or missing structured data.
Step 2: Does AI match you to the right buyer questions?
The second layer, Algorithmic Alignment, connects your business to the questions buyers really ask.
When a buyer asks an AI one question, the engine often splits it into several smaller questions behind the scenes. This is called query fan-out. Algorithmic Alignment maps those sub-questions for your industry so you can answer them.
It also includes a knowledge-graph audit. A knowledge graph is the web of facts AI holds about your business. If it holds wrong facts, the engine may state them as truth. That is a hallucination, and fixing it is part of the job.
Step 3: Is your content built to be quoted?
The third layer, Targeted Extraction, structures your pages so an engine can lift a clean answer.
Open every page with a bottom-line-up-front answer. Phrase headings as the questions buyers ask. Add FAQ content that answers real objections. In the benchmark, 66% of businesses had no FAQ content answering buyer questions. This layer maps to the Recommended stage, which carries 35% of the score.
Step 4: Can AI verify what you claim?
The fourth layer, Citation & Corroboration, proves your claims outside your own website.
AI trusts what it can confirm in more than one place. That means seeding Wikidata, Crunchbase, relevant directories, press and reviews with consistent facts. It also means publishing proprietary statistics and frameworks that others cite back to you. Original material gives an engine a reason to name you instead of a competitor with the same generic copy.
Step 5: What happens when the buyer arrives?
The fifth layer, Human Handoff, makes sure the AI referral turns into revenue.
Ahrefs reported in June 2025 that AI search visitors were 0.5% of its traffic but 12.1% of its sign-ups, converting 23 times better than organic. These buyers arrive pre-sold. Track AI referrals so you know where they come from. Then give them human-led sales copy and a conversion-tuned site that answers the last question before they book.
What mistakes keep businesses from being recommended?
The most common mistake is doing one layer well and assuming the work is done.
- Redesigning the website for looks while leaving it unreadable to crawlers.
- Writing about services in brand language instead of the words buyers use.
- Burying the answer in the third paragraph, where an engine will not lift it.
- Claiming expertise on your own site with no confirmation anywhere else.
- Sending AI referrals to a page that does nothing to convert them.
Each of these breaks the chain at a different stage. That is why measuring stage by stage beats guessing.
What does a done-for-you engagement look like?
It is a six-month engagement where Modern Asset Lab runs all five layers for you.
Engagements carry the guarantee "Top Recommended or You Don't Pay" and are built for businesses with $500K+ in annual revenue. Start by measuring where you stand across ChatGPT, Google AI Overviews, Perplexity and Gemini. The AI search optimization page explains the scope.
Frequently asked questions
Can I pay to be recommended by ChatGPT or Perplexity?
No. Recommendations are generated from what the engines can read, match and verify. You earn them by making your business easy to understand and confirmed across the web, not by buying a placement.
Do all four engines work the same way?
They differ in details, which is why the Match Protocol Score™ tests ChatGPT, Google AI Overviews, Perplexity and Gemini separately. The five layers apply to all of them because each engine needs to read, match, quote and verify you.
How do I know if AI is recommending me now?
Ask the engines buyer questions without naming your business, and record who they name. The Match Protocol Score™ does this at scale with up to 32 blind buyer conversations per business.
Next step
Measure first, then fix in order. Book Your Match Protocol™ Strategy Session or Get Your Match Protocol Score™.
Clint Rogers is the founder of Modern Asset Lab and the developer of Match Protocol™.
