# The AI-search visibility checklist we run on this site

> Everything on this list ships on this site right now — the schema graph, the llms.txt files, the markdown twins, the glossary built for quoting. This is the checklist version, so it can be checked.

AI search is not one channel but three behaviours: assistants that fetch pages live to answer a question (ChatGPT search, Perplexity, Claude), answer engines that compile citations from their index, and the SEO fundamentals that still decide what gets found at all. Each needs different plumbing, and most sites are set up for exactly one of them.

The honest framing before the list: nobody has proven which of these moves the needle and how much, because the systems change monthly and attribution is near-impossible. The items marked as proven also earn their keep on plain SEO and accessibility; the ones marked as bets are cheap enough to place anyway. That honesty is itself one of the items — an answer engine asked 'is this accurate' rewards content that says what it knows.

## Proven: the crawlable, quotable surface

Server-rendered content the crawler sees without executing JavaScript; a clean sitemap and robots.txt; definitional content where the answer is the first sentence (the part that gets quoted); structured data restating the page honestly (Organization, Article, FAQPage, DefinedTerm); real author and organization entities with sameAs links so the machine knows who is speaking.

## Working so far: the machine-readable extras

llms.txt and llms-full.txt as a site map for models; .md twins of key pages so a fetch gets markdown instead of a canvas; speakable markup marking the passage to read aloud; named AI-crawler rules in robots.txt documenting a deliberate allow. Early signs say the .md twins get fetched by assistant crawlers — the rest is inexpensive enough to keep.

## Unproven bets, cheap enough to place

Directly answering the phrasing of questions people ask assistants; keeping facts consistent everywhere the entity appears (Knowledge Graph work by another name); fresh content so crawlers re-check often; and content so quotable that being cited is the default outcome. None of these has a guaranteed return — all of them cost less than a month of ads.

## What to take away

- AI search = live-fetch assistants + compiled answer engines + classic SEO plumbing.
- Proven: server-rendered quotable content, honest schema, real entities.
- Working: llms.txt, .md twins, named crawler rules — assistants do fetch markdown.
- Say what is proven and what is a bet — honest hedging is itself citable.

## Tags

AEO, AI search, llms.txt, SEO

## We build this for clients

https://dfieldsolutions.com/en/services/web-performance

## More from the lab

- https://dfieldsolutions.com/en/lab/structured-data-aeo.md — Structured data an answer engine can use
- https://dfieldsolutions.com/en/lab/llms-txt-ai-crawlers.md — What we did for the AI crawlers
- https://dfieldsolutions.com/en/lab/hreflang-trilingual-site.md — One site, three languages, zero duplicate-content problems
- https://dfieldsolutions.com/en/lab/migration-without-loss.md — Move the site, keep the rankings

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Source: https://dfieldsolutions.com/en/lab/ai-search-visibility-checklist
DField Solutions — Dunakeszi, Hungary — dezso@dfieldsolutions.com
Booking: see https://dfieldsolutions.com/en/contact