Technical AI search readiness: what to check before chasing AI citations

Classic SEO audits usually answer questions like: can Google crawl this page, does the title tag exist, is the sitemap discoverable, and are canonical tags configured correctly? Those checks still matter.

But AI-assisted search adds a slightly different question: Can an answer engine crawl, understand, extract, and trust this page?

I have been building a free technical checker for that question. The goal is not to predict rankings or promise citations from ChatGPT, Perplexity, Gemini, Claude, Bing, or Google AI Overviews. The goal is much narrower: surface concrete readiness issues a site owner can actually fix.

Checks Included

• Whether robots.txt blocks common AI and search crawlers.
• Whether LLMs.txt or LLMs-full.txt exists and is readable.
• Whether sitemap discovery works.
• Whether important pages expose structured data.
• Whether headings and page sections are answer-ready.
• Whether the publisher/entity is clear.
• Whether trust signals such as about, contact, author, dates, and references are visible.

AI Visibility vs. AI Readiness

The most useful part so far has been separating "AI visibility" from "AI readiness." Visibility is the downstream result. Readiness is the technical and editorial surface that makes the site easier to crawl, parse, and cite.

That distinction matters because site owners cannot directly force an AI system to cite them. But they can remove crawler blocks, expose clearer files, improve content structure, add useful schema, and make sources and ownership easier to verify.

Resources

I published the checker here:
https://ai-search-readiness.s01071233604.workers.dev/tools/aeo-checker

Methodology:
https://ai-search-readiness.s01071233604.workers.dev/methodology

References:
https://ai-search-readiness.s01071233604.workers.dev/references

Feedback welcome, especially on false positives, missing crawler rules, and which checks are genuinely useful in production SEO workflows.