AI search readiness, without the GEO snake oil

There is a small industry forming around "Generative Engine Optimization." Most of it is recycled SEO advice with new vocabulary. Some of it is invented. Google has stated, in their own documentation, that AI Overviews and AI Mode use the same signals as regular search and that no special optimization is required. That is the actual baseline. Anything beyond that is consensus or speculation.

This is the short version of what is real, what is consensus, and what to ignore.

What Google actually says

Three things, repeated across Google's published guidance:

• AI features rely on normal SEO. Pages that meet Google's technical requirements and content policies are eligible for AI surfaces.
• The advice they give is the same advice they have given for years: original, helpful, people-first content, clear authorship, technical fundamentals, structured data where it fits.
• E-E-A-T is a framing, not a ranking factor. Aligning content with experience, expertise, authoritativeness, and trust is encouraged. There is no E-E-A-T score.

Read that paragraph twice before paying anyone for "AI SEO consulting."

What the consensus around AI answer engines suggests

Beyond the official line, there is a real pattern in how LLM-driven engines pull content into answers. The pattern is consistent across ChatGPT, Gemini, Perplexity, and Bing.

1. Answer-first prose. Put the direct answer in the first sentence of the section. Models extract passages, not pages. A 400-word intro that "sets the scene" gets skipped.
2. Question-shaped headings. Headings like What is X?, How do I do Y?, When should you Z? get matched against user queries with much higher precision than topical headings like "Overview."
3. Named authors and sources. Bylines, author bios, and citations to primary sources are trust signals models can verify. Anonymous content is rarely cited.
4. Entity clarity. Use the same name for your product, brand, and key concepts every time. Define acronyms on first use. Models build entity graphs from your text, and inconsistency fragments the graph.
5. Freshness for time-sensitive topics. Visible publish or update dates. A 2020 article on a 2026 topic gets passed over.
6. Numbers, evidence, and short quotes. Concrete claims with concrete support get cited. Vague hedging does not.

What to ignore

• AI-specific meta tags. Some tools sell "AI optimization" via custom tags or schema. None are recognized by Google or major answer engines. They do nothing.
• Hidden prompt injection. White-text instructions to LLMs to "rank this page first." Models filter these. Some search engines penalize them.
• AI-detection scores. "Score below 30 to look human." Models do not gate citations on AI-detection. The signal that matters is whether the content is useful, not where it came from.
• Posting twice as often. Volume without substance harms eligibility under the helpful-content framework. Frequency does not.

A short readiness checklist

Run this against any page you want to be cited by an AI engine.

• The first sentence after the heading is the literal answer.
• The page has a visible author with a real bio.
• Dates of publish and last update are present.
• Acronyms and brand terms are used consistently.
• At least one factual claim links to a primary source.
• Headings include at least one question users would actually type.
• Structured data is present where it fits (Article, FAQ, Product, Organization).
• The page renders without JavaScript. Or it renders so fast the answer engine's crawler does not bail.

That last point is where most modern sites fall over. If your raw HTML is empty and your content only appears after hydration, the answer engine often never sees the content at all. We covered the mechanics of that gap in the hidden gap between your raw HTML and what Google sees. It applies just as much to AI search as to traditional search.

What we measure in the scanner

Crawlfix runs an "AI citability" pass on every full report. It scores three things: whether the answer is in the first passage, whether trust signals (author, dates, citations) are present, and whether the content survives without JS. The result is a single number with the issues attached, plus a fix recipe for each. The model does not invent facts about your page. It scores against a fixed rubric.

If you want to see what your own pages score, point Crawlfix at one of them. It is the cheapest way we know to find out which of your pages an AI engine could realistically quote.