Answer engine optimization is the practice of structuring content so AI assistants can find, extract, trust and cite it. Search engine optimization competes for a position on a results page. Answer engine optimization competes to be the source a model quotes when it answers directly.

The distinction matters because the success conditions are different. In traditional search there is a page two — being tenth is worse than being first, but it is not nothing. In AI answers there is no page two. A model cites two or three sources. You are either one of them or you are invisible.

The foundations overlap heavily. Crawlability, clear structure, semantic markup and genuine expertise serve both. But a page can rank respectably and still be almost impossible to quote, and that is the gap an AEO checker measures.

Can a machine identify what this page is without inference? JSON-LD declaring Organization, Article, Product or FAQPage removes guesswork. Pages with no structured data force models to infer everything from prose, and inference is where misattribution starts.

Do your headings match how people actually ask? A model matching a user's question against your page finds a question-shaped H2 far more readily than a heading like "Our methodology". This is the strongest single signal in the set.

Is there something short enough to quote? A question heading followed by four hundred words of build-up gives a model nothing extractable. A question followed by two or three sentences that resolve it gives a model exactly what it needs.

Lists and tables lift cleanly. Comparison tables, numbered processes and bulleted criteria are disproportionately likely to be reproduced in AI answers because they survive extraction without rewriting.

Specific figures, percentages, prices and dates get quoted. Vague claims do not. A page saying "significantly improves performance" offers a model nothing to attribute; a page saying "reduced load time from 4.2s to 1.1s" offers something worth citing with your name attached.

Does the page make clear who is speaking? Organization or Person schema, a coherent H1, a title and meta description all contribute. Models are cautious about citing sources they cannot identify, and entity ambiguity is a common reason well-written pages go unmentioned.

• 80 and above — strong citation candidate. The structure is doing its job; focus on distribution and authority.
• 55 to 79 — structurally sound but under-optimised. Usually the answers exist but are buried, or schema is missing.
• Below 55 — a model will struggle to extract anything useful. Start with headings and direct answers before touching anything technical.

The score is a diagnostic, not a ranking prediction. A page scoring 90 with no authority behind it will not out-cite an established source scoring 60. What the score tells you is whether your content is legible enough to compete at all.

Worth being explicit, because tools that overstate their reach cause bad decisions.

This checker analyses the HTML you paste. It measures how extractable a page is. It cannot tell you whether AI assistants currently mention your brand, how you compare to competitors in AI answers, or whether the sources models rely on in your category say anything about you at all.

Those questions need live data from the assistants themselves. Structure is necessary but not sufficient: being quotable gets you nothing if nothing quotes you. Presence on the sources models trust — review platforms, forums, comparison content, reference sites — is the other half of the work.

1. Grade your top ten pages by traffic or commercial value. Start where impact is highest.
2. Fix headings and answers first. Cheapest change, largest movement.
3. Add schema to the pages that lack it — Organization sitewide, then page-type specific markup.
4. Add specificity. Replace vague claims with figures, dates and named examples.
5. Introduce structure where prose is doing work a table would do better.
6. Re-grade and move on. Diminishing returns arrive quickly; do not chase 100.

Before optimising for extraction, confirm the extractors can reach you. Plenty of sites blocked AI crawlers in 2023 when the conversation was about training data, then spent the following years wondering why assistants never mentioned them. Training crawlers and answer crawlers are different bots doing different jobs, and most robots.txt files do not distinguish between them.

A well-argued, deeply researched page can score poorly here, and that is not a flaw in the measurement. Extractability and quality are independent properties, and the best content frequently fails on the first because it was written for a reader who arrives at the top and works down.

Long-form argument builds toward conclusions. Extraction wants conclusions first. A model scanning your page for an answer to a specific question does not read your carefully constructed setup — it looks for a self-contained passage that resolves the question and moves on.

The resolution is not to write worse. It is to front-load. State the conclusion, then build the argument beneath it. Readers benefit from this too: most abandon a page within seconds if they cannot tell whether it answers their question.

The mechanics vary by platform, but the pattern is broadly consistent across systems that cite sources.

1. Interpret the question and often break it into sub-questions.
2. Retrieve candidate sources, frequently leaning on search results, indexed content and platform-specific crawls.
3. Extract passages that appear to answer each sub-question.
4. Assess confidence — is this source identifiable, specific, and consistent with other sources?
5. Compose an answer and attribute the passages it used.

Every one of the six factors this checker grades maps onto a step in that sequence. Question headings help step three. Entity clarity and specific data help step four. Structured data helps steps two and four together.

Across many categories the same domains appear repeatedly in cited sources: community platforms like Reddit, review aggregators, reference sites such as Wikipedia, and established publications. Your own domain is one input among many.

This has an uncomfortable implication. A brand that is well described on the sources models trust can be cited favourably even with a mediocre website, while a brand with an excellent website and no third-party presence can be invisible. Optimising your own pages is necessary work, but it is not the whole job.