An AEO score is a 0–100 rating of how extractable a page is for AI answer engines, calculated from on-page signals like direct answers, structure, entity density, extractability and schema. It is a readiness proxy: because no engine publishes how it picks sources, you measure the signals you control and use the score to compare drafts and catch weak spots. A common publish bar is 80+, but the score reflects extractability — not a guarantee that ChatGPT or Perplexity will cite you.
What does an AEO score actually measure?
An AEO score measures the on-page characteristics that make content easy for an AI answer engine to extract and attribute. Rather than guessing at an engine's internal ranking, it evaluates signals you fully control — how directly each section answers its question, how scannable the structure is, how clearly entities are named, and whether machine-readable schema is present.
The result is a single 0–100 number plus a per-dimension breakdown. The number is useful as a relative benchmark across drafts; the breakdown is where the value is, because it tells you which specific dimension is dragging the page down.
It is deliberately a deterministic, on-page analysis. It does not query live answer engines, so it is fast and repeatable, but it measures readiness — not real-world citation frequency.
What are the dimensions behind the score?
Scores like this break extractability into distinct dimensions so you can act on them individually. The recurring ones map directly to the AEO fundamentals: a direct-answer lead, question-led structure, entity density, extractability of standalone passages, schema, and supporting signals like readability and source-grounding.
Each dimension is scored against its own maximum, and a low dimension is a concrete to-do rather than a vague 'write better'. For example, a low schema score means add Article/FAQPage JSON-LD in the HTML; a low structure score means break the wall of text into question-led sections and lists.
- Direct answer — does each section open with a liftable answer?
- Structure — question-led headings, scannable sections, lists and tables.
- Entity density — specific, named entities a model can attribute.
- Extractability — passages that stand alone when quoted.
- Schema — Article/FAQPage JSON-LD in the served HTML.
- Readability, freshness and source-grounding.
What counts as a good AEO score?
There is no industry-standard threshold, so treat any number as a relative bar you set, not a certified grade. Many teams use 80+ as 'publish-ready' for extractability, because by that point the direct answers, structure and schema are usually in place.
Below that, read the breakdown rather than the headline number. A page at 65 with a strong structure but no schema needs a different fix from a page at 65 with schema but buried answers. The point of the score is to direct your next edit.
Re-score after each meaningful edit. The fastest way to raise a score is almost always to add direct-answer leads and fix structure, followed by adding schema.
Why isn't a high score a guarantee of citations?
Because the score measures only what is on the page, and citation depends on factors the page cannot encode. An engine still has to be able to crawl the page, trust the domain, and find the answer accurate — none of which an on-page score can certify.
This is the honesty line that matters: a high AEO score means your content is easy to extract and quote, which makes you eligible. It is a strong, controllable input, not a deterministic output. Anyone selling a score as a citation guarantee is overstating it.
Used correctly, the score is a readiness and regression tool — it tells you the content is as extractable as you can make it, so the remaining variables are access, authority and accuracy.
How do you use the score in a content workflow?
Score early and often. Run a draft through the checker, fix the weakest dimensions, and re-score until you clear your bar — the same way you'd run a linter before shipping code. It turns 'is this AI-ready?' from a judgment call into a measurable step.
Then pair the score with the things it can't see: confirm AI crawlers can reach the page, that the content is in the static HTML, and that your claims are grounded in real sources. Score plus access plus accuracy is the complete picture.
You can check any URL or paste a draft in our free AEO score checker, then read the complete AEO guide for the underlying playbook.
- 1Paste a URL or draft into the AEO score checker.
- 2Read the breakdown and fix the lowest-scoring dimensions first.
- 3Re-score until you clear your publish bar (e.g. 80+).
- 4Verify crawl access and that content and schema are in the served HTML.
Frequently asked questions
What is a good AEO score?+
There is no official standard. Many teams treat 80+ as publish-ready for extractability. Below that, use the per-dimension breakdown to fix the weakest area first rather than chasing the headline number.
Does the AEO score query ChatGPT or Perplexity?+
No. It is a deterministic, on-page analysis of structure, entities and schema. It does not query live answer engines, so it measures extractability readiness rather than confirmed citations.
How is an AEO score different from a content quality score?+
A general quality score rates readability and style; an AEO score specifically rates extractability for answer engines — direct answers, structure, entities and schema that let a model lift and attribute your content.
How quickly can I raise my score?+
Usually fast. Adding direct-answer leads and fixing structure tends to move the score most, followed by adding Article/FAQPage schema in the HTML. Re-score after each edit to confirm.