Domain-Wide Audit

Domain-Wide Audit scores a site against a broad set of GEO and AEO checks, grouped into categories: the fastest way to see, at a glance, which parts of your domain have the biggest technical blockers standing between them and AI citation.

Running an audit

• URL field + Run Audit: the domain to audit.
• vs: a second input, letting you audit two domains side by side for comparison.
• Download PDF: export the full result as a shareable report.

Categories

Checks are grouped into categories, each shown as its own row. Each row shows how many of that category's checks passed, the points earned out of the category's total, and a letter grade. Expand a row (▶) to see the individual checks behind it.

GEO Readiness

What it checks: things like llms.txt page coverage and declared AI content usage signals, whether your site explicitly tells AI crawlers what content exists and how it can be used.

Why it matters: AI crawlers and agents need a way to discover your content and confirm it's usable before they'll cite it. Without these signals, otherwise excellent content can simply be invisible to, or excluded from, the systems this whole audit is trying to get you cited by.

AEO Readiness

What it checks: query-answer alignment, such as question-style headings paired with direct answers.

Why it matters: answer engines extract the text that most directly answers a question. Content that technically contains the answer but buries it in unstructured prose is far less likely to get pulled into a cited response than content that states the answer plainly right under a matching heading.

Structured Data & Schema

What it checks: Article/BlogPosting schema, BreadcrumbList schema, and similar markup.

Why it matters: structured data gives machines an explicit, unambiguous description of what a page is and how it fits into your site, removing the guesswork an AI system would otherwise have to do to correctly interpret the page.

Content Quality

What it checks: fact density (data points, dates, attributions) and definition patterns that AI models can extract cleanly.

Why it matters: models preferentially cite content with concrete, verifiable specifics over vague or promotional language, since a citation needs to be defensible as an answer, not just a source that mentions the topic.

Content Structure

What it checks: how well the page's structure supports scanning and extraction, headings, lists, and logical section breaks.

Why it matters: models parse structure to isolate the specific chunk of a page relevant to a query. Information that's technically present but buried in a dense, unstructured block is harder to extract cleanly than the same information broken into scannable sections.

Authority & Trust

What it checks: author information and content licensing signals.

Why it matters: authorship and licensing are part of how AI systems judge whether a source is credible enough to cite, the same way a human reader trusts a byline over an anonymous page.

Technical SEO

What it checks: fundamentals like RSS/Atom feed availability and semantic HTML5 elements.

Why it matters: these are baseline crawlability signals. Without them, content may not reliably reach the indexing or training pipeline in the first place, making every other category's score moot for that page.

Overall score

The right-hand panel shows the audit's overall score (checks-passed points out of the category totals combined), an overall letter grade, how many individual checks passed out of the total, which URL was audited, and who ran it.

Below that, two separate tiles break the same audit into its two underlying models:

• AEO Score: a weighted score (the "aeorank" model) reflecting answer-engine readiness.
• GEO Score: a score reflecting general AI-engine readiness.