Content Analysis

Content Analysis scores any page for how well it's optimized for AI-driven answer engines. Point it at a URL, a Genezio article, or a document you've drafted, and it tells you how likely the page is to be cited by an answer engine — and what to fix to improve that.

It's the input side of the loop. The rest of the platform measures what answer engines say about your brand; Content Analysis measures the pages on your side that they read.

Why It Exists

For most of the platform, the unit of analysis is the answer — what ChatGPT, Perplexity, Claude, and others say about your brand and competitors. That tells you whether you're being recommended, cited, or ignored.

But the cause of those answers is on your side: the pages on your site (and the documents you publish) that answer engines crawl, retrieve, and quote. Content Analysis gives you a way to evaluate those pages directly:

Which pages are ready to be cited by an answer engine?
Which ones aren't, and why?
What specifically should you change to fix them?

Pages you improve here should later show up as citations in your dashboards — closing the loop between input quality and measured visibility.

What You Can Analyze

Content Analysis accepts every common starting point as a first-class input:

a URL — any public page on your site (or elsewhere)
a document — uploaded directly
an existing Genezio article — any article in your Content Hub library

This means you can run Content Analysis at any point in the content lifecycle: before publishing, after publishing, on a draft an agency sent you, or on a competitor's published page.

What You Get Back

Each analysis returns:

An Overall Score

A single headline score that tells you, at a glance, how citable the page is for answer engines.

Per-Dimension Scores

The overall score is broken down into the dimensions that drive it:

Readability — how cleanly the page reads, including sentence structure and clarity
Structure — heading hierarchy, sections, lists, tables, and other signals answer engines use to navigate content
Claim-supporting signals — whether claims in the content are backed by evidence answer engines can recognize (data, sources, examples)
Citability — how likely an answer engine is to pick this page as a source in a response

Seeing both the overall score and the breakdown lets you target the dimension that's holding the page back, rather than guessing.

Actionable Recommendations

Alongside the scores, Content Analysis surfaces specific recommendations for what to change — for example, breaking up a long paragraph, adding a comparison table, surfacing a missing definition, or strengthening a claim with a source.

The recommendations are tied to the dimension they would improve, so you can prioritize the fixes that move the score the most.

A Typical Workflow

Content Analysis is designed for a daily workflow, not one-off reports. The natural loop is:

Run the analysis — paste a URL, upload a document, or pick an existing article from Content Hub.
Read the overall score and the breakdown — see which dimension is weakest.
Import as a Content Hub draft — for external inputs (URL or uploaded document), click Import as Hub draft directly from the analysis result. The content becomes a regular Content Hub article you can edit.
Apply the recommendations — inside the Content Hub editor, fix the issues the analysis flagged.
Re-analyze from inside the draft — Content Analysis runs directly on the Hub draft, so you can see the new score without leaving the editor.
Iterate — repeat fix → re-analyze until the score is where you want it.
Publish (or re-publish) — and watch your dashboards for new citations of the improved page.

The Import as Hub draft action turns Content Analysis from a dead-end report into the front door to content production: every common starting point flows directly into the edit-and-improve loop.