Technical

Page Analysis

Audit any URL for the technical signals that influence how AI engines read, cite, and trust your content.

Overview

Page Analysis lets you run a deep technical audit on any public URL to evaluate how AI-ready that page is. Unlike traditional SEO audits focused on ranking signals, Page Analysis specifically checks the attributes that AI engines rely on when deciding whether to cite a page in a generated answer.

Running an Analysis

Enter any public URL into the analysis bar and click Analyse. Citatra fetches the live page and evaluates it across five signal categories.

Signal Categories

Structured Data

Checks for Schema.org markup that AI engines use to understand the page's subject matter and authority:

• Article / NewsArticle — date, author, publisher, headline
• FAQPage — well-formed question/answer pairs
• Product, Review, HowTo, BreadcrumbList
• Organization and WebSite markup for entity recognition

Each schema type found is shown with a pass/fail indicator and a preview of the parsed values.

Content Extractability

Evaluates whether AI crawlers can actually extract meaningful content from the page:

• Content-to-HTML ratio (thin pages score poorly)
• Presence of <main>, <article>, or <section> semantic landmarks
• Render mode — server-rendered content is more reliably indexed than client-only rendered content
• Content behind authentication or paywalls (detected via status codes and login-form indicators)

Crawlability

Checks for signals that may prevent AI agents from accessing or indexing the page:

• robots.txt rules targeting common AI user agents (GPTBot, PerplexityBot, Google-Extended, ClaudeBot)
• X-Robots-Tag and <meta name="robots"> noindex / nofollow directives
• Canonical tags pointing elsewhere
• HTTP status code (redirects, 4xx, 5xx)

Speed for AI

AI engine crawlers have limited time budgets per domain. This section flags performance issues that could cause incomplete indexing:

• Server response time (Time to First Byte)
• Page weight (HTML payload size)
• Presence of blocking resources that delay content delivery

Meta Signals

Additional signals used for entity and topic recognition:

• <title> and <meta name="description"> quality and length
• Open Graph and Twitter card completeness
• Canonical URL consistency
• hreflang for multi-language pages

Results Summary

The analysis returns a score (0–100) for each category and a composite AI Readiness Score. Individual issues are listed with a severity label (Critical, Warning, Info) and a suggested fix.

Batch Analysis

From the Tools Hub, the Structure Auditor tool supports batch analysis — paste a list of URLs to audit multiple pages simultaneously and export the results as CSV.