AI Optimization Analysis

This module is part of the Analyze ecosystem and included in DXPR CMS.

AI Answer Engines Cannot Cite What They Cannot Parse

ChatGPT, Perplexity, Gemini, and Google AI Overviews are replacing traditional search for millions of queries. These systems extract answers from pages with clear structure: proper headings, structured data, semantic HTML, and FAQ patterns. Pages that lack these signals get skipped, regardless of how good the content is. This module scores every page against the factors that determine AI answer engine visibility.

You need AI Optimization Analysis if

• Your content ranks well in traditional search but rarely appears in AI-generated answers
• You want to understand which pages are structured for AI extraction and which are not
• Your site lacks structured data (JSON-LD) and you need to prioritise where to add it
• You want to measure and improve AI readiness across hundreds of pages systematically

What You Get

• AI optimisation score per page

  Every content entity gets a percentage score across eight checks, displayed as a visual gauge in the Analyze tab. Scores update automatically when content changes.

• Progressive-disclosure full reports

  Checks are grouped by severity: needs attention, can be improved, and passed. Each failing check explains why it matters for AI answer engines specifically, and gives an actionable recommendation.

• Eight deterministic checks

  Heading hierarchy (with question-shaped heading detection), JSON-LD structured data (including AI-friendly schemas like FAQPage and HowTo), meta tag completeness, semantic HTML elements, FAQ pattern detection, and internal link density.

• Optional AI-powered checks

  With an AI provider configured, two additional checks score citation readiness (whether content is structured for AI extraction) and tone authority (expertise signals and authoritative voice).

• Batch analysis for existing content

  Score hundreds of pages at once to find which content needs attention first. Results are cached with content-hash invalidation, so repeat views are instant and re-analysis only runs when content actually changes.

• AI Coding Assistant Integration

  AI optimisation analysis is available to AI coding assistants
  through the Analyze module's built-in
  Agent
  Skills file. Run
  drush analyze:setup-ai to enable, then ask
  naturally:

  • "Analyse AI optimisation readiness on all articles"
  • "Check structured data on the about page"
  • "Run AIO and SEO analysis together"

  Compatible with Claude Code, Codex CLI, Gemini CLI,
  GitHub Copilot, Cursor, and other tools supporting the
  standard.

• Site-wide AI optimisation report

  A filterable Views report shows AI readiness scores across all content, sortable by score, content type, and date, so you can spot patterns and prioritise improvements.

Getting Started

1. Enable the analyzer per content type at /admin/config/content/analyze-settings
2. Configure which checks to run at /admin/config/analyze/aio
3. Open any content entity's Analyze tab to see the score
4. (Optional) Set up an AI provider at /admin/config/ai/providers for citation and tone checks

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Additional requirements

This module requires:

• Analyze module (>=1.3.0)
• Views Color Scales module (>=1.1.0)

Optional:

• AI module with a configured chat provider (for citation readiness and tone authority checks)

Analyze AIO is included in DXPR CMS, which ships the full Analyze suite pre-configured so marketing teams can start optimising for AI answer engines immediately.

FAQ

Does AI Optimization Analysis require an AI provider?

Eight of ten checks run without any AI provider; they inspect heading hierarchy, structured data, meta tags, semantic HTML, FAQ patterns, and internal link density using deterministic rules. Two optional checks (citation readiness and tone authority) require a configured AI chat provider through the Drupal AI module, but the core scoring works out of the box.

How does AIO Analysis differ from SEO Analysis?

SEO Analysis targets traditional search engine ranking factors such as title tags, meta descriptions, image alt text, and URL structure. AIO Analysis targets AI answer engine visibility: whether the page is structured so that ChatGPT, Perplexity, Gemini, and Google AI Overviews can extract and cite its content. Both modules run independently and complement each other.

How often does AIO Analysis rescan my content?

Scores are cached using a content hash and only recomputed when the content actually changes. Editing and saving a page triggers automatic rescoring. For existing content, run a batch analysis via Drush or the admin UI to score hundreds of pages at once; subsequent views load instantly from cache.

Related Modules

Required dependencies; this module directly uses their APIs:

• Analyze - Content analysis framework; this module extends AnalyzePluginBase and implements BatchableAnalyzerInterface
• Views Color Scales - Colour-coded score display in the bundled AIO Views report

Optional integration:

• AI - LLM provider layer; enables citation readiness and tone authority checks

Sibling Analyze plugins:

• SEO Analysis - Traditional SEO scoring
• AI Brand Voice - Brand voice consistency scoring
• AI Sentiments Analysis - Multi-dimensional content tone analysis
• Broken Links Analyzer - Link health monitoring per page
• Search Console Analyzer - Google Search performance per page