cgiar.org — AI Search Visibility Report
Overall score: 58/100
AI search visibility analysis for cgiar.org. LLMao scored cgiar.org 58/100 across 8 LLM-readiness categories including crawlability, semantic content, structured data, authority signals, and answer-engine clarity.
Analyzed URL
Category breakdown
- readability: 75/100 — Content is professional and clear, though some sentences are long and contain sector-specific jargon without immediate definitions.
- schema_markup: 0/100 — No JSON-LD script blocks were found in the provided HTML, representing a significant missed opportunity for LLM indexing.
- authority_trust: 60/100 — Strong social proof through its network of centers, but lacks explicit publication dates and editorial transparency pages on the homepage.
- citation_sources: 50/100 — High internal linking to research centers, but lacks external primary source citations or a bibliography on the landing page.
- content_freshness: 40/100 — Mentions '2025-2030 Strategy' which is current for 2026, but lacks specific day/month timestamps for updates.
- content_structure: 85/100 — Excellent use of semantic HTML and clear navigation hierarchy, though H1/H2 nesting could be tighter.
- entity_definition: 70/100 — Strong brand consistency and clear 'About' sections, but lacks structured Person schema for its vast expert network.
- technical_accessibility: 70/100 — Good mobile optimization and social meta tags, but lacks a standard meta description tag.
Top recommendations
- Implement JSON-LD Structured Data (Schema Markup): Implement comprehensive JSON-LD schema including Organization, ResearchOrganization, and Article types. Currently, no structured data was detected in the HTML.
- Add Visible Publication Dates (Content Freshness): Add visible 'Last Updated' or 'Published' dates to homepage content and research updates to signal recency to LLM crawlers.
- Establish Editorial Transparency Pages (Authority & Trust): Create a dedicated 'Editorial Standards' or 'Fact-Checking Policy' page to strengthen E-E-A-T signals for scientific content.
- Optimize Meta Descriptions for AI Bots (Technical Accessibility): Ensure the meta description is explicitly defined in the HTML head; currently, it relies on Open Graph tags which some LLM crawlers may deprioritize.
- Link Researchers via Person Schema (Entity Definition): Use Person schema for leadership and researchers mentioned in the 'Global Leadership Team' and 'Our Centers' sections to link expertise to the entity.