ground.news — AI Search Visibility Report
Overall score: 75/100
AI search visibility analysis for ground.news. LLMao scored ground.news 75/100 across 8 LLM-readiness categories including crawlability, semantic content, structured data, authority signals, and answer-engine clarity.
Analyzed URL
Category breakdown
- readability: 85/100 — Content is highly readable with short sentences and clear language, suitable for a general audience. Technical jargon is minimal.
- schema_markup: 40/100 — The site uses Open Graph and Twitter cards extensively, but lacks JSON-LD (Organization, WebSite, Article) which is the primary signal for LLMs.
- authority_trust: 70/100 — Strong social proof with 10,000+ reviews and major media mentions, but lacks detailed author credentials and explicit trust pages in the immediate scrape.
- citation_sources: 75/100 — The platform is built on citations, but they are presented as news cards rather than formal academic or journalistic citations. Claims are verified by volume of sources.
- content_freshness: 95/100 — Excellent freshness with content dated Feb 20, 2026 (yesterday relative to current date). Clear publication dates are visible.
- content_structure: 70/100 — Uses H1 and H2 tags, but the hierarchy is slightly flat. Semantic HTML is present but could be more descriptive for LLM parsing.
- entity_definition: 65/100 — Brand consistency is high, but the lack of a structured 'About' entity in schema limits LLM understanding of the organization's specific attributes.
- technical_accessibility: 80/100 — Meta descriptions and social meta are well-implemented. Content is largely accessible, though heavily reliant on Next.js hydration.
Top recommendations
- Add JSON-LD Structured Data (Schema.org Markup): Implement structured data (Article, NewsArticle, Organization) in JSON-LD format. Currently, the site relies on meta tags which are less effective for LLM entity extraction than explicit JSON-LD.
- Strengthen Entity & Author Profiles (Entity Definition): Create a dedicated 'About' page with an Organization schema and detailed 'Author' pages for editorial staff to improve E-E-A-T signals for LLMs.
- Formalize Citation Structure (Citation & Source Quality): While the site aggregates sources, adding a 'References' or 'Sources' section with direct outbound links to the original reporting in a structured list would help LLMs verify claims.
- Optimize Heading Hierarchy (Content Structure): Ensure a strict H1-H2-H3 hierarchy. The current page uses an H1 for a descriptive sentence rather than a concise entity-focused title.
- Optimize AI Crawler Access (Technical Accessibility): Explicitly allow AI crawlers like GPTBot and ClaudeBot in the robots.txt file to ensure full indexing of the bias analysis data.