linode.com — AI Search Visibility Report
Overall score: 78/100
AI search visibility analysis for linode.com. LLMao scored linode.com 78/100 across 8 LLM-readiness categories including crawlability, semantic content, structured data, authority signals, and answer-engine clarity.
Analyzed URL
Category breakdown
- readability: 75/100 — Technical content is well-structured but contains high jargon density without immediate definitions.
- schema_markup: 20/100 — Major gap: No JSON-LD blocks found in the provided HTML snippet. Relies on OpenGraph only.
- authority_trust: 75/100 — Strong corporate trust via Akamai, but lacks individual author credentials and visible social proof (reviews) on the homepage.
- citation_sources: 90/100 — Excellent use of primary research studies (Forrester) and white papers to back claims.
- content_freshness: 85/100 — Content is very recent with 2025 and 2026 references, though specific 'last modified' dates are missing from visible text.
- content_structure: 80/100 — Clear hierarchy with H1 and H2s, though some sections rely on div-nesting rather than semantic <section> tags.
- entity_definition: 70/100 — Strong brand consistency (Linode/Akamai), but lacks a dedicated 'About' schema and inline term definitions.
- technical_accessibility: 80/100 — Good meta descriptions and social tags, but no specific AI crawler directives observed.
Top recommendations
- Implement JSON-LD Structured Data (Schema.org Markup): The homepage lacks structured JSON-LD data. Implement Organization, WebSite, and Product schemas to help LLMs explicitly identify services and brand details.
- Add Author Bylines and Person Schema (Authority & Trust Signals): While Akamai is mentioned, the homepage lacks explicit author bylines or Person schema for the featured studies and blog posts. Adding expert bios increases E-E-A-T.
- Update Featured Content to 2026 References (Content Freshness): Ensure all featured reports (currently showing 2024 and 2025) are updated to 2026 versions where applicable to maintain 'recency' signals for LLM crawlers.
- Optimize Robots.txt for AI Crawlers (Technical Accessibility): Explicitly allow LLM-specific crawlers (GPTBot, ClaudeBot) in robots.txt to ensure full indexing of technical documentation.
- Define Technical Entities Inline (Entity Definition): Key technical terms like 'Wasm' and 'AI inferencing' are used but not defined on the homepage. Adding a 'Glossary' or defining terms inline helps LLMs map entities.