seohive.co — AI Search Visibility Report
Overall score: 68/100
AI search visibility analysis for seohive.co. LLMao scored seohive.co 68/100 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, using active voice and clear, agency-focused language.
- schema_markup: 60/100 — Basic Organization schema is present, but missing Service, Product, and Review schemas.
- authority_trust: 65/100 — Strong social proof with testimonials and agency counts, but lacks deep author credentials and a dedicated About page.
- citation_sources: 40/100 — Lacks outbound links to primary data or industry authorities to back up service claims.
- content_freshness: 50/100 — Copyright is current (2026), but specific publication or modification dates for content are missing from the homepage.
- content_structure: 90/100 — Excellent use of H1-H3 hierarchy and clear logical sections for different service paths.
- entity_definition: 60/100 — Brand consistency is high, but lacks a dedicated About page and formal term definitions.
- technical_accessibility: 95/100 — Excellent metadata and social tags; content is accessible and well-described for crawlers.
Top recommendations
- Implement Service and Review Schema (Schema Markup): Expand JSON-LD to include Service, Review, and FAQPage schemas to help LLMs understand specific offerings and trust signals.
- Create Comprehensive About/Entity Page (Authority & Trust): Add a dedicated 'About Us' page with detailed founder bios, professional certifications, and links to verified social profiles.
- Add Authoritative Outbound Citations (Citation & Source Quality): Include outbound links to authoritative industry sources (e.g., Google Search Central, Search Engine Journal) when making technical claims.
- Display Visible Modification Dates (Content Freshness): Add 'Last Updated' dates to service descriptions and blog posts to signal content recency to LLM crawlers.
- Implement Technical Term Definitions (Entity Definition): Define technical terms like 'MRR', 'White-Label', and 'GBP' in a glossary or via inline definitions to improve LLM semantic mapping.