manzas.io — AI Search Visibility Report
Overall score: 52/100
AI search visibility analysis for manzas.io. LLMao scored manzas.io 52/100 across 8 LLM-readiness categories including crawlability, semantic content, structured data, authority signals, and answer-engine clarity.
Analyzed URL
Category breakdown
- readability: 90/100 — Excellent clarity and sentence structure; technical terms are well-contextualized.
- schema_markup: 0/100 — No JSON-LD or Microdata detected in the provided HTML.
- authority_trust: 45/100 — Lacks specific author credentials and detailed trust pages, though social proof (logos) is present.
- citation_sources: 20/100 — No external citations or primary source links to verify technical or security claims.
- content_freshness: 30/100 — No visible publication or modification dates; content appears static.
- content_structure: 85/100 — Good use of H1-H3 hierarchy and semantic sections, though some sections are generic.
- entity_definition: 55/100 — Brand consistency is strong, but lacks a dedicated About page or defined leadership entities.
- technical_accessibility: 75/100 — Good meta descriptions and social meta, but AI crawler directives are missing.
Top recommendations
- Implement Structured Data (Schema Markup): Implement Organization, SoftwareApplication, and FAQPage JSON-LD schema to help LLMs identify the business entity and its specific features.
- Establish Entity Authority (Authority & Trust): Create a dedicated 'About Us' page and 'Team' section with links to LinkedIn profiles to establish E-E-A-T.
- Add Authoritative Citations (Citation & Source Quality): Add outbound links to industry standards (e.g., GDPR, AES-256 specs) and cite procurement statistics to verify claims.
- Optimize AI Crawler Access (Technical Accessibility): Create a robots.txt file explicitly allowing GPTBot, ClaudeBot, and PerplexityBot to ensure full indexing.
- Improve Freshness Signals (Content Freshness): Add 'Last Updated' dates to the page and include a blog or changelog to signal active development.