manzas.io — AI Search Visibility Report
Overall score: 79/100
AI search visibility analysis for manzas.io. LLMao scored manzas.io 79/100 across 8 LLM-readiness categories including crawlability, semantic content, structured data, authority signals, and answer-engine clarity.
Analyzed URL
Category breakdown
- readability: 90/100 — The content is clear, uses active voice, and handles technical procurement jargon well. There are minor improvements in sentence length and jargon handling, leading to a slightly higher overall score.
- schema_markup: 95/100 — JSON-LD is present with Organization, WebSite, FAQPage, and SoftwareApplication types. The FAQPage schema is complete and valid. All core schemas are present and correctly implemented. Schema completeness and validity have significantly improved, with no errors detected. The addition of the SoftwareApplication schema is a notable improvement. The only remaining issue is the absence of a more specific content schema like Article or Product, which could further enhance the markup.
- authority_trust: 70/100 — Strong social proof with enterprise logos and contact info, but lacks deep author credentials and a dedicated About page.
- citation_sources: 40/100 — Lacks outbound links to primary sources or third-party verifications for security claims.
- content_freshness: 75/100 — Copyright is current (2026), but there are no visible 'last updated' dates for specific content sections.
- content_structure: 90/100 — The content structure is excellent, with a clear heading hierarchy and well-organized sections. Semantic HTML usage has improved significantly, and paragraph structures are generally good, though some minor improvements could still be made for optimal readability.
- entity_definition: 80/100 — Brand consistency is high and the founder is identified in schema, though a dedicated About page is missing.
- technical_accessibility: 85/100 — Good meta tags and social meta, though JS dependency for the demo/pilot sections is a minor concern.
Top recommendations
- Fix Truncated JSON-LD and Add FAQ Schema (Schema.org Markup): The current JSON-LD script is truncated in the HTML. Ensure the full script is properly closed and includes FAQPage schema for the 'Frequently Asked Questions' section to improve LLM 'rich snippet' extraction.
- Create Dedicated About and Author Pages (Authority & Trust Signals): While the founder is mentioned in schema, there is no visible 'About' page or detailed author bio on the site. Create a dedicated About page to establish E-E-A-T.
- Add Authoritative Outbound Links (Citation & Source Quality): The site makes several claims about 'Enterprise-grade protection' and 'EU Data Residency' without linking to external certifications or official GDPR documentation. Add outbound links to authoritative sources.
- Implement Visible Content Update Signals (Content Freshness): The blog link is present but no recent posts are visible on the homepage. Regularly update the blog with 2026 dates to signal active maintenance to LLM crawlers.
- Optimize Meta Description for Intent Keywords (Technical Accessibility): The meta description is present but could be more keyword-rich for LLM intent matching (e.g., including 'RFP automation', 'procurement software', 'vendor evaluation').
- Use semantic list elements for lists (content_structure): Replace text-based lists (e.g., the bullet points under 'Siloed stakeholders...' and 'Define it right') with appropriate <ul> (unordered list) or <ol> (ordered list) HTML tags. This improves accessibility and semantic meaning, making it easier for screen readers and other assistive technologies to interpret the content.
- Enhance semantic structure of 'Cookie Preferences' (content_structure): Review the 'Cookie Preferences' section and consider using more semantic HTML elements to structure its content. For example, using <form> for the preferences, <fieldset> and <legend> for grouping options, and appropriate input types for user choices.
- Shorten longer sentences (readability): Break down complex sentences into two or more simpler sentences to improve readability and Flesch-Kincaid score. For example, 'The structured way to evaluate complex software, when demos and decks fall short.' could be rephrased.
- Review and simplify complex sentences (readability): Identify and rephrase sentences that are particularly long or contain multiple clauses to enhance clarity and flow. This will directly impact the sentence length score.