The technical quality of a website is not an aesthetic opinion.

This page is the technical report for marioliguori.it (category Web Agencies). It separates perceived identity (design, brand fame) from the raw technical data measured with Google PageSpeed Insights and from source-code analysis: the same parameters, in the same order, as the Audit 360° tool.

⚡ Performance
3° su 5
category average 70
🧠 AI Readiness
2° su 5
category average 56
🌱 CO₂ (lower is better)
5° su 5
category average 0.16 g
Performance (Lighthouse)
65
out of 100
Carbon Footprint
0.359g
CO₂ per single visit
AI Readiness (Graph) LLM
84
out of 100

Summary: what works and what doesn't

Assessment generated from the values measured on marioliguori.it in the latest test: every item has a verifiable reference threshold.

✅ Strengths

  • Hosting certified as renewable-powered: carbon intensity of 50 g/kWh instead of 442.

⚠️ Priorities for action

  • A 12.01 MB page: above 2 MB you pay in load time and CO₂ emissions.

Detailed Audit Data

The six audit areas with the parameters recorded in the latest scan. Open the deep-dives to see every value.

⚡ Performance and Core Web Vitals

Performance Score: 65 / 100
First Contentful Paint: 3.7 s
Largest Contentful Paint: 6.1 s
Total Blocking Time: —
Cumulative Layout Shift: —
Total Page Weight: 12.01 MB
Accessibility
—
Best Practices
—
Technical SEO
—
Lab metrics and server
Speed Index: —
Server response (TTFB): —
DOM nodes: —
Network requests: —
Weight in KB: 12298 KB
Simulated device: MOBILE
Real-user data (Chrome UX Report)

Value not recorded in this test.

What slows the page down

Value not recorded in this test.

🌱 Sustainability

Renewable Hosting: ✅ Certified
Hosting provider: —
CO₂ per visit: 0.359 g
Annual emissions (10k visits): 3.6 kg
Calculation details
CO₂ per 1,000 visits: 359.0 g
CO₂ per 100,000 visits: 36 kg
Applied carbon intensity: 50 g/kWh
Method: Sustainable Web Design (SWDM)
Date recorded: 07/10/2026

📝 On-page SEO

Title length: —
Meta description length: —
Canonical: —
H1 / H2 headings: —
Structure and content
Language (lang): —
Mobile viewport: —
Indexable: —
Hreflang: —
OpenGraph / Twitter tags: —
Images without alt: —
Internal / external links: —
Words in text: —
HTML weight: —

🔍 Semantic Markup

Syntax Status: VALIDO
JSON-LD errors: 0
JSON-LD blocks: —
Robots & Sitemap: ✅ / ✅
Detected entities and types
Schema.org types (total): —
Microdata / RDFa: —
Top-level entities:
WebSite, WebPage, ProfessionalService, Person
Robots.txt and Sitemap
Robots.txt: ✅ Present
Disallow rules: —
XML Sitemap: ✅ Present

🧠 Knowledge Graph

Main Entity: ✅
Crawl Optimization: 60 / 100
Topical Authority: 100 / 100
Overall AI Readiness: 84 / 100
Knowledge Graph details
Architecture: Unified @graph
sameAs links: —
knowsAbout competences: —
significantLink: —
isPartOf/hasPart hierarchy: —
about/mentions: —
Entities described: —
@ids to entities outside the page: —
Possible @id typos (same page): —

🛡️ Basic security

HTTPS: —
HSTS: —
Content-Security-Policy: —
Security headers: —
All checks
X-Content-Type-Options: —
Anti-clickjacking (XFO / frame-ancestors): —
Referrer-Policy: —
Redirect on the tested URL: —

Overall Assessment

Performance Analysis

Based on the simulations run, the technical architecture of the site shows solid performance, with concrete room for frontend optimisation.

Dive into the technical performance analysis
Most Core Web Vitals are met, but you are still some distance from excellence: there is room to reduce weight, trim CLS and speed up first render. At this level every extra point has a measurable impact on mobile bounce rate and organic traffic quality.

It is worth remembering that low performance is not isolated: it severely affects real SEO ranking, increases the cost per click (CPC) of Google Ads campaigns, and depresses conversion rate.

Semantics for Artificial Intelligence

On the semantic and structured-data side (essential for LLMs like ChatGPT, Gemini and Claude), the domain has excellent AI Readiness: coherent ontology and authoritative content for LLMs.

Dive into the semantic and Knowledge Graph analysis
The site is a recognised source for LLMs: entities are clear, the graph is coherent, and content is citable. This level requires maintenance: update content, expand the graph, monitor citations. The value now is the systematic way leadership is sustained.

The detected Knowledge Graph infrastructure is classified as: Unified @graph, with a Schema.org markup status of VALIDO.

Improvement Plan

Six concrete actions for each metric, in order of impact. Metrics in the critical band should be tackled first.

🎯 Performance Fair

  1. Aim for 100/100: replace heavy JS libraries (jQuery, moment.js) with native alternatives.
  2. Reduce CLS by reserving explicit dimensions for images, video and iframes.
  3. Introduce a performance budget in CI/CD to prevent regressions.
  4. Use system fonts or a limited subset (max 2 families, 2 weights each).
  5. Check that inner pages, not just the homepage, stay fast.
  6. Document optimisation rules for the editorial team.

🌱 Sustainability (CO₂) Insufficient

  1. Introduce a CO₂ budget for every new page published.
  2. Prefer external video platforms (YouTube) with lazy embed.
  3. Use responsive images (srcset) to avoid unnecessary downloads on mobile.
  4. Replace JS animations with CSS when possible (less CPU, less energy).
  5. Periodically verify your provider's Green Web Badge.
  6. Communicate the ESG results: digital sustainability is a brand asset.

🔍 Semantic Markup Excellent

  1. Keep a register of published entities to avoid duplication.
  2. Extend the schema with advanced properties and explicit relations.
  3. Integrate data with Wikidata to strengthen recognition.
  4. Document the ontological model for the editorial team.
  5. Regularly test the graph with the Schema Markup Validator.
  6. Consider exposing the graph as static JSON-LD (faster to parse).

🧠 AI Readiness Excellent

  1. Systematically monitor AI citations of your brand.
  2. Continue expanding the graph with new entities and relations.
  3. Publish original data (research, benchmarks) that LLMs like to cite.
  4. Maintain coherence between the internal Knowledge Graph and Wikidata.
  5. Document AI-first editorial guidelines for the team.
  6. Consider APIs or structured feeds to power specialised models.

The Alternative: WAA Enterprise Framework

While most traditional CMSs (WordPress, Magento, etc.) suffer from severe architectural bottlenecks that cost organic visibility, advanced JAMStack frameworks like IM Web (WAA) systematically guarantee near-100/100 performance, near-zero CO₂ emissions and native readiness (Semantic Graph) to feed Artificial Intelligence databases.

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