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

This page is the technical report for eneba.com (category E-commerce). 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 12
category average 41
🧠 AI Readiness
3° su 12
category average 7
🌱 CO₂ (lower is better)
1° su 12
category average 0.64 g
Performance (Lighthouse)
49
out of 100
Carbon Footprint
0.054g
CO₂ per single visit
AI Readiness (Graph) LLM
12
out of 100

Summary: what works and what doesn't

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

✅ Strengths

  • Page weight kept in check (1.80 MB).
  • Hosting certified as renewable-powered: carbon intensity of 50 g/kWh instead of 442.

⚠️ Priorities for action

No issues detected on the measured parameters.

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: 49 / 100
First Contentful Paint: 5.0 s
Largest Contentful Paint: 6.6 s
Total Blocking Time: —
Cumulative Layout Shift: —
Total Page Weight: 1.80 MB
Accessibility
—
Best Practices
—
Technical SEO
—
Lab metrics and server
Speed Index: —
Server response (TTFB): —
DOM nodes: —
Network requests: —
Weight in KB: 1843 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: 0.054 g
Annual emissions (10k visits): 0.5 kg
Calculation details
CO₂ per 1,000 visits: 54.0 g
CO₂ per 100,000 visits: 5 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:
Organization, Website
Robots.txt and Sitemap
Robots.txt: ✅ Present
Disallow rules: —
XML Sitemap: ✅ Present

🧠 Knowledge Graph

Main Entity: ✅
Crawl Optimization: 0 / 100
Topical Authority: 20 / 100
Overall AI Readiness: 12 / 100
Knowledge Graph details
Architecture: Fragmented
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 stays below the acceptability threshold: the site works but loses visits on every load.

Dive into the technical performance analysis
Performance sits in a middle band that feels acceptable but is not: Core Web Vitals fail on at least one axis and mobile suffers. There is concrete room for recovery and, within weeks, the score can climb above 80. Discipline on assets, correct caching and attention to render-blocking components are what closes the gap.

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 insufficient AI Readiness: generative assistants cannot cite it.

Dive into the semantic and Knowledge Graph analysis
The site has some structured elements but lacks depth, authority and semantic clarity. LLMs cannot confidently classify the entity or cite it as a source. Priority interventions concern building a knowledge graph, adding factual content and ensuring terminological coherence.

The detected Knowledge Graph infrastructure is classified as: Fragmented, 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 Insufficient

  1. Enable Brotli/Gzip compression for HTML, CSS and JS.
  2. Convert all images to WebP or AVIF and set explicit dimensions.
  3. Move rendering of non-critical components to the server (SSR/SSG).
  4. Reduce HTTP requests: bundle CSS and JS, use sprites where it makes sense.
  5. Confirm the hosting copes with traffic: slow shared CPUs mean high TTFB.
  6. Monitor real Core Web Vitals via CrUX or in-house RUM, not just synthetic tests.

🌱 Sustainability (CO₂) Excellent

  1. Officially certify the site with Carbon Analytics or the Website Carbon Badge.
  2. Include the CO₂ metric in the annual sustainability report.
  3. Keep hosting on documented 100% renewable providers.
  4. Justify every new asset on energy grounds before publishing it.
  5. Train the editorial team on multimedia asset weight.
  6. Share the case study: it is a real competitive advantage.

🔍 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 Severe Issues

  1. Enrich pages with factual data (numbers, dates, certifications).
  2. Add authors with structured bios (Person schema + sameAs).
  3. Create dedicated pages for each service/product with specific schema.
  4. Update content regularly: LLMs reward freshness.
  5. Build semantic internal links between related content.
  6. Publish an updated, verified XML sitemap.

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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