ls.app 65 C
🛡️ SEO 71 🤖 GEO 65 ⚡ Perf 42 🏗️ Arch 83
Radar → Domains → ls.app

ls.app — Global SEODiff Score 65/100

ls.app
📊

At 82/100, the ACRI for ls.app indicates strong fundamentals in AI extractability, surpassing the majority of indexed sites. In the developer sector, ls.app outperforms the average (57), suggesting strong competitive positioning in AI search. Content is delivered server-side, meaning bots and AI agents can parse the full page without executing JavaScript. Heavy markup overhead (50.0× bloat) forces AI systems to wade through excess code before finding useful information. The site includes 2 schema blocks, providing adequate structured data for basic entity recognition. The site maintains an open-door policy for AI crawlers — GPTBot, ClaudeBot, and other major agents are all allowed.

65
C — Global SEODiff Score
Comprehensive search visibility assessment
Strong foundations, but Performance (42) is your bottleneck.
🎯 Top Fix: Reduce token bloat (70×) → +5–10 pts
71
🛡️ Traditional SEO
65
🤖 AI Readiness
42
⚡ Performance
83
🏗️ Architecture
🔬 Automated SEODiff Assessment · Snapshot: Mar 15, 2026 · 📋 API
📈 ACRI Trend 3 snapshots
Mar 5 Mar 15
🔔 Recent AI Indexing Activity
No recent changes detected by adaptive crawler.
Does your site score higher than ls.app?
Run the same 40-signal audit on your own domain — free, instant results.
Scan Your Site Free →
🧮 Score Transparency — How is this calculated?
🛡️ Traditional SEO (25% weight)71 × 0.25 = 17.8
🤖 AI Readiness / GEO (40% weight)65 × 0.40 = 26.0
⚡ Performance (20% weight)42 × 0.20 = 8.4
🏗️ Architecture & Trust (15% weight)83 × 0.15 = 12.4
Weighted sum = 17.8 + 26.0 + 8.4 + 12.4
Global SEODiff Score = 65 (C)
📊 ACRI Sub-Scores (AI Readiness Detail)
100
Bot Access
avg 92
99
Rendering
avg 93
39
Structure
avg 35
84
Schema
avg 9
70
Tech Stack
avg 63
🔀
Visibility Delta: Google vs AI
Google (Tranco)
Top 9%
Rank #86204
Aligned
Gap
AI (ACRI)
Top 4%
Score 82/100

ls.app has balanced Google and AI visibility — both rank roughly in the same tier. ACRI measures technical crawler readiness. Read the methodology →

Why ls.app ranks here

Tech stackNext.js
Industrydeveloper
RenderingSSR
Schema coverage2 blocks
Token bloat50×+

Fastest improvements

  • Reduce token bloat (navigation/footer/code) so agents reach your main content faster (see Token Bloat).
  • Create an llms.txt file so AI crawlers can discover your content structure without heavy crawling. Generate llms.txt →
  • Run a full entropy audit to find which DOM regions waste the most tokens. Run Entropy Audit →
🧪

JavaScript Rendering Check

We check what AI crawlers miss when they skip JavaScript execution.

Running headless browser to simulate AI extraction…
See other Next.js sites → developer industry → Full leaderboard → Domain A–Z →
🛡️

Traditional SEO

71/100 25 % of Global Score 🟢 High Confidence

📝 Title Tag

55 chars
Good length

Optimal range: 30–60 characters for SERP display.

📋 Meta Description

229 chars
Too long

Optimal range: 120–160 characters for snippet control.

🔤 Heading Hierarchy

  • ✓ Exactly 1 <h1> tag — found 1
  • ✓ Has <h2> headings — found 8
  • ✓ <h2> not before <h1>

🔍 Indexability

  • ✓ Canonical tag present → https://ls.app/
  • ✓ No noindex directive
  • ✓ Meta viewport set
  • ✓ HTML lang attribute → en
  • ✅ Hreflang tags
  • ✓ Googlebot allowed by robots.txt

🌐 Social / OpenGraph

  • ✗ og:title
  • ✗ og:description
  • ✗ og:image
  • ✗ twitter:card
📐 How the SEO Pillar score is calculated

SEO Pillar = Title (20 pts) + Meta Desc (20 pts) + Heading Hierarchy (20 pts) + Indexability (20 pts) + Social/OG (20 pts)

Each sub-score is derived from the checks above. Canonical tag, lang attribute, og:image, and a single H1 are the highest-impact items.

🤖

AI Readiness / GEO

65/100 40 % of Global Score 🟢 High Confidence

This pillar aggregates citation share, hallucination risk, bot access, schema health, and content extractability. The individual diagnostic sections below contribute to this score.

🔗

Citation Alternatives

Research
💡
Insight: In the developer sector, hikkoshizamurai.jp (ACRI: 88) currently has stronger AI extractability. AI models tend to prefer sources with higher semantic structure and schema coverage. Domains with ACRI < 40 see 3.5× more hallucinations. Read the research →
ls.app
57
Your ACRI Score
→
hikkoshizamurai.jp
88
Industry Peer ACRI
AI models prioritize pages with strong semantic structure and schema coverage. hikkoshizamurai.jp has schema coverage of 5 blocks and uses Custom / Proprietary. Improve your score by implementing the remediation patches below.
📊 Side-by-Side Comparison →
🚨

Hallucination Risk

Research

Is AI lying about your brand? This panel measures how likely LLMs are to hallucinate facts when extracting information from your page.

Analyzing hallucination risk…

🤖 Bot Access Matrix

✅
GPTBot (OpenAI)
Allowed
✅
ClaudeBot (Anthropic)
Allowed
✅
CCBot (Common Crawl)
Allowed
✅
Google-Extended
Allowed
✅
Googlebot
Allowed

👻 Rendering (Ghost Ratio) Docs

Ghost Ratio 5%
0% — Safe 50% 100% — Risk
Status Server-Side Rendered (Safe)
Rendering Type SSR

📊 Structure & Information Density Docs

Structure Grade 39/100 — Low
Structured Elements 96 elements (96 lists, 0 rows, 0 headers)
Total Words2096
Raw Density4.6%
💡Low structure score (39/100). Your content appears as a wall of text with few structured HTML elements. You have 96 list items, 0 table rows, 0 table headers. Convert features into <ul> lists and data into <table> elements to help AI models extract structured information.

🏷️ Schema Health Docs

Organization Schema ✅ Present
Product / Service Schema ✅ Present
Total Schema Blocks2 block(s) — Basic (low value for AI)

Schema Coverage Map

4/7 schema types detected
✅ Organization
✅ Product/Service
❌ Breadcrumb
✅ FAQ
❌ Article
❌ WebSite
💡BreadcrumbList schema missing. AI cannot understand your site hierarchy or how pages relate to each other.
💡WebSite schema missing. Add WebSite + SearchAction so Google can generate a Sitelinks Search Box for your brand in AI results.

📐 AI Efficiency Metrics Docs

59
AI Extractability
High
Crawl Cost
None
Blocklist Risk
Extractability59/100 — AI models can partially extract answers from this page
Crawl CostHigh (100/100) — expensive for AI crawlers to process
Blocklist RiskNone — 0 of 5 AI crawlers blocked

Token Bloat Research

1%
🗑️ 99%
Useful Content (13.0 KB)Bloat (904.9 KB)
Token Bloat Ratio50×+ — Bloated

Multimodal Readiness

Visual Context100% Optimized for Vision
Image Alt Coverage11 / 11 images have alt text

TDM Rights

TDM-Reservation HeaderNot set
X-Robots-Tag: noaiNot set
💡Your HTML is 917.9 KB, but only 13.0 KB is text. 1% useful / 99% bloat. AI crawlers have limited context windows (e.g. 128k tokens). This level of bloat (50×+) risks context-window truncation by ChatGPT, Claude, and Gemini. Reduce inline scripts, CSS, hydration payloads, and tracking code.

🔥 Structural Entropy Check Research

0 Entropy
Poor Token Bloat: High
Noise Ratio: 98.6% · SNR: 0.01 · Signal: 3331 / Noise: 231657 tokens
Run Full Entropy Audit →

🔬 AI-Crawler Simulation

See your website the way AI crawlers do. CSS stripped, structure labeled, content chunked.

🌐
This is what humans see — styled, branded, visual.
Toggle to "AI Agent View" to see what GPTBot, ClaudeBot, and other AI crawlers actually extract from this page.
🤖

AI Answer Preview

NEW

See how AI models summarize your site. Left: your actual content. Right: what the LLM extracts and says about you.

Simulating AI extraction…
🧠

The LLM Interpretation

AI-VERIFIED

SEODiff AI analyzed the extracted content of ls.app and produced this structured business intelligence. Fields marked SEMANTIC VOID indicate information the AI could not find — a critical gap in your site’s machine-readability.

Core Offering
Antidetect Browser helps users automate tasks and manage multiple accounts securely.
Target Audience
Web automation professionals, affiliate marketers, and multi-account managers.
Pricing Model
⚠ SEMANTIC VOID
🏆 Competitive Moat
Advanced anti-detect technology for secure web automation and account management.
📊 Content Depth
5/10
⚡ Key Pain Points
• Thin landing pages for features
• Lack of detailed pricing information
Analyzed by SEODiff AI · 2026-03-04

🔧 Tech Stack

FrameworkNext.js
AI-Readiness Score70/100
Server—
CDN—
HTTP Status200
Load Time1149 ms
Raw HTML Size917.9 KB
Visible Text Size13.0 KB
⚡

Performance & Speed

42/100 20 % of Global Score 🟢 High Confidence

⏱️ Time to First Byte

1149 ms
Slow — bots may time out or deprioritise

Google considers <200 ms "good". AI crawlers may have even shorter timeouts.

📦 Page Weight

1328
DOM nodes
918 KB
HTML payload
Heavy page — consider reducing DOM complexity

🗄️ Cache & CDN

  • ✓ Cache-Control header → private, no-cache, no-store, max-age=0, must-revalidate
  • ✗ CDN cache status
  • ✗ CDN detected

🔬 Tracker Tax

0
tracker scripts
0
third-party domains
0.0%
token overhead
Minimal tracker load — clean signal for bots
📐 How the Performance Pillar score is calculated

Perf Pillar = TTFB (35 pts) + Page Weight (25 pts) + Cache/CDN (20 pts) + Tracker Tax (20 pts)

TTFB <200 ms = full marks. DOM >3000 or payload >300 KB incurs heavy penalties. Tracker scripts beyond 5 reduce score.

🏗️

Architecture & Trust

83/100 15 % of Global Score 🟢 High Confidence

🗺️ Sitemap & Robots

  • ✓ Sitemap declared in robots.txt → https://ls.app/sitemap.xml
  • ✓ Googlebot allowed
  • ✓ GPTBot allowed
  • ✓ ClaudeBot allowed

🔗 Linking

52
internal links
4
external links
Good internal linking — helps crawlers discover content

🔒 Security & Trust

  • ✗ HSTS header (Strict-Transport-Security)
  • ✗ Content-Security-Policy header
  • ✓ HTTP status 200 OK (got 200)

♿ Accessibility Signals

  • ✓ HTML lang attribute → en
  • ✓ Meta viewport for mobile
  • ✓ Single H1 for screen readers
📐 How the Architecture Pillar score is calculated

Arch Pillar = Sitemap & Robots (30 pts) + Linking (25 pts) + Security (25 pts) + Accessibility (20 pts)

Having a valid sitemap, allowing AI bots, HSTS, and a good internal link count are the highest-impact items.

🏅 AI-Verified Trust Badge

Your site scores 57/100. Reach 80+ to unlock the green "AI-Verified" badge. Fix the issues below to improve your score.

AI-Verified badge for ls.app
Pending Audit — score below 80 threshold 
<a href="https://seodiff.io/radar/domains/ls.app" rel="noopener"><img src="https://seodiff.io/api/v1/badge?domain=ls.app" alt="AI-Verified by SEODiff" width="280" height="52"></a>

💡 Paste in your site footer, GitHub README, or email signature. Badge updates automatically as your score changes.

� Deep Crawl Analysis 7 pages · Deep-10

Homepage ACRI
57
Single-page score
-57
Severe hidden bloat
Δ delta
Site-Wide ACRI
0
Avg across 7 pages · Range 0–0
🔍
Hidden Bloat Detected

Homepage scores 57, but internal pages average only 0 — a -57-point gap. Blogs, docs, and legacy content are dragging down AI readability site-wide.

Total Words
0
Avg Bloat
0.0×
Page Type ACRI Token Bloat Words Status
https://ls.app/about about 0 0.0× 0 ✓
https://ls.app/products product 0 0.0× 0 ✓
https://ls.app/features product 0 0.0× 0 ✓
https://ls.app/docs docs 0 0.0× 0 ✓
https://ls.app/case-studies social-proof 0 0.0× 0 ✓
https://ls.app/contact support 0 0.0× 0 ✓
https://ls.app/integrations integrations 0 0.0× 0 ✓
📂
Health by Sub-Directory
Average ACRI and top issues aggregated by URL path prefix
Path Pages Avg ACRI Ghost % Bloat Top Issue
/about/ 1 0 0% 0.0× Low AI Readiness
/products/ 1 0 0% 0.0× Low AI Readiness
/features/ 1 0 0% 0.0× Low AI Readiness
/docs/ 1 0 0% 0.0× Low AI Readiness
/case-studies/ 1 0 0% 0.0× Low AI Readiness
/contact/ 1 0 0% 0.0× Low AI Readiness
/integrations/ 1 0 0% 0.0× Low AI Readiness
🔄 Re-Crawl & Update 📡 Track this Domain

Scores update automatically each month. Create a free account for on-demand re-crawls (3/month free).

🔌 API Access

Pull this data programmatically. All sub-page metrics are available via our public API. 

curl https://seodiff.io/api/v1/deep10/domain/ls.app

Get your free API key — 100 requests/month included.

🔗 Similar developer Sites

Domains with a similar tech stack, industry, and AI readiness profile to ls.app. Compare side-by-side.

Domain ACRI AI Score Tech Stack Token Bloat Schema
ls.app (this site) 57 82 Next.js 70.5× 2 —
kika.ee 82 87 Next.js 4.3× 5 Compare →
deadwoodstudios.com 82 97 Next.js 9.2× 10 Compare →
bllv.de 81 80 TYPO3 1.4× 2 Compare →
berg-freunde.at 81 87 Express 3.8× 2 Compare →
earlyamerica.com 81 92 Custom / Proprietary 4.3× 2 Compare →
Compare All 5 Similar Sites →

📊 Semantic Share of Voice

How often would an AI cite ls.app when users ask about topics in this domain's niche? We run entity queries through our 188k-page search index and measure citation probability.

Analyzing citation landscape…

🩹

Remediation Patches

COPY-PASTE

Auto-generated code fixes tailored to ls.app. Copy and paste these into your codebase to improve AI visibility. These patches are mathematically proven to increase extraction accuracy →

Add WebSite + SearchAction JSON-LD
High Impact ⏱ 5 min
Enables the Sitelinks Search Box in Google and allows AI to understand your site structure.
html 
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "WebSite",
  "name": "Ls",
  "url": "https://ls.app",
  "potentialAction": {
    "@type": "SearchAction",
    "target": "https://ls.app/search?q={search_term_string}",
    "query-input": "required name=search_term_string"
  }
}
</script>
Reduce Token Bloat
Medium Impact ⏱ 1–2 hrs
Only 1% of your HTML is useful content. AI crawlers waste context window tokens on bloat.
html 
<!-- Move inline CSS to external stylesheets -->
<link rel="stylesheet" href="/css/main.css">

<!-- Move inline scripts to external files with defer -->
<script src="/js/app.js" defer></script>

<!-- Remove duplicate navigation blocks -->
<!-- Keep only ONE <nav> in the <header> -->

<!-- Ensure <main> wraps your primary content -->
<main>
  <!-- Your content here — this is what AI sees first -->
</main>
📈

Projected Impact

ROI EST.

If you apply the patches above, here's the estimated improvement for ls.app:

Current Score
82
Projected Score
91
Improvement
+9 pts
Add WebSite schema +4 pts
Reduce token bloat +5 pts

*Estimates based on SEODiff's scoring model. Actual results depend on implementation quality.

📋 Data Export

Download scores and metadata for audits, client reports, or CI/CD pipelines. Exports contain computed metrics only (no copyrighted content).

📄 Generate llms.txt 📊 Export JSON Data 📸 Download Visual

All data is generated automatically and updated with each crawl. JSON exports contain scores and metadata only (no copyrighted content).

Is this your company?

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Start Free →

🧭 Self-Diffing (Private Layer)

For owned domains, combine this world snapshot with private drift + regression history.
Template Drift
Track in My Site
Drift → Traffic Impact
In development coming soon
Regression Incidents
Track in My Site
Internal Linking
Deep Audit graph
Semantic Structure
GEO view in Deep Audit
Content Quality
Thin/duplicate tracking
Open My Site report → Open Self-Diffing incidents → Open History timeline →

🕒 History

Score over timeAvailable in My Site history
Drift eventsTemplate timeline + incidents
Drift → Revenue AttributionComing soon
Schema/rendering/extractability changesTracked per scan in project history
🔍 Found indexing issues?
Run a free deep audit to diagnose crawled-not-indexed, soft 404s, redirect errors, and more.
Free Deep Audit → GSC Error Guide →

📚 Research & Documentation

🔬
The Science of ACRI
Methodology behind the AI-Crawler Reality Index scoring model.
🚨
Hallucination Risk Study
How poor crawl access increases AI fabrication rates by 3.5×.
🔥
Structural Entropy Paper
The median web page wastes 55% of tokens on noise. Here's the data.
🧪
Extraction Lab Paper
Causal evidence that semantic HTML structure increases AI extraction accuracy.
📖
Documentation Hub
Metrics definitions, fix guides, and API reference for CI/CD integration.
🛠️
Free Tools
llms.txt generator, hallucination checker, entropy audit, and more.