hivemq.com 62 C
🛡️ SEO 54 🤖 GEO 65 ⚡ Perf 59 🏗️ Arch 73

hivemq.com — Global SEODiff Score 62/100

hivemq.com
📊

hivemq.com achieves a 81/100 on the AI-Crawler Reality Index, reflecting above-average readiness for AI-driven discovery. Within the developer vertical, this places hivemq.com above the industry average of 57 —, suggesting strong competitive positioning in AI search. The low ghost ratio (5%) confirms that what crawlers see matches what users see — a hallmark of strong SSR implementation. A 22.3× token bloat ratio means crawlers must process significantly more tokens to reach the actual content — a drag on extraction efficiency. Minimal structured data (1 block) limits the site's ability to communicate entity relationships to AI systems. Robots.txt grants unrestricted access to the key AI user-agents, which is the strongest starting position for AI visibility.

62
C — Global SEODiff Score
Comprehensive search visibility assessment
Strong foundations, but Traditional SEO (54) is your bottleneck.
🎯 Top Fix: Reduce token bloat (22×) → +5–10 pts
🔬 Automated SEODiff Assessment · Snapshot: Mar 16, 2026 · 📋 API
📈 ACRI Trend 7 snapshots
Feb 23 Mar 16
🔔 Recent AI Indexing Activity
📉 Mar 11 ACRI -2 (59→57)
Does your site score higher than hivemq.com?
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)54 × 0.25 = 13.5
🤖 AI Readiness / GEO (40% weight)65 × 0.40 = 26.0
⚡ Performance (20% weight)59 × 0.20 = 11.8
🏗️ Architecture & Trust (15% weight)73 × 0.15 = 10.9
Weighted sum = 13.5 + 26.0 + 11.8 + 10.9
Global SEODiff Score = 62 (C)
📊 ACRI Sub-Scores (AI Readiness Detail)
100
Bot Access
avg 92
99
Rendering
avg 93
62
Structure
avg 35
42
Schema
avg 9
75
Tech Stack
avg 63
🔀
Visibility Delta: Google vs AI
Google (Tranco)
Top 3%
Rank #33018
Aligned
Gap
AI (ACRI)
Top 5%
Score 81/100

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

Why hivemq.com ranks here

Tech stackSvelte/SvelteKit
Industrydeveloper
RenderingSSR
Schema coverage1 blocks
Token bloat22.3×

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…
🛡️

Traditional SEO

54/100 25 % of Global Score 🟢 High Confidence

📝 Title Tag

56 chars
Good length

Optimal range: 30–60 characters for SERP display.

📋 Meta Description

240 chars
Too long

Optimal range: 120–160 characters for snippet control.

🔤 Heading Hierarchy

  • ✓ Exactly 1 <h1> tag — found 1
  • ✓ Has <h2> headings — found 14
  • ✓ <h2> not before <h1>

🔍 Indexability

  • ✓ Canonical tag present → https://www.hivemq.com/
  • ✓ No noindex directive
  • ✓ Meta viewport set
  • ✓ HTML lang attribute → en
  • ➖ Hreflang tags — N/A (single language site)
  • ✓ Googlebot allowed by robots.txt

🌐 Social / OpenGraph

  • ✓ og:title — HiveMQ – Stream Data. Build Intelligence. Activate AI.
  • ✓ og:description — HiveMQ is the real-time industrial data platform powering Agentic AI. Built on MQTT, the HiveMQ platform connects, contextualizes, and analyzes operational data to create clean, validated, and trusted AI-ready pipelines from edge-to-cloud.
  • ✓ og:image — preview
  • ✗ 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 →
hivemq.com
57
Your ACRI Score
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 62/100 — Good
Structured Elements 121 elements (121 lists, 0 rows, 0 headers)
Total Words1055
Raw Density11.5%

🏷️ Schema Health Docs

Organization Schema ❌ Missing
Product / Service Schema ✅ Present
Total Schema Blocks1 block(s) — Basic (low value for AI)

Schema Coverage Map

2/7 schema types detected
❌ Organization
✅ Product/Service
❌ Breadcrumb
❌ FAQ
❌ Article
❌ WebSite
💡Organization schema missing. AI models cannot identify your brand entity. Without it, your brand won't appear in Knowledge Panels or be associated with your content.
💡BreadcrumbList schema missing. AI cannot understand your site hierarchy or how pages relate to each other.
💡FAQ schema missing. Adding FAQPage schema lets AI models directly extract Q&A pairs for Featured Snippets and chatbot answers.
💡WebSite schema missing. Add WebSite + SearchAction so Google can generate a Sitelinks Search Box for your brand in AI results.

📐 AI Efficiency Metrics Docs

58
AI Extractability
Medium
Crawl Cost
None
Blocklist Risk
Extractability58/100 — AI models can partially extract answers from this page
Crawl CostMedium (50/100) — moderate for AI crawlers to process
Blocklist RiskNone — 0 of 5 AI crawlers blocked

Token Bloat Research

4%
🗑️ 96%
Useful Content (8.3 KB)Bloat (176.3 KB)
Token Bloat Ratio22.3× — Heavy

Multimodal Readiness

Visual Context17% Optimized for Vision
Image Alt Coverage9 / 52 images have alt text

TDM Rights

TDM-Reservation HeaderNot set
X-Robots-Tag: noaiNot set
💡Your HTML is 184.5 KB, but only 8.3 KB is text. 4% useful / 96% bloat. AI crawlers have limited context windows (e.g. 128k tokens). This level of bloat (22.3×) risks context-window truncation by ChatGPT, Claude, and Gemini. Reduce inline scripts, CSS, hydration payloads, and tracking code.
💡Only 17% of images have alt text. Add descriptive alt attributes so multimodal AI (ChatGPT Vision) can understand your images.

🔥 Structural Entropy Check Research

0 Entropy
Poor Token Bloat: High
Noise Ratio: 95.5% · SNR: 0.05 · Signal: 2116 / Noise: 45124 tokens

🔬 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 hivemq.com 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
HiveMQ is an industrial data platform for AI, connecting and analyzing real-time operational data at scale.
Target Audience
Data scientists, IoT engineers, OT/IT teams, automotive engineers, pharmaceutical researchers
Pricing Model
Not specified - likely tiered based on usage and features.
🔗 Integration Partners
MQTT
🏆 Competitive Moat
Scalable data streaming and unified OT/IT data pipelines for AI-driven operations.
📊 Content Depth
6/10
🔄 Programmatic SEO Signals
Brand Resources (logo downloads)Newsletter sign up
⚡ Key Pain Points
• Pricing model not specified
Analyzed by SEODiff AI · 2026-03-02

🔧 Tech Stack

AI-Readiness Score75/100
ServerNetlify
CDNnetlify
HTTP Status200
Load Time738 ms
Raw HTML Size184.5 KB
Visible Text Size8.3 KB

Performance & Speed

59/100 20 % of Global Score 🟢 High Confidence

⏱️ Time to First Byte

738 ms
Slow — bots may time out or deprioritise

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

📦 Page Weight

931
DOM nodes
184 KB
HTML payload
Moderate weight — acceptable for most scenarios

🗄️ Cache & CDN

  • ✓ Cache-Control header → public,max-age=0,must-revalidate
  • ✗ CDN cache status
  • ✓ CDN detected → netlify

🔬 Tracker Tax

1
tracker scripts
1
third-party domains
0.0%
token overhead
Minimal tracker load — clean signal for bots
js.hs-scripts.com
📐 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

73/100 15 % of Global Score 🟢 High Confidence

🗺️ Sitemap & Robots

  • ✗ Sitemap declared in robots.txt
  • ✓ Googlebot allowed
  • ✓ GPTBot allowed
  • ✓ ClaudeBot allowed

🔗 Linking

116
internal links
17
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 hivemq.com
Pending Audit — score below 80 threshold
<a href="https://seodiff.io/radar/domains/hivemq.com" rel="noopener"><img src="https://seodiff.io/api/v1/badge?domain=hivemq.com" 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 88 pages · Deep-10

Homepage ACRI
57
Single-page score
+13
Subpages outperform homepage
Δ delta
Site-Wide ACRI
70
Avg across 88 pages · Range 0–85
Topical Cohesion
14%
Topical Drift
TF-IDF cosine similarity
Total Words
161622
Avg Bloat
28.6×
RAG Fractures [?]
86
⚠️
86 RAG-Chunking Fractures Detected

Poorly formatted tables or pricing grids on 86 pages will be split incorrectly during RAG chunking, causing AI models to hallucinate prices and features.

Page Type ACRI Token Bloat Words Status
https://www.hivemq.com/blog/why-your-manufacturing-strategy-needs-agentic-ai/
Why Your Manufacturing Strategy Needs Agentic AI
pricing 85 9.3× 3944 ⚠️ RAG Fracture
https://www.hivemq.com/blog/how-hivemq-iso-27001-soc-2-certifications-support-gxp-compliance/
How HiveMQ’s ISO 27001 and SOC 2 Certifications Support GxP Compliance
blog 85 9.5× 4604 ⚠️ RAG Fracture
https://www.hivemq.com/blog/establishing-governance-frameworks-agentic-ai-industrial-operations/
Establishing Governance Frameworks for Agentic AI in Industrial Operations
blog 75 10.8× 3317 ⚠️ RAG Fracture
https://www.hivemq.com/blog/establishing-multi-agent-frameworks-coordinated-industrial-intelligence/
Establishing Multi-Agent Frameworks for Coordinated Industrial Intelligence
blog 75 13.1× 2519 ⚠️ RAG Fracture
https://www.hivemq.com/blog/enabling-contextual-intelligence-agentic-ai-industrial-operations/
Enabling Contextual Intelligence for Agentic AI in Industrial Operations
blog 75 13.4× 2658 ⚠️ RAG Fracture
https://www.hivemq.com/blog/establishing-real-time-data-flow-agentic-ai-streaming-unified-namespace/
Establishing Real-Time Data Flow for Agentic AI Through Streaming and Unified Namespace
blog 75 11.3× 3146 ⚠️ RAG Fracture
https://www.hivemq.com/blog/how-to-centrally-manage-hivemq-edge-with-barbara/
How to Centrally Manage HiveMQ Edge With Barbara
pricing 75 15.2× 2465 ⚠️ RAG Fracture
https://www.hivemq.com/blog/example-of-a2a-over-mqtt-scale-agentic-ai-collaboration-part-4/
Example of AI Agent-to-Agent (A2A) Communication Over MQTT
blog 75 16.2× 2082 ⚠️ RAG Fracture
https://www.hivemq.com/blog/a2a-enterprise-scale-agentic-ai-collaboration-part-1/
A2A for Enterprise-Scale AI Agent Communication: Architectural Needs and Limitations
pricing 75 12.2× 2839 ⚠️ RAG Fracture
https://www.hivemq.com/blog/enterprise-ai-readiness-starts-better-data-context/
Enterprise AI Readiness Starts with Better Data Context
blog 75 11.2× 3051 ⚠️ RAG Fracture
https://www.hivemq.com/blog/why-embracing-cloud-native-iot-business-imperative-enterprise-architecture/
Why Embracing Cloud-Native IoT is a Business Imperative for Enterprise Architecture
blog 75 15.8× 1771 ⚠️ RAG Fracture
https://www.hivemq.com/blog/hivemq-on-raspberry-pi-as-mqtt-broker-and-mqtt-client/
HiveMQ on Raspberry Pi: As an MQTT Broker and an MQTT Client
pricing 75 15.5× 2165 ⚠️ RAG Fracture
https://www.hivemq.com/blog/ai-operational-technology-unlocking-value-data-architecture-autonomy/
AI in Operational Technology: Unlocking Value Through Industrial Data
blog 75 10.3× 3883 ⚠️ RAG Fracture
https://www.hivemq.com/blog/coordinating-edge-microservices-hivemq-mqtt/
Coordinating Edge Microservices with HiveMQ and MQTT
blog 75 10.9× 3677 ⚠️ RAG Fracture
https://www.hivemq.com/blog/ai-at-scale-part-2-rethinking-data-centers-as-data-problem/
AI at Scale: Rethinking Data Centers as a Data Problem
blog 75 14.6× 1847 ⚠️ RAG Fracture
https://www.hivemq.com/blog/ai-at-scale-part-1-why-data-centers-will-define-next-industrial-era/
AI at Scale: Why Data Centers Will Define the Next Industrial Era
blog 75 14.4× 1850 ⚠️ RAG Fracture
https://www.hivemq.com/blog/architecture-alignment-transforming-agentic-ai-company-outside-in/
The Architecture of Alignment: Transforming into an Agentic AI Company from the Outside In
blog 75 14.9× 1955 ⚠️ RAG Fracture
https://www.hivemq.com/blog/understanding-mqtt-message-ordering/
Understanding MQTT Message Ordering
pricing 75 10.1× 3395 ⚠️ RAG Fracture
https://www.hivemq.com/blog/building-unified-namespace-why-mqtt-outperforms-amqp/
Building a Unified Namespace: Why MQTT Outperforms AMQP
blog 75 14.3× 2322 ⚠️ RAG Fracture
https://www.hivemq.com/blog/building-industrial-iot-data-streaming-architecture-mqtt/
Building Industrial IoT Data Streaming Architecture with MQTT
pricing 75 15.2× 2161 ⚠️ RAG Fracture
Showing 20 of 88 pages. Unlock full subpage table →
📂
Health by Sub-Directory
Average ACRI and top issues aggregated by URL path prefix
Path Pages Avg ACRI Ghost % Bloat Top Issue
/blog/ 76 73 0% 18.8× High JS Bloat
/pricing/ 4 56 0% 132.6× High JS Bloat
/docs/ 1 62 0% 31.3× High JS Bloat
/contact/ 1 44 0% 107.8× High JS Bloat
/case-studies/ 1 41 1% 227.4× Bot Blocked
/products/ 1 67 0% 42.4× High JS Bloat
/features/ 1 67 0% 42.4× High JS Bloat
/faq/ 1 64 0% 44.7× High JS Bloat
/about/ 1 54 0% 58.2× High JS Bloat
/integrations/ 1 0 0% 0.0× Low AI Readiness
🔗
Outbound External Citations
0 unique external domains cited across 88 pages
g2.com ×86
university.hivemq.com ×86
docs.hivemq.com ×86
x.com ×86
youtube.com ×86
console.hivemq.cloud ×86
github.com ×86
sourceforge.net ×86
🔄 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/hivemq.com

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

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📊 Semantic Share of Voice

How often would an AI cite hivemq.com 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 hivemq.com. Copy and paste these into your codebase to improve AI visibility. These patches are mathematically proven to increase extraction accuracy →

Add Organization JSON-LD
High Impact ⏱ 5 min
AI models cannot identify your brand entity without Organization schema. This is the #1 fix for AI visibility.
html
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Hivemq",
  "url": "https://hivemq.com",
  "logo": "./favicon.png",
  "sameAs": []
}
</script>
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": "Hivemq",
  "url": "https://hivemq.com",
  "potentialAction": {
    "@type": "SearchAction",
    "target": "https://hivemq.com/search?q={search_term_string}",
    "query-input": "required name=search_term_string"
  }
}
</script>
Reduce Token Bloat
Medium Impact ⏱ 1–2 hrs
Only 4% 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>
Add FAQ Schema
Medium Impact ⏱ 10 min
FAQ schema lets AI models directly extract Q&A pairs. This is the easiest way to get featured in AI responses.
html
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is Hivemq?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add your answer here — describe what Hivemq does in 1-2 sentences."
      }
    },
    {
      "@type": "Question",
      "name": "How does Hivemq work?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Explain the key features and how users interact with Hivemq."
      }
    }
  ]
}
</script>
📈

Projected Impact

ROI EST.

If you apply the patches above, here's the estimated improvement for hivemq.com:

Current Score
81
Projected Score
99
Improvement
+18 pts
Add Organization schema +6 pts
Add WebSite schema +4 pts
Reduce token bloat +5 pts
Add FAQ schema +3 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).

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

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🧭 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

🕒 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
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