AI Visibility Scorecard
jbd.dev · Other
Borderline visible. AI bots can crawl jbd.dev, but the structured-data signals are thin - you are at real risk of being skipped when buyers ask ChatGPT, Claude, or Perplexity for a recommendation.
Monitoring for AI engine activity
In the Engagemii AEO index
4
AEO Visibility
iBorderline · 4.2/10
5
Muse Index Score
iAI agent readiness
Basic · 5/100
0
AI Adoption
iNone detected · 0/100
from our crawl and measurement
"jbd.dev linux NUMA Core dumps Google: Persistent disks practices Production readiness Benchmarks are hard Microservices instrumentation Debugging latency Google: SRE Written by JBD mostly." That is how jbd.dev introduces itself. To AI engines like ChatGPT and Perplexity, though, it is barely visible: it scores 4.2 out of 10.
Our crawl found a sitemap and a readable heading structure. It is missing structured data describing the business and an llms.txt file.
Strong · Good · Fair · Weak
Structured Data
Weak
Organization / LocalBusiness JSON-LD that AI can read.
Content Structure
Strong
Clear headings and answer-style content.
Entity Clarity
Weak
How clearly your brand identity reads to AI.
E-E-A-T Signals
Experience, Expertise, Authority, Trust
Fair
Experience, Expertise, Authority, Trust markers.
Technical AEO
Fair
robots.txt, llms.txt, and AI-bot crawl access.
AI Discoverability
Good
Sitemaps and entity links AI can follow.
How the web signals your brand to AI
Backlinks
Weak
No inbound links found yet.
Domain Authority
Weak
Little domain authority yet.
Reference Presence
Weak
Not in AI knowledge graphs yet.
News & Press
Weak
No press coverage found yet.
Community
Weak
No community discussion yet.
Social Mentions
Weak
No social discussion found yet.
Your AEO score measures whether AI search engines - ChatGPT, Claude, Perplexity, Gemini - can actually read your site and cite it in answers. Roughly two-thirds of sites are invisible to them. At 4.2/10, jbd.dev is crawlable but under-signaled - fixable, and the signals above are where to start.
When to review a service for production readiness?
Production readiness reviews is not only useful right before pushing to production, they can be a protocol when handing off operational responsibilities to a different team or to a new hire. Use reviews when: Launching a new production service. Handing off the operations of an existing production service to another team such as SRE. Handing off the operations of an existing production service to new individuals.
Is there unexpected latency?
In order to detect anomalies in latency, we need to first answer what would be considered as expected latency. Every service has different requirements what unexpected latency can be. It is almost impossible to have services with 100% reliability, hence we need to first determine what latency range would give user an experience that they wouldn’t recognize there is a problem.
What is the source of latency?
When an alert is triggered or a customer is reaching out to you, an on-call person is expected to take a look. At this point, they know that there is a latency violation or poor experience. We often know what specific service/method it is but no idea the underlying cause. At this point, we can look at the latency distribution heat map for the service/method.
Is this your brand?
The exact fixes for jbd.dev
Which AI engines already crawl you
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The search landscape has fundamentally shifted. While Google still dominates, millions of users now ask questions to ChatGPT, Gemini, and Claude instead of typing into a search bar.
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Scored by Engagemii on September 30, 2026. Methodology: engagemii.com/aeo/methodology
Source URL: https://engagemii.com/aeo/brands/jbd-dev
Cite this score: Engagemii (2026). "AEO Score for jbd.dev." Retrieved from https://engagemii.com/aeo/brands/jbd-dev
Licensed under CC BY 4.0. You may reuse this data with attribution: a visible link to engagemii.com.
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