AI Visibility Scorecard
simonhessner.de · Education
Borderline visible. AI bots can crawl Simon's blog, 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.
AI engines read this profile 5 times
Meta AI · Claude
#669,785 of 937,045 in Education for AI visibility
4
AEO Visibility
iBorderline · 4.1/10
5
Muse Index Score
iAI agent readiness
Basic · 5/100
6
AI Adoption
iBasic · 6/100
from our crawl and measurement
Simon's blog is a business whose own site puts it this way: "It covers many topics of the LLM lifecycle in three weeks of videos, labs and quizzes." To AI engines like ChatGPT and Perplexity, though, it is barely visible: it scores 4.1 out of 10.
Our crawl found a sitemap, a readable heading structure, substantial page content. It also found the gaps that keep answer engines guessing: structured data describing the business, 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
Fair
How clearly your brand identity reads to AI.
E-E-A-T Signals
Experience, Expertise, Authority, Trust
Weak
Experience, Expertise, Authority, Trust markers.
Technical AEO
Fair
robots.txt, llms.txt, and AI-bot crawl access.
AI Discoverability
Fair
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 4.1/10, Simon's blog is crawlable but under-signaled - fixable, and the signals above are where to start.
Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem?
July 19, 2018June 12, 2019 Simon 15 Comments In a recent project I was wondering why I get the exact same value for precision, recall and the F1 score when using scikit-learn’s metrics. The project is about a simple classification problem where the input is mapped to exactly \(1\) of \(n\) classes.
Is this your brand?
The exact fixes for Simon's blog
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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 July 31, 2026. Methodology: engagemii.com/aeo/methodology
Source URL: https://engagemii.com/aeo/brands/simonhessner-de
Cite this score: Engagemii (2026). "AEO Score for Simon's blog." Retrieved from https://engagemii.com/aeo/brands/simonhessner-de
Licensed under CC BY 4.0. You may reuse this data with attribution: a visible link to engagemii.com.
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