Concepts
Original thinking models and methodological approaches for analyzing and shaping visibility in traditional and AI-based search systems.
ARTS SEO Formula
The classical distinction between onpage and offpage is no longer sufficient to describe the mechanics of modern search systems. SEO requires a structured, user-oriented logic that puts the "why" before the "what".
The ARTS SEO Formula structures the analysis based on four guiding questions:
Accessible
Is the URL technically accessible?
Relevant
Is the thematic relevance clearly recognizable?
Technically Important
Is the page technically and structurally measurably important?
Satisfactory
Does the page meet user expectations?
This results in concrete advantages for strategic work:
- Structured analysis based on clear guiding questions
- Focus on search intent and user satisfaction
- Contemporary weighting of links
- Reduction of purely technical misinterpretations
- Simplified strategic prioritization
Publication
The ARTS SEO Formula – The New SEO Framework
Website Boosting, Issue 92 – Cover Story
Prompt Decoding
Prompt Decoding was developed by Hanns Kronenberg, is based on millions of real prompts, and is licensed exclusively through Rankscale.
The methodological foundation consists of model-internal simulations with ChatGPT and Gemini. Representative clusters, frames, and response paths become visible. The results are reproducible, privacy-compliant, and are not based on personal data.
In the OpenAI/Harvard study "Who People Use ChatGPT" (September 2025), central usage clusters were reproduced partly word-for-word and in comparable frequencies that had already been identified by Prompt Decoding in April 2025. This underscores the methodological validity.
ChatGPT and Gemini provide consistent cluster structures. This creates a robust, system-spanning perspective on user behavior.
Prompt Decoding was presented at the G50 Summit 2025 before the international SEO expert community and was recognized for its contribution.
Publication
From Keyword to Prompt: Why SEO Experts Must Understand the Black Box of AI
Website Boosting, Issue 95 – Cover Story
Semantic Resonance Analysis
Semantic Resonance Analysis examines how strongly a brand or topic is embedded in the model knowledge of AI systems. Unlike traditional visibility measurement, it is not about rankings but about the probability of being mentioned in a generated answer.
The foundation consists of systematic prompt series that make response patterns comparable across different models. This reveals strengths, gaps, and shifts in how AI perceives a brand.
The method combines qualitative analysis with quantitative reproducibility and provides a reliable basis for strategic decisions in the field of AI Search.
On-Model / Off-Model SEO
On-Model SEO describes measures that directly contribute to representation in the model knowledge of AI systems. Off-Model SEO encompasses traditional optimization that works in search engines but is not necessarily visible in generative answers.
The distinction helps allocate resources more effectively: Which measures work in both worlds? Which only in one? And where does the shift toward AI Search create a blind spot?
The model provides clarity for teams that need to manage SEO and GEO in parallel without doubling budgets.
The concepts do not stand in isolation, but are part of a consistent perspective on the transformation of search and answer systems. The goal is strategic decision-making capability in the transition from ranking logic to answer logic.