From conceptual models to explainable AI — logic and semantics, built in.
🚀 Start a ConversationThis site explores the integration of conceptual modeling, logic-based reasoning, and intelligent data systems into the fabric of modern applications. It operates on the philosophy that systems, especially those at the heart of decisions, automation, or AI, ought to embed knowledge, semantics, and logic as fundamental components.
This involves developing tools and methods for:
This initiative welcomes collaboration with researchers, developers, and open-source projects aligned with its mission. If exploring knowledge-driven AI, designing schema-first architectures, or building semantically aware platforms, collaboration and conversation are always welcome.
A practical perspective on using AI responsibly in writing, where it helps, where it fails, and how to keep authorship and integrity clear.
Read full articleDesign precise and flexible conceptual models to form the foundation for intelligent systems.
Develop semantic models to represent complex domains, enabling reasoning, explainability, and inference.
Integrate symbolic logic with learning systems to create explainable and robust AI applications.
Interested in working together? Let’s explore ways to collaborate or contribute to like-minded initiatives.
All articles on this site are authored by G. Sawatzky, who developed the conceptual framework and guided the content. For many of the articles, AI language models were used to generate initial drafts and formalize technical prose under the author's direction.