Bridging rigorous conceptual foundations and real-world intelligent systems
In many AI projects, the hardest problems are not technical but conceptual: the underlying concepts, rules, and responsibilities were never made explicit. This site focuses on formal conceptual modeling for AI systems: making their foundations clear enough to support explanation, governance, and accountability before automation turns hidden assumptions into operational decisions.
My approach combines Object-Role ModelingORM (Object-Role Modeling) is a fact-oriented conceptual modeling method that represents domain knowledge through object types, fact types, explicit roles, and constraints, enabling precise conceptual schemas independent of data structures, processes, or system implementation. for conceptual schema design, DEMODEMO (Design & Engineering Methodology for Organisations) is a theory-based approach for modeling the essential structure of organizations in terms of actor roles, commitments, and coordination acts, explicitly separating what an organization does from how it is implemented or supported by technology for organizational essence modeling, and an Intent-Driven AI DelegatesIntent-Driven AI Delegates are AI systems that execute delegated intent within defined authority and constraints, grounded in conceptual schemas, with responsibility and accountability remaining human. framework for governing how AI systems act within human-defined authority. The goal is AI systems that are auditable by design, not as an afterthought. The framework articles below explain the intellectual foundation. The demos show it running.
My background includes over a decade in Banking as a Service and Ad Tech, where I applied enterprise architecture and integration methodologies to complex business problems.
These articles form the intellectual core of the work on this site. The first establishes the vocabulary. The second applies it to AI governance. The third shows the same broader position in a compact implementation-focused example. Read them in order for the full argument, or start with whichever matches your immediate question.
Start with Knowledge, Information, and Semantics for the vocabulary, continue to Intent-Driven AI Delegates for the governance architecture, then move into implementation and foundations through When Prolog Beats the LLM That Created It, Knowledge Engineering Beyond SQL, and The Relational Model Still Matters. For organizational application, see Enterprise Ontology for AI Reinvention.
A compact demonstration of the Intent-Driven AI DelegatesIntent-Driven AI Delegates are AI systems that execute delegated intent within defined authority and constraints, grounded in conceptual schemas, with responsibility and accountability remaining human. framework. A natural language request is interpreted by an LLM, mapped into a structured, jurisdiction-aware conceptual schemaConceptual Schema is a formal model that captures the relevant conceptual distinctions of a domain—objects, relationships, constraints, and rules—independent of implementation or execution. context, evaluated by a bounded delegate against explicit rules, and recorded in a full audit trail.
A neuro-symbolic AI demonstration built using the ORM Toolkit with AI code assistance. It shows how formal conceptual schemasConceptual Schema is a formal model that captures the relevant conceptual distinctions of a domain—objects, relationships, constraints, and rules—independent of implementation or execution. can drive intelligent decision systems that combine neural and symbolic reasoning with explainable logic.
Note: This demo was built before the Intent-Driven AI Delegates framework was formalized. It demonstrates the neuro-symbolic capability that the delegates architecture is designed to govern.
Web-based conceptual modeling tool for Object-Role Modeling. Features AI-powered assistance for model creation and validation.
Note: This public version is available with guest access (5 AI requests/hour) or Google sign-in (unlimited). Publishing features are not enabled in the public demo.
These pieces focus on formal modeling foundations, implementation strategy, reasoning systems, and practical AI architecture.
These pieces are more useful if your primary question is organizational design, interoperability across business contexts, or how AI changes enterprise structure and accountability.
Explicit conceptual schemas and semantic foundations for systems that need explainability, governance, and durable shared meaning.
Experience with complex API integrations for Banking as a Service, Ad Tech, and multi-provider systems using systematic methodology.
AI-assisted workflows in which interpretation, execution, authority, and accountability are clearly separated and auditable.
Working prototypes that use AI-assisted development and formal modeling to validate feasibility and demonstrate technical approach.
If you would like to discuss knowledge modeling, conceptual schemasConceptual Schema is a formal model that captures the relevant conceptual distinctions of a domain—objects, relationships, constraints, and rules—independent of implementation or execution., and semanticSemantics refers to the explicit modeling of conceptual distinctions in a conceptual schema, supporting shared meaning, reasoning, and action through use, interpretation, and refinement over time. foundations, feel free to reach out.