Nu-Logos does not blend system models, semantics, relationship graphs, data models, connectors, and AI into one black box. Each has a defined role, because results are only trustworthy when you know which part produced them.
These components do not substitute for one another. The model defines structure, the ontology defines meaning, the graph follows relationships, and the data model and provenance records keep each value in context. We are currently validating this architecture in a limited-scope proof of concept (PoC).
Defines requirements, functions, and system and component allocation as models, and establishes the traceability between them.
Gives engineering objects, properties, and relationships a common meaning, so different tools read the same object as the same thing.
Explores the relationships between objects. When a design parameter changes, it finds the paths the impact can travel across the graph.
Stores each design value with its unit, source, version, status, and evidence, so no number floats free of its context.
Connects existing engineering tools and analysis codes to the Nu-Logos information model. It generates the inputs a tool needs and imports the results.
Assists with extracting candidate requirements, exploring impact, and drafting documents. Engineers make the final decisions.
Records the lineage of each result: which inputs, model, code, and version produced it, and who reviewed and approved it.
AI suggestions enter the baseline only after an engineer has reviewed them.
Every analysis result has the inputs, model, code, and version that produced it, and the people who reviewed and approved it. Nu-Logos records these links as a single chain, so the same question never sends you back to reconstruct everything from scratch.
Our information model and terminology reference international standards and ontologies published by research institutions.
Criteria for how requirements are written and verified
A way to express requirements, functions, and structure in one model
A way to express lineage between inputs, processes, and results
A nuclear data integration ontology published by Idaho National Laboratory (INL)
We welcome inquiries about pilot projects, technology partnerships, and anything else by email.