I Built an End-to-End Enterprise Intelligence Simulation to Answer One Question
Most enterprise AI architecture diagrams look the same. An AI agent sits at the top, utilizing "Ontology", "Knowledge Graph", "Certified Data" and "Vector Index", and connections promise that every answer will be grounded, governed and traceable.
I wanted to find out whether these integrations actually work. So I simulated the full environment using open source tools just answering one question, end to end: "Which overloaded transformers have the highest customer impact risk?"
The domain is for an electricity distribution grid, and every architectural simulation decision served that one question.
Overview of the system
The design splits "enterprise intelligence" into two tracks.
- Semantic resolution - works out what the user means or intent. An OWL (Web Ontology Language) ontology, loaded with RDFLib, maps terms included on the question to a governed concept. A YAML metric registry holds the certified metric definition. A Neo4j graph captures the grid topology including transformers,...
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