File-Based Knowledge Graphs and Retrieval-Augmented AI for Complex Project Delivery

abstract

Complex engineering and transformation projects generate heterogeneous knowledge that is difficult to integrate, trace, and reuse across multi-year lifecycles. Although knowledge graphs (KGs) and retrieval-augmented generation (RAG) have independently matured, many teams still lack a practical path from fragmented documents to explainable AI-assisted decision support. This paper develops an end-to-end design pattern for file-based project knowledge graphs: KGs whose canonical representation resides in structured files (Markdown + YAML + explicit links), rather than in dedicated graph databases. It is based on a prototype built for a real-case, multi-year, multi-site industrial MES implementation project, and presents a detailed design that covers ontology governance, graph encoding patterns, agentic retrieval loops, provenance rules, human-in-the-loop write controls, and production-oriented cost optimisation.

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