Digital Engineering AI

This proposal presents a novel approach to enhancing Government-owned digital engineering environments through a dual Retrieval-Augmented Generation (RAG) architecture integrated with a Modular Open Systems Approach (MOSA). The system addresses critical inefficiencies in current digital engineering practices, particularly in documentation generation, requirements traceability, and cross-project knowledge sharing.áThe core innovation combines a graph-based RAG implementation optimized for structured relationships with a vector-based RAG for natural language processing. This hybrid approach enables comprehensive context for AI operations while maintaining strict data provenance. The system's architecture employs a modular design with standardized interfaces, allowing third-party integration while ensuring enterprise reliability.áUsers interact with the system through three primary interfaces: a conversational interface that enables natural language queries across the entire project knowledge base, a document generation interface that creates standardized documentation directly from project data, and a batch analysis capability where AI assistants can process project data at scale to identify patterns, inconsistencies, and potential issues. All interactions maintain complete traceability to source data while operating within IL5 security requirements.