A Five-Layer Reference Architecture for First-Party Enterprise Knowledge Graphs with GraphRAG Integration and Governance Controls

Organizations produce significant amounts of first-party data as part of their day-to-day operations in manufacturing, clinical, and customer experience domains, which are not accessible to generic large language models. The paper discusses the five-layer approach to using this information asset: (1) data ingestion and quality assurance, (2) domain ontology engineering, (3) knowledge graph engineering and population, (4) GraphRAG-enabled AI augmentation, and (5) downstream application enablement. The paper focuses on the design considerations, implementation tactics, and lessons learned from real-world applications in manufacturing, healthcare, and professional networks rather than presenting original research results. The implementation results show up to 70–80% query time reduction and close to 85% fewer hallucinations on average for GraphRAG over the standard RAG for most of the use cases analyzed. The main barriers to adoption are the substantial manual effort involved in ontology engineering, 73–94% entity resolution accuracy across different industries, and 3–5× higher computational costs for GraphRAG compared to RAG. The framework gives practitioners and researchers a reference architecture for designing, evaluating, and governing enterprise knowledge graph deployments built on proprietary organizational data.