Graph analytics are becoming increasingly important for enterprises to advance their AI enablement initiatives, but operationalization and post-production maintenance pose significant challenges. Enterprises often lack the required skill sets and tools in their current architectures, leading them to seek high-level architecture designs to get started with a minimal setup that can be scaled as needed. The Graph Analytics Platform architecture has been designed using Neo4j and GCP, which can also be deployed on other major cloud vendors or on-prem infrastructures. The Minimal and Scaled architectures are two design options for enterprises, with the former focusing on enriching data and generating insights and the latter enabling operational applications such as real-time fraud detection and border control. A key aspect of any modern Data Analytics Platform is the data life cycle, which drives the actions performed by different parts of the platform. The architecture diagrams illustrate the functions served by each section of the Graph Analytics Platform, providing a comprehensive solution for enterprises to advance their AI enablement initiatives.