As network complexity continues to grow, there is an increasing demand for more advanced methods to manage and operate these systems, focusing on improving efficiency, reliability, and security. In response, a broad range of Artificial Intelligence (AI) and Machine Learning (ML) models are being developed. These models are crucial for automating decision-making, performing predictive analyses, proactively managing networks, enhancing security, and optimizing network performance. They collectively form what is known as Network Intelligence (NI), which is fundamental to shaping the future of networks. Leading Standard-Defining Organizations (SDOs) are incorporating NI into upcoming network architectures, particularly emphasizing a closed-loop approach. However, current methods for seamlessly integrating NI into network architectures are not yet fully effective. To address this, a paper introduces an in-depth architectural design for a Network Intelligence Stratum (NI Stratum). This stratum is supported by a novel end-to-end NI orchestrator designed to facilitate closed-loop NI operations across various network domains. The primary objective of this design is to streamline the deployment and coordination of NI throughout the entire network infrastructure, tackling challenges related to scalability, conflict resolution, and effective data management. The paper details comprehensive workflows for managing the NI lifecycle and presents a reference implementation of the NI Stratum.