Modern CSPs are swiftly transitioning to cloud-based solutions, attracted by the potential of real-time scalability and adaptability. However, the intricacies of cloud-native architectures, combined with the data volumes and talent shortages, can be daunting for CSPs when it comes to assuring services. Leveraging AI/ML can demystify this landscape, optimizing processes and bridging knowledge and resource gaps to navigate this transformation seamlessly. AI/ML-based service assurance can revolutionize CSP operations. By intelligently analyzing vast amounts of data in real-time, it identifies and predicts network issues before they escalate. This not only ensures optimal network performance and uptime but also significantly reduces manual intervention and troubleshooting efforts. For proactive, predictive, and preventive problem identification and resolution, Service Assurance tools must grasp the intricate interplay between all layers, from virtualized applications and cloud-native infrastructures to the network layer, all in near real-time. Beyond just understanding, these solutions must also assimilate observability data such as logs, metrics, traces, and topology from a myriad of sources. By synthesizing this data, they provide a unified perspective on the entire business service, paving the way for swift and efficient interventions. This Catalyst project aims to demonstrate how AI and ML can be used in conjunction with a digital twin of the virtualized services, network, and infrastructure to automate Service Assurance for converged Telco Cloud CaaS and CNFs. The Catalyst project seeks to showcase how BMC's Helix AIOps solution, in conjunction with Exfo's digital twin service topology, can meet the above-mentioned needs and provide a future-proof service assurance solution, eliminating the need for manual problem resolution and remediation. The main benefits of our solution are reduced cost of operations and improved customer satisfaction: * Automated problem detection, correlation, and root-cause analysis replace the formerly manual problem identification and diagnostic processes. * The automated assurance solution makes it more cost-effective to manage complex cloud-based services and networks, with a 50x reduction in the volume of anomalies needing manual analysis and intervention, enabling a 30% OPEX saving for cloud-based service operations. * Problem detection time is accelerated (90% reduction in MTTD and RCA), resulting in fewer problems going unnoticed or unactioned until they impact customers. * Problem resolution time (MTTR) is improved by 37%, resulting in an improved customer experience and an improved NPS from those customers who may be impacted. The solution also improves the scalability and sustainability of operations, enabling more cloud-based services to be introduced without requiring a linear increase in Ops personnel and corresponding costs.