Network digital twins (NDTs) are virtual representations of network assets and processes, such as in 5G or 6G networks, synchronized with physical properties. NDTs can be leveraged for network monitoring, automation, and optimization tasks. Simulations and predictions from the NDT can be provided to scout the feasibility of important processes from a network connectivity quality perspective. The real-time modeling of a key performance indicator (KPI) enables continuous network management. This article presents results and insights from a proof-of-concept NDT for an enterprise use case using real-time data from commercially available communication equipment. Neural networks, assisted by comprehensive feature selection and extraction, are integrated into the NDT to model the KPI, namely the downlink user throughput. KPI evaluations from the NDT are provided based on requests from the real-world generated demanding scenarios. The results provide general insights into an accurate real-time N DT that can support continuous network configuration updates.