Abstract: In some implementations, a device may receive, from a user device, a query for information from a user. The device may receive, from an enterprise system, data identifying products and services to be offered by the user. The device may process the query and the data, with a model, to identify a subject of the query and a context for the subject. The device may process the subject and the context, with a model, to determine a workflow for the user. The device may process the data, the subject of the query, and the context for the subject, with a model, to determine a key performance indicator or a decision associated with one of the products or the services. The device may provide information identifying the workflow, the key performance indicator, and/or the decision to the user device via a virtual assistant or a user interface.

Abstract: In some implementations, a device may receive, from a user device, a query for information from a user. The device may receive, from an enterprise system, data identifying products and services to be offered by the user. The device may process the query and the data, with a model, to identify a subject of the query and a context for the subject. The device may process the subject and the context, with a model, to determine a workflow for the user. The device may process the data, the subject of the query, and the context for the subject, with a model, to determine a key performance indicator or a decision associated with one of the products or the services. The device may provide information identifying the workflow, the key performance indicator, and/or the decision to the user device via a virtual assistant or a user interface.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to predict future handover success rates of cells of a wireless network and to determine updated network parameter values that may be used to improve the future handover success rates. Clustering algorithms may be applied to handover success rates associated with the cells to group the cells into different clusters. Machine learning (ML) models may be trained based on historical handover data associated with cells of the clusters to predict future handover success rates based on current handover success rates. The output of the ML models may be used to determine one or more updated network parameter values to be implemented by the wireless network to improve future handover success rates at one or more cells.