Abstract: A system for determining context and intent in a conversation using machine learning (ML) based artificial intelligence (AI) in omnichannel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and a ML-based AI subsystem to communicate with the server and the data store in the network. The ML-based AI subsystem may comprise a data access interface to receive data associated with a conversation with a user via a communication channel. The ML-based AI subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation by applying hierarchical multi-intent data labeling framework, training at least one model with training data, and generating and deploying a production-ready model based on the trained and retained at least one model.

Abstract: A system for determining context and intent in a conversation using machine learning (ML) based artificial intelligence (AI) in omnichannel data communications is disclosed. The system may comprise a data store to store and manage data within a network, a server to facilitate operations using information from the one or more data stores, and a ML-based AI subsystem to communicate with the server and the data store in the network. The ML-based AI subsystem may comprise a data access interface to receive data associated with a conversation with a user via a communication channel. The ML-based AI subsystem may comprise a processor to provide a proactive, adaptive, and intelligent conversation by applying hierarchical multi-intent data labeling framework, training at least one model with training data, and generating and deploying a production-ready model based on the trained and retained at least one model.