Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors perform: receiving, by a chat bot and from a user, an indication to talk to a live agent; calling, by the chat bot, an automatic call distribution server to initiate a live-agent session for the user; storing metadata for the live-agent session in a distributed table and a distributed queue; pulling, from an automatic call distribution connector, one or more messages from a conversation between the user and the chat bot based on the metadata; and connecting the live agent to the user in the live-agent session, wherein the user communicates with the live agent in the live-agent session through a same interface for the chat bot. Other embodiments are disclosed.

Abstract: A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include receiving, via a computer network, a user interaction signal from a user device for a user. The method further can include after receiving the user interaction signal, determining, in real-time via a machine learning model, a plurality of user intent labels based at least in part on transaction data, interaction data, and incident data for the user. The machine learning model can include pre-trained based on historical input data and historical output data associated with multiple users comprising the user. The historical input data can comprise historical feature embedding vectors for historical transaction data, historical interaction data, and historical incident data associated with the multiple users.