Abstract: A method of evaluating performance of a chatbot includes identifying a test scenario including a request and an expected outcome, initiating an automated conversation between a first machine learning model and the chatbot based on the test scenario, storing a recording of the automated conversation, providing the recording of the automated conversation and the test scenario to a second machine learning model, and receiving an evaluation of the automated conversation from the second machine learning model based on the recording of the automated conversation and the expected outcome.

Abstract: A method for configuring a selected application of a contact center to facilitate handling of incoming interactions. The method may include: collecting data; generating individual customer models and aggregated customer models, wherein the aggregated customer models each comprises an aggregation of a grouping of the individual customer models; generating individual agent models and aggregated agent models, wherein the aggregated agent models each comprises an aggregation of a grouping of the individual agent models; from the customer models, generating a customer predictor configured to predict customer behavior; from the agent models, generating an agent predictor configured to predict agent behavior; using the customer predictor to make a customer prediction; using the agent predictor to make an agent prediction; and modifying an allocation of a contact center resource based on the customer and the agent predictions.

Abstract: A method for automating interactions with enterprises includes: receiving, through a user interface, a natural language request from a customer; determining a customer intent from the natural language request; conducting an automated interaction by: determining, by a personal bot controller circuit, an enterprise to service the customer intent; loading an enterprise library for interacting with an application programming interface (API) of the enterprise, the enterprise library including one or more library functions; identifying a library function for servicing the customer intent from among the one or more library functions; generating a request based on the identified library function and the customer intent; transmitting the generated request to the enterprise to initiate an interaction; receiving a response from the enterprise; and notifying the customer of a result of the interaction.

Abstract: A method for generating a predictor of customer behavior for a contact center includes: collecting, by a processor, data from a plurality of different applications of the contact center, the data being stored in a plurality of different formats, the data corresponding to a plurality of recorded interactions between a plurality of customers and the contact center; converting, by the processor, the data from the plurality of different formats into a common format; generating, by the processor, a plurality of customer models for the customers by, for each customer of the customers: identifying, from the data from the plurality of different applications, identified data associated with the customer; and aggregating the identified data in an individual customer model of the plurality of customer models, the individual customer model being associated with the customer; and generating, by the processor, a predictor in accordance with the customer models.