Abstract: Systems and methods for artificial intelligence communications agents are disclosed. Implementations relate to capturing individual agent's behaviors and modelling them in artificial intelligence (AI) learning models so that the agent's behavior can be easily replicated. Some implementations further relate to systems and methods for capturing human-computer interactions (HCl) performed by agents and using robotic process automation (RPA) to automate tasks that would otherwise require human interaction. The combination of AI learning models and RPA are used to provide artificial intelligence communications agents capable of responding to a variety of topics of conversation over a variety of communication mediums.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries town independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include an Al based system configured to determine which of the inquiries should be routed to the independent group of experts. The expert system is optionally trained based on scored responses to prior customer service inquiries.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include methods of classifying customer service inquiries, training a machine learning system, and/or processing customer service requests.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include an expert system configured to identify clusters of inquiries that can be responded to using predetermined response content. The expert system is optionally trained based on scored responses to prior customer service inquiries.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by providing the inquiries to an independent group of experts. These experts are typically able to resolve the inquiry before the inquiries are routed to a traditional contact center. The customer service inquiries are routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. In some embodiments, more than one expert can contribute to the resolution of an inquiry.

Abstract: In a crowd sourcing approach, responses to customer service inquiries are provided by providing the inquiries to an independent group of experts. These experts are typically able to resolve the inquiry before the inquiries are routed to a traditional contact center. The customer service inquiries are routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. In some embodiments, more than one expert can contribute to the resolution of an inquiry.