Computer-implemented techniques for answer time prediction in a contact center service of a provider network. While a delayed processing timing has not been met, a set of contact queuing context-actual answer time data for a set of contact inquiries serviced is received as a first set of contact queuing context-actual answer time data. When the delayed processing timing has been met, a new queuing model is learned based on the first set of contact queuing context-actual answer time data and a previous set of contact queuing context-actual answer time data for a previous set of contact inquiries serviced or a previous version of the queuing model. A request to predict an answer time for a target contact queuing context is received and an answer time for the target contact queuing context is predicted based on the new queuing model. The predicted answer time is provided to a contact via a contact communications channel.

Methods to reduce agent effort and improve customer experience quality through artificial intelligence. The Agent Assist tool provides contact centers with an innovative tool designed to reduce agent effort, improve quality and reduce costs by minimizing search and data entry tasks The Agent Assist tool is natively built and fully unified within the agent interface while keeping all data internally protected from third-party sharing.

The technology disclosed herein enables incorporation of feature sets of a unified communications system and a contact center system. In a particular embodiment, a method includes, in a contact center system, in response to an agent logging into the contact center system, establishing a first connection between the contact center system and a unified communications endpoint operated by the agent. The first connection is established using a unified communications system that services the unified communications endpoint. After establishing the first connection, the method includes identifying a call on a second connection between the contact center system and a second endpoint operated by a user. In response to selecting the agent to handle the call, the method includes bridging the first connection and the second connection.

A method of providing artificial intelligence (AI) functionality to target legacy customer outreach platforms of a plurality of tenant enterprises includes storing a plurality of AI templates, each of which is associated with one or more AI routines, generating a campaign object associating one or more of the AI templates with a tenant enterprise from among the plurality of tenant enterprises, transforming a communication on a switching network associated with the tenant enterprise according to the one or more AI templates associated with the campaign object, and providing the transformed communication to a target legacy customer outreach platform of the tenant enterprise.

A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

A sentiment-based score is determined for a contact center engagement between a first contact center service operator and a contact center user. The sentiment-based score is indicated within a graphical user interface displaying information associated with multiple contact center engagements at a device of a second contact center service operator. Based on a request to participate in the contact center engagement received from the device of the second contact center service operator via the graphical user interface, information associated with the contact center engagement is transmitted to the device of the second contact center service operator, and a contact center session involving a device of the contact center user and the device of the second contact center service operator is established.

Apparatuses and methods concerning providing a data-communications contact center virtual agent are disclosed. As an example, user-data-communications between client and participant stations are facilitated as follows, which may be implemented using a data communications server and associated communications circuitry. Service request data is received from users at a participant stations, and context information is identified for user-data-communications between a client station and the participant stations based on the service request data at least one communications-specific characteristic associated with the user-data-communications. The identified context information is aggregated for the client station and used for choosing a data routing option routing data with each user at the participant stations, based on the service request data and the aggregated context information.

An on-premises system for pre-processing data for forecasting according to an embodiment includes at least one processor and at least one memory having a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the on-premises system to receive a request to forecast contact center data using a cloud system, determine a first number of interactions per unit of time for a source interval, determine a second number of units of time in a destination interval, and determine a third number of interactions in the destination interval based on the first number of interactions per unit of time for the source interval and the second number of units of time in the destination interval.

A system, method, and computer program product to accurately route a call is disclosed, by providing a resource interface having one or more interface elements mapping a resource to a call based on current calls and predicted calls, determining a dynamic handling profile from a plurality of dynamic handling profiles that include call information which maps to a resource to provide a ruleset for handling one or more types of call requests, identifying a routing rule comprising one or more routing rules of the ruleset based on caller information, receiving an activation of a resource interface element of the resource interface to initiate a call routing request to forward an assigned call to the resource, and controlling the call routing request according to the routing rule of the dynamic handling profile to perform a call function associated with the assigned call.

A system and methods are provided to analyze audio signals from an incoming voice call. The system includes a processor and a computer readable medium operably coupled thereto, to perform voice analysis operations which include receiving a first audio signal comprising a first audio waveform of a first speech between at least two users during the incoming voice call, accessing speech segment parameters for analyzing the audio signals, determining one or more talk-over segments in the first audio waveform using the speech segment parameters, extracting audio features from each of the one or more talk-over segments, determining, using a machine learning (ML) model trained for interruption analysis of the audio signals, whether each of the one or more talk-over segments are a negative interruption or a non-negative interruption based on the audio features, and determining whether to output a first notification for the negative interruption or the non-negative interruption.