Abstract: A virtual assistant server receives an utterance provided by an end user via a channel of a virtual assistant rendered in a client device. The virtual assistant server identifies a current-node of execution from a plurality of nodes of a conversation definition of the virtual assistant and identifies a first set of language models from a group of language models of the virtual assistant to interpret the utterance. Further, the virtual assistant server executes the first set of language models in an order based on the current-node until an intent of the utterance is determined. Subsequently, the virtual assistant server generates a response based on the intent and outputs the response to the client device.

Abstract: A virtual assistant server receives conversational inputs as part of a conversation from a customer device and generates a version of a data record of the conversation upon: the receiving of each of the conversational inputs, or receiving each output generated by one of a plurality of software modules when the one of the software modules receives a system input from the conversation management framework. The virtual assistant server provides each of the generated versions of the data record to a communication orchestrator and receives for each of the generated versions of the data record, execution instructions from the communication orchestrator. Further, the virtual assistant server communicates with one or more of the software modules based on the received execution instructions, and provides based on the communicating, one or more of the outputs of the software modules to the customer device, when the one or more of the outputs comprise one or more responses to one or more of the conversational inputs.

Abstract: A server manages conversation data for a plurality of conversations between agent devices and customer devices and provides to the agent devices, one or more automated response recommendations to one or more customer messages by executing one or more dialog flows. The server, using a classification model, determines for each of the messages when there are one or more changes between the corresponding one or more automated response recommendations and one or more agent responses. Further, the server associates one or more tags to the one or more agent responses when the one or more changes are determined. Subsequently, the server identifies one or more patterns in the one or more tags associated to the one or more agent responses and provides one or more modification recommendations to the one or more dialog flows to one or more enterprise user devices based on the identified one or more patterns.

Abstract: A virtual assistant server executes a dialog flow corresponding to a use case of one or more utterances received from a customer device. Further, a large language model (LLM) is selected from a plurality of LLMs to perform response generation based on an execution state of the dialog flow. Further, a plurality of outputs is received from the selected LLM based on a plurality of prompts provided to the selected LLM to fulfill one or more execution goals of the dialog flow. Further, when one or more of the plurality of outputs comprise: one or more entities extracted from the one or more utterances and a response to be transmitted to the customer device, the adherence of: the extracted one or more entities to one or more business rules and the response to one or more conversation rules, are validated. Subsequently, the response of the one or more of the plurality of outputs is transmitted to the customer device when the corresponding validation is successful.

Abstract: A virtual assistant server determines one or more use cases of one or more utterances received from one or more customer devices. The virtual assistant server executes one or more dialog flows corresponding to the one or more use cases of the one or more utterances, where the executing comprises providing one or more inputs to a fine-tuned large language model comprising: use case context, customer context, conversation context, business rules, and exit conditions. The virtual assistant server receives one or more outputs corresponding to the one or more inputs from the fine-tuned large language model, and determines one or more responses based on the one or more outputs. Subsequently, the virtual assistant server provides the one or more responses to the one or more customer devices.

Abstract: A contact center server manages conversation data for a plurality of conversations between agent devices and customer devices and provides to the agent devices, one or more automated response recommendations to one or more customer messages by executing one or more dialog flows. The contact center server, using a classification model, determines for each of the one or more customer messages when there are one or more changes between the corresponding one or more automated response recommendations and corresponding one or more agent responses. Further, the contact center server associates one or more tags to the one or more agent responses when the one or more changes are determined. Subsequently, the contact center server identifies one or more patterns in the one or more tags associated to the one or more agent responses and provides one or more flow modification recommendations to the one or more dialog flows to one or more enterprise user devices based on the identified one or more patterns.

Abstract: A contact center server manages conversation data for a plurality of conversations between agent devices and customer devices and provides to the agent devices, one or more automated response recommendations to one or more customer messages by executing one or more dialog flows. The contact center server, using a classification model, determines for each of the one or more customer messages when there are one or more changes between the corresponding one or more automated response recommendations and corresponding one or more agent responses. Further, the contact center server associates one or more tags to the one or more agent responses when the one or more changes are determined. Subsequently, the contact center server identifies one or more patterns in the one or more tags associated to the one or more agent responses and provides one or more flow modification recommendations to the one or more dialog flows to one or more enterprise user devices based on the identified one or more patterns.

Abstract: A contact center server provides to an agent device one or more automated response recommendations determined by an executable virtual assistant platform to correspond to customer message data received from a customer device as part of conversation data. Further, the contact center server receives a selection of one of the automated response recommendations from the agent device and identifies agent response data to the customer message data transmitted from the agent device to the customer device. Further, the contact center server, using one or more classification models, determines when there is a change between the selected response recommendation and the identified agent response data. Further, the contact center server associates one or more tags to the identified agent response data in the conversation data when the determination indicates the change.

Abstract: A virtual assistant server determines a subset of test data corresponding to changes between a first version of training data of a virtual assistant and a second version of the training data of the virtual assistant. Subsequently, the virtual assistant server creates a test suite with the subset of test data and runs the test suite on a second language model of the virtual assistant created using the second version of the training data. Based on the running the test suite, the virtual assistant server generates one or more executable corrective actions to be implemented at the user device and provides the one or more executable corrective actions to the user device to implement to train the virtual assistant.

Abstract: A virtual assistant server receives an utterance provided by an end user via a channel of a virtual assistant rendered in a client device. The virtual assistant server identifies a current-node of execution from a plurality of nodes of a conversation definition of the virtual assistant and identifies a first set of language models from a group of language models of the virtual assistant to interpret the utterance. Further, the virtual assistant server executes the first set of language models in an order based on the current-node until an intent of the utterance is determined. Subsequently, the virtual assistant server generates a response based on the intent and outputs the response to the client device.