Abstract: Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Abstract: Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Abstract: Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Abstract: Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Abstract: Methods, apparatuses, or computer program products provide for processing multi-channel service data objects to initiate automated resolution actions via an intent engine. A first service message object is received via a first communication channel of a plurality of communication channels. The first service message object defines a first feature dataset associated with the first communication channel. Additionally, a second service message object is received via a second communication channel of the plurality of communication channels. The second service message object defines a second feature dataset associated with the second communication channel. Based on the first feature dataset and the second feature dataset, support labels for the first service message object and the second service message object are generated.

Abstract: Methods, apparatuses, or computer program products provide for generating an automated resolution action using a knowledge base system and a trained machine learning model. In some examples, a service message object is received via a communication channel of a plurality of communication channels and the service message object defines a feature dataset associated with a service request for an application framework, a knowledge base system is queried based on the feature dataset, a knowledge base data structure is received from the knowledge base system in response to the query, the knowledge base data structure is input to a machine learning model trained for reading comprehension to generate a resolution data object associated with a service resolution for the service request, and a resolution action is initiated for the service request based on the resolution data object.

Abstract: Methods, apparatuses, or computer program products provide for generating classifications for a service message via multiple artificial intelligence (AI) models. In some examples, a service event data structure related to a service message provided to an application framework is received, the service event data structure is input to a first AI model to generate an information technology (IT) support intent classification associated with the service event data structure, the IT support intent classification is compared to execution criteria for a second AI model, the service event data structure is input to the second AI model to generate an escalation classification associated with the service event data structure in response to a determination that the IT support intent classification satisfies the execution criteria, and transmission of a notification for a user device is caused based on the escalation classification.

Abstract: Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Abstract: Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Abstract: Methods, apparatuses, or computer program products provide for generating automated resolution actions using intent provided by a trained machine learning model. A service message object via a communication channel of a plurality of communication channels. The service message object defines a feature dataset associated with a service request for an application framework. Additionally, a machine learning model trained for intent recognition is applied to the feature dataset to generate an intent support label for the service message object. The intent support label is then correlated to a resolution data object related to a service resolution for the service request. Based on whether the resolution data object satisfies defined resolution criteria for the intent support label, a resolution action for the service request is initiated.

Abstract: Methods, apparatuses, or computer program products provide for processing multi-channel service data objects to initiate automated resolution actions via an intent engine. A first service message object is received via a first communication channel of a plurality of communication channels. The first service message object defines a first feature dataset associated with the first communication channel. Additionally, a second service message object is received via a second communication channel of the plurality of communication channels. The second service message object defines a second feature dataset associated with the second communication channel. Based on the first feature dataset and the second feature dataset, support labels for the first service message object and the second service message object are generated.

Abstract: Methods, apparatuses, or computer program products provide for training a virtual agent artificial intelligence model. An information technology (IT) support label dataset is generated based on service event data structures related to respective services messages provided to an application framework configured to manage respective application components for IT service management. Additionally a training dataset associated with a set of candidate questions provided by a classification model is generated and an artificial intelligence (AI) model is trained based on the IT support label dataset and the training dataset to generate a trained AI model. An intent recognition engine for a virtual agent system is then configured based on the trained AI model.