Hyper personalized virtual assistant with conversational artificial intelligence capabilities

A system comprising:

• one or more processors;
• a memory coupled to the one or more processors, the memory including instructions that, when executed by the one or more processors, cause the one or more processors to:
• establish a virtual communication session with a user having a user profile;
• extract, based on the user profile, session data associated with the user from a plurality of data sources;
• determine, using an orchestration model comprising at least one machine learning (ML) model, contextual information associated with the session data;
• partition, using the orchestration model and based on the determined contextual information, the session data into one or more predefined categories thereby creating a plurality of categorical datasets;
• generate, using an insights network comprising a plurality of ML models, a plurality of insights for each categorical dataset of the plurality of categorical datasets;
• score, using a scoring algorithm, each insight of the plurality of insights, wherein a respective score of each insight is associated with a respective probability that the insight is of interest to the user;
• identify a first insight as a best insight based on the respective probability of the first insight being a best score of a plurality of scores;
• and
• output, for display on a user interface, the plurality of insights in a display order based on the score, wherein the display order prioritizes the first insight.

A non-transitory computer-readable medium embodying program code that is executable by one or more processors to cause the one or more processors to:

• establish a virtual communication session with a user having a user profile;
• extract, based on the user profile, session data associated with the user from a plurality of data sources;
• determine, using an orchestration model comprising at least one machine learning (ML) model, contextual information associated with the session data;
• partition, using the orchestration model and based on the determined contextual information, the session data into one or more predefined categories thereby creating a plurality of categorical datasets;
• generate, using an insights network comprising a plurality of ML models, a plurality of insights for each categorical dataset of the plurality of categorical datasets;
• score, using a scoring algorithm, each insight of the plurality of insights, wherein a respective score of each insight is associated with a respective probability that the insight is of interest to the user;
• identify a first insight as a best insight based on the respective probability of the first insight being a best score of a plurality of scores;
• and
• output, for display on a user interface, the plurality of insights in a display order based on the score, wherein the display order prioritizes the first insight.