Utilizing machine learning and natural language generation models to generate a digitized dynamic client solution

A device, comprising:

• one or more memories;
• and
• one or more processors, coupled to the one or more memories, configured to:
• receive historical project data identifying experiences and/or work product from previous projects and client data identifying a client with a problem,
• process the historical project data and the client data, with one or more machine learning models, to generate recommendations for the problem of the client and confidence scores for the recommendations, wherein the historical project data and the client data are processed by:
• applying weights to the recommendations based on historical success rates associated with the recommendations;
• and
• generating the confidence scores for the recommendations based on applying the weights to the recommendations;
• wherein each of the recommendations includes:
• a template structure,
• one or more content sections for the template structure,
• a design for the template structure, and one or more templates generated by subject matter experts;
• process the recommendations and the confidence scores, with a natural language generation model, to generate a solution to the problem of the client and content for the solution;
• generate a digitized dynamic client solution to the problem based on the solution and the content for the solution;
• provide the digitized dynamic client solution to one or more user devices;
• receive editing of the digitized dynamic client solution from the one or more user devices;
• generate a final digitized dynamic client solution based on the editing of the digitized dynamic client solution;
• and
• perform one or more actions based on the final digitized dynamic client solution.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive historical project data identifying experiences and/or work product from previous projects and client data identifying a client with a problem,
• wherein the client data includes data identifying one or more of:
• a financial analysis associated with the client,
• key financial insights associated with the client,
• earnings call transcripts associated with the client,
• priorities associated with the client,
• operational key performance indicators associated with the client, or
• value propositions associated with the client;
• process the historical project data and the client data, with one or more machine learning models, to generate recommendations for the problem of the client and confidence scores for the recommendations, wherein the historical project data and the client data are processed by:
• applying weights to the recommendations based on historical success rates associated with the recommendations;
• and
• generating the confidence scores for the recommendations based on applying the weights to the recommendations;
• process the recommendations and the confidence scores, with a natural language generation model, to generate a solution to the problem of the client and content for the solution;
• generate a digitized dynamic client solution to the problem based on the solution and the content for the solution;
• provide the digitized dynamic client solution to one or more user devices;
• receive editing of the digitized dynamic client solution from the one or more user devices;
• generate a final digitized dynamic client solution based on the editing of the digitized dynamic client solution;
• and
• perform one or more actions based on the final digitized dynamic client solution.