Systems and methods for data integration and predictive analytics using tokenized artificial intelligence agents

A computer program product for data integration and predictive analytics using tokenized artificial intelligence agents, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to perform the following:

• extracting data comprising invoicing data from a plurality of sources comprising an invoicing database, service ticket data from a service ticket system, and contract data from a contract warehouse using a tokenized API interface, wherein the extracted data from the plurality of sources is in varying formats;
• normalizing and integrating the extracted data in the varying formats within a data rationalization warehouse generating integrated data;
• processing the integrated data generating processed data, using a proprietary tokenized AI agent to identify one or more patterns and trends;
• applying a machine learning model to the processed data to generate a predictive maintenance recommendation, wherein the machine learning model incorporates external factors comprising historical weather data and geolocation-specific attributes to enhance prediction accuracy of the predictive maintenance recommendation;
• presenting the predictive maintenance recommendation and underlying data to end users through a presentation layer;
• and
• allowing the end users to interact with and adjust the predictive maintenance recommendation, with changes tracked and updated in the system.

A method for data integration and predictive analytics using tokenized artificial intelligence agents, the method comprising:

• extracting data comprising invoicing data from a plurality of sources comprising an invoicing database, service ticket data from a service ticket system, and contract data from a contract warehouse using a tokenized API interface, wherein the extracted data from the plurality of sources is in varying formats;
• normalizing and integrating the extracted data in the varying formats within a data rationalization warehouse generating integrated data;
• processing the integrated data generating processed data, using a proprietary tokenized AI agent to identify one or more patterns and trends;
• applying a machine learning model to the processed data to generate a predictive maintenance recommendation, wherein the machine learning model incorporates external factors comprising historical weather data and geolocation-specific attributes to enhance prediction accuracy of the predictive maintenance recommendation;
• presenting the predictive maintenance recommendation and underlying data to end users through a presentation layer;
• and
• allowing the end users to interact with and adjust the predictive maintenance recommendation, with changes tracked and updated in the system.