Aggregator model for contact center

A system for scheduling and resource optimization comprising:

• receiving, by an interaction module of a scheduling and resource optimization system executed by one or more processors, a plurality of communications comprising a call, a text, an email, or a chat, wherein each communication comprises a record;
• storing, by the interaction module, the record in a database;
• determining, by the interaction module, a plurality of tasks based on the record by comparing the record with each of a plurality of model tasks, each task being associated with a duration;
• determining, by an administrative module executed by the one or more processors, an availability of a plurality of employees based on an event and a work schedule;
• determining, by a machine learning model of an optimizer executed by the one or more processors, a real-time demand plan based on the one task and the availability;
• determining, by the machine learning model, an employee of the plurality of employees for each task based on a semantic similarity of a worker profile and each task and an availability of the employee;
• and
• scheduling, by the administrative module upon receiving the employee determination from the optimizer, the employee for each task.

A non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:

• receiving, by an interaction module of a scheduling and resource optimization system executed by one or more processors, a plurality of communications comprising a call, a text, an email, or a chat, wherein each communication comprises a record;
• storing, by the interaction module, the record in a database;
• determining, by the interaction module, a plurality of tasks based on the record by comparing the record with each of a plurality of model tasks, each task being associated with a duration;
• determining, by an administrative module executed by the one or more processors, an availability of a plurality of employees based on an event and a work schedule;
• determining, by a machine learning model of an optimizer executed by the one or more processors, a real-time demand plan based on the one task and the availability;
• determining, by the machine learning model, an employee of the plurality of employees for each task based on a semantic similarity of a worker profile and each task and an availability of the employee;
• and
• scheduling, by the administrative module upon receiving the employee determination from the optimizer, the employee for each task.