Abstract: Systems and methods are directed to training and utilizing a generative language model that is constrained by a predetermined template that is used to train the generative language model. Once trained, customer data is accessed and transmitted to an evaluation component associated with the generative language model. The generative language model generates one or more sentences based on a feedback input of the plurality of feedback inputs, whereby the one or more sentences each include a sentiment, a target, and a reason for the sentiment in a format defined by the predetermined template. The evaluation component then identifies the sentiment, the target, and the reason from a sentence of the one or more sentences. A communication is then presented, on a device of a user, based on at least the sentiment and the reason identified from the sentence. The communication can be an alert or a report.

Abstract: A system provides notification of a time range for an order delivery. The system identifies estimated maximum delivery dates for each order in historical order data, and generates estimated minimum delivery dates for a first portion of the historical order data using a predictive model, wherein the predictive model is trained on a second portion of the historical order data. The system evaluates accuracy of the predictive model by determining a proportion of the first portion of the historical order data that had an actual delivery date between its corresponding estimated minimum delivery date and its corresponding estimated maximum delivery date, and revises a feature matrix to improve the accuracy of the predictive model. The system also estimates a maximum delivery date for the order delivery based on a plurality of lead times, estimates a minimum delivery date for the order delivery using the predictive model with improved accuracy.

Abstract: A system provides notification of a time range for an order delivery. The system identifies estimated maximum delivery dates for each order in historical order data, and generates estimated minimum delivery dates for a first portion of the historical order data using a predictive model, wherein the predictive model is trained on a second portion of the historical order data. The system evaluates accuracy of the predictive model by determining a proportion of the first portion of the historical order data that had an actual delivery date between its corresponding estimated minimum delivery date and its corresponding estimated maximum delivery date, and revises a feature matrix to improve the accuracy of the predictive model.