Abstract: Apparatuses, systems, and methods described include receiving data related to an availability for a shift, automatically triggering initiation of a communications session related to the shift, conducting the communications session, and receiving and storing a plurality of audio or audio and visual signals from the communications session. Machine learning (ML) sentiment analysis is performed on data of the communications session and based on the sentiment analysis, a reliability score is determined.

Abstract: Disclosed is a platform that makes use of hybrid model employing both heuristic and machine learning models to adaptively generate recommendations based on requested circumstances in a temporary staffing platform. The hybrid model is based on a set of training data surrounding historical temporary staffing outcomes. The heuristic model portion identifies matches between current queries to past outcomes and the machine learning model portion trains to derive new recommendations where no match exists. Queries are received and executed upon in real-time as opposed to pre-computing based on the frequency of changes to the recommendation to what would otherwise be the same query. The hybrid model is therefore configured to optimize for real-time responses to individual queries. The data surrounding the historical temporary staffing outcomes includes data relating to users, data relating to shifts, and data derived from a combination of both.

Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an AI machine learning model. The machine learning model predicts dispatch outcomes of a plurality of pairings of worker users to potential shifts. A dispatch outcome predicts whether a worker will show up for and work a given shift. The machine learning model is based on a set of training data surrounding historical dispatch outcomes. The data surrounding the historical dispatch outcomes includes data relating to users, data relating to shifts, and data derived from a combination of both. An implementation of the machine learning model stitches together multiple shifts for up to a schedule horizon based on predicted dispatch outcomes.