Abstract: Various embodiments of the present invention disclose techniques for optimizing a plurality of machine-learning based models for an end-to-end investigative process. The techniques include generating a profile for an input data object associated with an investigative process. A first machine-learning based model is used to select a predictive data analysis sub-routine for processing the profile. A second machine-learning based model is used to determine a predictive entity for performing an investigative process for the profile. An investigative outcome is received from the predictive entity. A historical optimization data object is augmented with the investigative outcome, the profile, the predictive data analysis sub-routine, or the predictive entity and the first machine-learning based model and the second machine-learning based model are trained using the historical optimization object.

Abstract: Various embodiments of the present invention disclose techniques for orchestrating a complex data processing scheme for an investigative process using a machine-learning based orchestration model that is trained to optimize the use of computing resources based at least in part on a feedback loop. An input data object associated with an investigative process can be selected for investigation; a predictive data analysis sub-routine for processing the input data object can be intelligently selected from a plurality of predictive data analysis sub-routines by the machine-learning based orchestration model; and a processing orchestration action can be initiated based at least in part on an investigative score output by the predictive data analysis sub-routine. The processing orchestration action can include closing the input data object, continuing to process the input data object with additional predictive data analysis sub-routines, or passing the input data object to a predictive entity for further processing.

Abstract: Systems and methods for applying customer-specific labels to an unprinted or non-displayed side of printed products.

Abstract: A computerized system, method, and computer-readable media implementing a method for mass producing variable shaped printed products is presented. A set of cutlines defining a shape for the ordered item is associated with each ordered item. A ganging system selects a plurality of ordered items to be simultaneously produced. Individual cutter-ready outline files corresponding to the associated set of outlines for each of the items selected for production are rendered and inserted into a gang template to generate an aggregated cutlines gang file. The gang file and at least one sheet of substrate are submitted to a cutting system. The cutting system cuts the individual finished products from the substrate sheet(s).

Abstract: Systems and methods for applying customer-specific labels to an unprinted or non-displayed side of printed products.

Abstract: An embroidery workstation utility cart for optimizing the positioning of supports for the embroidery machine, thread spool rack, monitor, workpiece queue, and other accessories desired for optimal embroidery processing.

Abstract: A computerized system, method, and computer-readable media implementing a method for mass producing variable shaped printed products is presented. A set of cutlines defining a shape for the ordered item is associated with each ordered item. A ganging system selects a plurality of ordered items to be simultaneously produced. Individual cutter-ready outline files corresponding to the associated set of outlines for each of the items selected for production are rendered and inserted into a gang template to generate an aggregated cutlines gang file. The gang file and at least one sheet of substrate are submitted to a cutting system. The cutting system cuts the individual finished products from the substrate sheet(s).