A computer-implemented method of processing a digital model for manufacture processes, comprising: receiving, at a compute device having a predictive engine, an electronic file with a digital model representative of a physical object, wherein the predictive engine includes at least one machine learning model, and wherein the electronic file is received from a client compute device; extracting mesh scheme data points from the electronic file and generating a mesh scheme of the digital model with a mesh analysis engine; instantiating in a memory of the compute device, a mesh scheme data structure with data values corresponding to a discretized version of the digital model; determining, based on the mesh scheme data structure, a set of geometric parameters corresponding to the physical object; computing a three dimensional point cloud from the digital model with a point cloud analysis engine; instantiating in the memory of the compute device, a point cloud data structure with data values corresponding to three-dimensional coordinates downsampled from the digital model; determining, based on the point cloud data structure, a set of volumetric parameters of the physical object, the volumetric set of parameters including a set of shape distributions related to one or more shapes identified within the digital model; inferring based on the set of geometric parameters, the volumetric parameters, and the at least one machine learning model, a set of axioms associated with a manufacture process of the physical object, wherein the set of axioms includes an identifier of a fixture type corresponding to a support device used during the manufacture of the physical object; determining, based on the set of axioms, a set of predicted outcomes associated with the manufacture process of the physical object based on the set of axioms, wherein the set of predicted outcomes includes the feasibility to manufacture the physical object using one or more fabrication materials and at least one of: a predicted cost to manufacture the physical object; different materials that can be used to manufacture the physical object; and available prototyping technologies that can be used to manufacture the physical object; and wherein the method further comprises sending the set of predicted outcomes to the client compute device in near real-time. receiving, at a compute device having a predictive engine, an electronic file with a digital model representative of a physical object, wherein the predictive engine includes at least one machine learning model, and wherein the electronic file is received from a client compute device; extracting mesh scheme data points from the electronic file and generating a mesh scheme of the digital model with a mesh analysis engine; instantiating in a memory of the compute device, a mesh scheme data structure with data values corresponding to a discretized version of the digital model; determining, based on the mesh scheme data structure, a set of geometric parameters corresponding to the physical object; computing a three dimensional point cloud from the digital model with a point cloud analysis engine; instantiating in the memory of the compute device, a point cloud data structure with data values corresponding to three-dimensional coordinates downsampled from the digital model; determining, based on the point cloud data structure, a set of volumetric parameters of the physical object, the volumetric set of parameters including a set of shape distributions related to one or more shapes identified within the digital model; inferring based on the set of geometric parameters, the volumetric parameters, and the at least one machine learning model, a set of axioms associated with a manufacture process of the physical object, wherein the set of axioms includes an identifier of a fixture type corresponding to a support device used during the manufacture of the physical object; determining, based on the set of axioms, a set of predicted outcomes associated with the manufacture process of the physical object based on the set of axioms, wherein the set of predicted outcomes includes the feasibility to manufacture the physical object using one or more fabrication materials and at least one of: a predicted cost to manufacture the physical object; different materials that can be used to manufacture the physical object; and available prototyping technologies that can be used to manufacture the physical object; and a predicted cost to manufacture the physical object; different materials that can be used to manufacture the physical object; and available prototyping technologies that can be used to manufacture the physical object; and wherein the method further comprises sending the set of predicted outcomes to the client compute device in near real-time.