Abstract: In some implementations, a system may host an application that includes a host container and multiple micro frontend (MFE) components that each include an interface associated with the application. The system may receive, at the host container and from a client device, a request to initiate the application. The system may identify a set of attributes associated with a user that provided the request to initiate the application. The system may determine a flow associated with the application based on the set of attributes associated with a user that provided the request to initiate the application, wherein the flow associated with the application includes a sequence in which a subset of the multiple MFE components is presented to the user. The system may present, to the client device, the subset of the multiple MFE components in accordance with the flow based on the set of attributes associated with the user.

Abstract: In some implementations, a system may host an application that includes various micro frontend (MFE) components. The system may define various user segments that are each associated with a combination of attributes corresponding to a path, of multiple paths supported by the application. The system may determine a flow associated with the application based on a mapping between a user segment corresponding to a set of attributes associated with a user that provided a request to initiate the application and a path, of the multiple paths supported by the application, associated with the user segment, wherein the flow associated with the application defines a subset of the MFE components and a sequence in which the subset of the MFE components is presented to the user. The system may present, to the client device, the subset of the plurality of MFE components in accordance with the sequence associated with the flow.

Abstract: A system for proactively extracting data from complex documents is disclosed. The system may include one or more processors, an NLP device, a trained machine learning device, and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to, receive one or more documents from a client device, extract one or more extractable data entries from the one or more data entries, generate, one or more normalized data entries, and proactively generate and add one or more completed data entries in place of one or more placeholders in a first document template. The system may receive a natural language prompt from a user device and determine a machine-readable semantic representation. The system may identify sensitive data entries and generate a graphical user interface identifying completed data entries and associated confidence intervals.

Abstract: A system for proactively extracting data from complex documents is disclosed. The system may include one or more processors, an NLP device, a trained machine learning device, and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to, receive one or more documents from a client device, extract one or more extractable data entries from the one or more data entries, generate, one or more normalized data entries, and proactively generate and add one or more completed data entries in place of one or more placeholders in a first document template. The system may receive a natural language prompt from a user device and determine a machine-readable semantic representation. The system may identify sensitive data entries and generate a graphical user interface identifying completed data entries and associated confidence intervals.

Abstract: A system for proactively extracting data from complex documents is disclosed. The system may include one or more processors, an NLP device, a trained machine learning device, and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to, receive one or more documents from a client device, extract one or more extractable data entries from the one or more data entries, generate, one or more normalized data entries, and proactively generate and add one or more completed data entries in place of one or more placeholders in a first document template. The system may receive a natural language prompt from a user device and determine a machine-readable semantic representation. The system may identify sensitive data entries and generate a graphical user interface identifying completed data entries and associated confidence intervals.