Abstract: A curated portable datamart system, method, and apparatus are provided. The method, implemented by one or more processors, comprises: invoking a security and client context layer to authorize a client to access an individual-specific raw data that is specific to a client query, the security and client context layer stored on the one or more computer memories and comprising computing instructions configured to access a semantic layer; responsive to authorizing the client query, invoking the semantic layer to curate the individual-specific raw data to generate a curated data deliverable, the curated data deliverable comprising temporal data generated from one or more raw data items accessed by the semantic layer; and outputting, via the API, the curated data deliverable, wherein the semantic layer is stored on the one or more computer memories and comprises computing instructions configured to generate temporal data from the one or more raw data items.

Abstract: A curated portable datamart system, method, and apparatus are provided. The method, implemented by one or more processors, comprises: inputting, via an application programming interface (API), a client query of a client, the client query requesting a temporal output able to be calculated by individual-specific raw data identifiable within the raw data of a virtual raw datamart; invoking a security and client context layer to authorize the client to access the individual-specific raw data that is specific to the client query; responsive to authorizing the client query, invoking a semantic layer to retrieve the individual-specific raw data from the virtual raw datamart, and curate the individual-specific raw data to generate a curated data deliverable, the curated data deliverable comprising temporal data generated from one or more raw data items accessed by the semantic layer from the virtual raw datamart; and outputting, via the API, the curated data deliverable.

Abstract: System, method, and apparatus for transforming computer files are provided. The method, implemented by one or more processors, comprises: inputting a source file and a source file specification, the source file including one or more source data items, the source file specification defining formats of the one or more source data items; generating, using a generative AI model based on the source data and the source file specification, a source file model including one or more source nodes, each of one or more source nodes associated with a respective data item of the one or more source data items; generating, using the generative AI model, a transform map mapping the one or more source nodes to one more target nodes of a target file model; invoking, a semantic model-based transformation service to generate an output file; and invoking a comparison module to compare the output file to an output sample.

Abstract: A curated portable datamart system, method, and apparatus are provided. The method, implemented by one or more processors, comprises: inputting, via an application programming interface (API), a client query of a client, the client query requesting a temporal output able to be calculated by individual-specific raw data identifiable within the raw data of a virtual raw datamart; invoking a security and client context layer to authorize the client to access the individual-specific raw data that is specific to the client query; responsive to authorizing the client query, invoking a semantic layer to retrieve the individual-specific raw data from the virtual raw datamart, and curate the individual-specific raw data to generate a curated data deliverable, the curated data deliverable comprising temporal data generated from one or more raw data items accessed by the semantic layer from the virtual raw datamart; and outputting, via the API, the curated data deliverable.

Abstract: Systems and methods for dynamically generating optimized application programming interfaces (APIs) using artificial intelligence (AI). The system may include an API database storing API data of a plurality of APIs of an API platform, and a generative AI model trained to generate an optimized API having at least a portion of the operational characteristics of one or more APIs of the API platform. The system may obtain an API call data stream of API calls from a user device to the APIs, and analyze the API call data stream to generate user-specific API transaction data. The generative AI model may receive the user-specific API transaction data and the API data to determine, and generate the optimized API. The system may configure the optimized API for execution, and provide the optimized API for execution by a user of the API platform.