Abstract: The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Abstract: The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Abstract: Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.

Abstract: The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Abstract: Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.

Abstract: The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Abstract: Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.