Citibank, N.a.

Ramee S Karthikeyan

Ganesh Prasad Bhat

Wei Jie Ng

Subramanian Sankaran

William Franklin Cameron

John E Ortega, Jr

Jason Ryan Engelbrecht

Alberto Cetoli

Joshua Adam Goldman

Ines Teixeira

Yael Man

Minjeong Cho

Alex Michael Eng

Cameron Paul Lim

Matthew Ryan Mitsui

Ryan Bergeron

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.

Robust artifacts mapping and authorization systems and methods for operating the same

Systems and methods disclosed herein generate validated responses using artificial intelligence (AI)-based models. The system obtains/receives an output generation request (e.g., from a graphical user interface (GUI)) that can include a document set and a query set. The system classifies the query set by partitioning it into multiple query subsets and assigning a complexity score. Based on the classification, the system generates a computational workflow set using a first AI model set to retrieve a resource set responsive to the query set. The system executes the workflow using a second AI model set (the same as or different from the first AI model set) and validates the retrieved resources against predefined criteria (e.g., rules, guidelines). If the resources satisfy the criteria, the system generates a response using a third AI model set. The system can display a graphical layout on the GUI showing the request, retrieved resources, and/or generated response.

Using a multi-model architecture for retrieval-augmented generation (RAG)

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Monmouth Junction, NJ, United States of America

Ramee S Karthikeyan