Citibank, N.a.

Nimrod Barak

Miriam Silver

Prag Sharma

Avi Gefen

Stuart Murray

Biraj Krushna Rath

Payal Jain

Avi Levin

Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.

Systems and methods for searching and storing data over a computer network

Systems and methods for novel approaches and/or improvements to real-time data processing of unstructured data. In particular, the systems and methods describe real-time data processing of unstructured data without interstitial standardization. For example, the systems and methods describe real-time data processing of unstructured data in which both the input and the output to the data processing pipeline is unstructured data.

Systems and methods for real-time data processing of unstructured data

Systems and methods for streamlining model risk documentation platform outputs using natively sourced kernels

The disclosed data generation platform enables generation of an output in response to an output generation request based on tuning a routing model that enables model selection in a dynamic, system-sensitive manner. For example, the disclosed data generation platform receives an output generation request for a user device and generates a risk indicator associated with the output generation request. The platform can determine a current system state and generate a set of performance indicators and associated weighting values based on the risk indicator and the system state. The data generation platform can select a first routing model based on the weighting values. The data generation platform can provide the output generation request to the first routing model to generate an indication of a model with which to generate a model output responsive to the input. The data generation platform can enable access to the generated model output.

Latency-, accuracy-, and privacy-sensitive tuning of artificial intelligence model selection parameters and systems and methods of the same

Systems and methods are described herein for a prompt engine microservice. The system may segregate a received request into a search function, a calculation function, and a schema selection function. The system may also create a new aggregation function that aggregates the results of the various processes into an input for an artificial intelligence model. By doing so, the system may process the new plurality of tasks in parallel and without the initial use of the artificial intelligence model.

Systems and methods for implementing an artificial intelligence-based solution for prompt orchestration by segregating processing requests into non-serialized tasks

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New York, NY, United States of America

Nimrod Barak