Abstract: Disclosed herein are systems, methods, and computer-readable media for generating and transmitting event artifacts based on events that are detected using artificial intelligence (AI) agents. One or more AI agents can monitor a plurality of sources to detect events associated with available interactions with one or more public entities. In response to detecting an occurrence of a first event, the one or more AI agents can assign a first priority metric to the first event. The first priority metric can be based on particular properties of a primary entity. In response to the first priority metric falling within a first predetermined metric range, the one or more AI agents can generate a first event artifact based on the first event and the properties of the primary entity. The first event artifact can be transmitted to the primary entity via one or more engagement channels of a multimodal interface.

Abstract: The systems and methods disclosed herein provide an architecture for deploying artificial intelligence (AI) avatars that generate audio and video responses to user interactions across various infrastructure environments. The systems and methods disclosed herein establish connections between client applications and a first infrastructure layer that invokes multiple agents trained on domain-specific knowledge bases to retrieve associated data records. The retrieved data and user input are routed to a second infrastructure layer hosting a generative AI model that identifies relevant data fields and generates avatar responses as audio or video streams. The systems and methods disclosed herein are enabled to evaluate the user inputs and/or avatar responses by applying one or more validation criteria. The systems and methods disclosed herein adjust the user inputs and/or avatar responses based on the evaluation.

Abstract: Example systems and methods for analyzing and summarizing legal documents using artificial intelligence are disclosed. A system receives a legal document such as a contract or agreement, and utilizes multiple AI models in combination to process and summarize the document. This includes identifying the type of legal document using a natural language model, and then extracting key information like parties, dates, and monetary values. Related clauses within the document are consolidated using a clause consolidation algorithm even if they are not contiguous. Summaries are generated for each section of the document. Additionally, a question-answering module preemptively generates common questions and answers about the agreement to highlight key considerations for the parties involved. By leveraging an ensemble of customized AI models, the system aims to simplify comprehension of legal documents.

Abstract: Various examples are directed to systems and methods for operating an autonomous vehicle. A matching cycle may be executed to match respective job requests of a plurality of job requests to at least one respective vehicle of a plurality of vehicles comprising the autonomous vehicle. A set of job requests may be selected from job requests that were matched to the autonomous vehicle during the matching cycle. Verification data may be received indicating that the autonomous vehicle is suitable for the set of job requests. A first offer message may be sent to a first user device associated with a first user making a first job request of the set of job requests. A reply message responsive to the first offer message may be received from the first user device. An instruction may be sent to the autonomous vehicle to begin executing a selected job from the set of job requests. The identity of the selected job may be based at least in part on the first reply message.

Abstract: Technologies for selecting a gateway for processing a request from a user system are described. Embodiments receive the request from the user system, where the request includes user profile data including a transaction request and associated user data. Embodiments identify gateways that are available to process the request. Embodiments sample historical gateway data for the available gateways using a sampling function optimized for at least two different optimization parameters. Embodiments select, by a first trained machine learning model trained based on the sampled historical gateway data, a gateway from the available gateways. Embodiments communicate the request to the selected gateway. Embodiments receive, from the selected gateway, a response that indicates a success or failure of the request.

Abstract: Among other things, embodiments of the present disclosure discussed herein may be used to identify and classify new web page content based on metadata in the content. The embodiments of the present disclosure help provide a classification solution for web content that is scalable to large numbers of content attributes, has the flexibility to predict values for a wide range of diverse attributes, and provides consistent and highly accurate results.