Abstract: Disclosed herein are mobile device, method, and computer program product embodiments for an improved integrated mobile AI assistant. The mobile device may launch a mobile application including an integration component, where the integration component is in communication, through the mobile application, with a data service and a user interface (UI) service. The integration component may receive a response including data and a data type from the data service generated by a large language model responsive to a natural language query. The integration component may customize an interface at the integration component using a rendering configuration received from the UI service to display the data, the rendering configuration generated by decomposing the data type into a predefined type.

Abstract: Disclosed herein are system, method, and computer program product embodiments for implementing AI driven application navigation recommendations based on user behavior. An embodiment operates by generating a trained machine learning model using training data obtained based on historical navigation logs corresponding to the web application. The embodiment deploys a reduced machine learning model within an instance of the web application, and the reduced machine learning model is generated by compressing the trained machine learning model. The embodiment then generates the page navigation recommendation using the reduced machine learning model based on an encoded navigation breadcrumb data corresponding to the instance of the web application.

Abstract: Disclosed herein are system, method, and device embodiments for providing a mobile interface powered by artificial intelligence. A user remains on a single user-interface page, conducting interactions with a customer relationship management tool using natural language. The technique leverages a large language model as an intermediary middle-layer, allowing a user to engage core functions. The technique builds an appropriate prompt including the natural language and uses the large language model to build an execution plan that references tools and tasks performable in the customer relationship management tool. By chaining prompts, the technique incorporates prior interactions into subsequent prompts. Mobile-specific information such as location, images, and scanned barcodes may be included in a prompts. Running the large language model on the client device allows the user to perform CRM functions while operating in an offline mode, a mode that secures user data and enhances privacy.

Abstract: Disclosed herein are system, method, and computer program product embodiments for implementing AI driven application navigation recommendations based on user behavior. An embodiment operates by generating a trained machine learning model using training data obtained based on historical navigation logs corresponding to the web application. The embodiment deploys a reduced machine learning model within an instance of the web application, and the reduced machine learning model is generated by compressing the trained machine learning model. The embodiment then generates the page navigation recommendation using the reduced machine learning model based on an encoded navigation breadcrumb data corresponding to the instance of the web application.