Abstract: A device receives user interface information associated with a user interface to be provided for a particular platform, and receives design information for a design of the user interface to be provided for the particular platform. The device receives a request to visually compare the user interface information and the design information, and utilizes, based on the request, a trained machine learning model to visually compare the user interface information and the design information. The device generates information, indicating defects in the user interface information, based on utilizing the trained machine learning model to visually compare the user interface information and the design information, where the defects include user interface information that does not visually match the design information. The device provides the information indicating the defects in the user interface information.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing a search result to a seller using a sales management tool that narrows an inventory to items that simultaneously meet consumer-affordability and lender-policy requirements. By considering numerous permutations of available financial structures associated with the inventory and a potential customer's financial constraints, a seller may recommend items that a customer is already pre-approved for. This removes guesswork and human error from the process of determining affordability while meeting lender-set policies. One embodiment describes providing search results tailored to a particular customer based on lender-policy requirements within a vehicle dealership researching vehicles to recommend to a potential customer.

Abstract: A vehicle analysis platform may cause one or more image capture devices to capture a plurality of images. The vehicle analysis platform may cause one or more sensors to provide measurement data associated with one or more operational characteristics of a vehicle. The vehicle analysis platform may determine, based on the plurality of images, one or more features of the vehicle and obtain, based on the one or more features, reference information associated with the vehicle. The vehicle analysis platform may analyze the vehicle based on the plurality of images and the reference information to determine an image score associated with the vehicle. The vehicle analysis platform may analyze the vehicle based on the measurement data and the reference information to determine an operational score associated with the vehicle. The vehicle analysis platform perform an action associated with the vehicle based on the image score and the operational score.

Abstract: A vehicle analysis platform may cause one or more image capture devices to capture a plurality of images. The vehicle analysis platform may cause one or more sensors to provide measurement data associated with one or more operational characteristics of a vehicle. The vehicle analysis platform may determine, based on the plurality of images, one or more features of the vehicle and obtain, based on the one or more features, reference information associated with the vehicle. The vehicle analysis platform may analyze the vehicle based on the plurality of images and the reference information to determine an image score associated with the vehicle. The vehicle analysis platform may analyze the vehicle based on the measurement data and the reference information to determine an operational score associated with the vehicle. The vehicle analysis platform perform an action associated with the vehicle based on the image score and the operational score.

Abstract: A device receives user interface information associated with a user interface to be provided for a particular platform, and receives design information for a design of the user interface to be provided for the particular platform. The device receives a request to visually compare the user interface information and the design information, and utilizes, based on the request, a trained machine learning model to visually compare the user interface information and the design information. The device generates information, indicating defects in the user interface information, based on utilizing the trained machine learning model to visually compare the user interface information and the design information, where the defects include user interface information that does not visually match the design information. The device provides the information indicating the defects in the user interface information.

Abstract: A device receives user interface information associated with a user interface to be provided for a particular platform, and receives design information for a design of the user interface to be provided for the particular platform. The device receives a request to visually compare the user interface information and the design information, and utilizes, based on the request, a trained machine learning model to visually compare the user interface information and the design information. The device generates information, indicating defects in the user interface information, based on utilizing the trained machine learning model to visually compare the user interface information and the design information, where the defects include user interface information that does not visually match the design information. The device provides the information indicating the defects in the user interface information.

Abstract: A device receives user interface information associated with a user interface to be provided for a particular platform, and receives design information for a design of the user interface to be provided for the particular platform. The device receives a request to visually compare the user interface information and the design information, and utilizes, based on the request, a trained machine learning model to visually compare the user interface information and the design information. The device generates information, indicating defects in the user interface information, based on utilizing the trained machine learning model to visually compare the user interface information and the design information, where the defects include user interface information that does not visually match the design information. The device provides the information indicating the defects in the user interface information.