Computer-Implemented Method and System for Analysis and Validation of Consistency Across Documents Including Images and Language Data Using Artificial Intelligence (AI)

A computer-implemented method for training an artificial intelligence (AI)-driven quality assurance (QA) model for validating data across different sources, the method comprising:

• receiving, by an AI-based QA model trainer including instructions stored in non-transitory memory of a computer system and executable by a processor of the computer system, training data comprising a photo document, a claim document, and a reference document associated with a damaged property;
• extracting, by the AI-based QA model trainer, one or more images and a textual description of the damaged property from the photo document;
• processing, by the AI-based QA model trainer, the one or more images using a computer vision model to extract first damage information associated with the damaged property;
• processing, by the AI-based QA model trainer, the textual description using a first machine learning (ML) model to extract second damage information associated with the damaged property;
• validating, by the AI-based QA model trainer, an accuracy of the photo document based on a consistency between the first damage information and the second damage information to generate a first consistency check result;
• combining, by the AI-based QA model trainer, the first damage information and the second damage information into third damage information;
• processing, by the AI-based QA model trainer, the claim document using a second ML model to extract claim information associated with the damaged property, wherein the claim information comprises a geographical location of the damaged property and one or more line items associated with restoring the damaged property;
• processing, by the AI-based QA model trainer, the reference document using a third ML model to extract contextual information associated with at least one of a policy, a regulation, or a guideline associated with a specific geographical location;
• validating, by the AI-based QA model trainer, an accuracy of the claim document based on a consistency of the claim information against the claim document against the contextual information, and the third damage information to generate a second consistency check result;
• initiating, by the AI-based QA model trainer, a large-language model (LLM) to generate, based on the first consistency check result, the second consistency check result, and the reference document, a QA report for the training data;
• receiving, by the AI-based QA model trainer, from the LLM, the QA report indicating an issue in at least one of the photo document, the claim document, or the reference document;
• evaluating, by the AI-based QA model trainer, at least one of the extracted first damage information, the extracted second damage information, the extracted claim information, the extracted contextual information, or the QA report against reference assessment information;
• and
• causing, by the AI-based QA model trainer, based on the evaluating, an update of one or more parameters associated with at least one of the computer vision model, the first ML model, the second ML model, the third ML model, or the LLM.