Apparatus, system and method for providing a digital device health platform for optimizing an offering of a product. Disclosed embodiments include: a plurality of supply chains, each of which contributes a different aspect of the product; a plurality of input modules; a digital device health engine that receives processed data from each of the input modules, and that processes the received processed data to generate a holistic digital twin of all aspects of the product corresponding to the plurality of the inputs, which includes the supply chain data from each of the plurality of supply chains, wherein the corresponding comprises at least a correlating to an optimized level of performance of the purpose, a comparing to a plurality of rules governing the performance of the purpose, and feedback from machine learning regarding anonymized prior similar iterations of other products similar to the product as reflected by the third party data.

SmartBuyer AI is an automated quote analysis and scoring system for current or previous vehicle purchases. The system accepts car deal quote images or data inputs, extracts product and pricing details, and audits these against a rule-based engine. It generates a SmartBuyer Score (0-100) and a detailed PDF report flagging overpriced or missing items such as GAP insurance, vehicle service contracts, maintenance plans, and add-ons. The system provides real-time consumer education and optional challenge-based incentives to promote fair and transparent automotive transactions.

Systems, apparatuses, methods and computer program products are disclosed for using a mobile driver's license (mDL) to enhance insurance data exchange for an insurance event. An example method includes transmitting an identity verification request to a user device. The example method further includes receiving, from the user device, an identity verification response comprising a mDL. The example method further includes verifying, based on the identity verification response, authenticity of the mDL. The example method further includes, generating, using a risk determination model, a secondary verification protocol. The example method further includes, verifying, using the secondary verification protocol, user identity. The example method further includes identifying an insurance entity associated with the mDL. The example method further includes transmitting to the insurance entity, an insurance data request. The example method further includes receiving, by a provider device, and in response to the insurance data request, insurance data associated with the user.

A method includes receiving an indication that a record has been updated and determining a confidence score. The method includes, in response to a determination that the confidence score has not met a threshold, filtering a set of communications. The method includes determining whether the record change has met one or more of a set of validity criteria. The method includes, in response to a determination that the user record change has not met the set of validity criteria, based on a first outcome of a set of reporting criteria, automatically generating a prompt with a first suggested revision of the first change. The method includes, in response to a determination that the record change has not met the set of validity criteria, based on a second outcome of the set of reporting criteria, automatically revising the first change with the first suggested revision.

A method includes receiving an indication that a record has been updated and determining a confidence score. The method includes, in response to a determination that the confidence score has not met a threshold, filtering a set of communications. The method includes determining whether the record change has met one or more of a set of validity criteria. The method includes, in response to a determination that the user record change has not met the set of validity criteria, based on a first outcome of a set of reporting criteria, automatically generating a prompt with a first suggested revision of the first change. The method includes, in response to a determination that the record change has not met the set of validity criteria, based on a second outcome of the set of reporting criteria, automatically revising the first change with the first suggested revision.

Systems, apparatuses, methods and computer program products are disclosed for using a mobile driver's license (mDL) to enhance insurance data exchange for an insurance event. An example method includes transmitting an identity verification request to a user device. The example method further includes receiving, from the user device, an identity verification response comprising a mDL. The example method further includes verifying, based on the identity verification response, authenticity of the mDL. The example method further includes, generating, using a risk determination model, a secondary verification protocol. The example method further includes, verifying, using the secondary verification protocol, user identity. The example method further includes identifying an insurance entity associated with the mDL. The example method further includes transmitting to the insurance entity, an insurance data request. The example method further includes receiving, by a provider device, and in response to the insurance data request, insurance data associated with the user.

A data processing system for recommending insurance plans implements obtaining an electronic copy of demographic information associated with a user; analyzing the demographic information with a first machine learning model to recommend a bundle of insurance policies based on the demographic information, wherein the first machine learning model is configured to group insured people having similar demographics into clusters and to generate the bundle of insurance policies based on predicted medical insurance consumption associated with a respective group into which the model predicts that the first user falls; customizing the recommended bundle of insurance policies based on the demographic information associated with the user to generate a customized bundle of insurance policies; generating an insurance recommendation report that presents the customized bundle of insurance policies to the user; and causing a user interface of a display of a computing device associated with the user to present the insurance recommendation report.

An agentic artificial intelligence system processes insurance claims, medical claims, financial transactions, and sales leads by receiving and preprocessing claimant, patient, transaction, and prospect data to standardize formats, remove sensitive identifiers, and enrich records. It uses machine learning, deep learning, natural language processing, and computer vision to analyze both structured and unstructured data, identify errors, inconsistencies, or fraudulent patterns, verify eligibility and compliance, and assign relevant codes based on historical and contextual information. The system calculates expected payouts or reimbursements, assesses transaction feasibility, and generates risk scores while adapting its predictions to market conditions, contractual factors, or clinical guidelines. A multi-agent framework coordinates specialized agents for eligibility verification, coding, pricing, fraud detection, and sales outreach, supporting multi-channel communication, lead prioritization, and natural language generation of outreach messages and decision-making explanations. Continuous learning is achieved via retraining, feedback loops, federated learning, and blockchain-based recordkeeping, ensuring secure, transparent, and compliant operations across multiple domains.