Systems and methods for personalizing bundles based on personas

1 . A computerized method for executing a personalized bundling process, comprising:

receiving user inputs specifying preferences for product bundling;

retrieving, from a Real-Time Data Mesh (RTDM), real-time data relevant to a user's preferences;

retrieving and processing data from one or more vendor systems via the RTDM, the RTDM comprising a distributed data architecture including a plurality of Purposive Datastores (PDSes) deployed within a cloud-native data mesh, each PDS configured for optimized retrieval based on at least one of data classification, access frequency, or computational workload;

dynamically harmonizing, by the RTDM, the retrieved data into a federated data layer comprising the plurality of PDSes, the federated data layer enabling parallelized query execution over the distributed data architecture;

analyzing, by an Advanced Analytics and Machine Learning (AAML) Module, one or more vendor-specific bundle recommendation information comprising one or more user inputs, a vendor-specific data, and a historical user interaction data;

generating the one or more vendor-specific bundle recommendations through a Personalized Bundling Module by applying a decision tree algorithm to select one or more optimal product combinations based on structured data attributes retrieved from the RTDM, the structured data attributes including at least one of the vendor-specific data, and the historical user interaction data, wherein the vendor-specific data comprises product availability information and compatibility constraint information, to generate personalized bundle recommendations based on market data and the analyzed one or more user input;

displaying the personalized bundle recommendations to the user via a Single Pane of Glass User Interface (SPoG UI);

receiving input from the user to perform completion of a personalized bundle;

transferring a personalized bundle order to at least one vendor system of the one or more vendor systems based on the completed personalized bundle;

executing the personalized bundle order by integrating data from the SPoG UI, RTDM, and the at least one vendor system,

wherein executing the personalized bundle order comprises querying, by a configure-to-order engine in communication with the RTDM and SPoG UI, the federated data layer of the RTDM to retrieve harmonized bundle configuration data from the PDSes, and generating a hierarchical bundle composition using a recursive Depth-First Search (DFS) traversal based on the harmonized data and user-selected configuration parameters;

storing computed decision tree adjustments based on the decision tree algorithm in the RTDM to refine future bundle recommendations based on iterative machine learning processes, wherein the method is executed by a computer system with a unified platform that integrates data from multiple sources for real-time personalization.

2 . The method of claim 1 , further comprising validating the personalized bundle using rules and algorithms by the AAML Module to ensure relevance of the personalized bundling recommendations.

3 . The method of claim 1 , wherein the AAML Module utilizes dynamic machine learning algorithms to adapt the personalized bundling recommendations based on changing user preferences and market conditions.

4 . The method of claim 1 , wherein the RTDM is continuously updated with real-time inventory, user behavior data, and market trends.

5 . The method of claim 1 , further comprising generating one or more real-time reports related to the personalized bundling process, wherein the one or more real-time reports comprise user engagement metrics and/or personalized bundle success rates.

6 . The method of claim 1 , wherein the vendor system for fulfilling the personalized bundle order is selected based on criteria including product availability and/or delivery capabilities.

7 . The method of claim 1 , further comprising sending a notification to the user based on a successful completion of the personalized bundle order.