Automated system for data mapping and migration in CRM integrations

Abstract

Automated system (100) for data mapping and migration in CRM integrations, consisting of a discovery module configured to connect to source and target CRM platforms and extract schema metadata including objects, fields and relationships; a mapping engine configured to automatically match and map source data structures to target structures, including user-defined fields and relational references; a transformation module configured to normalize data and perform format conversions based on configurable rules; a migration module configured to transfer data from the source to the target CRM system while maintaining referential integrity; a validation module configured to verify the accuracy and completeness of the migrated data through pre- and post-transfer comparisons; a user interface configured to enable configuration, monitoring, and logging of the data migration process.

Description

The present invention relates to customer relationship management (CRM) systems and, more particularly, to an automated system for mapping and migration of data in CRM integrations.Customer Relationship Management (CRM) platforms are important tools for companies for managing customer interactions, distribution, service, and marketing activities. As companies grow, fuse, or switch between CRM systems, the critical need arises to migrate large amounts of structured and unstructured data such as contacts, leads, opportunisticities, cases, and custom objects. Traditional migration methods often include extensive manual assignments, custom scripts, and post-transmission matching, leading to higher costs, delays, and potential data loss or damage.Existing solutions lack automation in identifying relationships between data models of different CRM platforms (e.g., Salesforce, Microsoft Dynamics, HubSpot), and often cannot handle complex dependencies, validation rules, and custom logic. These challenges make data integration risky and inefficient, particularly in large-scale or enterprise-wide environments. Therefore, there is a need for an intelligent and automated system that can dynamically associate, convert, and validate data objects during the CRM integration processes to minimize human errors, increase speed, and ensure end-to-end data integrity.To solve this problem, the present invention provides an automated system for data allocation and migration in CRM integrations.The system is designed to intelligently recognize source and target data models, dynamically map the corresponding entities and fields, and perform the necessary transformations to obtain relationships, formats, and integrity.The system also aims to validate data before and after migration, manage dependencies such as lookups and reference fields, and create detailed audit protocols for transparency.The system provides a scalable and configurable architecture that supports various CRM providers, integration scenarios, and business rules, thereby simplifying the migration process and reducing the risk of operation.The system performs pre-migration validation, post-migration matching, and automatic rollback in the event of errors, with detailed reports and audit protocols to comply with regulations.The system performs pre-migration validation, post-migration matching, and automatic rollback in the event of errors, with detailed reports and audit protocols to comply with regulations.In one embodiment, the present invention provides an automated system for mapping and migrating data in CRM integrations. The system simplifies the process of integrating heterogeneous CRM environments by automatically recognizing data models, mapping corresponding objects and fields, and applying transformation rules to maintain data integrity and format consistency. It eliminates the complexity of manual scripting and the risk of data loss through the use of prefabricated connectors and dynamic mapping algorithms that ensure accurate matching of data between source and target platforms.In addition to migration, the system has integrated validation, error detection and adjustment tools that check the accuracy of the data sets before and after transmission. The system supports configurable workflows, audit logging, rollback functions, and an intuitive dashboard interface to monitor real-time status, exceptions, and progress. The invention was developed to speed up CRM transitions, merges, or upgrades while still providing minimal business interruptions and high data fidelity in all enterprise applications.The invention is explained again below with reference to the figure. The following shows: FIG. 1 : shows an automated system for data mapping and migration in CRM integrations.The system (100) is comprised of multiple integrated modules that collectively perform automated CRM data mapping and migration. In essence, the system (100) has a discovery engine that connects to the source CRM with certainty, extracts schema definitions, and analyzes object relationships and metadata. A mapping engine then identifies compatible structures within the target CRM, either through predefined templates or through smart inference, and creates mappings between source and target fields, including support for nested structures, user-defined objects, and multi-valued fields.The system (100) also includes a hardware aware backend that generates optimized code for various target platforms such as CPUs, GPUs, FPGAs, and custom AI chips. It employs low-level optimization techniques such as loop scrolling, register allocation, vectoring (e.g., AVX or NEON), and memory tiles. The system (100) is also capable of detecting hardware configurations and appropriately adapting the matrix core generation. Optional profile creation and automatic optimization modules allow the system to evaluate multiple configurations and select the most efficient execution path based on runtime metrics. By combining CGA-aware algebraic transformation with platform specific optimization and the extensibility of LLVM, the system enables high performance real-time matrix computations for applications such as pose estimation, geometric deep learning, SLAM, and other AI tasks with complex geometric transformations.List of reference characters100 System

Claims

An automated system (100) for data allocation and migration in CRM integrations, comprising: a recognition module configured to connect to source and destination CRM platforms and extract schema metadata including objects, fields, and relationships; a mapping engine configured to automatically match and map source data structures to destination structures including user-defined fields and relational references; a transformation module configured to normalize data and perform format conversions based on configurable rules; a migration module configured to transfer data from the source to the destination CRM system while preserving reference integrity; a validation module configured to verify the accuracy and completeness of the migrated data by comparisons before and after transmission; a user interface configured to allow configuration, monitoring and logging of the data migration process.The system (100) of claim 1, wherein the mapping engine includes a machine learning model to suggest optimal field mapping based on historical migration data.The system (100) of claim 1, wherein the transformation module supports rule-based scripting to manage complex data format conversions.The system (100) of claim 1, wherein the validation module performs record level comparisons and generates alignment reports.The system (100) of claim 1, wherein the user interface provides real-time dashboards that indicate migration progress, exceptions, and audit records.The system (100) of claim 1, wherein the migration engine supports rollback operations in the event of a validation error.The system (100) of claim 1, wherein the system is integrated with external APIs and CI / CD pipelines for automated provisioning.

Images