LLM-Based IT Infrastructure Modernization
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Solution Overview
Problem
Existing methods for modernizing IT infrastructure face challenges due to lack of adequate documentation and skilled technicians, particularly in reverse engineering legacy systems to identify workflows, computation logic, and software requirements.
Innovation Solution
A method and system utilizing a Domain-Trained Large Language Model (LLM) to analyze and generate IT specification data from existing IT infrastructure data, facilitating the modernization process by reducing computational inefficiencies and accelerating the transition to modern standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reverse engineering of legacy systems is performed, then identification of workflows and computation logic is achieved, but the process is time-consuming and requires skilled technicians
Solution Approach 1:
The patent replaces manual reverse engineering processes with an automated LLM-based system. The LLM analyzes legacy system code, documentation, and data to automatically identify workflows, computation logic, and software requirements, eliminating the need for skilled technicians to manually dissect legacy systems while maintaining high accuracy in identification.
Solution Approach 2:
The system enables self-service modernization by allowing the LLM to autonomously analyze legacy systems, generate modernization plans, and produce updated documentation without requiring skilled technicians. The LLM serves itself by automatically extracting insights from legacy code and generating appropriate modernization strategies.
2Loss of information
If comprehensive analysis of legacy IT infrastructure is performed, then complete identification of artifacts is achieved, but computational redundancy increases
Solution Approach 1:
The patent changes the parameter of analysis depth by using LLMs to perform comprehensive analysis in a single pass rather than multiple iterative analyses. The LLM's sophisticated natural language processing capabilities enable it to extract complete artifact information from legacy systems efficiently, reducing computational redundancy while maintaining completeness.
Solution Approach 2:
The system replaces redundant manual analysis processes with an automated LLM-based approach that comprehensively identifies artifacts in one comprehensive analysis phase, eliminating the need for multiple iterative review cycles and reducing overall computational redundancy.
3Productivity
If legacy systems are modernized without adequate documentation, then modernization can proceed, but accuracy and reliability of modernized systems decreases
Solution Approach 1:
The LLM performs self-service documentation generation by automatically analyzing legacy systems and creating comprehensive documentation of workflows, computation logic, and software requirements. This self-generated documentation maintains high reliability and accuracy while enabling fast modernization, as the LLM directly extracts information from the legacy code rather than relying on pre-existing documentation.
Solution Approach 2:
The system performs preliminary comprehensive analysis and documentation generation before the actual modernization coding begins. The LLM first analyzes the legacy system, identifies all artifacts, and creates detailed documentation, which then serves as an accurate foundation for the modernization process, ensuring both speed and reliability.
Data Source
AI summary
The present disclosure provides a method of facilitating provisioning of Information Technology infrastructure data. Further, the method may include receiving one or more IT infrastructure data infrastructures from a client device. Further, the method may include analyzing the one or more IT infrastructure data using a first Large Language Model. Further, the first LLM may be trained on a training data comprising an association of two or more IT infrastructure data and two or more IT specification data. Further, the method may include generating one or more IT specification data based on the analyzing. Further, the one or more IT specification data includes a functionality data representing one or more functionalities provided by the one or more IT infrastructures implemented according to the one or more IT specification data. Further, the method may include transmitting the one or more IT specification data to the client device.