Recorded live from the AWS Summit in Washington, D.C., Badri Sriraman, head of our in-house Innovation Center, sits down with John Gilroy and the Federal Tech Podcast to discuss artificial intelligence, cloud, and the future of legacy system modernization. A core component of their conversation: accelerating modernization with AI and hyperlocal contextualization. 

Badri describes how as part of the innovation center’s mission, their research and development team developed a new AI solution to accelerate the migration and modernization process for mainframe systems. The ReDuX AI toolkit addresses common issues related to modernizing these older, more complex legacy systems. Throughout the interview, Badri and John discuss the costs and security risks associated with staying on current systems and the opportunity for AI to provide insight through hyperlocal contextualization to tackle these challenges.

Agile, Incremental Modernization Accelerated

In the interview, Badri shares a key component of the team’s AI-accelerated modernization methodology. He begins by acknowledging that the analysis required to peel away each part of a legacy system could slow down modernization into a years-long process. In a traditional modernization project, the team might optimize as they modernize to reduce costs. Throughout the process, these teams must untangle how different parts of the system integrate and work together. 

Alternatively, using AI teams continue their incremental process, and concurrently they use AI to identify and map these relationships within legacy systems. This helps them move at a rapid pace. Working together with the human team and stakeholders, the AI can quickly create a fuller understanding of the system, and its impact on the mission and provide new insights into optimization opportunities during the modernization process. This analysis is the first step in using AI for hyperlocal contextualization.

Hyperlocal Contextualization Plus Code Generation

The next phase incorporates these hyperlocal insights into the recommendations provided by code generation tools. Built specifically to modernize mainframe systems rapidly, ReDuX transforms legacy code, like COBOL, into modern production ready code. It uses advanced large language models (LLMs) available via tools like AWS Bedrock. However, our ReDuX engineers ensure the model goes deeper ensuring that the code will integrate with the system based on its earlier assessment. Plus, for agile, self-guided teams, it can also incorporate best practices, templates and security policies into those recommendations. Using a tool like ReDuX, the security optimization recommendations are customized to the complexities of that legacy system, resulting in a modernization process designed to optimize costs and security. 

Tune in and hear Badri discuss hyperlocal contextualization in greater depth in this insightful interview. Contact us and connect with our enterprise modernization experts.