Legacy Code Modernization

Legacy source code and documentation analyzed to automate refactoring, migration, and testing for faster modernization.

Business impact

• Migration speed — Speeds up code migration by up to 50-60 times, drastically reducing project duration
• Cost of modernization — Lowers capital and operational expenditures by automating complex legacy transformations
• Code quality — Enhances maintainability and reduces defects through AI-assisted refactoring and testing
• Risk reduction — Implements governance and safeguards to minimize risks during modernization efforts
• Delivery cycle time — Shortens software delivery timelines by integrating AI into development workflows

Data requirements

• Legacy source code repositories (Code) — Provide the raw code for analysis, refactoring, and migration
• System documentation and metadata (Text) — Support understanding of legacy system architecture and dependencies
• Runtime logs and telemetry (Numeric) — Inform performance and usage patterns to guide modernization priorities
• User feedback and issue trackers (Text) — Highlight pain points and defects to target during modernization

AI methods and techniques

• Generative AI — Generates updated code and automated refactoring scripts to modernize legacy systems
• Agentic AI — Orchestrates multi-step migration workflows and automates complex transformation tasks
• Predictive AI — Predicts potential code defects and migration risks to prioritize remediation efforts

AI models and model families

OpenAI Codex, GPT-4o, Custom agentic AI models, LLMs

Industries

Real-world evidence

7 documented case studies on record.

Companies using this: Caylent, Cognizant Technology Solutions Corporation, JPMorgan Chase, Lombard Odier, Pega, Top, Unnamed.