The Client
A global web technology client partnered with Radiant to modernize its legacy application stack using AI-powered solutions and advanced engineering frameworks. Radiant applied hybrid AI-human methodologies to accelerate feature delivery, enhance system scalability, and reduce operational costs, while preserving institutional knowledge and improving development workflows. These efforts enabled the client to cut time-to-market by 30–40% and operational costs by 30%, while laying the groundwork for long-term modernization across its technology landscape.
The Challenge
The client’s legacy .NET and standalone application architecture resulted in high maintenance costs, slow-release cycles, and inconsistent user experiences. Institutional knowledge was concentrated among a few Subject Matter Experts (SMEs), increasing risks related to staff turnover and onboarding delays. Missing documentation further hindered feature expansion, while outdated systems posed security and scalability concerns. The client needed a way to modernize its architecture, reduce reliance on SMEs, and deliver faster, more secure solutions without compromising stability.
Our Solution
Radiant implemented an AI-accelerated modernization framework focusing on discovery, documentation, and automation:
- AI-Driven Reverse Engineering & Discovery
Radiant’s AI scanned millions of lines of legacy .NET code, extracting APIs, workflows, and schemas into structured models. Automated generation of UML diagrams, entity relationship diagrams, and dependency maps reduced discovery timelines from 3–4 months to 4–6 weeks while minimizing SME dependency.
- Automated Documentation
AI converted codebases into detailed product requirements, user stories, and design documentation. Traceability was ensured by linking requirements with code artifacts, while human engineers refined validation criteria, saving 6–8 weeks of manual effort.
- API & Schema Modernization
AI identified redundant modules, optimized schemas, and generated OpenAPI specifications. This enabled backend re-architecture while reducing schema cleanup by 40%.
- Code Generation
AI scaffolded modern Angular 18 frontends and Java/Spring Boot backends, generating over 2,500 lines of code. With human oversight, logic optimization, and UI enhancements were applied, leading to a 30% cost reduction and faster sprint completions.
- Refactoring & Standards Enforcement
AI flagged security gaps and redundant logic, supporting engineers in enforcing industry standards such as OWASP and esilent and husky rules, resulting in improved maintainability and reduced defect leakage.
- Test Case Generation
AI generated over 1,800 unit and integration tests, achieving 70% coverage in days rather than weeks, which accelerated regression testing and reduced QA effort by 40%.
- Deployment Automation
AI-generated CI/CD pipelines, Terraform scripts, and YAML configurations. Engineers further refined them with compliance hooks and security measures, reducing deployment setup time by 20%.
Technologies Used
- AI-driven reverse engineering and code analysis
- OpenAPI specification generation
- UML, ERD, and dependency graph tools
- Angular 18, Java Spring Boot frameworks
- DevSecOps integrations including esilent and husky rules and OWASP standards
- Terraform, Jenkins, and GitHub Actions for CI/CD pipelines
Outcomes
- Reduced operational costs by 30%.
- Accelerated time-to-market by 30–40%.
- Cut discovery and documentation phases from months to weeks.
- Increased test coverage from 30% to over 70%.
- Improved onboarding efficiency and knowledge retention by 30%.
Conclusion
Through its AI-accelerated modernization approach, Radiant enabled the client to transform a legacy architecture into a scalable, secure, and cloud-ready platform. The solution preserved institutional knowledge, reduced costs, and accelerated delivery, while laying the foundation for future modernization projects. Radiant’s hybrid model demonstrated how AI, combined with engineering expertise, can reshape enterprise systems for the digital era.
