From SAS to React: How We Modernized National Grid's EPO Tracking Application

Introduction

The National Grid MDS Consolidation EPO Tracking Application was built on SAS — a legacy desktop system that had served its purpose but no longer met the scalability, accessibility, or user experience requirements of a modern enterprise. The project goal was to migrate it to a React.js + .NET Core web platform. What made this different from a typical modernization was the use of GenAI-assisted code generation as the initial development baseline.

The Migration Approach

Phase 1 — GenAI baseline generation

Using AI-assisted code generation tools, an initial codebase was scaffolded from the SAS system specifications and business logic documentation. This accelerated the starting point significantly compared to writing from scratch. The generated code covered the primary data models, API contracts, and frontend component structures.

Phase 2 — Stabilization and hardening

This is where the real work happens — and where most AI-generated code fails in enterprise contexts. The generated baseline contained architectural misalignments, performance anti-patterns, incomplete error handling, and business logic discrepancies. Each module was reviewed, corrected, and refactored to meet enterprise coding standards and production reliability thresholds.

Phase 3 — Integration and deployment

The React.js frontend was integrated with the .NET Core API backend and deployed to Azure. Cross-functional collaboration with US-based National Grid stakeholders ensured the final product met user needs and performance standards.

Results

The platform replaced the legacy SAS system with a modern, accessible web application that improved user experience, scalability, and maintainability. The AI-assisted approach reduced initial development time while the stabilization process ensured the output was production-ready.

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Key Takeaways

• GenAI can dramatically accelerate initial scaffolding.
• Stabilization requires senior engineering judgment — it is not optional.
• The combination of AI speed + senior review = faster AND reliable delivery.
• Legacy modernization is an ideal use case for AI-assisted development.

Conclusion

The National Grid project demonstrated that GenAI-assisted development is not about replacing engineers — it is about using AI to handle the scaffolding while senior engineers focus on the judgment-intensive work: architecture, edge cases, integration, and production reliability.