This is the validation Page
This is the Analysis Page
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This is the transformation Page
This is the Modernization Page
This is the Issues Page

About ModernIQ

Inspiration

Many organizations still rely on legacy applications that are difficult to maintain, understand, and modernize. Updating these systems can require significant time and effort because developers must first understand existing code, identify potential issues, evaluate architectural limitations, and determine a suitable modernization approach.

We built ModernIQ: AI Legacy Modernization & Transformation Platform to explore how AI can assist developers throughout this process. Our goal is to make legacy application analysis more accessible and help developers move from understanding existing systems to planning potential improvements.

What It Does

ModernIQ is an AI-powered platform designed to assist with legacy application modernization. It brings code analysis, issue identification, architectural insights, modernization recommendations, and transformation workflows into a unified interface.

Key capabilities include:

Code Analysis: Examine legacy application code to help developers understand its structure and identify potential improvement areas.
Issue Identification: Highlight potential code quality, maintainability, and modernization concerns.
Architecture Insights: Present architectural information to support understanding of existing applications and possible changes.
AI-Powered Recommendations: Generate modernization suggestions based on the application's code and analysis context.
Transformation Support: Explore potential approaches for adapting legacy code and application structures to modern technologies.
Interactive AI Assistance: Provide a conversational interface for discussing analysis findings and modernization decisions.

How We Built It

ModernIQ combines a web-based frontend, a backend API, and an AI integration to support its modernization workflow.

Frontend: Built with React, TypeScript, and Vite to provide an interactive interface for exploring projects, reviewing analysis results, and navigating modernization workflows.
Backend: Built with Python and FastAPI to handle application requests and coordinate analysis, project management, and AI-assisted operations.
AI Integration: Designed to use NVIDIA Nemotron through Nebius Token Factory for AI-assisted code analysis, recommendations, and modernization guidance.
Application Workflow: Organized around project management, code analysis, issue review, architecture exploration, modernization planning, and transformation-related functionality.

The platform is designed to bring these activities together rather than requiring developers to approach every modernization task as an isolated step.

Challenges We Faced

One of the central challenges was structuring a complex modernization workflow into an interface that developers can navigate effectively. Code analysis, architecture exploration, issue identification, and transformation planning involve different kinds of information, so presenting them coherently requires careful coordination between the frontend and backend.

Another challenge was integrating AI capabilities into a broader application workflow. AI-generated recommendations must be presented as guidance that developers can review, rather than automatically treating every suggestion as correct or suitable for production.

The project also required attention to configuration and integration between application components, including the AI service and backend APIs. Keeping these components organized and making the workflow understandable were important parts of the development process.

What We Learned

Building ModernIQ helped us explore how AI can be integrated into software engineering workflows beyond simple question-and-answer interactions. We gained experience connecting a modern web interface to backend services, organizing multi-step application workflows, and designing features around AI-assisted analysis and recommendations.

We also recognized an important limitation: AI-generated modernization suggestions need validation. Legacy systems often contain business rules, dependencies, and architectural constraints that cannot be understood reliably from isolated code snippets alone. Human review, testing, and contextual evaluation remain essential.

What's Next

ModernIQ can be extended with deeper dependency analysis, stronger validation of generated transformations, automated testing support, and more comprehensive modernization workflows. Future improvements could also focus on measuring the correctness of recommendations and assessing whether proposed changes preserve the original application's behavior.

Our vision is to help developers understand legacy applications more efficiently and make better-informed modernization decisions with AI as an assistant—not a replacement for engineering judgment.