This project was delivered for a client requiring rapid development and accelerated time-to-market. The entire development lifecycle was heavily AI-driven, enabling efficient design, implementation, and delivery with minimal manual overhead.

Development Approach

Given that the application is an internal administrative dashboard, the traditional Quality Engineering (QE) process was streamlined. Development, implementation, and testing were executed in parallel, followed by direct deployment to User Acceptance Testing (UAT).

AI-Driven Engineering

The project leveraged advanced AI tools throughout the software development lifecycle. Claude Code and Claude Desktop were primarily used for system architecture design, requirement analysis, and wireframing.

Backend development was carried out using VS Code, IntelliJ IDEA, and Cursor, integrated with tools such as GitHub Copilot and AI models including Claude Opus and GPT-4.6 Codex.

Frontend development was primarily handled using Cursor, leveraging Claude Opus (Thinking Mode), GPT-4.6 Codex, and Composer 2 Think to accelerate UI implementation and enhance code quality.

Automation & Productivity

AI agents were extensively utilized with predefined rules, skills, and MCP servers to streamline development workflows, automate repetitive tasks, and support continuous code review and validation processes.

DevOps & Deployment

The CI/CD pipeline was implemented using GitHub Actions, ensuring automated build and deployment workflows. The application was containerized using Docker and deployed on scalable AWS infrastructure, utilizing container clusters for efficient resource management and high availability.

Summary

This project demonstrates a modern, AI-first software engineering approach, showcasing how intelligent tooling and automation can significantly reduce development time while maintaining high-quality deliverables in a fast-paced environment.