Projects

Manual test case creation is time-consuming, inconsistent, and difficult to scale across large codebases. Teams spend significant effort writing boilerplate test cases that could be systematically generated.

A full-stack web application that integrates with the Gemini API to automatically generate structured test cases from user requirements. The platform provides a management interface for organising, reviewing, and exporting generated test cases.

Built with a React/Next.js frontend and a Flask/Python backend. The AI pipeline processes requirement descriptions through Gemini API to produce structured test cases with preconditions, steps, and expected outcomes. Test case data is persisted in PostgreSQL with a Prisma ORM layer.

Securely transmitting hidden information within video content requires combining steganography algorithms with reliable, scalable cloud infrastructure, a non-trivial engineering challenge.

A cloud-native service that accepts video uploads, processes them through an OpenCV-based steganography pipeline to embed or extract hidden data, and returns the processed output. The entire service is containerised and deployed on AWS Fargate.

The processing pipeline is built in Python using OpenCV for frame-level steganography. Docker containers are pushed to AWS ECR and orchestrated via AWS Fargate for serverless execution. Infrastructure is provisioned with Terraform. The deployment pipeline is automated through GitHub Actions.

Manual test case creation is time-consuming, inconsistent, and difficult to scale across large codebases. Teams spend significant effort writing boilerplate test cases that could be systematically generated.

A full-stack web application that integrates with the Gemini API to automatically generate structured test cases from user requirements. The platform provides a management interface for organising, reviewing, and exporting generated test cases.

Built with a React/Next.js frontend and a Flask/Python backend. The AI pipeline processes requirement descriptions through Gemini API to produce structured test cases with preconditions, steps, and expected outcomes. Test case data is persisted in PostgreSQL with a Prisma ORM layer.

Securely transmitting hidden information within video content requires combining steganography algorithms with reliable, scalable cloud infrastructure, a non-trivial engineering challenge.

A cloud-native service that accepts video uploads, processes them through an OpenCV-based steganography pipeline to embed or extract hidden data, and returns the processed output. The entire service is containerised and deployed on AWS Fargate.

The processing pipeline is built in Python using OpenCV for frame-level steganography. Docker containers are pushed to AWS ECR and orchestrated via AWS Fargate for serverless execution. Infrastructure is provisioned with Terraform. The deployment pipeline is automated through GitHub Actions.