E2E Automation
Playwright provides browser-level testing for critical user journeys and application behavior.
CASE STUDY · QA AUTOMATION · AI
QAForge AI
An AI-powered quality engineering platform designed to explore how modern automation, software testing, APIs and artificial intelligence can work together in a unified QA workflow.
01
THE PROBLEM
Testing workflows are often fragmented.
Modern QA teams frequently work across separate tools for test execution, API validation, reporting, defect analysis and CI/CD. This fragmentation makes it harder to understand quality from a single engineering perspective.
QAForge AI was created as an engineering project to investigate how those workflows could be brought together while incorporating intelligent automation.
02
THE SOLUTION
A centralized QA engineering platform.
QAForge AI combines a modern web interface, backend services, persistent data and automated testing concepts into a single platform.
The architecture was designed to support test management, automation workflows, execution visibility and future AI-assisted quality analysis.
03
QA STRATEGY
Quality treated as an engineering system.
API Validation
Backend endpoints can be validated independently from the user interface, improving defect isolation.
Regression Testing
Automated checks are structured to protect existing behavior as the platform evolves.
CI/CD
Automated validation can be integrated into the delivery pipeline to provide faster feedback.
04
ARCHITECTURE
Full-stack architecture built for experimentation.
01Frontend
Next.js interface and QA workflows
02API
FastAPI backend services
03Data
PostgreSQL / Supabase persistence
04Automation
Playwright and automated validation
05Delivery
GitHub-based CI/CD workflow
05
TECHNOLOGY
Technology stack.
PlaywrightPythonFastAPINext.jsPostgreSQLSupabaseDockerGitHub ActionsAI
06
ENGINEERING CHALLENGES
Building beyond a simple demo.
Full-stack integration
Coordinating frontend, API and database layers while keeping clear boundaries between components.
Testability
Designing application behavior so that critical workflows can be validated automatically.
Environment consistency
Managing local development, dependencies and production deployment across different environments.
Continuous improvement
Structuring the platform so additional testing and AI capabilities can be incorporated incrementally.
07
RESULT
A portfolio project demonstrating end-to-end QA engineering.
QAForge AI demonstrates the ability to approach quality from more than the perspective of individual test scripts. It combines software architecture, automation, backend development, data, deployment and quality engineering into one technical project.