Case Study

TestFlow: AI Testing Automation Workspace

Developer Tools · QA Automation — 8 weeks engagement · 3 engineers

90%
Test writing time saved
Repo-aware
Contextual test generation
Live
Execution tracking
Zero
Manual script authoring

Challenge

TestFlow set out to eliminate the most time-consuming part of the development pipeline: writing and maintaining test cases. Existing testing tools required manual script writing, disconnected CI/CD pipelines, and no centralized visibility into test execution. Teams were shipping code with low confidence and expensive post-deployment bugs.

Solution

We built TestFlow as a repository-aware AI testing automation platform that connects directly to GitHub repos, analyzes codebases, and generates comprehensive test cases using Google Gemini. The platform auto-generates Playwright scripts, executes browser tests in Browserless cloud environments, and provides live execution tracking with detailed logs, screenshots, and trace artifacts — all from a single dashboard.

Before → After

Before JH Global Tech
Test writingManual, hours per feature
Test executionLocal, inconsistent envs
CI/CD integrationDisconnected pipelines
Test visibilityScattered logs, no dashboard
After JH Global Tech
Test writingAI-generated in minutes
Test executionCloud-based, reproducible
CI/CD integrationGitHub-native, automated
Test visibilityLive dashboard with traces

Architecture

  • Next.js 16 with React 19 and TypeScript
  • Google Gemini for AI-powered test case generation
  • Playwright for automated browser testing
  • Browserless cloud for scalable test execution
  • Clerk for authentication and user management
  • Neon PostgreSQL with Drizzle ORM
  • Encrypted token storage for secure repo access
Tech Stack
Next.js 16React 19GeminiPlaywrightBrowserlessClerkNeon PostgreSQLDrizzle ORM
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