60% reduction in QA cycle timeAI / ML

AI-Powered QA Platform

Built an automated testing pipeline that uses LLMs to generate test cases from user stories, execute them against staging environments, and report findings with suggested fixes.

ai-powered-qa-platform.app
60%
Faster QA Cycles
3x
More Test Coverage
85%
Bug Detection Rate
< 5min
Per Test Suite

The Challenge

Generating contextually relevant test cases without hallucinating non-existent UI elements. The system needed to understand the actual application structure.

The Solution

1

Built a RAG pipeline grounded in the app's component tree and design system docs

2

Implemented code-aware indexing extracting component props, routes, and state shapes

3

Created multi-stage pipeline: context retrieval → test outline → full test → validation

4

Added feedback loop where failed tests refine the model's understanding

// Tech Stack

Built With

Next.js
OpenAI GPT-4
LangChain
MongoDB
Docker
Redis
Playwright
TypeScript
// Results

Impact

Reduced QA cycle time by 60% while increasing test coverage 3x. Bug detection rate improved to 85% on first pass.

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