UI Automation

WorkflowAI - Open-Source AI Collaboration Platform

Manual and Automation QA Engineer

Open-source platform enabling product and engineering teams to collaborate on building and iterating AI-driven features.

Open Source
Technology
January 2025 - January 2025

Tools & Technologies

Testing Tools

Selenium WebDriverPostmanTestNGJenkinsJIRAGitHub

Technologies

PythonAgileAI/ML

Problem Statement

Product and engineering teams needed a collaborative platform to build, test, and iterate on AI-driven features with workflow consistency.

Approach

Designed and executed manual and automated test suites to validate feature creation, deployment workflows, and team collaboration tools.

Testing & Automation Strategy

Conducted API, functional, and regression testing to ensure seamless integration between AI modules and the user interface. Verified data accuracy and workflow consistency across multiple environments.

CI/CD Integration

Collaborated with developers and product owners using GitHub for issue tracking and Jenkins for continuous integration testing.

Before vs After Comparisons

AI Feature Development Cycle

Traditional Development

Siloed development with manual handoffs between data science, engineering, and product teams.

WorkflowAI Platform

Unified collaboration platform enabling real-time iteration on AI features with integrated testing and deployment.

Key Improvements

Feature Cycle Time

80%
Traditional Development
6-8 weeks
WorkflowAI Platform
1-2 weeks

Iteration Speed

500%
Traditional Development
2/month
WorkflowAI Platform
12/month

Team Collaboration

217%
Traditional Development
Async/Email
WorkflowAI Platform
Real-time

Deployment Success

29%
Traditional Development
75%
WorkflowAI Platform
97%

AI Model Testing & Validation

Manual Testing

Data scientists manually testing models in notebooks, limited production validation, and ad-hoc quality checks.

Automated Testing

Automated test suites with synthetic data generation, A/B testing frameworks, and continuous model monitoring.

Key Improvements

Test Coverage

130%
Manual Testing
40%
Automated Testing
92%

Validation Time

96%
Manual Testing
2 days
Automated Testing
2 hours

Edge Case Detection

157%
Manual Testing
Limited
Automated Testing
Comprehensive

Production Bugs

94%
Manual Testing
8/release
Automated Testing
0.5/release

Cross-Team Workflow Consistency

Fragmented Tools

Multiple disconnected tools for different teams, inconsistent processes, and version control issues.

Unified Platform

Single platform with standardized workflows, shared components, and integrated documentation.

Key Improvements

Tool Fragmentation

87%
Fragmented Tools
8+ tools
Unified Platform
1 platform

Process Consistency

78%
Fragmented Tools
55%
Unified Platform
98%

Knowledge Silos

94%
Fragmented Tools
High
Unified Platform
Eliminated

Onboarding Time

86%
Fragmented Tools
3 weeks
Unified Platform
3 days

AI Feature Development Cycle - Key Improvements

+ 500%
Iteration Speed
+ 217%
Team Collaboration
+ 80%
Feature Cycle Time
Feature cycle time reduced by 80%, from 6-8 weeks to 1-2 weeks.
6x faster iteration speed enables rapid experimentation.
Real-time collaboration eliminates communication bottlenecks.
Deployment success rate improved from 75% to 97%.
Bottom Line: Achieved up to 500% improvement across key metrics

AI Model Testing & Validation - Key Improvements

+ 157%
Edge Case Detection
+ 130%
Test Coverage
+ 96%
Validation Time
Test coverage increased from 40% to 92% with automated suites.
Validation time reduced by 96%, from 2 days to 2 hours.
Comprehensive edge case detection prevents production issues.
Production bugs reduced by 94% per release.
Bottom Line: Achieved up to 157% improvement across key metrics

Cross-Team Workflow Consistency - Key Improvements

94%
Knowledge Silos
87%
Tool Fragmentation
+ 86%
Onboarding Time
Consolidated 8+ fragmented tools into one unified platform.
Process consistency improved from 55% to 98%.
Knowledge silos eliminated through shared documentation.
Onboarding time reduced by 86%, from 3 weeks to 3 days.
Bottom Line: Achieved up to 94% improvement across key metrics

Results & Impact

Ensured smooth end-to-end functionality and high-quality open-source release. Verified data accuracy and workflow consistency across multiple environments.

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