The objective was to transition from fragmented QA processes to a unified, scalable Quality Engineering (QE) model that could improve efficiency, reduce costs, and support faster releases. By standardizing frameworks, increasing automation, and strengthening governance, the organization enabled consistent quality, faster validation cycles, and improved operational performance across its ecosystem.
Transformation Timeline
Drag
The Challenge
The organization operated a complex digital ecosystem with multiple applications and platforms, but lacked standardized QA processes across portfolios. This led to inconsistent quality, execution inefficiencies, and challenges in scaling delivery.
Automation coverage was limited and fragmented across different tools and frameworks, resulting in high regression effort and duplication of work. Test data creation across distributed data sources was slow and complex, further delaying validation cycles.
ETL validation processes were time-consuming, impacting release timelines. Additionally, reliance on physical devices increased the cost and complexity of mobile testing.
Without a unified QE approach, these challenges limited efficiency, increased costs, and slowed the organization’s ability to deliver high-quality digital services.
Our Approach
Delivered an enterprise-wide QE transformation that standardized processes, increased automation, and improved validation speed across platforms.
Standardized QE Operating Model
Established a TMMi-aligned QE framework with shared processes, governance, and guidelines to ensure consistency across portfolios.
Unified Automation Framework
Implemented an enterprise-wide automation framework supporting web, mobile, thick-client, Salesforce, and API applications.
Scaled Automation Coverage
Automated over 22,000 test cases using Selenium (Java), TestComplete, Jacobs, and supporting tools to improve coverage and reduce manual effort.
Enabled Continuous Validation
Integrated CI/CD pipelines to enable continuous testing across on-prem and cloud environments, improving release speed and reliability.
Accelerated Data & API Validation
Delivered API and ETL validation automation to significantly reduce data validation time across distributed data sources.
Partner & Technology Ecosystem
The engagement was delivered using Coforge’s AI-led engineering capabilities and cloud-native delivery framework.

Impact to Date
The QE transformation delivered significant improvements in efficiency, automation coverage, and quality while reducing costs across the enterprise.
94%+ Regression Automation Coverage
77% Reduction in Manual Testing Effort
<3 Minutes ETL Validation Time
30% Reduction in Cost of Quality
Business Impact
-
Improved consistency and quality across applications and platforms
-
Reduced regression effort and improved execution efficiency
-
Accelerated test data creation and validation cycles
-
Enhanced mobile testing scalability while reducing costs
- Improved release timelines through faster validation
- Reduced production defect leakage to less than 1%
Looking Ahead
With a unified QE framework and high automation coverage in place, the organization is well-positioned to further enhance digital service quality, accelerate releases, and drive continuous improvement across its ecosystem.
Value Delivered
-
Enterprise QE Foundation: Established a scalable, standardized QE model to support complex, multi-platform environments.
- Automation-Led Efficiency: Enabled significant reductions in manual effort and improved execution speed through large-scale automation.
- Faster Data Validation: Reduced ETL and API validation timelines, accelerating release cycles.
- Improved Quality & Cost Optimization: Enhanced application quality while reducing the overall cost of quality across the enterprise.
