Reimagining Quality Engineering at Scale with Testing-as-a-Service
Implementation of a governance-led, automation-driven QE model to unify practices, optimize resources, and accelerate delivery across enterprise platforms.
Client
A leading diversified financial services company offering employee benefit solutions, retirement, health and sustainability consulting, and investment and wealth management services to corporate clients, advisers, and individual customers across global markets.
Problem Statement
Fragmented testing practices, high reliance on contractors, and inconsistent governance across digital platforms were driving up costs and limiting scalability. The client needed a standardized, automation-led Quality Engineering model that could deliver measurable ROI and sustained efficiency improvements.
Industry
Financial Services
Consulting
Quick Summary
- Delivered a governance-led Testing-as-a-Service (TaaS) model integrating functional, non-functional, and security testing.
- Embedded Software Testing Governance Assurance Framework (STGAF) across delivery workflows.
- Standardized tooling and processes to reduce fragmentation and enable automation-led scalability.
- Achieved 30% month-on-month cost savings and 25% quarterly improvement in testing output.
Client Profile
As a global financial advisory leader, the client manages multiple mission-critical platforms supporting advisory services, policy management, and customer engagement. Ensuring consistent quality across these systems was crucial for maintaining regulatory compliance, operational reliability, and customer trust across geographies.
Challenges: Inconsistent QE Practices
- Heavy dependence on individual testers and contractors with no unified governance model
- Limited automation and CI/CD adoption across platforms
- Gaps in reporting, risk management, and tooling standardization
- Misaligned stakeholder priorities and lack of QE governance awareness
- Rising QE costs and slower time-to-market for critical projects
QBurst Solution: Governance-Led TaaS Framework
We implemented a Software Testing Governance Assurance Framework (STGAF) to establish standard practices, enforce governance, and embed automation-driven efficiencies across all enterprise testing operations.
Key solution components:
- Governance model with defined templates, roles, and compliance reviews
- End-to-end test coverage across functional, integration, security, API, and performance layers
- Standardized tooling using open-source frameworks and in-house accelerators
- Scalable TaaS delivery model with flexible resourcing and burst capacity (+25% for critical cycles)
- Integration of CI/CD pipelines for automated health checks and regression testing
- Centralized repository for test artifacts and documentation, reducing silos and promoting reusability
- Training and enablement programs to upskill internal test engineers
Implementation Approach: Designed for Simplicity and Scale
- TMMi-based maturity assessment to benchmark existing QE practices
- Phased STGAF rollout – from assessment to policy definition, process standardization, pilot, and continuous improvement
- Embedded RACI-based governance and risk-based prioritization for automation and performance testing
- Continuous reporting and dashboards for monthly, half-yearly, and annual quality tracking
- Parallel chatbot testing stream to validate enterprise-grade AI solutions within a governed framework
Value-Added Initiatives
- AI-Driven Automation Enablement: Conducted GenAI automation pilots and tool evaluations for Microsoft Dynamics 365 and other enterprise environments, improving scalability and cost efficiency.
- Conversational AI Policy Assistant: Built a Copilot Studio–powered Teams assistant integrated with the Policy Master Knowledge Base to deliver accurate, context-aware responses for up to 100 concurrent users.
Impact
- 30% month-on-month resource cost savings since TaaS implementation
- 25% quarterly improvement in testing output and efficiency
- Expanded automation coverage across regression cycles, reducing manual effort and release cycles
- Reduced defect leakage and higher execution rates across platforms
- Improved QE governance maturity with a clear roadmap toward TMMi advancement
- Reusable, scalable QE framework embedded into enterprise delivery value chain
- AI-ready QE ecosystem supporting safe and governed adoption of conversational AI solutions
Client Profile
Challenges
QBurst Solution
Implementation Approach:
Value-Added Initiatives
Impact