Enterprise Assurance Services

LTIMindtree’s Enterprise Assurance Services focus on early testing, thus enabling clients to strengthen their Business-to-IT connect and institutionalize a culture of ‘first time right’. We ensure meeting and exceeding all the enterprise assurance needs by leveraging our technology-agonistic platform, supporting one-click automation and ensuring continuous quality in DevOps.

Our Enterprise Assurance Service focuses on following key areas:

• Strengthening the core
  • Setting up a custom operating model with cross-trained and cross-skilled resources.
  • Building common ‘Standard of Reference’ – Process, Tools, Frameworks.
  • Ensuring a centralized test repository and QA platform for cross leverage.
  • Framing and implementing lean and standard QA processes, with automated gating in accordance to client needs.
  • Setting up tool-based governance for ‘Single View of Quality’.
• Lifecycle automation
  • In-sprint and multi-stack automation enabled by technology/platform-agnostic LTIMindtree Canvas Engineering (Automation Framework).
  • Deploying fit-for-purpose automation tools and strategy that is based on client application technology and architecture.
  • Extending automation across the development lifecycle (Build & Deploy, Functional, Services, Performance, Test Ecosystem & Test Data and Test Reporting).
  • Leveraging our ready-to-use business scenarios and test cases to maximize end-to-end test coverage.
  • Ensuring a shift-left alignment for early defect detection and prediction of application hotspots.
• Ensuring resiliency by non-functional engineering
  • Setting up performance monitoring and resilience services including chaos engineering.
  • Log-analytics-based correlations and continuous governance to measure and monitor KPI & SLO.
  • Proactive site reliability engineering for failure prediction across business process and infrastructure.
• AI-based cognitive quality
  • Providing real-time insights to influence upstream and downstream quality.
  • Early application hotspot identification and defect predictions.
  • Impact-based testing through AI-led automated correlation of SDLC assets.

Key Outcomes

• Continuous improvement in production quality
• Robust and reliable applications
• Enabling business in rapid deployment for faster time-to-market
• >50% reduction in critical path to deployment through lifecycle automation
• Reduction in testing cycle time by 40%
• Improving application availability to >99.99% through resiliency
• 15-20% acceleration in defect-fixes and enhancements through AI-based analytics