Quality at Coforge begins before a line of code is written. The underlying objective of our Quality.NEXT service delivery platform is to enable quick releases and predictability, avoid excessive cost and risk of getting it wrong! We offer AI-powered, Lean-driven software quality engineering automation, process design, resources, and governance to eliminate waste and improve efficiencies. We provide quality governance led by Industry QE leaders augmented by agile credit score and technology-centric platform for software quality analytics, software quality intelligence and enterprise-wide software quality control. Our community of T-shaped Quality.NEXT engineers practice continuous learning of AI-powered, Lean QE skills with their teammates across organization-wide projects at our experimentation lab infrastructure. This enables them to implement things they learned at their project and share them with their project team for day-to-day continuous improvement efforts.

At Coforge, we provide ‘Product Quality and Risk Advisory Consulting’ to work with our customers to co-create quality engineering transformational roadmaps in a step wise manner. Our nSTAR (North Star Positioning Consulting Solution) for next-gen ADM services and testing services is operationalised by our matured best-in-class quality assessment model and hyper-automated capability solutions to step change improvement in quality engineering. Our 60+ solution accelerators and incubators enable continuous improvement in automation engineering, business assurance testing, enterprise and product testing, AI and ML infused testing with faster speed. The improvements are governed by Agile credit score for measuring team maturity. Our 50+ AI, machine learning and RPA use cases are the focused investments to bring quality intelligence and engineering efficiency in Agile and DevOps. During the last few years, we have applied AI, machine learning and RPA with lean in test automation, predicting test quality, prioritizing test cases, classifying defects, scientific risk-based test execution and end-to-end software quality governance.