AI-Native Quality Assurance
Quality assurance designed from the ground up for Artifical Intelligence-driven systems
We apply AI to hyper-accelerate end-to-end Quality Assurance
Winning has developed a selection of tools, procedures and custom agents that allow our analysts and programmers to leverage AI to introduce extreme efficiency improvements across the whole end-to-end QA value chain.
Examples of tools used
We select a toolkit tailored to the client’s context and existing management systems. The following is an illustration of a typical scenario:
AI-Native Quality Assurance as a Service
We propose an approach based on a fixed resource allocation model for three specialized testing profiles with complementary analysis, programming, and strategy skills complemented by AI.
Winning proposes the full-time allocation of a QA Analyst with experience in functional analysis, test case identification and prioritization, and the specification and execution of exploratory tests supported by AI, contributing to subsequent implementation.
Winning proposes the allocation of a full-time Software Engineer in Test who will manage and optimize the pipeline, monitor and improve reporting, and, above all, refine code produced by Artificial Intelligence based on the analyst’s specifications.
Winning proposes the allocation of a part-time Technical Manager to support strategy definition, prioritization, and technical review of solutions.
AI-Native Quality Assurance as a Service for a Large International Fashion Retailer
QA bottlenecks: Excessive manual effort for test development
Limited QA scalability due to a shortage of specialized staff
Unsustainable maintenance of QA assets requiring high ongoing effort
Winning AI-Native QA methodology designed around AI agents
Explorer Agents: AI that uses the applications to understand features, identify bugs, and define test case scenarios
Test Programmer Agents: AI that converts test cases into repeatable and highly structured Playwright/C# tests
Agents integrated with direct usage of systems under test, as well as supporting management tools such as Azure DevOps and Jira
Seamless CI/CD integration, with the produced automation fully compatible with existing pipelines
High test automation throughput: Over a 10x increase in test automation production rate
Broad functional coverage: Tests now cover a functional surface 5x larger than before
Sustainable model: Human effort is focused on understanding edge cases and user experience, rather than test case development
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