AI accelerates the work, while KMS quality engineers direct it, validate critical outputs, and apply quality gates. You keep governance and release sign-off in place, with KMS accountable for the quality outcome.
Gain a managed testing squad that combines experienced quality engineers with AI-powered workflows. Increase testing capacity, strengthen coverage, and release with confidence from requirements through release.
AI coding assistants, shorter release cycles, and growing application complexity increase the volume of change your QA team must validate. When testing still relies on manual effort and fragmented automation, the gap between how fast you build and how confidently you release keeps widening.
AI Testing Squad is a managed quality engineering service that connects requirements analysis, test design, execution, and reporting. You gain experienced KMS quality engineers supported by Velox, KMS’s AI delivery accelerator, with current strengths in BA and product workflows, testing automation, and team knowledge.
Each engagement starts with a pilot on an agreed workflow, with baseline metrics and success criteria defined upfront. Your squad then expands coverage and transitions into a steady managed service with defined SLAs, runbooks, and monthly service reviews.
AI accelerates the work, while KMS quality engineers direct it, validate critical outputs, and apply quality gates. You keep governance and release sign-off in place, with KMS accountable for the quality outcome.
Connect requirements to test cases, execution results, and defects. Your stakeholders gain a clearer view of coverage and unresolved issues, helping them make release decisions with evidence they can inspect.
Identify gaps in user stories and acceptance criteria before they move downstream. Your product, engineering, and QA teams gain a clearer, shared understanding of expected behavior and what testing needs to validate.
Your testing capacity grows through Velox-accelerated workflows rather than proportional hiring. Our model reduces manual work across test preparation, execution, and reporting, so quality engineering effort shifts toward higher-value validation.
From the pilot workflow to a steady managed service, you gain a testing capability configured to your environment, measured against your baseline, and supported by traceable coverage and regular service reviews.
The engagement starts with a review of your current QA practices to fully evaluate your current QE system. You receive improved user stories and acceptance criteria, a test strategy prioritized by risk, and agreed success criteria your stakeholders can measure progress against.
See how AI-assisted testing performs within your delivery environment through a focused pilot on agreed workflows. Results measured against defined success criteria help you decide where to expand and what needs refinement before committing to broader adoption.
Receive a regression coverage plan that prioritizes critical application behavior and identifies opportunities to reduce repetitive testing. Your team gains a clear basis for expanding automation where it can provide the greatest value to release validation.
Receive an expansion roadmap informed by pilot results and ongoing service performance. Your leaders gain a practical basis for deciding which products or coverage areas to address next and how to extend the service as testing demand grows.
Your first AI Testing Squad often begins with one product or release. As results prove out, you can add squads, products, and coverage areas to bring the same quality model to your broader portfolio.
AI is multiplying the volume of change your QA team must validate. Give your releases a testing model built for the speed your teams now work at.
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