Understand.
Agree on your product, critical journeys, and release risks. Review the tests and tools you already have.
A focused QA scopeMove fast. Know what works.
AI-powered testing and experienced QA leadership, built around your product.
From product context to purposeful coverage. Every cycle.
From your first smoke suite to your next release, get QA that solves the problem in front of you.
Find the right engagementMaintainable browser and API tests for the flows that matter. Strengthen what you have, fix unreliable checks, and connect useful feedback to your delivery pipeline.
Validate critical journeys against agreed requirements. Get reproducible defects, clear evidence, and an honest view of what passed, failed, or could not be tested.
Turn product requirements and real user journeys into a focused test strategy. Prioritize critical coverage and define what “ready to release” means for your team.
Hands-on QA direction for growing teams. Bring structure to test ownership, defect triage, automation reviews, and the decisions that keep delivery moving.
AI helps accelerate the work.
Product context and human review
keep it meaningful.
Agree on your product, critical journeys, and release risks. Review the tests and tools you already have.
A focused QA scopeUse AI to assist with test design, automation drafts, and failure investigation. Keep the implementation lean.
Useful, maintainable coverageReview assertions, run the checks, and inspect the evidence. Share findings and limitations before the next decision.
Clear, reviewed resultsAI helps us move faster. It never gets the final say on quality.
A focused starting point, with scope
and deliverables agreed before work begins.
Understand your coverage, your risks, and what deserves attention next.
Build or stabilize a useful test suite around your highest-priority flows.
Bring consistent QA thinking and hands-on support into your delivery cycle.
I'm Barış Irız, a QA lead and automation engineer. My work brings together test strategy, hands-on automation, and the judgment needed to understand what a test result actually means.
I use AI to help with the time-consuming parts of QA, while staying accountable for the scope, the assertions, and the evidence.
AI can assist with analyzing requirements, drafting tests, improving automation, and investigating failures. A QA professional reviews the work, validates the results, and owns the quality decisions. The tools and workflow are agreed for each engagement.
Yes. The starting point is your current product, test suite, and delivery process. Useful coverage is preserved. Changes focus on gaps, unreliable tests, and the flows that matter most, with a rebuild considered only when there is a clear reason.
Access, approved tools, and data boundaries are agreed before work starts. Use of AI tools with your code or product data is subject to that agreement. Testing should use approved environments and suitable test data.
Deliverables depend on the engagement: a prioritized QA plan, maintainable test code, execution evidence, reproducible defect reports, and a clear handover. Results distinguish verified behavior from blocked checks and anything outside the agreed scope.
Both depend on your product, existing coverage, access requirements, and the work involved. A brief helps define the need; scope, delivery expectations, and commercial terms are agreed before an engagement begins.
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