Requirements signal
Acceptance criteria, risk, product context, and hidden assumptions.
Quality systems for predictable delivery
Turn QA bottlenecks into operating systems for release confidence.
QualityOps Studio helps CTOs, VP Engineering, and product leaders redesign how quality works across teams: faster regression, clearer ownership, smarter automation signal, and safer releases.
10h -> 30mCross-team regression time reduced through automation, parallelization, test data work, and CI/CD improvements.
-50%QA automation costs reduced while improving coverage and execution performance.
30+Distributed QA engineers led across multiple product teams.
1-2 weeksOnboarding ramp-up improvement through a cross-functional onboarding system.
What gets diagnosed
Quality failures rarely come from one test suite. They come from the system around it.
The diagnostic follows how quality actually moves through the organization, from requirements and dependencies to release decisions and ownership.
Jira and dependencies
Cross-team changes, blockers, workflow gaps, and missed handoffs.
Regression model
Coverage, prioritization, execution time, flaky areas, and release scope.
Automation signal
Trust, maintenance cost, reporting, ownership, and false confidence.
CI/CD and environments
Pipelines, test data, infrastructure, observability, and feedback loops.
Release ownership
Who decides what is ready, what is risky, and what improves next.
Evidence
Concrete results, translated into operating lessons.
Regression became a release signal
Cross-team regression was too slow for confident frequent releases.
Automation triage, prioritization, CI/CD improvements, test data work, and ownership changes.
Regression time reduced from 10 hours to 30 minutes.
Automation became more economical
Automation was useful, but expensive to run, maintain, and trust.
Suite review, coverage cleanup, execution optimization, and governance around what should be automated.
QA automation costs reduced by about 50% while improving coverage and execution performance.
Onboarding became part of the quality system
New people needed too much informal help to become productive across QA, engineering, product, and project work.
Cross-functional onboarding flow, documentation, expectations, and review rhythm.
Ramp-up time reduced by 1-2 weeks.
Positioning
Not outsourced testing. Not a generic automation vendor.
The work is for teams where quality has become an organizational constraint. The product is moving, the team is growing, but release confidence depends on slow regression, fragile automation, unclear ownership, and too much manual coordination.
The consulting model combines senior QA leadership, engineering context, process design, automation strategy, and team enablement. The outcome is a quality system the team can operate without constant external help.
When to call
Useful when quality problems are already visible in delivery.
Regression is too slow to support frequent releases
QA depends on manual coordination, hidden knowledge, or heroic individuals
Automation exists, but nobody trusts its signal
Jira, Confluence, and TMS are present but do not create real visibility
QA engineers execute tasks but do not own product quality
AI tools are being used ad hoc without a quality workflow
Consulting products
Three clear ways to buy the work.
2-3 weeks
Quality System Diagnostic
A focused review of how quality actually moves through your team: requirements, Jira, regression, automation, environments, test data, release gates, and ownership.
- Current-state map of your QA and release flow
- Risk and bottleneck report with evidence
- 30/60/90-day improvement roadmap
- Executive workshop with practical next steps
4-8 weeks
Regression Rescue Sprint
A hands-on engagement for teams whose regression cycle has become slow, expensive, flaky, or hard to trust.
- Regression suite triage and prioritization
- CI/CD, parallelization, test data, and flaky-test plan
- Release confidence dashboard and quality gates
- Ownership model so the improvement survives
Part-time leadership
Fractional Head of QA
Senior QA leadership for scaling teams that need direction, standards, mentoring, and operating rhythm without a full-time hire.
- QA operating model and responsibilities
- Metrics, OKRs, review cadence, and stakeholder reporting
- Hiring, onboarding, mentoring, and lead development
- Automation governance and cross-team dependency process
Specialist modules
Focused work when the bottleneck is already visible.
These modules can be bought independently or attached to a diagnostic, rescue sprint, or fractional leadership engagement.
Autotest Audit
Audit of automated tests on any stack: architecture, coverage, flakiness, maintainability, execution cost, reporting, CI/CD integration, and ownership model.
Load and Performance Testing Audit
Review of load, stress, performance, and reliability testing across tools and technologies, with focus on scenarios, data, environments, bottlenecks, and useful reporting.
Jira Dependency Process
Design of a Jira-based process for breaking changes and cross-team dependencies, so teams cannot silently ship changes that block or break dependent teams.
TMS Implementation and Migration
Selection, setup, migration, and process design for a new test management system, including structure, fields, workflows, reporting, and team adoption.
Test Case Review Process
Design and rollout of a practical review process for test cases: quality standards, ownership, review cadence, checklists, metrics, and cleanup rules.
Method
From hidden quality risk to an operating roadmap.
Observe the real system
Interviews, workflow review, Jira/Confluence sampling, regression and CI/CD analysis, release incident patterns, and team ownership mapping.
Separate symptoms from causes
The goal is not more process. It is finding the few constraints that make releases slow, risky, expensive, or emotionally exhausting.
Design practical interventions
Prioritized improvements across people, test strategy, automation, data, environments, reporting, onboarding, and release governance.
Transfer ownership
The work ends with an operating model, documentation, review cadence, and team habits that continue after the engagement.
Fit
Built for teams that want ownership, not just extra QA capacity.
Good fit
- Scaling engineering team with visible release or regression pain
- Leadership wants a system the team can own
- Automation, TMS, Jira, or CI/CD exist but do not create enough visibility
- The team is ready to change workflow, not only buy hours
Not the right fit
- Only need cheap manual test execution
- Want isolated automation scripts without process ownership
- Need a staffing vendor or offshore QA capacity
Led by Viacheslav Melnikov
Head of QA with the technical depth to inspect the system and the leadership range to change it.
Experience includes scaling distributed QA teams, reducing regression runtime, designing onboarding systems, enabling QA ownership of CI/CD, building performance testing practices, and introducing AI-assisted workflows for Jira, Confluence, and test management.
Domain background spans telecom, fintech, trading systems, enterprise software, SDN/NFV, infrastructure, document security, and SaaS-style product delivery.
Next step
Start with a focused diagnostic, then decide whether implementation support is worth it.
The first commercial experiment should be a fixed-scope Quality System Diagnostic. It is easier to buy, easier to deliver, and creates a natural path into a Regression Rescue Sprint or Fractional Head of QA engagement.
Discuss diagnostic