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What clients valueAI-Augmented QA · Human QA judgment · Release confidence
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Top 1 Software Testing CompaniesBucharest, Romania
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Top 1 Software Testing CompaniesBucharest, Romania
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Published category scores5.00 across 4 categoriesBudget · Quality · Schedule · Communication
View profile (opens in a new tab)Bring together the designs, requirements, tickets, repositories, test history, and product knowledge your team approves. AQA uses AI to accelerate test design, automation, maintenance, and release evidence while experienced QEs remain accountable for what is accepted and communicated.
AI assists. Tools execute. Experienced QEs approve.
Test plans
Created in
Automation
Implemented with
Traceability
Mapped in
Defects
Opened in
Test signal
Published through
Release evidence
Shared in
Representative destinations. Final tools and publishing steps are agreed per engagement.
AQA brings together approved designs, requirements, tickets, repositories, existing tests, incidents, roles, business rules, integrations, and release history. AI helps connect the evidence; an Embedded QE confirms what is current, important, and true.
Human controlAI can infer impact. AQA decides whether the inference matters to the product.
AI expands happy paths into negative, boundary, state, permission, integration, recovery, and data scenarios. AQA QEs remove noise, add product intuition, define expected outcomes, and select the right validation layer.
Human controlNo AI-generated scenario becomes an accepted asset until a QE verifies its relevance, expected outcome, and maintenance value.
AQA uses AI to accelerate implementation, refactoring, documentation, and coverage expansion. The QA Architect chooses the layer and pattern; engineers review the code and keep approved assets in the client repository.
Human controlAI proposes and accelerates code. AQA owns architecture, review, deterministic execution, and whether the test proves meaningful behavior.
AQA can use approved change, risk, history, dependencies, and evidence freshness to recommend regression scope. When a check fails, AI accelerates investigation while QEs confirm the cause before escalating it.
Human controlAI can cluster and suggest a cause. A QE reproduces and validates it before engineering receives a product defect.
When a test breaks, the workflow compares approved product intent, the code change, current behavior, and previous evidence. AI can propose a repair; AQA determines whether the product changed intentionally, the test became stale, or a regression occurred.
Human controlZero silent heals. Every accepted repair is reviewable, attributable, validated, and reversible.
AQA combines the change, affected flows, executed evidence, known gaps, defects, and accepted risk into one release view so stakeholders can see what was validated, what was not, and what must happen next.
Human controlAI assembles the evidence. AQA leadership owns the interpretation and any recommendation.
AQA Change Review keeps the scope, AI destination, supporting evidence, and resulting QA direction visible at every step—so your team can inspect the reasoning before it acts.
Register an open GitHub pull request, add only the product context the repository cannot explain, and choose a configured AI model before any assessment begins.
Inspect commit pins, selected evidence, exclusions, redactions, token estimate, credential status, and AI destination. Provider transmission requires explicit approval.
Review the product brief, ranked risk hypotheses, prioritized QA scenarios, exploratory charters, and cross-service impact—with material claims linked back to approved evidence.
Manage trusted model configurations and reopen completed or in-progress assessments from local history. Credentials are handled by the operating-system credential manager.
Direction with a visible evidence trail.The Public Edition analyzes source and repository context; it does not execute the product or tests. Risks remain hypotheses for human review—not verified defects or a release decision.
Animation stays off when your device requests reduced motion or data.Use the Public Edition to uncover risk and shape your test plan. Managed Delivery turns that direction into implemented, executed, and maintained tests—with clear release evidence.
| Compare editions What’s included One capability list. Two ways to start. | Public Edition Open access A reusable, self-directed QA workflow for public and locally configured private GitHub repositories. Available on GitHub | Managed Delivery Outcome-led partnership A named AQA team builds and operates the workflows needed to reach the agreed QA outcome. Human governed |
|---|---|---|
| Context and direction How each edition understands a change and turns it into a useful QA direction. | ||
| Starting point | One open GitHub pull request per assessment | Your highest-value QA bottleneck, roadmap, changes, and releases |
| Sources | Pull request, changed files, and available repository context | Approved designs, docs, tickets, repositories, tests, incidents, and product signals |
| Product knowledge | One assessment with no persistent product model | Maintained and corrected across the engagement |
| Change impact | Evidence-backed hypotheses for user interpretation | Human-reviewed product and business interpretation |
| Test scenarios | Prioritized conceptual scenarios | Approved test cases, exploratory charters, and maintained assets |
| Build and operate The delivery work that turns QA direction into repeatable protection. | ||
| UI automation | Not included | Designed, implemented, executed, and maintained |
| API and contract automation | Not included | Designed, implemented, executed, and maintained |
| Accessibility, visual, and specialist testing | Relevant considerations may be suggested | Agreed automation and human specialist validation |
| Test data | Requirements may be identified | Fixtures and synthetic data implemented and maintained |
| Execution | Not included | Manual, exploratory, CI, and scheduled execution |
| Failure triage | Not included | AI-assisted and human-confirmed classification |
| Test repair | Not included | Governed proposals, human review, and deterministic rerun |
| Evidence and ownership What remains after the review and who stays accountable for the result. | ||
| Coverage memory | One report with no ongoing memory | Maintained requirement-risk-test-evidence map |
| Release evidence | No release recommendation or decision | Human-reviewed evidence and recommendation |
| Asset ownership | No implementation assets | Agreed test code and runbooks remain accessible to the client |
| Accountability | The user interprets the output | A named AQA team owns agreed delivery and evidence |
| Commercial model | Free and reusable; one pull request per assessment | A scoped investment tied to the agreed outcome, workflow coverage, and delivery capacity |
| Choose your starting point | View Public Edition | Book a fit call |
Managed capabilities apply only when included in the engagement scope. The client retains the release decision.
Start with the bottleneck costing your team the most confidence or time. AQA connects the right workflow groups into one human-governed delivery service—not a software tier or an à-la-carte tool.
Best forTeams that already have execution capacity but need better direction and senior QA thinking.
Best forTeams whose manual knowledge is not becoming repeatable protection.
Best forTeams that want AQA to operate the capability, not just build assets.
The Public Edition gives you immediate QA direction. Managed Delivery adds the people, implementation, operation, and accountability needed to turn direction into durable release protection.
No. AQA Masters is a QA service. We use AI inside human-governed workflows to accelerate analysis, design, implementation, maintenance, and reporting. Your team does not have to operate another QA platform.
AI can produce source-linked drafts and variations from approved context. AQA QEs review relevance, expected results, risk, duplication, and maintenance value before a test becomes an accepted managed asset.
Yes. Managed Delivery can use approved designs, requirements, tickets, code, existing tests, incidents, and product knowledge. We agree how each source is accessed—manually or through an approved integration—before it enters the workflow.
Depending on product risk and agreed scope, managed work can include UI, API, contract, accessibility, visual, performance, mobile, integration, data, and AI-product evaluation coverage. The QA Architect selects the useful layer rather than automating everything through the UI.
No. AQA does not silently accept AI-proposed repairs. We first distinguish product, test, environment, and data causes. Every accepted repair is explained, reviewed by a QE, committed through the agreed client workflow, and proven by a deterministic rerun.
The Public Edition is a free, reusable, self-directed workflow. Each assessment uses one pull request and available repository context to produce evidence-backed impact hypotheses and prioritized test scenarios for your interpretation. It does not execute tests, commit automation, or provide an AQA release decision.
Depending on agreed scope, Managed Delivery adds maintained product context, human-approved test design, automation implementation, manual and exploratory work, execution, triage, governed maintenance, coverage records, release evidence, and named QA accountability.
Not necessarily. AQA can work around an existing team, fill missing architecture or automation capability, or provide Embedded QE capacity with shared senior and specialist support.
Agreed managed assets are created in client-approved systems and repositories so the client retains access and ownership under the engagement terms. Exact ownership, licensing, access, and handover terms are confirmed in the agreement.
We define a baseline, current period, unit, formula, scope, exclusions, and source evidence for each selected workflow metric. We report the measured difference for comparable periods rather than applying a generic AI productivity percentage.
Bring one workflow and its current constraints to a fit call with AQA.
QA Architect
