UpDoer is an independent software testing and Quality Engineering company. Experienced QA engineers, test automation and AI-assisted quality engineering work together to help teams find risk, validate critical workflows and release with more confidence.
UpDoer Technology is a software testing company, in operation since 2016. We work alongside product and engineering teams as an outside QA function: testing early, putting effort where a failure would cost the most, and reporting what we found in a form a team can act on.
Our practice combines manual and automated testing with AI-assisted quality engineering. AI speeds up analysis, test generation and repetitive work. Our engineers decide what matters and verify the results.
Quality across the lifecycle
Testing planned from requirements through release, not squeezed in at the end.
Works with your team
We use your tracker, your builds and your release rhythm, alongside product and engineering.
Risk-focused testing
Effort goes to the workflows where a failure would matter most.
Manual and automated
Exploratory judgment and repeatable automation, each used where it pays.
AI-assisted QA
AI speeds up analysis and generation; people verify the output.
Clear findings
Evidence-backed reports that show what was tested and what remains at risk.
Shipping quickly is only an advantage when you can tell what you are shipping. Five pressures come up again and again, and each one shapes how we work.
Faster development increases the need for effective validation.
We test early and in step with each release, so validation keeps pace with delivery.
Complex integrations create risks across whole workflows.
We test critical workflows end to end, including the boundaries between systems.
Test automation needs upkeep and reliable execution.
We automate where it pays and maintain the suite so its results stay trusted.
AI-assisted development adds new things to verify.
We check AI-generated output, and our own AI-assisted work, against evidence a person has reviewed.
Teams need to know what was tested and what is still at risk.
Every report says what was covered, what was found and what remains open.
AI can accelerate parts of the testing lifecycle, but reliable software quality still requires risk-based thinking, evidence, technical judgment and human verification.
AI brings scale. QA expertise brings judgment. Our process brings them together.
AI-assisted testing
Where it helps, AI assists with test generation, test analysis, automation workflows and related quality engineering work. It speeds up the repetitive parts; it does not decide what ships.
Structured test execution
Testing follows defined requirements, risk priorities, critical workflows and agreed acceptance criteria, so results can be compared from one release to the next.
Human verification
Experienced QA professionals review the evidence, investigate unexpected behavior and validate important outcomes where automated checks alone are not enough.
Actionable quality reporting
Findings are documented clearly so engineering and product teams can decide what to fix, what to accept and what to retest.
IN THE UPDOER AI QA PRODUCT
The AI QA product is a separate application. This site describes how we work; it is not that product.
A selection of our published services. Each has its own page with the detail.
We also help teams set up automation frameworks and QA strategy.See every service →
The same seven steps run every engagement, from the first conversation to the final report. AI assists across them, and a QA engineer owns each result.
Different products fail in different ways. We adapt our testing to the workflows, integrations and risks of your product environment.
See every industry →Pick the model that matches how much structure you need today.
Dedicated Team
A QA capability aligned with your product and team, working alongside your release cycle on an ongoing basis.
Coverage-as-a-Service
QA coverage structured around your agreed testing needs, without maintaining a fully embedded team.
Scoped Quick-Start Sprint
A focused QA engagement around a clearly defined scope, suited to a specific release, feature, or audit.
HOW TO START
Discover
Understand your product, your current QA approach, and where AI-assisted testing could help most.
Align
Agree on scope, the engagement model, and what a good result looks like.
Pilot
Start with a focused trial on a real part of your product, so you can judge the approach on real work.
Scale
Widen coverage and cadence as the work and the fit are established.
An independent perspective
We are not the team that built the product, so we report what we find, not what is convenient.
Risk first
Testing effort follows where a failure would matter most.
Evidence over opinion
Results come with the evidence behind them.
Clear communication
Findings are written so engineers and product owners can both act on them.
Flexible engagement
Dedicated, coverage-based or scoped: the model follows your need.
Fits your workflow
We work in your tracker, your builds and your release cadence.
AI with judgment
Every AI-assisted result is checked by a QA engineer before it reaches you.
DELIVERED
ECOMMERCE & RETAIL
100% to 30%
REGRESSION TESTING TIME VS. BASELINE
A dedicated QA team built a Playwright automation framework, API checks and a CI pipeline across point-of-sale, inventory, purchasing and warehouse modules, cutting regression testing time by 70%.
Read the full case study
ONGOING · 4 YEARS
SAAS
Manual only to 85%
CRITICAL FUNCTIONALITY COVERED BY AUTOMATION
A dedicated team of three QA engineers built a QA practice from nothing for a cloud case management platform: 40% less defect leakage in year one, and the practice is still running four years on.
Read the full case study
PRE-RELEASE TESTING
STREAMING & GAMING
27
SPECIALIST TESTERS ON THE ENGAGEMENT
A team of 27 mobile-gaming testers ran exploratory, regression and performance testing on iOS and Android ahead of launch, surfacing performance, gameplay and UI/UX problems before players did.
Read the full case study
Tell us about your product, delivery process and quality challenges. We’ll discuss how UpDoer’s QA services and AI-assisted approach could fit your team’s needs.
