Not AI Added to QA. AI Woven Into QA.
At UpDoer, AI is not an add-on to our QA services. It is an intelligence layer we aim to integrate across the testing lifecycle, so dedicated QA engineers can make better decisions, investigate deeper and deliver more meaningful quality outcomes.
AI capabilities are applied according to the engagement, the available tools and data, and technical feasibility. Human QA ownership remains central throughout.
THREE LAYERS, ONE DELIVERY MODEL
AI Intelligence
Assists with analysis, test design, execution support, failure investigation and quality insights.
Dedicated QA Expertise
Provides product context, exploratory thinking, risk judgment and verification.
Structured Delivery
Connects the work through a defined methodology, traceable findings, reporting and continuous improvement.
AI brings scale. QA expertise brings judgment. Our process brings them together.
One methodology, seven steps, in every engagement
Each step has two parts: where AI assists, and what a QA engineer owns. The names and order are the same everywhere on this site.
Understand
Product context, requirements, workflows, existing coverage and history.
WHERE AI ASSISTS
Reads requirements, tickets and code changes to build context faster.
WHAT A QA ENGINEER OWNS
A QA architect reviews the gathered scope for gaps the AI would not know to flag.
Risk Mapping
Ranking what could fail by what it would cost, weighted by how features are used and how often they change.
WHERE AI ASSISTS
Scores where a release is most likely to break, from what changed.
WHAT A QA ENGINEER OWNS
A QA engineer decides what a failure costs this business. The risk score is an input, not the answer.
Test Strategy
Turning requirements into test scenarios and coverage, and deciding what is automated and what stays manual.
WHERE AI ASSISTS
Drafts test cases from that context, edge cases included.
WHAT A QA ENGINEER OWNS
Generated tests are reviewed against real user behavior before they enter the suite.
Execute
Running manual, automated, API, performance, accessibility or other relevant testing.
WHERE AI ASSISTS
Runs in parallel, and helps repair locators that break when the interface changes.
WHAT A QA ENGINEER OWNS
Flaky or ambiguous failures are triaged by a QA engineer before anything is logged as a defect.
AI-Assisted Quality Engineering
Grouping failures and comparing results across runs, so triage starts from patterns, not a long list.
WHERE AI ASSISTS
Classifies failures and scores release risk across runs.
WHAT A QA ENGINEER OWNS
Every AI output is reviewed, and anything wrong is discarded before it reaches a report.
Human Verification
Reproducing, classifying and recommending on every flagged result.
WHERE AI ASSISTS
Its output is the input to this step, never the conclusion.
WHAT A QA ENGINEER OWNS
The one step that is never automated: nothing becomes a finding without a person confirming it.
Report & Improve
Turning verified findings into a decision, then feeding what was learned into coverage and regression.
WHERE AI ASSISTS
Its risk score is one input to the release decision.
WHAT A QA ENGINEER OWNS
The go/no-go call is made by a person, using the risk score as one input and not the whole decision.
What makes the approach different
Many providers use AI to speed up a task, such as drafting test cases or scripts. Our aim is wider: to bring AI into every layer of a dedicated QA engagement, from understanding requirements to analysing results, while a named team stays accountable for the outcome.
Our goal is not to replace dedicated QA engineers with AI. It is to help them deliver deeper analysis, more effective coverage, faster feedback and better-informed quality decisions.
AI across the lifecycle
We look for where AI can help at every stage, not only in automation.
Dedicated QA ownership
A team stays accountable for the engagement, the product context and the outcomes.
Human-verified results
AI-generated scenarios and recommendations are inputs to evaluate, never conclusions to trust automatically.
Context-driven testing
Priorities follow your requirements, user journeys, business risks and release goals.
Continuous improvement
Findings from each cycle shape the next cycle's testing and regression coverage.
Working with your team
- We work alongside product and engineering, in your tracker and against your builds.
- Testing follows your release cadence, with risk set by your priorities.
- Findings are written so engineers and product owners can both act on them.
- A named team owns the engagement and the product context.
What you receive
- A clear account of what was covered, what was found and what remains at risk.
- Findings with the evidence behind them, so they can be reproduced and traced.
- Release-risk summaries that a QA engineer has verified.
- Learnings fed back into regression coverage for the next cycle.
Getting started
A typical path from first conversation to a full engagement. No part of it is fixed in advance; it follows what your team needs.
01
Discover
Understand your product, your current QA approach, and where AI-assisted testing could help most.
02
Align
Agree on scope, the engagement model, and what a good result looks like.
03
Pilot
Start with a focused trial on a real part of your product, so you can judge the approach on real work.
04
Scale
Widen coverage and cadence as the work and the fit are established.
Technologies we work with
Automation
Selenium · Playwright · Appium · Katalon · TestComplete
API & Performance
Postman · SoapUI · Swagger · Apache JMeter
CI/CD & Reporting
Jenkins · TeamCity · Allure · Maven