AI-Powered QA for Faster, Smarter Software Testing
AI analyzes requirements, accelerates test generation and execution, and helps identify risk — while QA engineers provide human verification before results become release decisions.
AI
- Analyzes requirements
- Accelerates test generation and execution
- Helps identify risk
QA ENGINEERS
- Check every flagged result
- Verify before results become decisions
- Make the go / no-go call
See UpDoer AI QA in action
Explore the QA intelligence dashboard, Jira insights, AI-assisted requirements analysis, test case generation and automation workflow in the current platform.
- DASHBOARD
- JIRA ISSUES
- REQUIREMENTS AGENT
- TEST CASE AGENT
- AUTOMATION
QA intelligence dashboard
Defect and status visibility, test case visibility, coverage insights, agent activity and recommendations.
Requirements Agent
Requirement and story analysis, risk analysis and test planning.
Test Case Agent
Test case generation and management, with AI-assisted updates.
Automation
Playwright-based automation generation, following a Page Object Model workflow.
Five ways AI shows up in the pipeline
Each pillar below maps to a specific step of the seven-step methodology described further down — none of it replaces the engineer at the end of it.
TEST SCENARIOS
Agentic Test Generation
Plain-English requirements become production test code. Agents generate test cases directly from requirements, user stories, and code diffs — no manual scripting needed.
Human check: a QA engineer reviews every generated test against real user behavior before it enters your suite.
Self-Healing Automation
Scripts that adapt when your UI changes. When a locator breaks because of a UI change, the framework repairs itself instead of failing the whole suite.
Human check: every self-heal is logged and periodically spot-checked by an engineer.
RELEASE
Predictive Risk Analytics
Know where a release is more likely to break — before it ships. ML models identify failure patterns from historical data and score release risk in real time.
Human check: the risk score is one input to a go/no-go call — a QA lead makes the final decision, not a dashboard.
Autonomous App Mapping
AI explores your app before anyone writes a single test. Agents crawl your application, document its workflows, and build a test inventory — then flag the gaps only a person could fill in.
Human check: the resulting map is reviewed before it becomes the basis for test coverage.
VALIDATION
LLM & Generative-AI Output Testing
Testing the parts of your product that don't have one "correct" answer. Chatbots, copilots, and generative features are evaluated against a rubric your team defines.
Human check: a QA engineer reviews the edge cases the evaluation flags as uncertain.
AI accelerates. A person verifies.
AI and automation handle analysis, generation, and execution at a pace no manual process can match. None of it reaches you unreviewed: every AI-flagged result converges on a QA engineer before it becomes a Quality Signal.
This isn’t a limitation we’re working around — it’s the design. AI is fast and tireless; judgment about what actually matters to your users is still a human skill.
The complete methodology
Every engagement runs through the same seven steps, start to finish. Human Verification is the step before the final report, carrying the same weight as every step around it.
AI reads your requirements, user stories, Jira tickets, and code diffs — building context before a single test is written.
Agents build context from everything that changed, including the code diff itself, and score where a release is most likely to break. A QA engineer decides what a failure would cost your business.
Test cases come straight from that context, edge cases included. A QA engineer reviews them against real user behavior before they enter the suite, and decides what is automated and what stays manual.
Everything runs in parallel across real devices and cloud environments — web, mobile, and desktop. When a locator breaks because of a UI change, the framework repairs itself instead of failing the whole suite, and QA engineers review those repairs.
ML models classify failures and score release risk in real time, surfacing exactly where the hotspots are.
Every AI-flagged result is checked by a QA engineer before it reaches you — the one step that is never automated away.
A go/no-go call, made by a person, using the AI's risk score as one input — not the whole decision. What was found feeds back into coverage and regression for the next cycle.
A signal moves through the quality engineering pipeline: Understand → Risk Mapping → Test Strategy → Execute → AI-Assisted Quality Engineering → Human Verification → Report & Improve. Every AI-flagged result is checked by a QA engineer at Human Verification before it becomes a Quality Signal.
How this fits into how we work
There is one methodology, not two: the same seven steps above are how an engagement runs day to day.
Understand → Risk Mapping → Test Strategy → Execute → AI-Assisted Quality Engineering → Human Verification → Report & Improve
See the full process →Where AI practically helps
Requirement analysis
Reading requirements and surfacing gaps before test creation begins.
Test scenario generation
Turning requirements into concrete test cases, edge cases included.
Automation assistance
Building and maintaining automated scripts, including self-healing.
Regression assistance
Flagging what's likely affected by a given change.
Defect analysis
Classifying failures and surfacing patterns across runs.
QA reporting
Summarizing results and risk for a release decision.
Risk identification
Scoring where a release is most likely to break.
See how it shows up in our services
Who it’s for
- Software product teams
- Engineering teams
- QA teams
- Teams scaling regression coverage
- Teams introducing AI into their QA workflow
AI accelerates the work. Automation expands coverage. QA engineers verify the signal.