Lower the cost of coverage
Let AI draft test cases and automation so your team spends fewer hours on repetitive setup and scripting.
AI-powered end-to-end testing
More testing.
Less effort.
Lower cost.
Turn requirements and manual test cases into reliable automation. Let AI assist with design, scripting and repair—so your team can deliver more coverage with less repetitive work.
What would 80% less repetitive work be worth?
Illustrative scenario. Measure your actual results in a pilot.
From intent to evidence
Your expertise.
AI for the groundwork.
Design the right coverageRequirements → AI test cases
AI assisted↳ Your team reviews and approves
Build reusable automationApproved cases → editable scripts
AI assistedRun. Inspect. Improve.Test execution → evidence & optional AI repair
ExecutionReview before pipeline reuseApproval stays separate from the test result.
Designed for a leaner QA operationAI-assisted creationHuman reviewRepeatable executionVisible evidence
The business case
Grow coverage.
Get engineering time back.
Reduce the routine work behind every release. Give your existing team more capacity for the testing that needs human judgment.
Automate sooner
Turn requirements and existing test cases into executable tests, with review built into the workflow.
Spend less time maintaining
Use failure evidence and optional AI repair to investigate broken tests and reduce repeat debugging.
Make informed release decisions
Review run results, screenshots and logs. Approve scripts for reuse while keeping failures visible.
Where the effort goes
Fewer manual handoffs.
One connected workflow.
AI assists with time-consuming creation and repair. Automated runners execute the tests. People define quality and approve what moves forward.
A typical manual-heavy workflow
Your team carries every step.
- Interpret requirements
- Write and review test cases
- Code scripts and helpers
- Run tests and gather evidence
- Investigate and maintain scripts
- Repeat for the next regression
Engineering time accumulates across creation, coordination and maintenance.
With AutoQAin
Automate the work around your expertise.
- 01Bring requirements or existing casesYour application context becomes reusable input.
- 02AI designs and generatesYour team reviews coverage before automation.
- 03Execute and investigatePlaywright runs tests; AI can assist diagnosis and repair.
- 04Review, approve and reuseSchedule approved scripts and keep run evidence visible.
Less repetitive work. More capacity for exploratory testing, risk and release decisions.
What changes for your team
A smaller manual workload.
A clearer path to release.
Scroll the table sideways to compare approaches →
| Work to be done | Manual-heavy automation | With AutoQAin |
|---|---|---|
| Test design | Write and refine each case by hand. | AI drafts or optimizes cases from your application context. Your team reviews coverage. |
| Automation development | Translate approved cases into scripts and helpers. | Generate editable automation from approved cases, with reusable helpers. |
| Debugging & maintenance | Reproduce failures, inspect changes and patch scripts. | Inspect captured evidence; use optional AI diagnosis and repair, then rerun. |
| Regression delivery | Coordinate scripts, repeat runs and assemble reports. | Schedule approved scripts in pipelines and review execution evidence in one place. |
| Team capacity | More coverage adds repetitive engineering work. | Shift routine work to automation so your team can focus on risk, review and exploration. |
Build your business case
What would less
repetitive work be worth?
Explore scenarios with up to 80% less effort on repetitive tasks. Adjust the assumptions to fit your team.
Illustrative planning model. This is not a measured AutoQAin result or a savings guarantee. Validate the reduction in a pilot.
Your illustrative scenario
Annual capacity value recovered$60,00025% of total QA effort could be redirected.
Current annual team cost$240,000
Value of remaining QA effort$180,000
Net annual modeled valueAdd your budget
Recovered capacity is time your team can redeploy. Payroll falls only if staffing or contractor spend changes. Ongoing review and other QA work remain.
Validate this in a pilotHow the estimate works
Annual team cost = people × monthly cost × 12. Recovered capacity value = annual team cost × repetitive-work share × assumed reduction. Remaining effort value = annual team cost − recovered capacity value. Net modeled value subtracts your annual platform and rollout budget from recovered capacity value and may be negative. Currency changes the unit only; no exchange-rate conversion is applied.
The model excludes release revenue, defect avoidance and other speculative benefits. Actual results depend on application complexity, coverage, review effort, maintenance and adoption.
Start with one business-critical journey
Prove the value.
Then grow your coverage.
Bring an existing test flow. Compare the time to create, maintain and rerun it—and review the evidence with your team.
A focused pilot. A measurable baseline. A decision grounded in your workflow.