Illustrative example — not a client result. This is a hypothetical scenario written to show our approach. The figures are example targets, not results achieved for a client.
The scenario
Imagine a B2B SaaS platform with 60 engineers spending two days of manual testing per release and still producing around nine production hotfixes a month. Nobody can state what is covered, so release scope negotiations are guesswork.
What we would do
- Write a test strategy and map the journeys that produce revenue or support load.
- Build a Playwright regression suite in the client repository, running on every pull request.
- Add k6 load tests with budgets that fail the build rather than warn.
- Introduce defect templates requiring reproduction steps and evidence, to cut triage time.
Example targets after two quarters
| Example metric | Example starting point | Example target |
|---|---|---|
| Release testing time | 2 days | 35 minutes |
| Production hotfixes per month | 9 | 1 |
| Automated journey coverage | 0% | 84% |
| Escaped defects per release | 14 | 2 |
What would matter most
Ordering the automation by cost of failure rather than ease of implementation. Automating the journeys responsible for most past incidents first moves the defect curve before the coverage number looks impressive.
Case studyIllustrative exampleQASaaS