ONGOING · 4 YEARSSAASDEDICATED TEAM

Building a QA Practice from Scratch for a Case Management SaaS Platform

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.

CRITICAL FUNCTIONALITY COVERED BY AUTOMATION

Manual only85%

The engagement

PRODUCT: Cloud-based case management system
USED BY: Professionals in rehabilitation, therapy and insurance
TEAM: Three dedicated QA engineers
DURATION: Four years and ongoing
STARTING POINT: No QA process, manual-only testing

WHERE WE STARTED

Five gaps, no process

No QA process
Testing was ad hoc, so results were inconsistent and defects were missed.
Limited automation
Entirely manual testing was slow and prone to human error.
High defect leakage
Many issues were found only after deployment, hurting reliability and user satisfaction.
Scalability concerns
Without structure, QA could not keep pace with growing load and user numbers.
No view of QA return
Nothing tracked what QA delivered, so the investment was hard to justify.

THE SETUP

Setting up QA from the ground up

STEP 1
Test strategy and plans
Defined how the product would be tested, then designed manual and automation frameworks to match.
STEP 2
Manual testing
Functional, regression and user acceptance testing, with test cases written and executed to a standard.
STEP 3
Automation framework
A scalable Selenium and TestNG framework; the automated regression suite covers 85% of critical functionality.

STEP 4
Defect management
Tracking and reporting in JIRA, so QA and development talk about the same defects in the same place.
STEP 5
Continuous integration
Automation connected to Jenkins, with daily smoke and regression runs protecting code quality.

RESULTS

Measured improvements

40%
REDUCTION IN DEFECT LEAKAGE
Achieved over the first year.
90%
TEST COVERAGE
Manual and automated testing combined.
30%
SHORTER TEST CYCLES
Through automation.
50%
INCREASE IN CLIENT SATISFACTION
Credited to more stable releases.

ROI AND BENEFITS

Return on the QA investment

Cost savings ~20 HRS / RELEASE
Automation removed roughly 20 hours of effort from every release cycle.
Faster time to market 25% FASTER
Release cycles ran 25% faster, so changes reached users sooner.
Actionable reporting
Detailed test reports and metrics gave the team what it needed to make release decisions.
Stakeholder confidence
A reliable QA process raised trust in release readiness.

AI-DRIVEN QA

How AI-driven QA builds on this foundation

Once a structured practice exists, AI-assisted quality engineering has solid ground to build on. Every AI suggestion is checked by a QA engineer before it affects a release.

Smarter regression prioritisation
AI weighs recent code changes and past defects to run the highest-risk tests first.
Test maintenance assistance
As screens evolve, AI suggests updates to Selenium locators and flags tests likely to be flaky.
Coverage gap analysis
AI compares requirements, test cases and defects to point out scenarios no test covers yet.
Release-readiness insight
AI summarises test and defect trends, and a QA engineer reviews them before a release decision.