Client background
The client is a global leader in housing management technology, serving thousands of clients worldwide through a proprietary property management system. With 150+ engineers across five delivery streams, the organization operates at significant scale and maintains a complex, high-availability software environment with continuous release pressure.
Business challenge
The client aimed to modernize its QA processes, accelerate testing workflows, and raise test coverage across a large and growing codebase while sustaining delivery momentum under product and incident pressure. With no shared measurement framework in place, there was no agreed baseline against which to assess the current QA state or demonstrate improvement over time. AI tooling adoption was in its early stages across the engineering teams. The client sought a structured approach to embedding AI-assisted quality practices into its software testing lifecycle at scale, without disrupting existing delivery commitments.
Implementation
Together with the client, the delivery team conducted QA AI modernization across the engineering teams, using a staged adoption framework, with a focus on eliminating bottlenecks and building a resilient software testing lifecycle. In particular, the delivery team assisted the client with the following:
Workflow acceleration. The delivery team identified and accelerated 15 workflows across the engineering teams, directly reducing manual effort in regression testing, test case generation, and incident investigation.
SDLC productivity integration. AI-assisted practices were embedded in the client's software development lifecycle, stabilizing the team over time and creating a sustainable delivery pace across all five work streams.
Quality gate implementation. The delivery team introduced new quality gates into the client's software testing lifecycle, replacing ad hoc manual review checkpoints with automated checks embedded directly in the delivery pipeline, raising the bar for confidence in code releases and enabling daily releases without compromising quality.
Test coverage expansion. The delivery team helped drive a significant increase in test coverage across more than 300 repositories, supported by a reduction in test case generation time.
Throughout the engagement, the delivery team addressed key adoption barriers, such as developer skepticism, implementing security guardrails in an enterprise environment, and creating an effective AI context for a complex legacy codebase.
Value delivered
Test coverage improved from 55% to 89% across more than 300 repositories (+34%), directly contributing to a 60% reduction in escaped bugs; the clearest measure of release confidence the team had ever had;
Team velocity nearly doubled, from 1.8 to 3.5 pull requests per developer per week (+94%), and incident investigation time fell from four hours to 30 minutes (−87.5%);
Average tool effectiveness rose from 3% to 75% (a 72-point increase), while adoption reached 64% at its peak, up from 15%, confirming that structured enablement drove the results;
Regression testing time reduced from 3 days to 4 hours (-94%), and test case generation time dropped from 3 hours to 15 minutes (-92%).
Expertise: AI development services.
