See how Coherent Solutions applied AI in real-world QA delivery to increase documentation speed, expand test coverage, and reduce rework across projects.
The client is a SaaS provider serving a specialized vertical, offering websites, e-commerce, payments, and management software to businesses in the death-care industry (funeral, cremation, and cemetery services). Coherent Solutions became its QA partner in 2021, embedding a team of up to 10 across QA, QA automation, DevOps, front end, project management, .NET, and Java.
Coherent stood up a unified QA governance model across the client's diversified product landscape in 2 to 4 months, then rebuilt automation around a BDD framework that reached 100% smoke test coverage and 80% regression coverage across every product, with regression automation wired directly into CI/CD. The engagement scaled with the client from 2021 into 2024, then wound down as the client's own team took over quality ownership.
The challenge
The client ran a diversified set of products and project teams without a shared QA strategy. Each team tested, documented, and released differently, which left automation coverage fragmented and process ownership unclear. That inconsistency created real risk. Releases moved at different levels of confidence across products, regression coverage varied team to team, and there was no common language for QA reporting across a distributed organization.
The client didn't need another team added to one product. It needed a governance model that could scale consistently across all of them, standardizing documentation, automation, and communication without slowing any single team down.
Our approach
Coherent treated the engagement as an operating model to build, not a set of test scripts to write. The team started with foundations, including standardized test strategies and test plans, a shared Definition of Ready and Definition of Done, and onboarding materials that let new QA and delivery staff ramp against one set of practices instead of several. A Confluence knowledge base and a Slack-based QA hub gave every team a common home for documentation and coordination, backed by structured QA meetings and KPI-based reporting so leadership could track quality trends across products rather than per-team snapshots. Training programs for both QA and development teams kept those standards in active use.
On automation, the team rebuilt test coverage around a BDD approach using Nightwatch.js and Gherkin, so scenarios could be written and read by QA and non-QA stakeholders alike. Automated runs were wired into GitHub Actions and CI agents, with Zephyr Scale handling test cycle management and reporting. Coverage expanded from web to mobile and across browsers and platforms, with automation embedded directly into each Scrum team's sprint workflow instead of run as a separate, after-the-fact activity.
The engagement scaled as the client's needs grew. QA squads expanded to cover six teams, .NET capacity was added mid-partnership, and a CloudOps phase supported infrastructure scaling later on. Throughout, the goal was maturity rather than permanence. As governance and automation matured, Coherent worked toward a planned handoff instead of an open-ended engagement.
The solution
Unified QA governance was in place across the client's product landscape within 2 to 4 months of engagement start. From there, the BDD automation framework matured in stages, with smoke test automation reaching 100% coverage across all products and regression automation reaching 80%, integrated into CI/CD pipelines so quality gates ran automatically with every release.
By 2023, in-sprint automation was standard practice across QA squads, and KPI-based reporting gave leadership a consistent view of release readiness across every product. Manual QA effort dropped as automation absorbed routine coverage, freeing engineers to focus on judgment calls rather than repetitive execution. As the model matured, the client began transitioning quality ownership in-house, completing a full switch to a local QA model in mid-2024, with Coherent's remaining CloudOps support rolling off by the end of that year.
The impact
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Unified QA governance implemented across a diversified product landscape in 2 to 4 months
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100% of manual test scenarios covered for key application functionality, including new features
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100% of smoke tests automated across all products
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80% of regression suites automated across all products, integrated into CI/CD pipelines
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Automation coverage scaled alongside new feature delivery instead of lagging behind it
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Cross-team synchronization improved through structured release coordination and standardized communication
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QA operating model matured enough for the client to fully own quality in-house by mid-2024
What's next
This engagement is a working model for what QA governance looks like when it's built for scale instead of for a single team, with standardized documentation and Definition of Ready/Done criteria that travel across products, automation that grows in step with delivery instead of trailing it, and a deliberate path toward client ownership instead of long-term dependency. For SaaS organizations running multiple products through distributed teams, it shows how to turn fragmented QA into a governed, CI/CD-integrated practice, and how to know when the job is done.
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