Transforming QA Infrastructure to Reduce Prior Authorization Delays by 60% 

About the client 

Our client is a US-based healthcare technology leader with over 75 years of expertise in utilization management. Their flagship platform automates prior authorization workflows across multiple payers, supporting over 1 million lives with timely, compliant, and efficient care delivery. 

Business Challenge 

Evolving platform demands and stagnant testing capabilities 

After 13 years in the market, the client’s SaaS product had evolved into a complex solution operating on a single codebase that supports prior authorization workflows for multiple pharmacy benefit managers (PBMs) and health plans. 

With 8–9 major releases a year and regular patch cycles, the delivery team was moving fast, but QA couldn’t keep up. 

The test suite had grown to 8000+ test cases, with about 1000 more added every month. However, test execution lacked focus and orchestration. Automation coverage hovered at 40–50%, with low ROI. Just 10–20% of test cases were run before releases, largely due to maintenance issues, test flakiness, and scattered tooling. 

Manual testing remained a challenge, creating bottlenecks and allowing critical defects to escape into production. Continuous integration and deployment practices were not in place, and nightly regression runs were not feasible with the current setup. With payer-specific variations growing, the QA function had become more reactive than preventive. 

Leadership’s goals were clear: 

• Move from patchwork automation to reliable, continuous validation 

• Scale regression coverage to 100% 

• Significantly reduce manual QA load 

• Set up automated testing that runs throughout development and deployment 

• Deliver zero-defect production quality with confidence 

The challenge wasn’t test automation volume. It was test automation value. 

The Zuci Solution 

Transforming fragmented QA into scalable, intelligent automation-first capabilities 

Conducted a comprehensive QA gap analysis with GOAL approach 

We began with our consulting-led GOAL (Gauge, Organize, Align, Lead) assessment to identify structural QA gaps, from brittle scripts to test duplication and lack of traceability. This enabled a tailored transformation plan aligned with client’s business velocity and compliance requirements. 

Built a contextual, scalable automation framework 

We introduced TAPE, a scalable AI-powered automation framework tailored for testing complexities. Applying the inverted testing pyramid, we focused on high-value validations especially at the unit and API level to improve test stability, reduce maintenance overhead, and catch defects earlier. 

Applied in-sprint automation to eliminate lag and improve feedback cycles 

Zuci introduced in-sprint automation practices, enabling QA to begin test development alongside feature implementation rather than waiting for the UI to stabilize. This reduced the typical lag between code completion and test readiness, helped enable nightly regression runs, and gave developers faster, more reliable feedback within the same sprint. 

Optimized the test suite using data-driven prioritization 

With 1000+ test cases growing each month, we helped rationalize and reorganize the automation suite. We applied 80/20 principle to isolate high-impact flows, reduced redundancy, standardized data sets, and increased execution reliability across complex client environments. 

Introduced continuous testing practices 

We integrated the automation suite into Jenkins pipelines, enabling regular and scheduled executions, paving the way for future CI/CD expansion. This brought in predictability and early defect detection across releases and patch cycles. 

Aligned frameworks with regulatory and product evolution goals 
Our approach wasn’t just about automation velocity. It was about sustainable quality engineering. The frameworks were built to support CMS compliance, easily scale from web to mobile, and evolve with the client’s product roadmap. 

Engineers first, testers by choice 
At the core of this transformation was a mix of AI-enabled SDETs and domain SMEs. They brought full-stack QA skills, acted like product owners, and thought like engineers. This enabled scalable QA processes that didn’t just serve today’s releases, but prepared client for future growth.