A leading healthcare SaaS provider serving health plans nationwide was facing a familiar enterprise challenge: quality had become a bottleneck to delivery velocity. As engineering teams scaled and product complexity increased, manual testing processes slowed releases, allowed defects to escape into production, and limited the organization’s ability to innovate with confidence.

Launch Consulting partnered with the organization to transform how software quality was designed, validated, and scaled. Through a structured test automation and quality engineering program grounded in AI-SDLC principles, Launch delivered an integrated validation framework that reduced manual effort by 80% and leveraged AI to enable teams to validate change efficiently at every stage of the software lifecycle.

The Challenge

With a new technology leadership team in place, the organization set ambitious goals: an 80% reduction in both defect escapes and manual testing effort. However, existing quality practices were not positioned to support that level of transformation.

The reality on the ground:

• Over 10,000 hours per month spent on manual QA across three product lines

• Inconsistent testing practices across teams

• A deeply embedded manual-first culture within sprint processes

• Quality issues routinely escaping into production

• Previous automation initiatives that failed to deliver sustained results

The challenge was not a lack of tools. It was the absence of the foundational infrastructure, patterns, and engineering discipline required to make automation effective, scalable, and repeatable.

The Solution

Launch designed and executed a comprehensive test automation initiative that applied AI-SDLC principles to transform how the organization validates software across its entire product portfolio.

Quality Engineering Driven by AI-SDLC

Rather than treating automation as a bolt-on activity, Launch embedded AI-native quality practices directly into development workflows:

• Shift-left quality moved validation earlier in the lifecycle, enabling developers to validate their own data in 30 minutes instead of 3–4 hours

• Automation-first standards established automation as the default approach, reserving manual testing for exploratory and edge-case scenarios

• AI-assisted test generation introduced repeatable, test-driven patterns using AI-enabled tooling, accelerating test creation by 4×

• AI-generated test data enabled data creation at design time, build time, or run time—eliminating one of the most persistent bottlenecks in enterprise testing

Integrated Validation Framework

Launch delivered an integrated validation framework that enables teams to validate change efficiently before, during, and after release:

• Test Data Management (TDM): AI agents automatically identify or generate test data at the point of need – design, build, or run time

• Production Transaction Testing (PTT): A/B validation models verify changes prior to release, with AI-driven filtering reducing test datasets by 40–50% while maintaining coverage

• Post-Deployment Verification (PDV): Automated validation in production ensures changes perform as expected after deployment

Together, these capabilities function as a continuous validation pipeline rather than isolated testing activities.

Embedded Enablement

Launch engineers partnered directly with internal teams across all three of the organization’s product lines to deliver a full capability transfer:

• Automation frameworks, patterns, and documentation delivered to every team

• AI-enabled tooling introduced and operationalized

• Knowledge transfer embedded into every workstream

The goal was long-term ownership of the capability, not dependency on external resources.

Looking Ahead

With a proven quality infrastructure and AI-SDLC patterns in place, the organization is positioned to extend these capabilities across additional products, teams, and workflows. The integrated validation framework scales naturally, expanding test data coverage, extending post-deployment verification, and replicating AI-assisted patterns across the portfolio.

By solving the quality foundation first, the organization has created the conditions for broader AI-native engineering adoption. Now AI supports testing, development, deployment, and ongoing optimization.