Wondr Health scaled QA coverage to 80% and cut regression time from days to minutes

From Manual Regression to Push-Button QA

Before Spur, QA at Wondr Health was heavily constrained by legacy infrastructure and developer dependencies.

Automation was slow to build and difficult to maintain, often requiring engineers to modify older code just to support testing. This created constant prioritization friction across teams and delayed automation efforts. Regression testing, when it happened, could take up to three days and required teams to pause other work and coordinate across functions.

As a result, testing was inconsistent. Smoke tests were not reliably run on every release, and regression was treated as a disruptive event rather than part of a continuous workflow.

With Spur, that dynamic changed.

Regression testing is now fast, repeatable, and built into the release process. What previously required days of effort can now be completed in about 30 minutes, allowing the team to run tests consistently before every release.

“The time saved is insane. What takes Spur about 30 minutes would have taken us half a day.”

Instead of scrambling around release cycles, QA is now part of a predictable system. The team runs regression ahead of releases, monitors early in the week, and deploys on a steady cadence, with a long-term goal of fully automated, push-button deployments.

Spur also enabled rapid expansion in automation coverage.

The team moved from roughly 30% automation to 75–80% across key frontend areas, reaching a point where they feel confident in their baseline coverage and can focus on edge cases rather than rebuilding core flows.

At the same time, QA became more reliable across environments.

Rather than catching only feature-level issues, the team now consistently identifies higher-level problems such as environment misconfigurations and DevOps gaps earlier in the process, reducing risk before deployment.

From Reactive Testing to Strategic QA

With regression and smoke testing automated, the QA team has shifted from repetitive execution to higher-value work.

They are now:

• Expanding support across more teams and systems
• Investing in performance testing initiatives
• Validating entitlement and access workflows
• Extending testing into marketing systems to verify user communications
• Building backend checks, such as validating data flows into AWS

Work that was previously difficult to prioritize due to time constraints is now a core part of their QA strategy.

Spur as a Catalyst for AI Adoption

Spur also became a practical entry point for AI within the organization.

While there were mixed feelings about AI across teams, Spur provided a concrete example of its value. It reduced repetitive work without eliminating roles and created space for engineers and QA specialists to focus on more meaningful problems.

“It made people’s jobs easier. No one was let go, and it created space to work on more interesting problems.”

This helped shift internal perception toward seeing AI as a tool to build better software, not replace people.

Looking Ahead

Wondr Health is continuing to expand Spur across the full product lifecycle.

The team is working toward deeper end-to-end coverage across the full user journey, moving from scheduled test runs to event-driven automation, and pushing testing earlier into feature development.

“If we can move this earlier and catch issues sooner, that’s where the real impact is.”

Their goal is to scale QA alongside development velocity without introducing new bottlenecks.

With Spur, that foundation is already in place.