How Fyscal Technologies Cut Test Execution Time by 60% and Reclaimed 600+ Engineering Hours Every Month

Overview

FyscalTech powers a large-scale fintech ecosystem built on a distributed, microservices-driven architecture, with a combined user base of 150M+ and 60M+ active users across its core platforms.

In a category where even small errors can affect financial accuracy, user trust, and regulatory compliance, predictable system behavior, especially during peak periods, is a top engineering priority.

Software quality, in this context, isn't a feature; it's the foundation of customer trust, financial correctness, and compliance readiness. Beyond functional correctness, the team also emphasises traceability and reproducibility, ensuring system behavior can be validated, explained, and audited across environments when required.

But as the platform scaled, the testing infrastructure couldn't keep up.

When Regression Cycles Stretched to 10 Hours, Release Decisions Got Stuck

As FyscalTech's systems scaled, regression cycles extended to 6–10 hours, creating long delays in feedback loops. Automation was already in place, but flakiness and inconsistent execution signals made test results harder to trust. Even when tests passed, there was hesitation in release decisions, and over time, that lag in feedback started affecting how quickly the team could move.

This became sharper during major releases and high-traffic periods, when the old system struggled to provide timely feedback. The lag caused delays in deployment decisions and lowered confidence in system stability, even when everything was actually working correctly.

The downstream impact on engineering was significant:

• Bugs were getting found late in staging or production instead of in early testing.
• Regression cycles kept growing; even a small change required re-testing everything.
• Tangled, unclear code made it difficult to write reliable test cases or know what "correct" looked like.
• Sign-offs were happening with low confidence, and releases were going out, hoping nothing breaks.
• Engineers were spending more time debugging than testing, chasing root causes instead of validating features.

Keeping the suite running was just as demanding. Approximately 35–40% of testing effort went into stabilising suites, chasing flaky failures, and managing execution environments, time that should have gone into building new validation coverage.

In a regulated environment, delayed or ambiguous validation signals also introduce friction with audit confidence and release predictability.

The team needed a fundamentally different testing foundation.

What FyscalTech Was Looking For

When the team started evaluating new solutions, the must-haves were clear: faster test execution without losing consistency, fewer flaky results, clear insight into why tests fail, smooth integration with existing CI/CD pipelines and microservices, and AI-assisted insights the team could actually trust and act on.

Two things made TestMu AI stand out:

1. Speed, through HyperExecute. Parallel execution cut wait times, sped up feedback, and shortened release cycles.
2. Clarity, through KaneAI. It didn't just flag failures; it explained them, surfacing readable insights instead of leaving engineers to dig through raw logs.

How HyperExecute Compressed Regression From 10 Hours to Under 1

Time-to-feedback was the constraint that mattered most. Before HyperExecute, regression took 6–10 hours. By the time results came back, the team had already moved on to other work, and slow feedback meant delayed releases.

With HyperExecute, the same regression suite now finishes in under 1 hour. Hundreds of tests run in parallel, and results land while the work is still fresh in the engineers' minds.

Practically, that compression changed how the team operates. Full regression can now be run multiple times a day if needed. The feedback loop for developers is dramatically tighter. And release decisions are no longer held up by testing.

Onboarding was straightforward. There were no test rewrites and no workflow changes; the team was up and running quickly, improving execution without disrupting anything that was already working.

Even more notably, regression cycles that once took 6–10 hours now complete in under an hour, a reduction of roughly 85–90% in execution time and the team saw substantial gains from the very first rollout, running on just 2 devices in parallel before scaling up further.

How KaneAI's Agentic Approach Reshaped Test Creation and Debugging

KaneAI has become the backbone of the team's QE workflow, reshaping how software validation is done day-to-day. It takes on two parts of the workflow that used to drain hours: writing tests and making sense of why they fail.

• Natural-language-based test automation. Engineers can express test intent more directly, reducing the overhead of writing and maintaining boilerplate code. Test creation is 60% faster, and the team can move from idea to validation in less time.
• Contextual, interpretable failure insights. KaneAI helps engineers understand why a failure occurred and correlate it with actual system behaviour, rather than forcing them to comb through raw logs.

Alongside KaneAI, the team uses HyperExecute for large-scale parallel test execution and CI/CD integrations for continuous validation pipelines.

A practical workflow shift came from a module-based approach the team adopted during onboarding: packaging common steps and reusing them across flows. That single change reduced duplication, made test creation easier, and made the entire workflow smoother to manage.

Faster Releases, Reclaimed Hours, Trusted Quality

For FyscalTech, this was effectively a fresh start on automation rather than a migration of an established setup. Flow prioritisation across critical journeys, bill payments, onboarding, money transfer, and others was handled internally by the team.

The team now runs 70 test cases in 1 hour 15 minutes, down from cycles that previously stretched across hours. Overall execution time dropped ~60%, enabling same-day validation and freeing up time for higher-value testing.

600–700 engineering hours saved per month. Effort that used to go into test maintenance, debugging inconsistent failures, and managing execution environments has been reclaimed, contributing to a ~30–35% increase in productive engineering throughput within QA and validation workflows.

Broader coverage, fewer escaped defects. Test cycles are faster and more predictable, and coverage has improved across more devices and environments. That has translated into catching more issues early and reducing escaped defects — because reliable tests catch real bugs. Previously, defects weren't escaping due to a lack of testing, but because the team couldn't fully trust what the tests were telling them. That trust gap is closed now.

Audit-ready by design. Logs, execution history, and failure details are all available, keeping things traceable and audit-ready. The App Automation section in TestMu AI has become a key debugging tool when the team needs to determine whether a failure originated from the app itself or from the platform, and real devices are used to validate device-specific issues.

Ready to improve your testing? Schedule a demo to see how TestMu AI can help your team achieve similar results.