Replaced a slow, unreliable manual QA process with a maintainable automated test suite for a fast-growing analytics SaaS platform — cutting regression testing from 4 days to 3 hours.
Northbeam Analytics was releasing new features every two weeks, but each release required four full days of manual regression testing across their growing platform — testing that was inconsistent between QA staff, frequently missed edge cases, and was the single biggest bottleneck in their release cycle. Two significant bugs had already reached production despite the manual process.
Digivance designed a risk-based test automation strategy, then built a maintainable Playwright-based automated suite covering the platform's highest-risk user flows and API surface, fully integrated into their GitHub Actions CI/CD pipeline. Regression testing that took four days now completes in under three hours, automatically, on every release candidate.
Every two-week release required four full days of manual regression testing, consistently becoming the critical-path bottleneck that delayed releases or forced QA to work weekends.
Different QA staff tested different things depending on who was assigned that week, meaning coverage was inconsistent and important edge cases were regularly missed.
Two significant defects had reached production in the six months before Digivance was engaged, each requiring an emergency hotfix and damaging customer trust in a still-growing product.
Testing happened entirely outside the deployment pipeline as a separate manual phase, meaning there was no automated quality gate before code reached production.
As the platform's feature surface grew, the manual testing burden grew faster than the QA team could scale headcount to match, threatening to slow the company's release cadence permanently.
There was no risk-based prioritisation of what to test — QA effort was spread evenly across all features regardless of actual business risk or usage frequency.
Before writing any automation, we mapped Northbeam's platform by business risk and usage frequency, identifying the roughly 20% of user flows responsible for 80% of real customer impact — and focused automation there first.
Built a Playwright-based automation framework using stable selector strategies and proper abstraction patterns, specifically designed to avoid the flaky, high-maintenance automation that causes teams to abandon test suites.
Added API-level testing beneath the UI layer for faster, more reliable coverage of business logic — catching backend regressions in seconds rather than waiting for slower, more brittle UI tests.
Implemented visual regression testing for the platform's most visually complex dashboards, catching unintended layout and rendering issues that functional tests alone would miss.
Integrated the automated suite directly into GitHub Actions, so every pull request triggers relevant tests automatically — creating a genuine quality gate before code can reach production.
Trained Northbeam's QA team to write and maintain new automated tests themselves going forward, ensuring the automation investment keeps growing after our engagement rather than stagnating.
Completed risk-based test strategy and stood up the core Playwright automation framework, with the first 15 highest-risk user flows automated and passing reliably in CI.
Expanded automated coverage to 85% of identified critical user flows, plus API-layer and visual regression testing for the platform's core dashboards.
Full suite integrated into GitHub Actions as a required release gate. Regression testing time dropped from 4 days to 3 hours, with post-release defects down 70% in the first quarter of operation.
"Our QA team used to spend every release week buried in manual regression testing, and bugs still slipped through. Digivance built us something we can actually maintain ourselves — three hours instead of four days, and we've shipped three times as often since, with fewer incidents, not more."
Let's build a test automation suite that actually scales with your release cadence.