Client background
The client is a US-headquartered Series A B2B SaaS company building a multi-tenant workflow product for mid-market services businesses. Their team of around fifteen engineers had a strong product instinct and shipped features quickly, but they had reached the stage in their growth where speed-without-safety was no longer a viable engineering posture. Their customer base had grown into the hundreds, contracts were getting larger, and every production bug now had a measurable revenue impact in churn risk.
The client approached Squash Apps for QA staff augmentation specifically — not full project delivery. They wanted senior QA engineers embedded inside their existing team, working alongside their feature engineers in the same sprint structure, but bringing the testing discipline and automation depth that a small fast-moving team had never developed on its own.
The challenge
The core problem was that test coverage was patchy and almost entirely manual. The product had grown to roughly 200 user-facing surfaces (forms, modals, dashboard widgets, billing flows) but only 12% had any automated test coverage. When the team shipped a release, the only way to know whether something had broken in a corner of the product was to wait for a customer to report it.
This had two compounding effects. First, the release cadence had slowed: engineers were spending more time manually clicking through the product after every PR rather than shipping forward. Second, founder confidence in releases was eroding — the CEO was personally testing flows the morning of every release, which was clearly unsustainable as the team grew.
A secondary technical issue: the codebase had test scaffolding from earlier attempts but no consistent patterns. Some areas used Jest with React Testing Library, some used Cypress (poorly), and an abandoned Playwright project sat half-built in a feature branch. The team needed not just more tests, but a single coherent automation strategy that could become the team's long-term testing discipline.
How we engaged
The engagement was structured as classic QA outstaffing: three Squash Apps QA engineers — one senior (8+ years), two mid-level (4–5 years) — joined the client's Slack, Linear, GitHub and staging environments on day three of the engagement. By day seven the senior QA had run a coverage audit against the live product, presented a ranked list of the eight highest-risk regression areas, and proposed Playwright + TypeScript as the unified automation stack (consolidating the abandoned scaffolds).
Daily standup ran on the client's ET schedule with one QA engineer on a partial-shift overlap. Sprint planning and retrospectives were attended by the senior QA pod lead as a full sprint participant — not as a vendor representative. From the client engineers' perspective the QA pod was indistinguishable from in-house QA hires; the only difference was that hiring three senior QA engineers locally would have taken 4–6 months and cost roughly 3× more.
Srijith reviewed the automation architecture in week one and again at the six-week milestone to make sure the patterns were durable.
What we built
Within the first six weeks the QA pod shipped:
• A consolidated Playwright + TypeScript test harness, replacing the abandoned Cypress and Playwright scaffolds. CI integration with GitHub Actions running the suite on every PR. • End-to-end test coverage for the eight highest-risk user surfaces: signup, billing checkout, subscription upgrade, invitation flow, multi-tenant data isolation, role-based access, the admin export pipeline, and the OAuth integration with the client's most-used third-party. • A flake-free smoke suite that ran in under 4 minutes — short enough to gate every PR without slowing engineering velocity. • Visual regression coverage on the top 12 dashboard views using Playwright's screenshot comparison. • Cross-browser smoke testing on BrowserStack covering Chrome, Safari, Firefox and Edge against a real-device matrix. • A written QA runbook documenting the test architecture, the bug-triage workflow, and the release-day checklist — so the client's own engineers could maintain and extend the suite if Squash Apps ever stepped away.
Beyond the automation work, the pod also acted as the team's manual QA layer for any features that were genuinely new (where writing tests against a still-changing design was wasteful). One QA engineer ran exploratory test charters against the staging environment for every release candidate.
Technical approach
Playwright was selected over Cypress for three reasons: better TypeScript ergonomics, native cross-browser support (Chrome / Safari / Firefox / Edge), and a more capable parallel-execution model that kept the full suite under 15 minutes even at 200+ tests.
The test architecture used a page-object pattern with TypeScript types shared between the test code and the application code — meaning that refactors to UI selectors or form field names in the React app surfaced as TypeScript errors in the test suite at compile time, rather than runtime test failures hours later. This single architectural decision was responsible for the eventual flake rate being under 1%.
CI was wired through GitHub Actions with two test profiles: a fast smoke suite (≤ 4 min) gating every PR, and a full regression suite (≤ 15 min) running on every merge to main and nightly. BrowserStack integration ran the smoke suite against a real-device matrix on a nightly schedule, catching mobile-Safari issues that desktop CI missed.
Test data management used database seed scripts run as part of the test setup, ensuring that every test ran against a known, reproducible baseline rather than relying on the staging database state.
Results
By week six, automated regression coverage rose from 12% to 71% — measured against a defined list of user-facing surfaces that the senior QA had compiled and the client engineering team had ratified.
The release cadence stabilised. The client went from a Friday-deploy-with-fear rhythm to a Tuesday-and-Friday deploy cadence with the smoke suite gating every PR. The CEO stopped manually testing before releases by week four.
Production incident rate dropped meaningfully: in the three months following engagement start, customer-reported defects fell by 58% compared to the prior three months. The client's customer-success team noted in their weekly review that the volume of "noticed a bug today" emails from customers had become "noticeably quieter."
The engagement transitioned from a six-week initial sprint into an ongoing QA retainer at the end of week six. Two of the three QA engineers stayed embedded long-term as the client's standing automation team; the senior pod lead transitioned to a part-time architect role, reviewing new test patterns and pairing on flaky-test triage.
This case study is a representative pattern for the QA outstaffing engagements Squash Apps runs: senior QA engineers embedded inside a fast-moving product team, bringing a coherent automation strategy and the discipline to make it stick. For B2B SaaS, FinTech and HealthTech companies in the US and UAE whose engineering velocity is being throttled by manual-QA fatigue, this is exactly the engagement shape we recommend.