EnGenious
Sage Company Case Study

Zero-to-Production QA in 3 Months — 97% Faster Regression, 53/53 Critical Flows Automated

 

At-a-Glance

 

  • Coverage: 53/53 P0 revenue-critical flows automated (100%) 

  • Regression Time: 16 h → 30 min (−97%) 

  • Velocity: 3.2× average improvement (peak 7.5 tests/week) 

  • Stability: 95% local pass rate; ~85% CI (improving through Q1 2026) 

  • Levers: API-first validation (85% API / 15% UI), AI-assisted development (~60% of framework; ~50% of tests) 

  • Timeline & Team: Zero → production-ready Oct–Dec 2025 by a lean 2-person team 

  • Stack: Playwright (TypeScript), CI/CD, Qase.io, Cursor AI, Playwright MCP 

 

1 img Sage At a Glance (1)

 

The Situation

 
SageCompany is an enterprise SaaS platform (formation, compliance, payments, banking, CRM). Testing was largely manual with a handful of unstable UI tests, no API-level validation, and frequent UI changes—making tests brittle, slow to build, and expensive to maintain. Third-party dependencies (Stripe, Salesforce) introduced flakiness and long debug cycles. There was no structured framework and no AI in the toolchain.

 

Goals & Objectives

 
Stand up a production-ready Playwright + TypeScript framework from scratch

  • Automate 53 P0: end-to-end flows tied to revenue 

  • Cut regression from 1–2 days to minutes 

  • Stabilize around Stripe and Salesforce integrations 

  • Increase delivery velocity via AI-assisted development 

 

2 img Sage Challenges (1)

 

What We Did (challenge → lever → effect)

 

1. No automation foundation → Framework-first, API-first architecture

  • Built a clean Playwright/TS framework with API abstraction, component library, and shared fixtures/utilities

  • Validated via API for ~85% of checks; kept UI to the true user path (~15%)

  • Effect: Fast, stable tests that don’t break on cosmetic UI changes

2. Complex third-party flows → Mocking & direct API verification

  • Stabilized Stripe flows with API mocks; verified backend state via platform APIs

  • Tamed Salesforce sync with direct validations and contract checks

  • Effect: Lower flake; shorter debug time on the hardest integrations

3. Tight timeline, lean team → AI-assisted delivery

  • ~60% of framework and ~50% of tests AI-assisted (Cursor, Copilot, Playwright MCP)

  • Effect: 3.2× average velocity; 7.5 tests/week peak without adding headcount

4. Weekly UI change → Reusable components & stable selectors

  • Componentized UI, standardized patterns, deterministic locators

  • Effect: Maintainability up; fewer locator breakages

5. Slow feedback → CI pipeline with quality gates

  • CI orchestration, artifacts, dashboards; improving CI stability (→ 85%+)

  • Effect: Minutes-level feedback; visible trends and faster merges

 

In three months, SageCompany went from zero usable automation to a production-ready platform that ships faster, with dramatically less manual effort—and protects every critical flow.

3 img Sage Partnership (1)

 

Timeline & Team

  • Oct: Framework & architecture; API abstraction; component library

  • Nov: Acceleration; API-first coverage; AI-assisted authoring standard

  • Dec: All 53 P0 flows automated; stabilization; CI dashboards live

  • Team: 1 QA automation engineer (full-time) + part-time delivery management

  • Engagement model: Managed Delivery with weekly demos, two-week cadence, one shared backlog

 

 

4 img Sage Outcomes (1)

Explore Our QA Services 

 

Why It Worked

 

  • Architecture first, migration second—no lift-and-shift of brittle patterns

  • AI where it matters—authoring, selectors, and reviews; humans keep judgment

  • Contracts + components—stability across third-party APIs and complex UI

  • Transparent CI—faster feedback and a single source of truth for quality

“It’s rare to find a partner who blends real technical depth with an equally sharp grasp of our business goals.”

Sage Company Team

Looking Ahead

 

Next, we’ll broaden coverage where it matters—expanding component-level and API suites across high-impact flows. We’ll scale AI-driven test generation as the default path for new scenarios, while increasing CI parallelization and adding test-impact analysis to cut wall-clock times. Automation will wire deeper into deployment gates (pre-merge, pre-prod, post-deploy smoke) to harden go/no-go decisions. Target outcomes: ≥90–95% CI stability, <10-minute build verification, and regression that stays measured in minutes, not days.

 

 

API-first automation that protects every critical flow.
What should we accelerate next?

 
Let’s redefine what quality can look like for your team.

 

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