Enterprise QE at Full Depth
1,000+ automated tests and end-to-end pod ownership. How a multi-year embedded QE engagement built a complete quality practice from zero.
Quality engineering for a leadership development platform.
The client is a leadership development and coaching platform designed to unlock the potential of individuals, teams, and organisations. It connects leadership growth directly to business performance through a combination of expert human coaching, a proprietary AI coaching agent, and organisational intelligence — helping companies lead through transformation, AI adoption, restructuring, and sustained resilience. Trusted by several Fortune 500 enterprises and leading consumer brands, the platform delivers measurable coaching programmes aligned to organisational goals — serving HR teams, leadership, and coaching professionals across enterprise clients globally. The platform spans a complex multi-service architecture — meeting services, assessment services, integration services, a RAG-powered AI interview system, video/audio interview workflows, and deep accessibility requirements — making quality engineering a critical enabler of platform reliability and growth.
Starting from zero, the team built everything — and earned trust from the C-suite down to customer support.
The numbers.
AUTOMATED TEST CASES
AUTOMATION COVERAGE
MANUAL EFFORT REDUCED
C-SUITE APPRECIATIONS
Build everything. Own everything.
This was a deeply complex, long-running QE engagement that required building a complete quality engineering practice from nothing — across a fast-moving platform with frequent releases, major UI overhauls, a multi-service architecture, AI-integrated features, and strict accessibility requirements.
No QE Process or Documentation
Zero QA infrastructure to start: no test cases, no feature matrix, no RTM, no documented process — while the platform kept shipping weekly. Every foundation had to be built live, without slowing the release train.
Rapid Development & Continuous UI Revamps
Frequent releases and major interface overhauls threatened to invalidate large portions of the suite overnight. Every revamp meant a race to re-stabilise coverage before the next release window closed.
Complex Multi-Service Architecture
Assessment, meeting, and integration services each demanded validation at the UI, API, job, and database layer. A single UI change to the meeting service meant re-validating Lambda jobs and database state end-to-end.
Major Dependency Upgrades
A single Cypress and dependency upgrade broke the majority of automation at once — nearly a month of stabilisation, run on an isolated branch so the main suite stayed protected while engineering and DevOps coordinated the fix.
Foundation, automation, AI, ownership.
The engagement covered the entire surface of the platform — not just UI, but Lambda jobs, microservices, accessibility, performance, AI features, and cross-functional collaboration with customer support. Each layer was treated as a first-class testing domain.
Built QE from Scratch
Stood up the entire QE practice from the ground up — feature matrix, RTM, Zephyr Scale test cases, release structures, Confluence documentation. Owned the pod, signed off releases, mentored new joiners.
1,000+ Cypress Automation Scripts
Automated 1,000+ test cases covering ~80% of the platform, wired into CircleCI and GitHub Actions for daily sanity and weekly regression, with weekend cron runs reporting to Slack and Cypress Cloud.
AI-Integrated Automation with Cursor AI
Cursor AI accelerated script authoring, review, and refactoring throughout — and powered full Cypress automation of the client’s AI-driven video/audio interview flows, a RAG-powered coaching feature.
Shift-Left Automation
Automation shipped in parallel with features, not after — compressing feedback loops and cutting the manual regression load on every weekly release.
AWS & Microservices Testing
Extended into the infrastructure layer: Lambda jobs, and the assessment, meeting, and integration services validated end-to-end, including database and job checks after every UI revamp.
Dependency Upgrades & Stabilisation
Led recovery after a major Cypress/dependency upgrade broke most of the suite — a month of methodical stabilisation on an isolated branch, merged back once fully green.
Deep Accessibility Testing
NVDA and VoiceOver for screen readers, axe and WAVE for automated checks, manual passes across Windows/macOS for colour and contrast, and BrowserStack for cross-browser/cross-device parity.
Customer Support Collaboration
Helped the customer support team diagnose production issues directly — an extra feedback loop that sped up resolution and reinforced client trust.
What the work delivered.
1,000+ automated test cases delivered in Cypress
covering ~80% of the test surface — with cron-based weekend execution, daily sanity runs, and on-demand pipelines in GitHub Actions and CircleCI, all reporting to Slack and Cypress Cloud.
Shift-left automation model implemented
scripts delivered in parallel with feature development, compressing feedback loops and reducing manual regression effort for every weekly release cycle.
AI-integrated automation built for the client’s interview application
including full Cypress automation of video and audio interview flows and RAG-powered coaching use case testing, with Cursor AI used throughout.
AWS and microservices layer validated
Lambda jobs, assessment service, meeting service, and integration service all tested end-to-end, including database and job validation after the meeting service UI revamp.
Major dependency upgrade stabilised
a full Cypress and dependency upgrade that broke the majority of scripts was resolved over approximately one month on an isolated branch, restoring full suite health.
Deep accessibility coverage delivered
NVDA, VoiceOver, axe, and WAVE testing across Windows and macOS, covering screen readers, colour gradients, themes, and cross-browser parity via BrowserStack.
QE pod fully owned
the team held release sign-off authority, coordinated with DevOps and the product team, mentored new engineering and QA joiners, and supported the customer success team directly.
Multiple appreciations received from CEO, CTO, development leads, and the product team
recognising the QE team’s contributions to platform reliability, process maturity, and cross-functional collaboration.
The stack.
This engagement represents the fullest expression of what a long-term, embedded QE partnership can deliver: 1,000+ automated scripts, AI-integrated coverage, microservices validation, shift-left workflows, accessibility testing, and full pod ownership — treated as a first-class function, not an afterthought.