Case study • E-Commerce • Quality Automation at Scale
95% QA Pass Rate Enables Predictable Scale for Retail AI Platform
We helped a Canadian e-commerce leader embed a quality governance model into their AI-driven product discovery platform. By defining QA ownership, automating critical validations, and establishing a safe release protocol, the organization improved data accuracy, maintained a 95% pass rate, and accelerated time to launch — with clear visibility into when to scale, pause, or pivot.
Success Highlights
Key Details
Industry: E-commerce / SaaS Geography: Canada
Platform: AI-driven product discovery platform
Business Challenge
The platform needed more than just testing — it needed governance at scale. Bugs were slipping into production, test ownership was unclear, and no one could confidently say if the next release was safe to ship.
Product leaders lacked a model to decide: Can we scale this? Should we pause? Or is it time to pivot?
Our Solution Approach
We reframed QA as a governance loop — with measurable signals, automated safeguards, and escalation clarity built into every release.
1 · Discover
Assess QA Gaps in Curation Pipeline
We identified breakdown points in the curation queue, mapped sources of error, and prioritized critical areas for test coverage and automation.
2 · Consolidate
Establish QA Strategy & Test Frameworks
We defined QA processes for Agile workflows, including onboarding, test planning, automation guidelines, and structured review procedures.
3 · Automate
Enable Full-Stack Quality Validation
We implemented regression, API, performance, and database testing to create end-to-end coverage—eliminating deployment risk at every layer.
4 · Accelerate
Streamline Product Launches
We enabled faster, error-free product releases with macro-automation and a consistent pass rate, leading to successful application launch in under 6 months.
Technical Highlights
QA enforcement integrated into Google Cloud Retail AI pipeline using pre-deployment test hooks and release gate logic for all curated data flows
Macro-automation framework with conditional test routing, rollback-safe deployments, and escalation logic tied to anomaly detection thresholds
Full-spectrum test suite covering regression, REST API validation, SQL-based data integrity checks, and performance benchmarking using k6 and Postman
Release scorecards generated via CI pipeline telemetry, exposing metrics like test pass rate, rework %, flaky test count, and error recovery time
// Governance-First Test Decision Flow
def evaluate_release(build):
if build.test_coverage < 85:
raise_blocker(“Coverage too low”)if build.pass_rate < 95 or build.flaky_tests > threshold:
notify_team(“Escalate to QA lead”)
mark_release(“hold”)
else:
approve_release(build)
Business Outcomes
Transformed ad-hoc QA into a governed, accountable release framework with metrics leadership could trust.
95%
QA Pass Rate:
Automated validation with clear rules for release readiness and rollback.
6 month
Time-to-Launch:
Fast, safe delivery through streamlined QA workflows and role ownership.
Built-in
Scale Decision Model:
Enabled product leaders to confidently scale, pause, or pivot using live test data and risk signals.
Aligned QA operations with board-level decision needs
Eliminated ambiguity around release ownership and agent safety
Want to Build a “Scale / Pause / Pivot” QA Model?
Let’s talk about building measurable guardrails and role-based ownership into your QA pipeline — so every release is defensible.