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Manual QA Engineering for an AI-Powered Recruiting Platform

Industry

HR Tech / Recruitment

Service We Provided

Manual QATest Management ImplementationCross-platform Testing
Manual QA Engineering for an AI-Powered Recruiting Platform

ABOUT THE PROJECT

An AI-driven recruiting platform that connects with 250+ job boards to source active candidates and runs targeted outreach campaigns for passive talent across email, direct mail, and social media. The platform integrates with multiple applicant tracking systems simultaneously and includes built-in analytics and market intelligence for funnel-level recruitment reporting.

Business Objectives

01Own the end-to-end QA function across the platform's full feature set.

02Implement a structured test management system to organize coverage and link it to development tickets.

03Validate new features against functional requirements and design specifications before release.

04Maintain backlog hygiene and keep defect tracking actionable.

Process Overview

Test Strategy Defined the QA approach and coverage scope aligned to the platform's release cadence.

Testing Execution Ran functional and non-functional testing, smoke, sanity, UI/UX, exploratory, regression, and confirmation cycles tied to each sprint.

Compatibility Testing Covered web and mobile across Windows, macOS, Android, iOS, Chrome, Safari, and Firefox.

Test Management Implementation Introduced X-Ray to structure test cases, link coverage to tickets, organize test sets by platform section, and maintain separate plans for general releases, fix versions, and regression cycles.

Backlog Management Implemented Aging Bugs tracking to flag unresolved defects that had been open too long, keeping the backlog focused and prioritization clear.

Design Validation Used PerfectPixel to verify implemented features against design specifications at pixel level.

Challenges & Solutions

Challenge 1 — No structured test management Test cases were unorganized and not linked to development tickets, making coverage gaps invisible. X-Ray implementation gave the team full traceability between what was tested and what was shipped.

Challenge 2 — Backlog becoming unmanageable Unresolved defects were accumulating without clear prioritization. Aging Bugs tracking surfaced stale issues and forced resolution decisions, keeping the backlog actionable.

Technology Stack

Test Management: X-Ray, Jira

Design Validation: PerfectPixel

Platforms Covered: Web, Mobile, iOS, Android

OS Covered: Windows, macOS

Browsers Covered: Chrome, Safari, Firefox

Result

  • Structured test coverage with full traceability between cases and development tickets
  • Pre-release defect detection reduced production issues
  • Regression planning optimized for each release cycle
  • Backlog kept clean and prioritization maintained throughout

Delivery Summary

The client left with a QA function that matched the pace and complexity of an actively scaling AI product. Test coverage was organized, traceable, and tied directly to the development workflow — with a clean backlog and a release process the team could rely on.

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