QA leadership for AI-powered interview intelligence platform
How we led and scaled the QA function for HireLogic, validating AI note-taking, candidate ratings, cross-platform compatibility, and end-to-end interview workflows across web and mobile browsers.
Client
HireLogic
Team
QA team led by QAble engineer
Engagement
Ongoing
Platforms
Web, Mobile Browsers
Technologies and tools
Jira
Confluence
Zoom
Microsoft Teams
Google MeetResults by the numbers
QA+
Team leadership
7+
Core features validated
3×
Platforms covered
Daily
Collaborative QA sessions
100%
Jira-managed test coverage
AI
Interview output validated
Here's a bit about HireLogic
Industry
HR Tech / AI
Engagement
Ongoing
Platforms
Web, Mobile Browsers
Integrations
Zoom, Teams, Meet
HireLogic is an AI-powered interview intelligence platform that helps recruiters, HR teams, and staffing firms capture smarter insights from every candidate conversation. Its suite of features, including instant interview notes, AI-generated summaries, candidate scoring, topic coverage analysis, and an interview chatbot, aims to reduce hiring bias, eliminate manual note-taking, and accelerate hiring decisions.
The platform integrates directly with Zoom, Microsoft Teams, and Google Meet, and supports both web and mobile browser experiences for recruiters on the go. As the product matured and its AI capabilities expanded, HireLogic needed a structured, expert-led QA function to ensure the reliability of its core AI outputs, cross-platform consistency, and overall product quality before every release.
Before and after QAble
Before QAble
With QAble
No structured QA process or ownership in place
AI-generated notes and summaries untested for accuracy
Candidate ratings and scoring untested across scenarios
No test cases documented or managed in any tool
No cross-platform (web + mobile browser) test coverage
Video integration testing (Zoom, Teams, Meet) unverified
Bugs discovered reactively in production
No visibility into testing status or progress
No collaborative testing cadence with the wider team
No repeatable QA framework or release-readiness gate
Our engagement
Our QA engineers embedded deeply into the HireLogic team from the outset, building the quality function from scratch. After the client observed the rigor and thoroughness of our individual testing approach, they extended trust to us to lead the entire QA team, overseeing test planning, execution, defect management, and reporting across all active workstreams. This progression from individual contributor to team lead reflects the depth of partnership we cultivated.
A core focus was validating HireLogic's AI capabilities, ensuring that interview notes, dynamic summaries, topic coverage analysis, and candidate ratings produced accurate, consistent, and unbiased outputs across diverse interview scenarios. Testing AI-generated content required building specialized test cases that went beyond traditional functional checks, accounting for variation in input quality, interview length, and video platform behavior.
Daily collaborative testing sessions kept the entire team aligned. Rather than isolated, asynchronous QA work, we worked side by side with HireLogic's engineers and product team, catching issues early, discussing edge cases in real time, and ensuring that every feature shipped was thoroughly validated. All test cases, bug reports, and task management were handled entirely within Jira, with regular status reports published to Confluence, giving the client full transparency into quality progress at every stage.
Engagement highlights
What QAble delivered
Services
AI output testing
HireLogic's core value proposition rests on the quality of its AI-generated outputs, interview notes, dynamic summaries, topic coverage detection, job description matching, and candidate scoring. Our team designed test cases specifically for these AI features, validating accuracy across short and long interviews, varied candidate profiles, and different interview formats. We tested that summaries were coherent, ratings were consistent, and topic detection correctly identified skills, industries, and job functions, ensuring the AI delivered reliable, trustworthy insights every time.
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