CASE STUDY

Scalable QA for AI Interviews

Executed scenario-driven automation testing across Admin, Tenant, and Candidate workflows, ensuring interview quality, scoring logic, system feedback, and AI behavior were production-ready, scalable, and aligned with real-world hiring and compliance use cases.

AI

ABOUT THE CLIENT

Scalable QA for Agentic AI Interview Platform

The client is a deep-tech startup offering Spectra, an Agentic AI interview platform designed to “hear, see, reason, and speak.” The platform enables scalable and hyper-personalized interviews across recruitment, compliance, research, and more. Our QA efforts centered on multi-portal functional validation and regression automation, delivering 80% coverage to support mission-critical pre-launch goals

HIGHLIGHTS

80%

Automation Coverage

165+

Test cases executed

90+ bugs identified and tracked
Visual PPT mapping amplified stakeholder understanding of automation scope
Ensured consistency across interrelated functionalities of the Spectra platform

Tools we Used

PROBLEM STATEMENT

Repetitive AI Questions and Inconsistent Resume Scoring

AI-generated interview questions often lacked variety, resulting in duplication within sessions and a diminished candidate experience. In parallel, the resume scoring algorithm produced inconsistent results, frequently inflating candidate scores without clear reasoning. These functional flaws risked undermining recruiter trust and platform credibility, especially in hiring-critical environments.

OUR SOLUTION

Test-Driven Root Cause Detection and Flow Stabilization

Conducted scenario-based functional testing to surface AI question repetition
Validated backend logic against expected diversity patterns in interview flows
Highlighted resume score discrepancies by running varied candidate profiles through controlled sessions
Flagged logic flaws and proposed guardrails for question and scoring randomness
Developed targeted automation around scoring APIs to test stability across edge cases
Collaborated closely with the dev team by presenting visual evidence of flawed flows via structured PPTs

What we did?

End-to-End Validation

Workflow-Based Automation

AI Behavior Testing

Strategic QA Roadmap

End-to-End Validation

We consolidated test coverage across Admin, Tenant, and Candidate portals to simulate real-world flows. From user creation and access control to candidate invitations and status transitions, we validated every touchpoint for accuracy, feedback, and data integrity. This helped detect critical issues early, including flow breakages and edge-case failures, reinforcing reliability for all user types.

Workflow-Based Automation

Automation was built not just around generic actions but actual usage scenarios spanning customer onboarding, candidate evaluation, and multi-role operations. Keyword-based validations were integrated to test AI-generated outputs for repetition and logic gaps. API-level tests complemented UI automation to validate backend resume scoring algorithms. This combined strategy provided confidence in both the technical workflows and the intelligence layer that powers the platform.

AI Behavior Testing

Beyond traditional automation, we emphasized validating the behavior of AI-generated content. Automated scripts were tailored to detect repetitive interview questions and measure the consistency of resume scoring across diverse candidate profiles. By integrating backend API validations with front-end flows, we ensured that both the user experience and the AI’s logic were reliable, scalable, and production-ready

Strategic QA Roadmap

Created a structured PPT that mapped test flows to Spectra’s modular use cases (e.g., SpectraHire, SpectraSeek). This helped stakeholders visualize QA depth and understand how each automated scenario contributed to reliability across sectors like hiring, legal prep, and research

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