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Case Study

NervAI Backend

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Case Study

Introduction

NervAI was created to automate the candidate interview process using AI. The backend was built to support asynchronous interview workflows, advanced integrations, and multimedia data storage. The platform empowers HR teams to scale candidate assessments with consistent evaluation standards while reducing interviewer workload.

Project Name / Code:

NervAI Backend

Category:

AI Interview & Recruitment Tech

Technology:

Django , Stripe , Google Search Console ,Google Cloud SQL

Location

Global (Cloud-hosted)

Project start date

23-Apr-2025

Project Name / Code:

NervAI Backend

Category:

Category: AI Interview & Recruitment Tech

Technology:

Django , Stripe , Google Search Console ,Google Cloud SQL

Location:

Global (Cloud-hosted)

Project Start Date:

23-Apr-2025

About

Problem Statement

Recruiters face challenges in scaling interviews while ensuring fair, consistent, and unbiased evaluation. Traditional interviews are resource-intensive, lack structured analytics, and often require multiple tools to manage video, transcripts, and scoring.

Objectives

  • Automate interviews using AI with predefined question sets.
  • Evaluate candidate responses (voice & text) through AI analysis.
  • Store both interview transcripts and video recordings for later review.
  • Provide seamless integrations with HRM and ATS platforms via Kombo APIs.
  • Implement secure authentication (Keycloak) and payment flows (Stripe).
  • Ensure scalability and responsiveness with asynchronous Django REST architecture.

Implementation

  • Core Backend: Developed using Django REST Framework (async) with PostgreSQL as the database.
  • Asynchronous Processing: Leveraged Django async views for real-time AI interview flows.
  • Signals: Implemented Django signals to trigger post-interview tasks (e.g., evaluation storage, notification triggers).
  • Integrations:
    • Kombo → unified API for HRM & ATS system integrations.
    • ChatGPT → generates interview questions and evaluates candidate answers.
    • ElevenLabs → converts text questions to natural voice for AI-led interviews.
    • Keycloak → authentication and secure user management.
    • Stripe → subscription and payment handling.
  • Interview Storage: Both video recordings and transcripts are stored for auditing and review.
  • Scalability: Built API endpoints optimized for concurrent interviews and integrations.

Key Takeaways:

  • Combined AI, HR, and ATS systems into a single backend.
  • Created a flexible and scalable asynchronous API-based architecture.
  • Enabled multimedia storage and real-time analysis.

Results / Outcomes

KPIs / Metrics:

  • Reduced interviewer workload by ~50% through AI-driven assessments.
  • Average candidate evaluation time cut by 40%.
  • Enabled integrations with multiple HRM/ATS systems through a single Kombo API layer.

    Before & After Comparison:

  • Before: Manual interviews, fragmented tools for transcripts, scoring, and HRM sync.
  • After: Automated AI-led interviews, unified backend, and streamlined HR integrations.
  •  

         Key Improvements:

  • Standardized candidate evaluation process.
  • Enhanced candidate experience with natural AI-driven voice (ElevenLabs).
  • Secure and scalable backend with async Django REST and Keycloak.
    •  

Technologies

Technologies / Tools that We used to achieve this task