January 9th 2026

AI Interviewer: How We Built an Autonomous Interviewer from 0 → 1 That Reduced Time-to-Hire by 70%

This case study is shared while respecting confidentiality agreement. I've ensured to keep it engaging while protecting sensitive information. I've structured it in chapters outlining the product development journey in brief.

A Little Context

This is the story of how we built a product from 0 → 1 — an AI interviewer that screens candidates and generates detailed analysis reports along with recommendations on who's actually worth interviewing. This reduced time-to-hire by 70%, saved team's effort, and reduced biasness in hiring.

The Problem

Traditional hiring processes are time-consuming, biased, and inefficient. Recruiters spend countless hours on initial screenings, often making decisions based on incomplete information or unconscious biases.

The Solution

We built an LLM-based multimodal personalized interviewer that:

  • Generates role-specific questions tailored to each position
  • Creates real-time follow-ups to keep candidates in conversational mode
  • Screens candidates autonomously without human intervention
  • Produces detailed analysis reports with actionable recommendations

Technical Implementation

Frontend

  • Built with Next.js + TypeScript from wireframe to production
  • React dashboards with advanced filtering and bulk actions
  • Socket.io-based video/audio recording system with WebSocket support

Backend

  • Clean REST APIs in Django for user management and interview data
  • Secure authentication systems
  • Django ORM with PostgreSQL for efficient database modeling
  • Optimized query performance and data integrity

Infrastructure

  • Containerized with Docker
  • Deployed on GCP Linux servers
  • Asynchronous interview reviews enabled

Results

  • 70% reduction in time-to-hire
  • 25% decrease in screening time through asynchronous interview reviews
  • Reduced bias in the hiring process
  • Saved significant effort for recruitment teams

Key Learnings

Building from 0 → 1 taught us the importance of:

  • Starting with user problems, not technology
  • Iterating quickly based on real feedback
  • Balancing automation with human oversight
  • Designing for scale from day one