Real-Time AI Interview

Context & Goals

Interview coaching platforms often lack live, contextual feedback. This project delivers a browser-based interviewer that listens, scores, and surfaces coaching tips in real time without sacrificing privacy or stability.

Architecture Overview

• Client: Next.js front-end with WebRTC publisher, noise gating, and timeline UI.
• Signaling: Node server establishing WebRTC sessions, handling TURN/STUN negotiation, and streaming audio frames to evaluators.
• Inference layer: GPT-based evaluators running in parallel, enriching transcripts with rubric checks (communication, technical depth, empathy).
• Storage: Redis for transient session data, PostgreSQL for final reports, highlights, and improvement suggestions.

Highlights

• Built adaptive scoring that adjusts rubrics mid-session based on candidate responses and job role.
• Implemented safe-words and privacy controls; sessions can redact sensitive topics before saving transcripts.
• Added playback controls that pair video segments with constructive coaching cards.

Next Steps

• Expand to support collaborative interviewer panels with shared annotations.
• Integrate HRIS exports to close the loop on performance tracking.