Build an AI Interview Coach That Gives Feedback from Video and Audio in Real-Time
In the hiring and interview preparation ecosystem, thousands of mock interviews are recorded each day but most are underutilized.
1. Problem Statement
In the hiring and interview preparation ecosystem, thousands of mock interviews are recorded each day—but most are underutilized. Candidates are left with raw recordings that they don’t know how to review or learn from. Critical feedback around clarity, tone, confidence, or body language is either missing or vague. Recruiters and coaches often lack the time to analyze every recording in depth.
There is a dire need for a scalable AI-powered solution that can analyze these audio/video recordings and generate actionable, structured feedback to help candidates improve interview readiness.
2. Tools Used :
Python
Core programming language
OpenAI Whisper
For high-quality audio transcription
OpenCV
To analyze body language using face detection
SentenceTransformer (BERT)
Semantic analysis of answers
Streamlit
Web-based feedback dashboard
PostgreSQL + psycopg2
To store and retrieve interview feedback records
tempfile : Handles file saving securely
re (regex) : Cleans HTML for database
whisper : Audio-to-text transcription
OpenCV : Video frame analysis
BERT (sentence-transformers) : Semantic feedback
PostgreSQL + psycopg2 : Saving interview data
In this project, I successfully developed an AI-powered Interview Feedback Analyzer that helps candidates improve by analyzing both their spoken answers and body language.
By integrating tools like Whisper, OpenCV, BERT, and Streamlit, I achieved:
Accurate audio transcription
Smart semantic feedback on answers
Basic body language detection
Personalized coaching-style feedback
Secure feedback storage using PostgreSQL
The project simulates a virtual interview coach, providing structured, real-time feedback to help candidates reflect and grow.
3. Why We Need This Use Case
Interview Coaching at Scale: With job seekers rising globally, interview coaching cannot stay 1:1. Automation through AI brings scalability to personalized feedback.
Objective & Unbiased Feedback: Human feedback may be inconsistent or subjective. AI-based analysis provides consistent evaluation metrics.
Self-Improvement for Job Seekers: Candidates preparing for interviews on their own often lack a benchmark. This tool gives them clarity on their strengths and areas for improvement.
Time-Saving for EdTechs & Recruiters: Institutions or platforms can use this tool to batch analyze hundreds of mock interviews and identify top talent or training gaps.
Hybrid Capability: It combines NLP, computer vision, and rule-based heuristics, making it a comprehensive interview evaluator.
4. When We Need This Use Case
During mock interview training sessions for job seekers.
In EdTech platforms where interviews are part of upskilling programs.
For HR teams or staffing agencies automating initial interview screenings.
In self-assessment tools where students or professionals upload their interview responses for instant AI feedback.
As a pre-screening tool before sending candidates to final technical interviews.