Building an AI-Powered 3D Interview Avatar: A Smarter Way to Practice Job Interviews

I recently built an AI-driven mock interview platform designed to help candidates prepare for real-world recruiter conversations through interactive, personalized interview simulations.

The core idea was simple: instead of practicing with static question lists, users upload their CV/resume and engage in a live conversation with an intelligent 3D avatar that acts like a recruiter.

What makes this system powerful is its ability to understand the candidate’s professional background and generate context-aware interview questions based on their actual experience, skills, projects, and career history—similar to how recruiters conduct interviews in real meetings.

How it works:

• Users upload their CV/resume • The system processes and extracts key professional information • A Retrieval-Augmented Generation (RAG) pipeline organizes the data into vector embeddings • The AI agent builds personalized interview flows based on resume context • A real-time 3D avatar conducts the interview through voice interaction • The avatar responds dynamically with lip-sync, facial expressions, and body animations for realistic conversation flow

This project combines conversational AI with immersive 3D interaction to create a much more natural and engaging interview preparation experience.

Tech Stack:

Backend & AI

Python

LangChain

OpenAI

Pinecone (Vector Database)

Retrieval-Augmented Generation (RAG)

Frontend & Avatar System

React

Three.js

Real-time avatar animation system

Lip-sync engine

Facial expression mapping

Body motion integration

Key Challenges Solved:

✔ Resume parsing and contextual knowledge extraction ✔ Dynamic interview question generation ✔ Real-time voice conversation pipeline ✔ Avatar lip-sync synchronization ✔ Emotion-driven facial expressions ✔ Memory-aware conversational flow

This project reflects how AI agents can move beyond text-based chatbots into immersive, human-like digital experiences.

AI agents + 3D avatars + contextual intelligence are opening new possibilities for training, education, customer support, and simulation systems.

This is one example of how I build AI-powered interactive systems that combine intelligence, design, and real-time user experience.

#AI #AIAgents #LangChain #OpenAI #ThreeJS #Python #React #MachineLearning #3DDevelopment #InterviewPreparation #RAG #Pinecone

A Telegram bot built as a parallel layer to a productivity system, designed to intercept moments of negative impulse and convert them into structured reflection conversations.

How it works:

Every user message triggers an AI intervention conversation grounded in the user's Chief Definite AIM, recent insights, current goal pacing, and reflection history

Voice messages auto-transcribed via OpenAI Whisper, treated as text turns in the conversation

/done command ends the session, prompts trigger word + outcome (avoided / indulged / unclear), persists everything to SQLite for downstream daily summary integration

/restart wipes session state for fresh context

System prompt is brother-tone, AIM-grounded, 80–200 words per response, deliberately not therapy-speak

Runs in the same Node process as a separate productivity bot — two bots, two tokens, one codebase.

Tech: Node.js, Telegram Bot API, Claude API, OpenAI Whisper, SQLite.