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AI INTERVIEW
COPILOT

Real-time AI assistance during interviews

🎧 Use headphones for best experience
✦◆✦
✦ AI-POWERED INTERVIEW COACH

ACE EVERY
INTERVIEW
IN REAL TIME.

Speak naturally. Whisper transcribes, RAG retrieves your resume, and LLM generates structured answers — all in under 2 seconds.

START FOR FREE →See how it works ↓
< 2s
RESPONSE TIME
Whisper
TRANSCRIPTION
RAG
RESUME-GROUNDED
LLM
LLM ENGINE
SCROLL
SCENE 01

UPLOAD
YOUR
RESUME

Drop your PDF. We chunk it into semantic sections, convert each piece into vector embeddings, and store them in FAISS — so every answer is grounded in your actual experience.

1
CHUNK
Resume split into semantic sections
2
EMBED
Each chunk → vector embedding
3
SEARCH
Questions matched via cosine similarity
UPLOAD RESUME
Connect your PDF to the interview engine.
Protected
PDF only
📄
RESUME.PDF
2.4 MB · Indexed ✓
Tell me about yourself
Why this role?
A challenge you solved
Your biggest strength
Teamwork · Ownership
Impact · Results
SCENE 02

LIVE
INTERVIEW
MODE

🎤
RECORDING
◉ QUESTION HEARD
✦ AI SUGGESTION
EXPECT THESE FOLLOW-UPS
LIVE INTERVIEW
Type a prompt and generate an interview answer.
idle
YOUR PROMPT
Press Enter to send, Shift+Enter for a new line.
AI RESPONSE

Your response will appear here.

Disconnected
SCENE 03

REAL-TIME
AI MAGIC

INTERVIEWER
"Tell me about yourself and your background."
WORKFLOW
How the answer is generated
1
User request
↓
2
Embedding generation
↓
3
Vector search
↓
4
Context assembly
↓
5
LLM response
↓
6
Answer returned
↓
User(req) → Embedding → Vector Search → Context → LLM → Answer → response
Whisper
Transcription
FAISS
Vector Search
LLM
Generation
< 2s
Total Latency
SCENE 04

MOCK
INTERVIEW
MODE

AI plays the interviewer. You answer. Copilot scores your response and gives specific, actionable feedback — just like a real coaching session.

AI ASKS ✦
"Tell me about yourself."
YOUR ANSWER
"I am a product-minded engineer who likes solving end-to-end problems. I care about ownership, communication, and shipping work that improves the user experience."
AI FEEDBACK
90/100
Good structure. Add one concrete example of impact to make it stronger.
MOCK INTERVIEW
Get scored feedback from the backend evaluator.
0/5
QUESTION

Start a mock interview to get your first question.

0/3000 characters
Backend session id: waiting to start
SCENE 05

SYSTEM
ARCHITECTURE

A production-grade system: WebSockets for real-time data flow, session state for persistence, FAISS for semantic retrieval, and LLM inference for sub-second responses.

Next.jsFrontendWebSocketReal-timeFastAPIBackendSession StateMemoryPostgreSQLDatabaseLLMGenerationFAISSPinecone
FastAPI ✦WebSockets ✦Whisper ✦LLM ✦FAISS ✦PostgreSQL ✦Next.js ✦Docker ✦LLaMA 3 ✦GitHub Actions ✦FastAPI ✦WebSockets ✦Whisper ✦LLM ✦FAISS ✦PostgreSQL ✦Next.js ✦Docker ✦LLaMA 3 ✦GitHub Actions ✦FastAPI ✦WebSockets ✦Whisper ✦LLM ✦FAISS ✦PostgreSQL ✦Next.js ✦Docker ✦LLaMA 3 ✦GitHub Actions ✦

YOUR NEXT
OFFER
STARTS HERE.

Upload your resume. Open a session. Speak. Let Copilot handle the rest.

GET STARTED ✦