Train for ONE specific company's exact culture and problems — before you ever apply.
Every top company has a unique engineering culture — how they write code, what problems they solve, how they review work, what they value in engineers.
The candidate who walks in already thinking like a Razorpay engineer always beats the one who just grinded DSA.
ShadowIntern makes you that person — for any company, without knowing anyone inside.
Student enters: "I want to work at Razorpay"
│
▼
┌─────────────────────────────────────┐
│ Agent 1 — Company Intelligence │
│ Scrapes GitHub repos, tech blogs, │
│ job descriptions, Glassdoor, │
│ LinkedIn posts, YouTube talks │
│ → Returns: "Company DNA" │
└──────────────┬──────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ Agent 2 — Culture Modeller │
│ Understands stack, values, │
│ what they reject, how they think │
│ → Returns: "Engineer Profile" │
└──────────────┬──────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ Agent 3 — Task Generator │
│ Creates REAL intern-level tasks │
│ based on actual company problems │
│ → Returns: Daily task assignments │
└──────────────┬──────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ Agent 4 — Shadow Mentor │
│ Reviews code like a senior │
│ engineer AT THAT COMPANY would │
│ → Returns: Company-style feedback │
└──────────────┬──────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ Agent 5 — Interview Simulator │
│ Uses ACTUAL questions their │
│ interviewers ask, gives verdict │
│ → Returns: Hire / No-hire + gaps │
└─────────────────────────────────────┘
🏢 Shadow Company: Razorpay 📅 Week 3 of 4
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
✅ Tasks Completed: 11/14
⭐ Senior Review Score: 74/100
🎯 Interview Readiness: 68%
📋 This Week's Task:
"Design a webhook retry system that handles
partial failures gracefully.
Razorpay processes 5M webhooks/day."
👨💼 Mentor Feedback:
"Your solution works but a Razorpay engineer
would flag this — you're not handling
idempotency. That's a P0 bug in payments.
Redo section 3."
📈 Weak Area: System Design — Distributed Queues
🔥 Strong Area: API Design, Error Handling
| What Exists | What ShadowIntern Does |
|---|---|
| LeetCode | Generic DSA, no company context |
| Mock interview platforms | Scripted questions, not company-specific |
| Internships | Luck-based, requires connections |
| ShadowIntern | Train for ONE company's exact culture & problems |
| Layer | Tool |
|---|---|
| Agent Framework | CrewAI + LangGraph |
| LLM | Claude API (Anthropic) |
| Scraping | Playwright + GitHub API |
| Code Review Agent | Claude with company style prompts |
| Frontend | React Dashboard |
| Auth + Payments | Supabase + Razorpay (ironic) |
See ROADMAP.md for full details.
- Phase 1 → Top 20 Indian tech companies
- Phase 2 → Any company globally with public data
- Phase 3 → ₹500/month per company (student plan)
- Phase 4 → Companies pay to access pre-trained candidates
This project also has an active research component exploring multi-agent frameworks for company-specific engineering culture extraction and task synthesis.
See research/ABSTRACT.md for the working paper draft.
Related work:
- MockLLM (2024) — Multi-agent mock interview generation
- TheAgentCompany (2024) — Benchmarking LLM agents on real workplace tasks
- MetaGPT (2023) — Multi-agent software development simulation
See ARCHITECTURE.md for the full agent pipeline breakdown.
This project is in early design phase. See CONTRIBUTING.md if you want to collaborate.
Areas where help is needed:
- Agent pipeline design
- Company data scraping strategy
- Frontend dashboard mockups
- Research/evaluation methodology
Built and researched by @RabbaniHacker
MIT © 2026 — See LICENSE