AI-powered placement intelligence platform for students
PlacementIQ analyzes your resume against job descriptions and gives you a placement readiness score, skill gap analysis, and a personalized learning roadmap — powered by a multi-layer scoring engine and Llama 3.
- Resume Parsing — Upload your PDF resume and extract skills, projects, and education automatically
- JD Analysis — Paste any job description and get an instant match score
- 4-Component Scoring Engine — Skill match, experience weight, project relevance, keyword context
- AI Insights — Powered by Llama 3 via Groq for personalized recommendations
- Learning Roadmap — Prioritized list of skills to learn with resources and time estimates
- Resume Tips — Specific suggestions to improve your resume for the role
| Layer | Technology |
|---|---|
| Frontend | Next.js 15, TypeScript, Tailwind CSS, Recharts |
| Backend | FastAPI, Python 3.11, SQLAlchemy (async) |
| Database | PostgreSQL + pgvector |
| AI/LLM | Llama 3 via Groq API |
| NLP | TF-IDF, scikit-learn, custom skill taxonomy (500+ skills) |
| Auth | JWT + bcrypt |
| Deployment | Railway (backend) + Vercel (frontend) |
Resume PDF → Text Extraction (PyMuPDF)
→ Section Parser (skills, experience, projects, education)
→ Skill Extractor (500+ canonical skills taxonomy)
→ Scoring Engine (weighted 4-component algorithm)
→ Groq LLM (insights, roadmap, tips)
→ Results Dashboard
- Python 3.11+
- Node.js 18+
- PostgreSQL 17+
- pgvector
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env # fill in your values
alembic upgrade head
uvicorn app.main:app --reloadcd frontend
npm install
echo "NEXT_PUBLIC_API_URL=http://localhost:8000" > .env.local
npm run devScore = 0.40 × skill_match
+ 0.20 × experience_weight
+ 0.20 × project_relevance
+ 0.20 × keyword_context
Shreesha H S — GitHub
MIT