AI-powered resume optimization that thinks like an enterprise ATS.
HireOrbit simulates the Applicant Tracking Systems (ATS) used by platforms like Workday — parsing your resume, scoring it against a job description, identifying keyword gaps, and generating a professionally formatted, ATS-optimized PDF. Powered by the Llama 3.3 70B model via Groq for ultra-fast, deterministic inference.
- Enterprise ATS Simulation — Mirrors real-world ATS workflows: resume parsing, keyword extraction, scoring, and gap analysis.
- Lightning-Fast LLM Inference — Powered by Groq and the Llama 3.3 70B model to instantly process resumes and provide real-time feedback.
- Deterministic Resume Parsing — Strict JSON schema enforcement ensures reliable extraction of skills, experience, education, projects, and keywords.
- ATS Match Scoring — Resume-to-job-description alignment score with missing keyword detection and actionable recommendations.
- AI-Powered Resume Generation — Dynamically generates ATS-optimized LaTeX resumes and exports them as polished PDFs.
- Cloud PDF Compilation — Uses a cloud-based LaTeX compilation API for reproducible, dependency-free document rendering.
Resume + Job Description
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Llama 3.3 70B (via Groq)
(Extraction & ATS Scoring)
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ATS Report + Suggestions
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Llama 3.3 70B (via Groq)
(LaTeX Resume Generation)
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Cloud LaTeX API
(ytotech.com)
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Optimized PDF
| Layer | Technologies |
|---|---|
| Frontend | Next.js 14, React, TypeScript, Tailwind CSS |
| Backend | Node.js, Express.js |
| AI Models | Llama 3.3 70B Versatile (via Groq API) |
| PDF Pipeline | Cloud LaTeX API (ytotech.com) |
1. Upload Resume
2. Paste Job Description
3. Extract Structured Candidate Data ← Llama 3.3 70B (Groq)
4. Calculate ATS Match Score ← Llama 3.3 70B (Groq)
5. Identify Missing Keywords
6. Generate Optimization Suggestions
7. Create ATS-Optimized LaTeX Resume ← Llama 3.3 70B (Groq)
8. Compile & Download Professional PDF ← Cloud LaTeX API
Reliable LLM Output Parsing LLMs frequently return malformed or inconsistent JSON. HireOrbit enforces strict output schemas and validation layers to guarantee deterministic downstream processing regardless of model variability.
High-Speed AI Inference Leveraged Groq's LPU inference engine with the Llama 3.3 70B model to deliver near-instantaneous resume parsing and generation, providing a snappy user experience.
Scalable PDF Generation Shifted from local LaTeX compilation to a robust cloud-based compilation API, eliminating heavy host dependencies and ensuring byte-for-byte reproducible PDF output across all environments.
ATS Optimization Logic Implemented keyword matching and gap detection algorithms that compare candidate profiles against job requirements and generate specific, prioritized improvement suggestions.
- Node.js v18+ and npm
- Docker & Docker Compose (optional)
Create the following .env files before running the project.
backend/.env
GROQ_API_KEY=your_groq_api_key_here
GROQ_API_KEY_LATEX=your_groq_api_key_hereGet your API keys at console.groq.com.
frontend/.env.local
NEXT_PUBLIC_BACKEND_URL=http://localhost:8040From the project root:
docker-compose up --build- Frontend → http://localhost:3000
- Backend → http://localhost:8040
Make sure tectonic is installed on your system, then:
# Backend
cd backend
npm install
npm start
# Frontend (in a separate terminal)
cd frontend
npm install
npm run dev- Multiple resume templates
- Cover letter generation
- Recruiter feedback simulation
- Resume version tracking
- Interview question generation
- RAG-based company-specific ATS optimization
Srishti Rawat — Full-Stack Developer focused on scalable backend systems, AI-powered applications, and production-ready software engineering.
This project is licensed under the MIT License.