⭐ If PROTEUS gave you a smarter way to think about resume optimization — a star helps other engineers find it. Takes 2 seconds.
PROTEUS is a JD-aware resume analyzer that runs a five-agent NVIDIA NIM pipeline to produce consistent, actionable outputs from a single job description and resume.
| Output | What you get |
|---|---|
| Semantic Match Score | Percentage alignment with category breakdown |
| Gap Analysis | Matched / partial / missing requirements ranked by impact |
| Bullet Rewrites | JD-aware rewrites with rationale and impact scores |
| Cover Letter | Tailored letter from the same parsed context |
| Priority Actions | Ranked steps to improve your application |
Every output reads from the same parsed JD context — no contradictions between your score, gaps, rewrites, and cover letter.
JD ──┐
├──→ [Parse] → [Calibrate] → [Map] → [Rewrite] → [Draft] ──→ Results
Resume┘
| Step | Agent | Model | Task |
|---|---|---|---|
| 01 | JD Parser | nvidia/llama-3.3-nemotron-super-49b-v1.5 |
Extract role, requirements, seniority |
| 02 | Resume Parser | nvidia/llama-3.1-70b-instruct |
Extract skills, experience, achievements |
| 03 | Gap Analyzer | nvidia/llama-nemotron-embed-1b-v2 |
Semantic similarity scoring |
| 04 | Rewriter | mistralai/mistral-nemotron |
JD-aware bullet rewrites |
| 05 | Cover Letter | nvidia/nemotron-3-super-120b-a12b |
Tailored letter generation |
Models auto-update via GitHub Actions health checks every 3 hours.
| Role | Model | Last Checked |
|---|---|---|
| jd-parser | nvidia/llama-3.3-nemotron-super-49b-v1.5 |
2026-07-24T05:52:53.571Z |
| resume-parser | nvidia/gliner-pii |
2026-07-24T05:52:53.571Z |
| gap-analyzer | nvidia/llama-nemotron-embed-1b-v2 |
2026-07-24T05:52:53.571Z |
| rewrite-suggester | nvidia/gliner-pii |
2026-07-24T05:52:53.571Z |
| cover-letter | nvidia/llama-3.3-nemotron-super-49b-v1.5 |
2026-07-24T05:52:53.571Z |
Core Analysis
- 3 ways to input a JD: paste, upload, or URL
- 2 ways to input a resume: paste or PDF upload
- Semantic match scoring (embedding-based, not keyword matching)
- Gap analysis ranked by impact with severity badges
- Bullet-level rewrite suggestions with before/after comparison
- Consistent cover letter generated from the same context
- Priority action items ranked by impact
Platform
- Per-user data isolation and analysis history
- Rate limiting (10 analyses/day)
- Streaming API (NDJSON) for real-time pipeline progress
- Mobile-responsive UI
- Dark theme with gold accent design system
- Three auth methods: magic link, Google, GitHub
Reliability
- Automatic model fallback on failure
- JSON retry with temperature escalation (3 attempts)
- 300s timeout on serverless functions
- Health checks every 6 hours via GitHub Actions
- Self-healing model registry (auto-replaces failed models)
| Layer | Technology |
|---|---|
| Framework | Next.js 16 · App Router · TypeScript |
| Styling | Tailwind CSS v4 · Dark theme · Geist fonts |
| AI/ML | NVIDIA NIM · LLaMA 3.1 70B · Embeddings |
| Database | better-sqlite3 (local) · Turso/libsql (Vercel) |
| Auth | NextAuth.js v5 · Magic Link · Google · GitHub |
| Validation | Zod v4 |
| PDF Parsing | unpdf |
| Deployment | Vercel · GitHub Actions |
| Rate Limiting | Custom per-user daily limits |
| Endpoint | Method | Description |
|---|---|---|
/api/analyze |
POST | Run full pipeline |
/api/analyze/stream |
POST | Run pipeline with SSE streaming |
/api/history |
GET | List past runs |
/api/history/:id |
GET | Get run details |
/api/history/:id |
DELETE | Delete a run |
/api/models |
GET | List configured models |
/api/health |
GET | Health check |
/api/health/nim |
GET | NIM connectivity test |
/api/usage |
GET | Daily usage stats |
Built by Daniel Deshmukh · Mumbai, India