Redrob AI helps recruiters and hiring managers instantly process massive datasets of resumes, ranking them with semantic understanding, skill matching, and explainable AI insights.
Instead of manually reading thousands of resumes, hiring teams can upload a candidate database and instantly explore the best fits, calculate custom weighted strategies, and view automated executive summaries through an interactive dashboard.
Rank candidates using contextual intent instead of keyword matching.
Example:
We need a Senior Python Developer with FastAPI and Docker experience who has built scalable APIs.
Redrob AI searches the candidate pool using vector similarity to find candidates with relevant work history and titles.
Adjust hiring weights in real-time. Move sliders to prioritize:
- Qualification (Core Technical Skills + Experience + Education)
- Compatibility (Semantic JD Overlap)
- Behavior (Recruiter Engagement & Responsiveness)
- Confidence (Data Completeness)
Leaderboards update instantly without invoking LLMs again.
Generate detailed executive summaries for candidates:
- Match Score & Verdict: Star ratings and recommendation statuses.
- Why Not Rank #1?: Automated competitive gap analysis detailing why a candidate didn't make the top spot.
- Ramp-up Prediction: Immediate, 2-week, or 1-month onboarding estimations.
- Strengths & Concerns: Bulleted business pros and cons.
- Interview Focus Areas: Custom questions tailored to the candidate's exact resume gaps.
┌───────────────┐
│ React Frontend│ (Vite + CSS Modules)
└───────┬───────┘
│
▼
┌───────────────┐
│ FastAPI Backend│ (Uvicorn)
└───────┬───────┘
│
┌───────────────────┼───────────────────┐
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Resume │ │ Vector │ │ Intelligence│
│ Ingestion │ │ Database │ │ Engine │
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘
│ │ │
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Structured │ │ FAISS │ │ Gemini 2.5 │
│ JSONL Parser│ │ Index │ │ Flash API │
└─────────────┘ └─────────────┘ └─────────────┘
- React 18
- Vite
- Recharts (Radar charts)
- Lucide React (Icons)
- CSS Modules (Scoped styling)
- FastAPI (Python 3.11+)
- Uvicorn
- FAISS (Vector Database)
- Sentence-Transformers (
all-MiniLM-L6-v2) - Pydantic v2
- Google GenAI SDK (Gemini 2.5 Flash)
| Capability | Description |
|---|---|
| Resilient Extraction | Resilient dual-stage parsing (Regex Baseline -> Gemini Enhanced) |
| Instant Uploads | Skips neural network embeddings on restart if vectors already exist on disk |
| Vector Ranking | Evaluates 100,000 resumes in milliseconds using FAISS |
| Explainable AI | Detailed gap analysis ("Why Not Rank #1?") and ramp-up predictions |
| Recalculation | Vector-matrix dot products for real-time slider re-ranking |
| Export | CSV export of final ranked leaderboards |
INDIA-RUNS/
│
├── backend/
│ ├── app/
│ │ ├── api/ — Routes (upload, rank, recalculate, export)
│ │ ├── config/ — App settings
│ │ ├── models/ — Schemas and JSON formats
│ │ └── pipeline/ — Ingestion, embedding, FAISS vector store, ranking & explainability
│ │
│ └── requirements.txt
│
├── frontend/
│ ├── src/
│ │ ├── components/ — Results panel, setup panel, sliders, radar charts
│ │ ├── pages/ — Main dashboard layout
│ │ ├── lib/ — API caller and mock defaults
│ │ └── index.css — Custom design tokens
│ │
│ └── package.json
│
└── README.md
git clone https://github.com/WebDoveleprrr/Redrob-AI.git
cd Redrob-AIcd backend
python -m venv venv
# Windows
venv\Scripts\activate
# Linux / Mac
source venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reloadBackend URL:
http://localhost:8000
cd frontend
npm install
npm run devFrontend URL:
http://localhost:5173
GEMINI_API_KEY=your_google_ai_studio_api_key| Method | Endpoint | Description |
|---|---|---|
| POST | /api/v1/upload_db | Upload candidates.jsonl database (Cached vectors load instantly) |
| Method | Endpoint | Description |
|---|---|---|
| POST | /api/v1/rank | Rank candidates against raw Job Description (Gemini Enhanced) |
| POST | /api/v1/recalculate | Fast vector re-ranking using customized slider weights |
| GET | /api/v1/export_csv | Export ranked candidates to CSV |
- Multi-recruiter team permissions
- Custom weighted score presets saving
- Dynamic JSONL resume schema support (auto-detecting resume fields)
- Direct PDF/Word resume uploads with OCR parsing
- Calendar integration for direct scheduling of top matches
Rohit Chowdary
Computer Science Engineering Student
Built to explore how hybrid search engines, vector indexing, and generative models can be combined to solve large-scale talent acquisition bottlenecks.
Redrob AI is not just another wrapper for an LLM.
It combines:
- Fallback Resilience: Offline Regex parsing automatically boots up if Gemini API limits are hit.
- Speed: Embeddings are computed only once, saved to a binary FAISS database, and reloaded instantly.
- Mathematical Precision: Instead of asking an LLM to guess a ranking order, a vector dot product combines semantic match with behavioral scores to order candidates deterministically.
- Explainability: Every ranking comes with concrete reasons, gap analysis, and tailored interview advice.