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Recruitt is a powerful, high-performance talent acquisition engine designed to optimize the intelligence matrix for modern recruiters.

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#  Recruitt - AI Talent Screening Platform

![Recruitt Banner](https://raw.githubusercontent.com/Yves-Developer/recruitt/main/apps/web/public/image.png)

Recruitt is a powerful, high-performance talent acquisition engine designed to optimize the intelligence matrix for modern recruiters. It leverages **Gemini 2.5 Flash** for deep semantic analysis of resumes and job requirements.

##  Local Development

To get the full Recruitt engine running on your machine, follow these steps:

### 1. Prerequisites
- **Node.js**: v18.0.0 or higher
- **pnpm**: Fast, disk-space-efficient package manager
- **MongoDB**: A running instance (local or Atlas)
- **Gemini API Key**: Required for the AI screening pipeline

### 2. Installation
```bash
# Install dependencies for the entire monorepo
pnpm install
```

### 3. Environment Configuration

Create an `.env` file in `apps/server/.env`:
```env
PORT=5000
MONGODB_URI=your_mongodb_connection_string
GEMINI_API_KEY=your_gemini_api_key
RESEND_API_KEY=your_resend_api_key
EMAIL_FROM="Recruitt <notifications@yourdomain.com>"
```

Create an `.env.local` file in `apps/web/.env.local`:
```env
NEXT_PUBLIC_API_URL=http://localhost:5000/api
AUTH_SECRET=your_next_auth_secret
PRELAUNCH_PASSCODE=your_preferred_passcode
DISABLE_PASSCODE_SCREEN=false
```

### 4. Running the Platform
You can start all services (Frontend, Backend, and Docs) simultaneously from the root:
```bash
pnpm dev
```
- **Dashboard**: `http://localhost:3000`
- **Server**: `http://localhost:5000`
- **Docs**: `http://localhost:3001`

---

##  Technical Architecture

###  Recruiter User Flow
The frontend is designed for high-velocity decision making, focusing on clarity and AI explainability.

```mermaid
journey
    title Recruiter Journey
    section Job Setup
      Create Job Posting: 5: Recruiter
      Define Requirements: 4: Recruiter
    section Talent Ingestion
      Upload PDF Resumes: 5: AI-Parser
      Bulk Import CSV/Excel: 5: AI-Mapper
    section Matrix Evaluation
      Adjust Screening Weights: 4: Recruiter
      Trigger Matrix Assessment: 5: Gemini-AI
    section Final Selection
      Review Leaderboard: 5: Recruiter
      Shortlist Candidates: 5: Recruiter
```

###  AI Decision Pipeline
The backend orchestrates semantic comparisons between talent profiles and job specifications using a weighted matrix approach.

```mermaid
graph TD
    A[Recruiter Triggers Screening] --> B[Fetch Job & Applicant Data]
    B --> C[Flatten Data for LLM Context]
    C --> D[Apply User Weights: Skills/Exp/Edu]
    D --> E[Gemini 2.5 Flash Processing]
    E --> F[Generate JSON Output]
    F --> G[Extract Strengths/Gaps/Scores]
    G --> H[Persist to MongoDB]
    H --> I[Notify Frontend Leaderboard]
```

---

##  Sub-Project Documentation

Explore more detailed documentation for each part of the platform:

- [🎨 **Frontend Dashboard**](apps/web/README.md) - Next.js 16 (App Router) + Tailwind 4
- [⚙️ **Backend Orchestrator**](apps/server/README.md) - Node.js + TypeScript + Mongoose
- [📚 **Overall Documenation**](https://recruitt-docs.vercel.app) - Built with Docus
- [📦 **Shared Library**](packages/shared/README.md) - Types and Talent Profile Specifications

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