Recruit Sphere: Advanced AI-Driven Recruitment Platform

Recruit Sphere is a professional-grade, full-stack recruitment platform designed to streamline the hiring process with automated ATS (Applicant Tracking System) scoring, multi-channel notifications, and high-performance dashboard analytics.

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🚀 Key Features

• Automated ATS Scoring: Intelligent resume parsing that ranks candidates based on job-specific criteria and keywords.
• Dynamic Hiring Pipelines: Configurable recruitment stages (Coding rounds, Interviews, Offers) for every job posting.
• High-Performance Architecture: Built with Next.js 14 (App Router) using a streaming architecture for near-instant perceived speed.
• Centralized Logic: Pure Service-Oriented Architecture (SOA) that decouples business logic from UI components.
• Real-time Notifications: Integrated automated email system (SMTP) and in-app alerts for candidates and recruiters.
• Comprehensive Candidate Profiles: Rich profile management including skill tracking, experience history, and resume hosting.

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🛠️ Technology Stack

• Frontend: Next.js 14, React 19, Tailwind CSS, Shadcn UI
• State Management: Zustand (Global UI), TanStack Query (Server State)
• Backend Orchestration: Next.js Server Actions with a dedicated Service Layer
• Database: MongoDB with Mongoose (Optimized Indexing)
• Validation: Zod (End-to-end type safety)
• Security: Server-side cookie-based sessions with Middleware protection

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🏗️ System Architecture

High-level overview of the Recruit Sphere service-oriented architecture.

▶ Technical Topology (Click to expand)

graph TD
    subgraph "UI Layer (Client/Server Components)"
        P[Pages] --> C[Components]
    end

    subgraph "Action Layer (Next.js Server Actions)"
        C --> A[Server Actions]
    end

    subgraph "Service Layer (Domain Business Logic)"
        A --> JS[JobService]
        A --> US[UserService]
        A --> AS[ApplicationService]
        A --> NS[NotificationService]
    end

    subgraph "Data Layer (Mongoose & Indices)"
        JS --> M[(MongoDB)]
        US --> M
        AS --> M
        NS --> M
    end

    style JS fill:#6366f1,color:#fff,stroke:#333,stroke-width:2px
    style US fill:#6366f1,color:#fff,stroke:#333,stroke-width:2px
    style AS fill:#6366f1,color:#fff,stroke:#333,stroke-width:2px
    style NS fill:#6366f1,color:#fff,stroke:#333,stroke-width:2px

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Core Design Principles:

1. Dumb Actions, Smart Services: Server Actions act only as thin entry points; all complex logic resides in the Service Layer for testability.
2. Streaming & Suspense: Critical data-heavy pages use loading.tsx and React Suspense boundaries to eliminate blank screens.
3. Database Indexing: High-performance MongoDB queries through strategic indexing on createdAt, status, and recruiterId.

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📁 Project Structure

src/
├── app/               # Next.js App Router (Routes & Skeletons)
├── components/        # Reusable UI components (Shared)
├── features/          # Domain-driven feature modules (Jobs, Auth, Profile)
├── lib/               # Utility functions & Database configuration
├── models/            # Mongoose Schemas & Database Models
├── services/          # Centralized Business Logic (The brain of the app)
└── shared/            # Shared types and Zod schemas

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⚙️ Installation & Setup

1. Clone the repository:

   git clone https://github.com/varni1512/Recruit_Sphere.git
   cd Recruit_Sphere

2. Install dependencies:

   npm install

3. Environment Setup: Create a .env.local file with the following:

   MONGODB_URI=your_mongodb_connection_string
   SMTP_HOST=your_smtp_host
   SMTP_PORT=your_smtp_port
   SMTP_USER=your_smtp_user
   SMTP_PASS=your_smtp_password

4. Run in development:

   npm run dev

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📊 Performance Optimization

• Database: Standardized on .lean() queries with indexing for maximum throughput.
• Rendering: Implemented Parallel Data Fetching (Promise.all) in the Dashboard to reduce initial load latency.
• Bundle: Optimized via dynamic imports for heavy components like charts.

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📝 License

Distributed under the MIT License. See LICENSE for more information.