AI Recruiter 🚀

The Next-Generation Hybrid Recruitment Platform

📝 Overview

AI Recruiter is a production-grade, privacy-first recruitment platform designed to revolutionize how talent is screened. Unlike traditional cloud-only solutions, AI Recruiter employs a novel Hybrid Architecture that combines the speed of server-side deterministic scoring with the depth of Local LLM Intelligence (Ollama).

This approach ensures Zero-Latency Screening while providing rich, human-like qualitative feedback without sending sensitive candidate data to external third-party AI APIs for deep analysis.

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✨ Key Features

🧠 Hybrid Intelligence Engine

• Phase 1: Deterministic Speed (Server-Side): Instantly parses resumes and ranks them based on hard skills and semantic matching. Zero latency.
• Phase 2: Cognitive Analysis (Client-Side Local LLM): Uses a locally running Qwen 2.5 model via Ollama to act as a "Senior Recruiter," providing nuanced, human-like feedback on candidate soft skills and potential red flags.

🎨 Premium User Experience

• Glassmorphism UI: A stunning, modern interface built with ShadCN UI and Tailwind CSS.
• Professional Dark Mode: Carefully calibrated "Neutral Zinc" palette for deep contrast and reduced eye strain.
• Reactive Dashboard: Real-time progress tracking, interactive charts, and seamless drag-and-drop uploads.

🛠️ Powerful Toolset

• ATS Score Finder: Deep-dive analysis of individual resumes against specific job descriptions.
• Interview Question Generator: Context-aware question generation tailored to the specific gaps found in a candidate's profile.
• Secure Data Handling: Powered by Firebase Auth and Firestore with strict security rules—your data belongs to you.

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🏗️ Architecture Diagram

The system uses a Progressive Enhancement pattern to balance performance and intelligence.

flowchart TD
    subgraph Client [💻 User Environment]
        Browser["Modern Browser"]
        LocalAI["🦙 Local LLM (Ollama)"]
    end

    subgraph Cloud [☁️ Cloud Infrastructure]
        Server["⚡ Next.js Server Actions"]
        DB[("🔥 Firestore")]
        Auth["🛡️ Firebase Auth"]
    end

    User[👩‍💼 Recruiter] -->|1. Upload JD & Resumes| Browser
    
    %% Phase 1: Fast Screening
    Browser -->|2. fast-screen (Phase 1)| Server
    Server -->|3. Deterministic Scoring| Server
    Server -->|4. Save Initial Rank| DB
    
    %% Phase 2: Deep Analysis
    Browser -.->|5. Realtime Subscription| DB
    Browser -->|6. Trigger Deep Analysis (Phase 2)| LocalAI
    LocalAI -->|7. Generate Human Feedback| Browser
    Browser -->|8. Secure Write| DB
    
    classDef client fill:#e0f2fe,stroke:#38bdf8,stroke-width:2px;
    classDef cloud fill:#f3e8ff,stroke:#a855f7,stroke-width:2px;
    
    class Browser,LocalAI client;
    class Server,DB,Auth cloud;

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Flow Breakdown

1. Fast Screen: The server instantly processes files to give immediate UI feedback.
2. Local Inference: The browser connects to localhost:11434 (Ollama) to generate expensive textual feedback, saving cloud costs and ensuring privacy.
3. Secure Sync: The authenticated client writes the final detailed feedback to Firestore, ensuring only authorized users modify data.

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🛠️ Tech Stack

• Frontend: Next.js 14 (App Router), React, ShadCN UI
• Styling: Tailwind CSS (Custom Zinc Theme)
• Local AI: Ollama (Model: qwen2.5:7b)
• Backend: Next.js Server Actions
• Database: Firebase Firestore
• Auth: Firebase Authentication

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🚀 Getting Started

Follow these instructions to set up the Hybrid AI environment on your local machine.

Prerequisites

• Node.js 18+ installed.
• Ollama installed and running. (Download)
• Firebase Project created.

1. Setup Local AI (Ollama)

This project relies on a local Large Language Model for Phase 2 feedback.

# 1. Install Ollama from ollama.com

# 2. Pull the Qwen model (Optimized for reasoning & coding)
ollama pull qwen2.5:7b

# 3. Start the Inference Server (Must align with CORS settings)
ollama serve

2. Clone the Repository

git clone https://github.com/your-username/ai-recruiter.git
cd ai-recruiter

3. Install Dependencies

npm install

4. Configure Environment

Create a .env.local file in the root directory:

# Firebase Configuration
NEXT_PUBLIC_FIREBASE_API_KEY=your_api_key
NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN=your_project_id.firebaseapp.com
NEXT_PUBLIC_FIREBASE_PROJECT_ID=your_project_id
NEXT_PUBLIC_FIREBASE_STORAGE_BUCKET=your_project_id.appspot.com
NEXT_PUBLIC_FIREBASE_MESSAGING_SENDER_ID=your_sender_id
NEXT_PUBLIC_FIREBASE_APP_ID=your_app_id

# Feature Flags
NEXT_PUBLIC_USE_EMULATORS=false 
NEXT_PUBLIC_ENABLE_LOCAL_LLM=true

5. Run the Application

npm run dev

Visit http://localhost:3000 to access the platform.

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📂 Project Structure

/src
├── app/                    # Next.js App Router (Pages & Layouts)
├── ai/
│   ├── flows/              # Server-side Deterministic Logic (Phase 1)
│   └── progressive-enhancement/ 
│       └── feedback-service.ts # Client-side Local LLM Logic (Phase 2)
├── components/             # Reusable UI (ShadCN + Custom)
├── styles/                 # Global styles & Tailwind config
└── services/               # Firestore Service Layer

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🤝 Contributing

We welcome contributions! Specifically, we are looking for:

• Prompt Engineering: Improving the "Senior Recruiter" persona in feedback-service.ts.
• Algorithm Tuning: Enhancing the semantic matching logic in rank-candidates-fast.ts.

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License

MIT