employee-onboarding-ai

A Unified Generative AI Solution for Streamlined Employee Onboarding Processes

This repository contains the research work titled “A Unified Generative AI Solution for Streamlined Employee Onboarding Processes”, which explores how modern AI techniques—particularly Generative AI—can be effectively integrated into HR management systems to automate and enhance the employee onboarding experience.

📄 Abstract

Employee onboarding is a critical process in HR management. Traditional onboarding often faces inefficiencies, delays, and communication gaps. This research proposes a unified Generative AI solution that combines natural language processing (NLP), machine learning (ML), and generative techniques to automate document handling, query answering, training content generation, and personalized onboarding paths for new hires.

📌 Objectives

• Identify onboarding pain points in traditional systems.
• Design and prototype a generative AI system for HR automation.
• Evaluate its efficiency, scalability, and user experience.
• Suggest future improvements and real-world implementation strategies.

🧠 Key Features

• Automated response generation for onboarding FAQs using NLP.
• AI-generated training materials and onboarding schedules.
• Seamless integration with HR systems to fetch and process employee data.
• Personalized experience using machine learning models.
• Enhanced efficiency and reduced HR workload.

🛠️ Technologies Used

• Generative AI (e.g., GPT-based models)
• NLP & BERT
• Reinforcement Learning
• React, Flask, PostgreSQL
• Docker & Kubernetes
• TensorFlow, PyTorch, Hugging Face Transformers

📁 Files Included

• Employee_Onboarding_Research.pdf – Full research paper

📄 Download Research Paper

You can view or download the full paper here:
👉 Employee_Onboarding_Research.pdf

📌 Note: For this link to work, make sure the PDF file is uploaded in the root directory of your GitHub repository with the exact name Employee_Onboarding_Research.pdf.

📩 Contact

Aman Kesharwani
📧 amankesharwani7100@gmail.com
🌐 Website: Kesharwani Services
🔗 LinkedIn: https://www.linkedin.com/in/aman-kesharwani-3300a2203/
🧳 Portfolio: https://amankesharwani7100.github.io/Portfolio/

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🌟 Acknowledgements

Special thanks to all contributors, mentors, and institutions involved in this research.

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