Multitenant Leave System

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Aayush Bhaskarwar; Akshat Dhanuka; Ziyaad Parkar; S.P. Shintre

Multitenant Leave System

Authors: Aayush Bhaskarwar, Akshat Dhanuka, Ziyaad Parkar, S.P. Shintre

Unique Paper ID: 169590
Volume: 11
Issue: 6
PageNo: 2687-2694

Keywords: leave management system team collaboration chatbot integration NLP GPT API SQL query automation cross-team dependencies employee productivity workflow optimization data-driven leave suggestions HR automation interdependent teams organizational efficiency.

Abstract:
Efficient leave management is critical for maintaining team productivity and meeting project deadlines in modern organizations, particularly when multiple teams work collaboratively. This paper proposes a dynamic, AI-driven team leave management system designed to streamline leave requests and approvals while minimizing disruptions to workflow. The system integrates a chatbot, utilizing natural language processing (NLP) to convert employee inputs—such as "Who is on leave today?" or "When can I next take leave?"—into SQL queries, providing instant responses and actionable insights. By incorporating cross-team dependency management, the system ensures that leave scheduling is optimized across interdependent teams, preventing bottlenecks and missed deadlines. Furthermore, the system tracks utilized and non-utilized leaves, offering data-driven suggestions for optimal leave timing, based on usage trends throughout the year. This paper also discusses the technical architecture, the integration of the GPT API for NLP, and the role of a backend database in automating leave management. The proposed solution aims to enhance team efficiency by offering a seamless, intelligent interface for managing leaves without affecting overall project timelines.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 6
PageNo: 2687-2694

Multitenant Leave System

Available:https://ijirt.org/article?manuscript=169590

21 May, 2025

Multitenant Leave System with Adaptive Leave Optimization and NLP Integration

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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