Unified Restaurant Management and AI-Assisted Customer Service Platform: Design, Implementation, and Evaluation of GTL Utsav Dining |
Author(s): |
| Pratik Mote , P.E.S. Modern College of Engineering Pune, India; Shripad Bhide, P.E.S. Modern College of Engineering Pune, India |
Keywords: |
| Restaurant Management System, Full-Stack Architecture, Workflow Integration, Multi-Factor Authentication, Conversational AI, CRM Automation, Operational Analytics, Hospitality Informatics |
Abstract |
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This paper presents the design, implementation, and evaluation of an integrated restaurant management and AI-assisted customer service platform, GTL Utsav Dining, that unifies customer workflows (reservations, ordering, tracking) with administrative operations (inventory, analytics, reporting) within a single full-stack architecture. The system comprises three independent subsystems: (1) a customer-facing React/Vite application supporting table bookings, online and dine-in ordering, real-time order tracking, and chatbot interaction; (2) an administrative React/Vite application providing menu management, order lifecycle control, inventory visibility, and operational reporting; and (3) a modular Python chatbot service using hybrid rule-based and machine-learning approaches for menu guidance, order tracking assistance, and FAQ handling. The backend employs Flask REST API organized into modular blueprints, MongoDB relational database with normalized schema, and independent scheduling for lead generation and event processing. Evaluation encompasses functional completeness across 45 API endpoints and 37 UI screens, automated testing of 23 chatbot scenarios achieving 100% pass rate with 94.3% intent classification accuracy, security assessment of OTP and TOTP multi-factor authentication achieving 95% and 92% success rates respectively, and operational analytics demonstrating 2.1-second order-to-dashboard synchronization time. The platform demonstrates that modern full-stack patterns can unify fragmented restaurant operations while maintaining security and conversational AI support, reducing manual operational overhead and improving decision-making latency. This implementation study contributes empirically to understanding integrated architecture patterns for hospitality domain challenges, with specific implications for small and medium-sized restaurant operations and hospitality informatics education. |
Other Details |
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Paper ID: IJSRDV14I40038 Published in: Volume : 14, Issue : 4 Publication Date: 01/07/2026 Page(s): 153-158 |
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