AI-Powered Restaurant Appointment Scheduling Chatbot

1. Challenge Overview

This challenge focuses on developing an AI-driven chatbot that enables users to schedule restaurant reservations seamlessly. The chatbot should handle booking requests, prevent overbooking, and provide an intuitive interface for both customers and restaurant managers.

2. Problem Statement

Build an AI-powered restaurant appointment scheduling chatbot that:

• Uses agentic behavior through LangGraph or CrewAI for intelligent interaction.
• Supports two interfaces:
  1. User Interface – Customers can book, modify, or cancel their reservations.
  2. Restaurant Interface – Restaurants can manage bookings, view upcoming reservations, and adjust availability.
• Stores booking information in a database (choice of SQL or NoSQL).
• Bounty Points for voice integration.
• Ensures no overbooking by validating real-time availability.

3. Technical Approach

Participants must implement:

1. Agent-Based Interaction – Use LangGraph or CrewAI to simulate restaurant assistant agents.
2. Database Integration – Store reservations in a SQL (PostgreSQL, MySQL) or NoSQL (MongoDB, Firebase) database.
3. Chatbot Interface – Develop two interfaces:
   • User Chatbot: Handles booking requests and modifications.
   • Restaurant Dashboard: Allows staff to manage bookings.
4. Availability Management – Prevent overbooking by dynamically checking reservation slots.
5. Notification System – Send confirmation emails or SMS notifications upon booking.
6. Performance Optimization – Measure response speed and efficiency of booking handling.

4. Restaurant Dashboard Features

The restaurant should be able to:

• Set Up Table Availability – Define the number of tables available for reservations.
• Manage Per Table Capacity – Specify the number of guests each table can accommodate.
• Define Total Capacity – Set the maximum number of people the restaurant can serve at any given time.
• Configure Banquet Capacity (if applicable) – Handle special large-group bookings separately from regular table reservations.
• View and Modify Reservations – See all upcoming bookings and adjust them as needed.
• Track Peak Hours & Trends – Gain insights into reservation trends to optimize operations.

5. References

• LangGraph for Multi-Agent Orchestration: LangGraph GitHub
• CrewAI for Agent Collaboration: CrewAI GitHub
• Twilio API for Notifications: Twilio Docs
• OpenTable API for Real-time Restaurant Data: OpenTable API

6. Evaluation Criteria

Submissions will be assessed based on:

• Booking Accuracy (40%) – Ensuring correct and conflict-free reservations.
• Agentic Behavior (20%) – Effective use of LangGraph or CrewAI for automation.
• User & Restaurant UX (20%) – Ease of interaction for both interfaces.
• Efficiency & Performance (10%) – Response time and handling of multiple requests.
• Code Quality & Documentation (10%) – Maintainable, well-documented code.

7. Deliverables

1. Source Code & README – Well-documented code with setup instructions.
2. Working Chatbot Application – Functional bot with booking and management features.
3. Evaluation Metrics – Report comparing chatbot performance and response times.
4. Demo Video (Optional) – Short video showcasing chatbot functionalities.

8. Implementation Guidelines

• Implement an agentic workflow using LangGraph or CrewAI.
• Use a database for reservation storage and real-time availability tracking.
• Ensure multi-user support so multiple customers can book simultaneously.
• Develop a React/Flask/Streamlit-based frontend for the restaurant interface.
• Integrate a notification system (SMS/Email) for booking confirmations.(Optional)