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AI-Driven Customer Appointment System

Automating Scheduling and Support with Conversational AI and API Integration

Tools Used: Python · Zendesk API · Acuity Scheduling · Claude (LLM)

Project Overview

In customer-facing businesses, responsiveness and efficiency are critical. I developed an AI-powered appointment system that integrates **Zendesk**, **Acuity Scheduling**, and **Anthropic’s Claude** to automate booking workflows and support interactions.

The system uses a conversational AI agent to understand customer requests, suggest available slots, and confirm appointments—all in natural language. A Python backend orchestrates real-time communication between APIs, enabling seamless ticket-driven scheduling and support resolution.

Data Preparation & Exploration

Using Python and API endpoints, I structured and synchronized data across platforms:

  • Customer queries and ticket metadata from Zendesk
  • Availability slots and booking history from Acuity
  • AI-generated appointment suggestions and confirmations
  • Polling mechanisms for real-time updates and status checks

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    I built logic to handle:

  • Intent recognition and slot matching
  • Conflict resolution and fallback suggestions
  • Ticket-to-appointment mapping
  • Confirmation syncing and notification triggers

    • This foundation enabled a responsive, scalable system that adapts to customer needs and backend changes.

    System Architecture & Integration

    The backend was designed for modularity and extensibility:

  • Conversational Layer: Claude interprets user intent and generates human-like responses
  • Scheduling Layer: Acuity API handles availability checks, booking, and updates
  • Support Layer: Zendesk API manages ticket creation, status tracking, and escalation
  • Sync Layer: Polling and event streaming ensure real-time data consistency across services

    • Each layer communicates via secure API calls, with error handling and fallback logic to maintain reliability.

    Key Insights & Strategic Recommendations

    The system delivered several strategic advantages:

  • Reduced manual scheduling effort by automating ticket-to-appointment workflows
  • Improved customer satisfaction through natural, responsive interactions
  • Increased accuracy and reduced double-bookings via real-time availability checks
  • Enabled scalability for high-volume support teams without increasing headcount

    • These outcomes informed broader automation strategies across customer service and operations.

    Operational Impact

    The project achieved measurable improvements:

  • Cut scheduling time by 70 percent
  • Reduced support resolution delays by 40 percent
  • Increased booking accuracy and reduced no-shows
  • Enhanced customer experience with faster, smarter interactions

    • Collaboration & Workflow

    I collaborated across departments to ensure alignment:

  • Support: To define ticket workflows and escalation logic
  • Operations: To configure scheduling rules and fallback protocols
  • Engineering: To build scalable backend modules and API connectors

    • The system was demoed in stakeholder meetings and integrated into live support workflows with minimal disruption.

    Lessons Learned

  • AI needs structure: Clear workflows and fallback logic are essential for reliability
  • Real-time matters: Polling and event streaming improve responsiveness and accuracy
  • Integration drives value: Seamless API connectivity unlocks automation potential