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AI Booking Optimization and Smart Agent Escalation System

Status Built With Focus License Last Update

Project Pitch:
I built an AI-driven system that optimizes online booking flows and customer support for travel transfers.
The system intelligently guides users through the booking process, auto-answers frequent questions, and escalates complex cases to human agents — using real-time confidence scoring and CRM integration to maximize speed and satisfaction.


System Diagram

Booking Optimization and Smart Escalation System Diagram


What It Does

  • Receives incoming booking inquiries or support questions
  • Classifies intent and routes simple cases to the AI agent
  • AI agent answers FAQs, booking edits, cancellations, and transfer confirmations
  • If confidence is low or user is unsatisfied, the system escalates to a human agent
  • Connects directly to the CRM and booking platform to pull or update real-time customer data
  • Uses A/B testing to continuously optimize AI messaging and flow efficiency

📊 Booking Optimization Flow Diagram

This system uses AI to route customers through the booking process with CRM integration, fallback nudges, and event logging:

Diagram

This diagram shows how the booking optimization flow orchestrates various services:

  • 🎯 AI Decision Engine: Guides the user based on preferences, timing, and historical booking data.
  • 🧠 Fallback System: Nudges users with alternate suggestions (routes, time slots) to reduce drop-off.
  • 🔄 CRM Sync: All actions update in real-time with the CRM, enabling personalized follow-ups.
  • 📅 Event Logging: Tracks decision points to improve conversion analysis and A/B testing.

Technologies Used

  • OpenAI (chat completion and classification)
  • n8n (workflow orchestration)
  • CRM API Integration (HubSpot, Zoho, or similar)
  • Booking system API connection
  • LangChain prompt routing (for escalation decision making)
  • Confidence score-based escalation triggers
  • A/B Testing layer for conversational experiments

Files

  • ai_booking_optimization_workflow.json — Exported n8n workflow
  • ai-booking-optimization-system-diagram.png — Visual diagram of system logic

KPIs Measured

  • Booking completion rate
  • AI self-resolution rate
  • Escalation to human (%)
  • Customer satisfaction score (CSAT)
  • Booking flow average time

Why This Matters

This system shows how a carefully designed AI agent can enhance customer experience without replacing humans — speeding up bookings, deflecting unnecessary support cases, and increasing customer loyalty.
It proves real-world AI deployment with measurable business value, aligned with Alps2Alps goals.


Future Work

  • Add multilingual support for AI booking conversations
  • Introduce dynamic confidence threshold adjustment based on live user feedback
  • Expand CRM integration to include loyalty program status and upsell recommendations
  • Build a predictive booking delay detection agent using live traffic and weather data

Demo built for AI Agent Implementation Manager portfolio presentation.

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Smart AI system for booking flow optimization, customer service automation, and intelligent human handoff in travel services.

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