Automate Appointment Scheduling with Twilio, Airtable and AI Agents

Opening Problem Statement

Meet Sarah, the customer service manager at PC Parts Ltd, a busy London-based PC and laptop repair company. Every day, Sarah receives dozens of SMS enquiries from customers wanting to book repair appointments. Currently, Sarah spends over 3 hours daily just juggling appointment requests, manually responding to questions, tracking conversations, and following up with potential clients who haven’t yet booked. Errors like double bookings or missed messages happen, frustrating both Sarah and customers. On top of this, customers occasionally text “STOP” to avoid further messages, and Sarah needs a way to respect these requests automatically. The sheer volume and manual effort slow down the booking process and cause lost revenue opportunities for PC Parts Ltd.

To transform Sarah’s workflow and save time, an automated, intelligent appointment scheduling system that communicates over SMS and manages all appointment interactions seamlessly is needed.

What This Automation Does

This n8n workflow harnesses Twilio, Airtable, Cal.com APIs, and AI-powered language models to fully automate appointment scheduling and follow-up messaging for PC Parts Ltd. When a customer sends an SMS enquiry, this workflow:

• Triggers on incoming SMS with Twilio Trigger node.
• Looks up any existing chat session for the customer in Airtable to maintain conversation context.
• Engages an AI Appointment Scheduling Agent (using Langchain and OpenAI GPT) to understand customer messages, answer questions, and schedule, reschedule, or cancel appointments via Cal.com API calls.
• Updates chat history and customer records in Airtable after each interaction.
• Sends SMS replies and appointment confirmations back through Twilio.
• Daily identifies customers with open enquiries but no scheduled appointment, generates personalized follow-up messages using AI, and sends these messages with opt-out instructions.
• Handles customer STOP commands, updating the Airtable status and confirming opt-out automatically.

This workflow saves Sarah 3+ hours daily by eliminating manual response handling, reduces booking errors, respects customer preferences automatically, and boosts conversion by re-engaging leads with personalized follow-ups.

Prerequisites ⚙️

• Twilio Account 📧 – to receive and send SMS messages.
• Airtable Account 📊 – stores customer session data, chat history, and appointment details.
• Cal.com Account 🔌 – provides the appointment scheduling API integration.
• OpenAI Account 🔐 – powers the AI Appointment Scheduling Agent and follow-up message generation.
• n8n Account 🔑 – to create and run this workflow. Can be self-hosted or cloud-hosted. For self-hosting, consider Hostinger’s n8n hosting options.

Step-by-Step Guide

Step 1: Add Twilio SMS Trigger Node

Navigate to Triggers → Twilio Trigger. Set it to listen for inbound SMS messages with the event com.twilio.messaging.inbound-message.received. Connect your Twilio account credentials. This node starts the workflow every time a customer texts your Twilio number.

You should see webhook URL generated to test Twilio integration. Incoming SMS payload includes sender phone number and message body.

Common Mistake: Not setting proper webhook URL in Twilio console or missing the inbound message event.

Step 2: Check for STOP Command with Switch Node

Add a Switch node → Check For Command Words right after the Twilio trigger. Configure it to check if the SMS body contains the text “STOP” (case-sensitive). Route the “STOP” output to a node that updates the customer’s status in Airtable. Non-STOP messages proceed to the chat session lookup.

This ensures customers can opt out of follow-up messages at any time.

Step 3: Get Existing Chat Session from Airtable

Add an Airtable node → Get Existing Chat Session. Configure with your Airtable base and table (Lead Tracker). Use a filter formula to find any existing session with the sender’s phone number as session_id. Return only one record.

This retrieves existing chat history and appointment info to provide context to the AI agent.

Step 4: AI Appointment Scheduling Agent Setup

Add the Langchain Agent node → Appointment Scheduling Agent1. Connect your OpenAI API credentials. Provide a comprehensive system message defining the assistant’s role: handling PC Parts Ltd appointment bookings, interacting only about appointments and company info, managing bookings via Cal.com API, and updating conversation state accordingly.

The node sends the conversation and session data to GPT models for conversational scheduling.

Step 5: Use Cal.com HTTP Request Tools for Bookings

Several HTTP Request nodes interact with Cal.com’s API to:

• Get availability slots
• Create new bookings
• Find existing bookings
• Reschedule or cancel appointments

Configure each HTTP tool node with Cal.com API credentials and appropriate endpoint URLs, methods, headers, and body as per the examples in the workflow. Use ISO 8601 datetime formats for appointment times.

Example for creating a booking:

{
  "eventTypeId": 648297,
  "start": "2024-01-01T09:00:00Z",
  "attendee": {
    "name": "John Doe",
    "email": "[email protected]",
    "timeZone": "Europe/London"
  },
  "bookingFieldsResponses": {
    "title": "PC repair enquiry"
  }
}

Step 6: Update Airtable with Conversation and Booking Data

Add an Airtable node → Create/Update Session to store or update the customer’s chat messages, appointment details, and metadata after each interaction. Match records by session_id. This keeps chat history persistent.

Step 7: Send SMS Reply to Customer

Add a Twilio node → Send Reply. Use the phone number from the event (From) and message generated by the AI agent. This keeps the conversation flowing in SMS.

Step 8: Daily Follow-Up to Open Enquiries

Use a Schedule Trigger set to every 24 hours to identify leads without confirmed appointments from Airtable using Find Follow-Up Candidates Airtable node with a formula filtering:

• No appointment scheduled
• Status not STOP
• Less than 3 follow-ups
• At least 3 days since last follow-up

Pass these leads to the AI language model to Generate Follow Up Message. Then update follow-up count and last follow-up date in Airtable using Update Follow-Up Count and Date. Finally send the personalized follow-up SMS with Send Follow Up Message Twilio node.

Step 9: Handle Customer STOP Requests

The workflow listens for “STOP” messages, updates the record status to “STOP” with the User Request STOP Airtable node, and sends a confirmation SMS with Send Confirmation Twilio node. This respects customer communication preferences.

Customizations ✏️

• Change Appointment Duration: In the Appointment Scheduling Agent1 node system message, edit the appointment length or rules for booking durations to match your business needs.
• Modify Follow-Up Frequency: Adjust the filter formula in Find Follow-Up Candidates Airtable node to change how many days between follow-ups or maximum follow-up attempts.
• Expand AI Responses: Customize the AI agent’s system prompt to include answers to common FAQs about repairs, pricing, or company policies.
• Switch SMS Provider: Replace the Twilio nodes with other SMS integration nodes if using a different messaging service.
• Update Cal.com Event Type ID: In the HTTP Request nodes for Cal.com, change the eventTypeId parameter to your specific event to reflect correct booking options.

Troubleshooting 🔧

Problem: “No matching session found” or empty results in Airtable search

Cause: The session_id filter value does not match the phone number format or missing data in Airtable records.

Solution: Verify that the incoming Twilio phone number format matches how phone numbers are stored in Airtable. Check the filter formula syntax and update Airtable data consistency.

Problem: AI Agent fails to schedule appointment or returns error

Cause: API credentials for Cal.com or OpenAI are incorrect or missing required fields in the AI prompt.

Solution: Double-check credentials in n8n for Cal.com and OpenAI. Review the AI system prompt for required parameters such as user email, date, and time. Also confirm ISO datetime formatting.

Problem: Follow-up messages sent to users who replied STOP

Cause: Airtable record status not updated correctly or logic flaw in filtering candidates.

Solution: Check the User Request STOP Airtable update node and formula in Find Follow-Up Candidates node. Make sure customers marked STOP are excluded.

Pre-Production Checklist ✅

• Confirm Twilio webhook URL works and triggers workflow on new SMS.
• Verify Airtable base and table schemas match expected fields for sessions and follow-ups.
• Test Cal.com API calls independently to confirm authentication and endpoints.
• Validate AI agent responses make sense with representative sample queries.
• Perform test SMS conversations including STOP command scenario.
• Check follow-up messages are personalized and sent only to eligible leads.
• Backup Airtable data before deploying workflow to production.

Deployment Guide

Activate your workflow by turning it ON in n8n. Link your Twilio webhook URL in your Twilio account to the exposed n8n webhook URL from the Twilio Trigger node.

Monitor incoming SMS logs in n8n’s execution monitor for errors. Use n8n’s error handling features to log or notify issues.

If self-hosting, set up persistent storage and secure API keys environment securely. Consider using the Hostinger n8n hosting option for stability.

FAQs

Q1: Can I use another SMS provider instead of Twilio?

Yes, you can substitute Twilio nodes with other SMS provider integrations available in n8n, but you will need to adjust webhook triggers and message sending nodes accordingly.

Q2: Will sending follow-up messages consume my Twilio credits?

Yes, each SMS sent through Twilio uses credits. Make sure to monitor usage in your Twilio account and adjust follow-up frequency accordingly.

Q3: Is customer data safe in this workflow?

All personal data is stored in Airtable, so ensure your Airtable and n8n environments are secured and access controlled. Communication with APIs is encrypted over HTTPS.

Q4: Can this workflow handle a large number of enquiries?

This workflow is scalable with n8n’s infrastructure but heavy volumes might require retries or queue mechanisms.

Conclusion

By implementing this n8n workflow, Sarah at PC Parts Ltd has automated the entire SMS-based appointment scheduling journey— from receiving enquiries, intelligently conversing with customers using AI, managing and updating appointment data, to personalized follow-up messages that re-engage potential clients.

This automation saves over 3 hours daily of manual effort, reduces double booking errors, increases appointment conversion rates, and respects customer communication preferences automatically.

Next steps could include integrating email notifications, expanding to other service types beyond PC repairs, or adding multilingual support for a broader customer base.

Start building this workflow now, and watch your appointment scheduling transform from a tedious task into a seamless customer experience.