Automatic Ticketing with Make.com

In this how-to guide, you will learn how to build an automatic ticket classification workflow in make.com that accepts incoming requests, analyzes their content with AI, and routes them to the correct support team without manual effort.

Overview: Why Use Make.com for Ticket Classification

Support teams often receive large volumes of messages across multiple channels. Manually reading, tagging, and assigning each ticket is slow and error-prone. A scenario in make.com can automate this process with a webhook, a data store, and an AI classifier so tickets are automatically labeled and forwarded to the right team.

The tutorial from the official make.com automatic ticket classification guide shows a reference implementation that you can adapt to your own stack, whether you use help desks, CRMs, or custom tools.

What You Will Build in Make.com

By following this article, you will create a scenario in make.com that:

• Receives new support tickets via an HTTP webhook.
• Extracts relevant data like subject, body, and channel.
• Classifies each ticket into categories such as billing, technical issue, or feedback using AI.
• Stores ticket data and classification results in a data store.
• Triggers actions like email notifications or API calls to your support tools.

The exact structure of the scenario can vary, but the essential steps remain the same, allowing you to automate ticket triage and routing.

Prerequisites for Building the Make.com Scenario

Before creating the workflow, prepare the following:

• A make.com account with access to the Scenario editor.
• Basic understanding of webhooks and HTTP requests.
• Sample support tickets you can use to test the flow.
• An AI service or module available in make.com for text classification.
• Optional: a target system such as a help desk, Slack, or email for routing the classified tickets.

Once these are in place, you can start building the scenario step by step.

Step 1: Design the Workflow in Make.com

Before touching the scenario editor, map out the logical flow of data. A typical make.com ticket workflow includes:

1. Ticket ingestion: An entry point such as a webhook or email module.
2. Pre-processing: Normalizing message text and extracting key fields.
3. Classification: An AI or rules-based step that labels the ticket.
4. Persistence: Saving results into a data store or database.
5. Routing: Forwarding the ticket to the correct team or system.

Defining the flow first helps keep the scenario clear and easy to maintain.

Step 2: Create the Webhook in Make.com

The first technical step is to configure an incoming connection that will receive tickets.

1. Open your make.com dashboard and create a new scenario.
2. Add a Webhook module as the first module.
3. Choose Custom webhook and create a new webhook.
4. Copy the generated URL; you will use this to send test tickets from your system.
5. Save the scenario to ensure the webhook is active.

With this in place, any HTTP POST request to the webhook URL will create a new bundle in the scenario and launch the automation.

Step 3: Normalize Incoming Ticket Data

Tickets may arrive from different systems with slightly different fields. In make.com you should normalize these fields so later modules work with a consistent structure.

1. Add a Data transformer or Set variables module after the webhook.
2. Map incoming fields such as subject, body, email, and channel into well-named variables.
3. Optionally, concatenate pieces of text into a single ticket_content field for easier classification.
4. Trim whitespace and remove unnecessary markup or signatures where possible.

This step increases accuracy for the classification module and simplifies maintenance.

Step 4: Build the AI Classification Step in Make.com

The core of the automation is the AI classifier that analyzes the ticket content and assigns a category. Make.com allows you to connect to AI services or use native AI modules, depending on your plan and integrations.

1. Add an AI or HTTP module, depending on the AI provider you use.
2. Send the ticket_content or similar field from the previous step.
3. Provide clear instructions in the prompt or parameters, for example: “Classify this support ticket into one of the following categories: Billing, Technical Issue, Account Access, General Question, Feedback.”
4. Map the AI response to a new field such as ticket_category.
5. Optionally, also capture confidence scores or secondary categories, if available.

This step converts unstructured text into structured, actionable labels.

Step 5: Store Tickets in a Make.com Data Store

To keep a record of all processed tickets and their classifications, use a data store in make.com.

1. Create a Data store in your make.com dashboard with fields like id, subject, content, category, status, and created_at.
2. Add a Data store > Create a record module to the scenario.
3. Map each normalized field from the earlier modules into the data store fields.
4. Include the AI-generated category so you can later filter or analyze tickets based on this value.

Storing tickets in a central data store makes reporting and troubleshooting much easier.

Step 6: Route Classified Tickets to the Right Team

Once the ticket is classified and stored, the scenario can route it to the appropriate destination based on the category. This is where make.com shines as an integration platform, since you can connect a wide range of services.

1. Add a Router module after the data store step.
2. Create routes for each key category, such as Billing, Technical Issue, or Account Access.
3. On each route, set a filter that matches the ticket_category.
4. Attach modules that correspond to each team’s preferred channel, such as sending a message to a Slack channel, creating a ticket in your help desk tool, or sending a notification email.
5. Optionally, add a default route for tickets that cannot be confidently classified, sending them to a general queue for manual review.

This routing logic ensures that every ticket ends up in front of the right people quickly.

Step 7: Test and Refine Your Make.com Workflow

Testing is essential before you roll the scenario out to your entire support operation.

1. Turn on the scenario in make.com in a test environment or with a limited data set.
2. Send several sample tickets via the webhook URL with different topics and tones.
3. Check the logs to confirm that each module runs as expected and that the AI classification output makes sense.
4. Adjust the prompt or categories if the AI misclassifies important cases.
5. Verify that records are correctly created in the data store and that routing actions reach the intended teams.

Iterate on this process until you are satisfied with the level of accuracy and reliability.

Best Practices for Make.com Ticket Classifiers

To keep your automation stable and useful over time, follow these best practices:

• Keep prompts clear: Provide explicit categories and examples to your AI model.
• Limit categories: Start with a small set of high-level categories and expand later.
• Monitor edge cases: Regularly review tickets that were misrouted and refine filters or prompts.
• Implement fallbacks: Use a default route for low-confidence classifications.
• Document the flow: Describe each part of the make.com scenario so future editors can understand your logic.

Extending Your Make.com Ticket Automation

After the basic classifier is running, you can extend it with additional features:

• Automatic priority scoring based on keywords or sentiment.
• Customer tier detection using data from your CRM.
• Scheduled reports that summarize ticket volume by category.
• Feedback loops where agents can correct misclassifications and improve the model.

For more advanced automation strategies and consulting around integrations and AI workflows, you can explore resources at Consultevo, which specializes in automation and optimization.

Conclusion: Streamline Support with Make.com

By combining webhooks, AI classification, data stores, and routing logic, make.com enables you to create a powerful automatic ticket classification system that reduces manual work and speeds up response times. Following the structure from the official tutorial and the steps outlined here, you can adapt the scenario to your own stack and continuously improve it as your support needs evolve.