Transform Voice Messages Into IT Support Tickets Automatically With n8n

Picture this: your team is buried in support requests. Employees send voice messages on Telegram because typing is too slow. Your IT team manually listens to each one, deciphers the request, creates a ticket, and then tries to remember who asked for what. It's messy. It's time consuming. And honestly, it's exhausting.

What if your support system could listen, understand, and organize these requests for you? What if every voice message automatically became a tracked ticket with all the details extracted and routed to the right people?

That's exactly what this n8n workflow does. It transforms chaotic voice messages into structured IT support tickets using AI transcription, intelligent data extraction, and seamless integrations. No manual data entry. No lost requests. Just automation that actually works.

What You'll Need Before You Start

Before diving into the workflow, you'll need access to a few key platforms. Think of these as your automation toolkit:

• n8n account for building the workflow
• Telegram Bot API to receive and send messages
• Google Gemini API for AI powered audio transcription
• OpenAI API (GPT 4.1 mini) to extract structured data from transcripts
• Jira Software Cloud to create and track support tickets
• Slack workspace for team notifications
• Google Drive API to backup audio files

Each of these services plays a specific role in the automation. Telegram serves as your user interface. Google Gemini turns speech into text. OpenAI makes sense of that text. Jira tracks everything. Slack keeps your team in the loop. And Google Drive preserves the original audio for reference.

Key Components That Power This Workflow

The workflow uses specific n8n nodes that handle different parts of the process. Here's what each one does:

• Telegram Trigger listens for incoming voice messages
• IF node validates that the message is actually audio
• Telegram node downloads the audio file and sends responses
• Google Drive node uploads and stores audio backups
• Google Gemini node transcribes audio to text
• Merge node combines transcript and file metadata
• Code node processes data before AI analysis
• AI Agent node with OpenAI analyzes transcripts
• Structured Output Parser formats data into predefined schema
• Jira node creates service request tickets
• Slack node notifies the IT support team
• Set node manages configuration variables

These components work together like a well oiled machine. Each handles its specific task and passes the result to the next node in line.

Building The Workflow Step By Step

Step 1: Set Up Your Telegram Listener

The workflow starts with a Telegram Trigger node. This node waits for messages from your Telegram bot. When someone sends a voice message, the trigger activates and captures the message data including the audio file ID, sender information, and chat details.

img_1.png

Right after the trigger, an IF node checks whether the incoming message contains audio. It looks for the audio/ogg format in the message structure. If it's not audio, the workflow sends a friendly reply asking the user to send a voice message instead. This prevents errors and guides users toward the right input method.

Step 2: Process and Backup The Audio

Once the workflow confirms it's dealing with audio, two things happen simultaneously. First, a Telegram node downloads the audio file using the file ID. Second, the workflow prepares to back up this file.

img_2.png

The downloaded audio then flows to a Google Drive node. This node uploads the file with a timestamped filename like audio 20241110 143022. The backup serves two purposes: it preserves the original request for compliance or reference, and it generates a shareable link that gets attached to the Jira ticket.

Step 3: Transcribe Audio With AI

While the audio is being backed up, a Google Gemini node receives the same audio file. Using the Gemini 2.5 Flash model, it transcribes the voice message into text. This happens in parallel with the backup process, saving precious seconds.

img_3.png

A Merge node then waits for both operations to complete. It combines the transcript text with the Google Drive metadata including the shareable link. This merged data becomes the foundation for the next step.

Step 4: Extract Structured Data With OpenAI

A Code node prepares the merged data by creating a clean JSON structure containing the transcript, audio URL, chat ID, and user's first name. This formatted data feeds into an AI Agent node powered by OpenAI's GPT 4.1 mini model.

The AI Agent acts like an intelligent assistant. It reads the transcript and extracts specific information: requester name and department, request category (Feature, Incident, Access Request, or Maintenance), priority level (Low, Medium, High, or Critical), a clear title summarizing the request, a detailed description with context, any mentioned due dates, and the original transcript for reference.

img_4.png

The system prompt instructs the AI to only extract information that's explicitly mentioned. If the speaker doesn't mention their name or department, those fields stay empty rather than making something up. This keeps the data honest and accurate.

A Structured Output Parser ensures the AI's response matches a predefined JSON schema. This guarantees consistency across all tickets and makes the data easy to work with in subsequent steps.

Step 5: Create The Jira Ticket

With structured data in hand, a Jira node creates a new Service Request ticket. The ticket summary uses the extracted title. The description includes the AI generated description, the full original transcript, and a link to the audio file in Google Drive.

img_5.png

This gives the IT team everything they need: a quick summary for prioritization, full context for understanding the request, and the original audio if they need to hear it themselves. The ticket is automatically created in the My Kanban Project with all fields properly populated.

Step 6: Notify Everyone Involved

After ticket creation, a Set node stores configuration values like the Jira base URL and IT support email. These values flow to two notification nodes.

A Slack node posts a formatted message to the IT support team channel. The message includes the requester's name and department, the category and priority, the title and description, and a direct link to the Jira ticket. The team sees everything at a glance and can jump straight into action.

img_6.png

img_7.png

Simultaneously, a Telegram node sends a confirmation message back to the person who submitted the request. This message acknowledges receipt, summarizes what was understood, provides the Jira ticket number, and includes a tracking link. The requester knows their request was received and can follow its progress.

Why This Workflow Changes Everything

This automation eliminates manual bottlenecks in IT support. Voice messages are faster and easier for employees than typing detailed requests. But they're harder for support teams to manage without transcription and organization.

The workflow solves this by making voice as structured as text. Every request gets logged, categorized, and tracked automatically. Nothing falls through the cracks. Response times improve because the team isn't spending time on data entry.

Beyond IT support, this pattern works for any scenario where voice input needs to become structured data. Customer feedback collection. Field service requests. Sales inquiry logging. Helpdesk operations. The core concept is the same: capture audio, transcribe it, extract meaning, create records, and notify stakeholders.

The beauty of n8n is that you can adapt this workflow to your specific tools. Swap Jira for another project management system. Replace Slack with Microsoft Teams or email. Use a different transcription service. The logic stays the same while the integrations flex to match your tech stack.

What used to take ten minutes of manual work now happens in seconds. That's the power of intelligent automation: not replacing humans, but freeing them to focus on solving problems instead of processing requests.