Customer support ticket triage, sentiment analysis, and draft response generation

A support team receives 150 tickets before lunch. Each one needs reading, categorising, and a thoughtful response. By 3pm, your best agent is burnt out, customers are waiting for replies, and nothing's been solved yet. This scenario plays out in thousands of companies every single day. The problem isn't that your team doesn't care. It's that they're doing work machines should handle. Ticket triage, sentiment analysis, and response drafting are perfect candidates for automation, yet most teams still rely on manual sorting and copy-pasting templates. The result: slower first response times, inconsistent quality, and staff turnover. What if every incoming ticket was automatically categorised, analysed for urgency and emotion, and presented to your agent with a ready-made draft response waiting for review? What if sensitive issues were flagged immediately, and routine requests were queued separately? You'd free up hours per week, reduce response time from days to minutes, and let your team focus on genuinely tricky problems that need human judgment. This is exactly what a well-built automation workflow can do. Let's build it. For more on this, see Customer support ticket analysis and response automation. For more on this, see Multilingual customer support ticket automation with resp.... For more on this, see Multi-language customer support ticket triage and respons....

The Automated Workflow

You'll need an orchestration platform to tie everything together. Zapier works for simple cases, but I'd recommend n8n or Make if your support system has a decent API. They give you better control over how data moves between tools and cost far less at scale. The workflow has five core steps: receive the ticket, summarise its content, analyse sentiment and urgency, draft a response, and notify your support agent. Here's how it flows.

Step 1: Receive the incoming ticket via webhook

Your support platform (Zendesk, Freshdesk, Help Scout, or even a custom form) triggers a webhook whenever a new ticket arrives. If you're using Make or n8n, set up a webhook module to listen for POST requests.

CopyPOST /webhook/support-ticket
Content-Type: application/json { "ticket_id": "TKT-12847", "customer_email": "alice@company.com", "subject": "Billing issue on invoice #4521", "body": "I was charged twice for my subscription this month. This is the second time this has happened and I'm very frustrated.", "created_at": "2026-03-14T09:23:00Z", "customer_name": "Alice Chen"
}

Store this payload in your workflow context. You'll need the ticket_id and full message text for later steps.

Step 2: Summarise the ticket content

Pass the ticket subject and body to Smmry. This tool condenses lengthy messages into bullet points, which saves your agent time and ensures nothing is missed. In Make, add an HTTP module and call Smmry's API:

CopyPOST https://api.smmry.com/SM_API
Content-Type: application/x-www-form-urlencoded sm_api_input={ticket.body}
SM_LENGTH=5

Smmry will return a concise summary. Store this as a variable for display later.

Step 3: Run sentiment and urgency analysis

This is where Twig comes in. Twig reads the message and flags urgency levels, emotional state, and whether the customer is likely to escalate or churn. Many support tools do this natively, but Twig is purpose-built for it and integrates well with automation platforms.

CopyPOST https://api.twig.io/v1/analyse
Authorization: Bearer YOUR_TWIG_API_KEY
Content-Type: application/json { "text": "{ticket.body}", "ticket_id": "{ticket.ticket_id}", "context": "support_ticket"
}

The response will include fields like sentiment_score (negative to positive), urgency_level (low, medium, high, critical), and suggested_category. If urgency is critical, flag the ticket for immediate human review. Otherwise, continue.

Step 4: Draft an intelligent response

This is where the real work happens. Send the ticket details, summary, and sentiment analysis to GPT-4o via the OpenAI API. Give it clear instructions for tone, length, and what information to include based on the category.

CopyPOST https://api.openai.com/v1/chat/completions
Authorization: Bearer YOUR_OPENAI_API_KEY
Content-Type: application/json { "model": "gpt-4o", "temperature": 0.7, "max_tokens": 500, "messages": [ { "role": "system", "content": "You are a professional customer support agent. Write clear, empathetic, concise responses. Match the tone to the customer's emotion: if they're frustrated, acknowledge it; if they're confused, clarify. Always offer a next step. Keep responses under 200 words." }, { "role": "user", "content": "Customer ticket:\n\nSubject: {ticket.subject}\n\nMessage: {ticket.body}\n\nTicket Summary: {smmry_output}\n\nCustomer Sentiment: {twig.sentiment_score}\n\nUrgency: {twig.urgency_level}\n\nPlease draft a professional response." } ]
}

GPT-4o will generate a draft. In your automation platform, store this as a variable and feed it to the next step.

Step 5: Notify the agent and present the full context

Finally, send a structured notification to your support agent via email or a Slack message (or both). Include the original ticket, the summary, the sentiment analysis, and the draft response side by side. Using Make's email module:

CopyPOST https://api.sendgrid.com/v3/mail/send
Authorization: Bearer YOUR_SENDGRID_API_KEY
Content-Type: application/json { "personalizations": [{ "to": [{"email": "{assigned_agent_email}"}] }], "from": {"email": "support-automation@company.com"}, "subject": "[Triage] {twig.urgency_level} - {ticket.subject}", "html": "<h3>Ticket #{ticket.ticket_id}</h3><p><strong>Customer:</strong> {ticket.customer_name}</p><p><strong>Sentiment:</strong> {twig.sentiment}</p><p><strong>Summary:</strong> {smmry_output}</p><h4>Suggested Response:</h4><blockquote>{gpt4o_draft}</blockquote><p><a href='https://yourSupport.com/tickets/{ticket.ticket_id}'>View full ticket</a></p>"
}

Alternatively, if you want responses to go straight into your support platform (Zendesk, Freshdesk), use their API to create a draft note attached to the ticket instead of sending an email. The complete workflow in n8n pseudocode: 1. Webhook receives ticket.

2. HTTP request to Smmry to summarise.

3. HTTP request to Twig to analyse sentiment.

4. Conditional branch: if urgency is critical, send immediate alert; otherwise continue.

5. HTTP request to OpenAI GPT-4o to draft response.

6. Send email or Slack notification with full context.

7. Optionally: POST draft back to your support platform as a note. Total execution time: 15 to 45 seconds per ticket, depending on API latency.

The Manual Alternative

If you don't want to automate the entire flow, you can build smaller workflows. For instance, you could automate only the sentiment analysis and draft generation, leaving categorisation to your agent. This requires less configuration and is a good starting point if you're new to automation. Alternatively, use ChatGPT Writer as a Chrome extension to compose responses directly in your support platform's web interface. It's not fully automated, but it removes the blank-page problem and ensures consistent quality. Okara AI works similarly for sensitive issues, keeping your drafts encrypted until you're ready to send. This approach is slower and still requires human decisions, but it eliminates some manual typing and gives agents better starting points.

Pro Tips

1. Test sentiment analysis thresholds carefully.

Twig's urgency levels are configurable. Before going live, run 50 real tickets through your workflow and check whether the critical flag is too sensitive. Too many false positives will annoy your team; too few and you'll miss genuinely upset customers.

2. Rate-limit your API calls.

If you get a spike of 200 tickets in one hour, you don't want to send 200 simultaneous requests to OpenAI. Use your orchestration tool's delay or queue module to space out requests: process 10 tickets per minute, for example. This avoids hitting rate limits and keeps costs predictable.

3. Store all drafts in a database.

Don't discard the GPT-4o outputs. Save them to a spreadsheet or database along with the original ticket and the agent's final response. Over time, this data shows you which drafts agents accept as-is, which ones they heavily edit, and which categories need better prompting. Use this feedback to refine your system prompt.

4. Handle API failures gracefully.

If Smmry is down, don't block the entire workflow. Set a timeout of 5 seconds per API call. If Smmry doesn't respond, skip the summary and let the agent read the original. Your workflow should degrade gracefully, not fail.

5. Watch your OpenAI usage.

At scale, GPT-4o's per-token cost adds up. If you're processing 500 tickets per day, consider using GPT-4o mini for routine categorisation and saving GPT-4o for genuinely complex or sensitive issues. You can branch your workflow: if sentiment is neutral and urgency is low, use mini; otherwise, use GPT-4o.

Cost Breakdown