Support Ticket Classification & Routing
New HubSpot support tickets are analyzed for sentiment and intent.
customer support teamsops and IT teams
What it does
You get new support tickets from HubSpot instantly analyzed for sentiment, churn risk, and purchase intent so you can prioritize effectively. The system summarizes customer messages, classifies issues like technical or fulfillment problems, and automatically creates enriched Jira tickets assigned to the right teams.
Step by step
- Configure the HubSpot webhook to trigger on new ticket creation with specific property filters.
- Define the static mapping rules for Jira project keys and issue types based on classification categories.
- Set up environment variables for API credentials of HubSpot, OpenAI, and Jira Software.
Where the LLM does the work
- Generate a concise summary of the customer's message to provide context for the support team.
- Analyze the text to detect emotional tone and assign churn risk scores based on nuanced language cues.
- Classify the ticket into specific categories like technical or fulfillment issues using few-shot examples.
Watch out for
Jira field IDs vary by instance, verify your custom fields exist before mapping data to them.
Sensitive customer data might be sent to OpenAI, ensure you filter PII before making the API call.
Tools that fit
OpenAI Chat Model LLM Summarize messages and generate ticket details
Jira Software Service Create and route classified tickets
AI Agent LLM Detect sentiment, churn risk, and classify intent
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Support Ticket Classification & Routing: New HubSpot support tickets are analyzed for sentiment and intent. ## Specification - What it does: You get new support tickets from HubSpot instantly analyzed for sentiment, churn risk, and purchase intent so you can prioritize effectively. The system summarizes customer messages, classifies issues like technical or fulfillment problems, and automatically creates enriched Jira tickets assigned to the right teams. - Trigger: Triggered by an event (Manual or Scheduled · cron) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - HubSpot: Fetch support tickets and metadata - OpenAI Chat Model: Summarize messages and generate ticket details - Jira Software: Create and route classified tickets - AI Agent: Detect sentiment, churn risk, and classify intent ## Known pitfalls, handle each one explicitly in your implementation 1. Jira field IDs vary by instance, verify your custom fields exist before mapping data to them. 2. Sensitive customer data might be sent to OpenAI, ensure you filter PII before making the API call. ## Reference implementation https://n8n.io/workflows/7981 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automate-support-ticket-classification-routing/ via usecasesforagents.com
Frequently asked questions
Can I use a different CRM than HubSpot?
Yes. HubSpot is only the example CRM in this recipe. The same flow works with Agile CRM, Attio, Close CRM and Freshworks CRM. Swap the CRM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for support teams drowning in inbound and looking to triage faster, not just harder.
Ops-IT teams triaging their own internal requests get the same pattern, different queue.