Quick answer: Let AI label and summarize tickets, but keep refunds, promises and external replies under human control until the workflow proves reliable.
A small support team’s problem
Imagine two people handle a shared inbox for a US online homewares shop. Queries arrive through email and a store form. Customers ask about shipping, returns, a damaged product and general product details. The objective is to route each message to the right person, not to generate an impressive-looking chatbot conversation.
Define four labels: shipping status, returns, product question and needs human review. The final category is essential. It catches new topics, conflicting information, emotional complaints and possible sensitive content.
Suggested data flow
- Receive the ticket through an approved connector.
- Remove unnecessary personal details from text sent to the classifier.
- Ask the model to return a constrained label, a short summary and an uncertainty flag.
- Validate the returned label against the four allowed values.
- Route unknown, risky or malformed results to human review.
- Show the original ticket to staff before any customer reply.
A usable classification instruction
You categorize inbound support messages. Return only one of SHIPPING, RETURN, PRODUCT or REVIEW and a sentence explaining the topic. Never infer refund eligibility, promise delivery dates or invent order status. If the message is ambiguous, angry, legal, sensitive or outside the categories, choose REVIEW.
This is a proposed instruction, not a claim about how a particular vendor’s model behaves. Test it against real ticket patterns after removing sensitive identifiers.
Measuring quality without gaming the result
Create a test set of 40–60 examples from historical tickets that staff have already labeled. Include terse messages, typos and messages that belong in two categories. Compare accuracy per label, especially false negatives in REVIEW. Do not call a system “98% accurate” unless you have actually measured it on representative data.
Failure handling
If the AI service is unavailable, route all tickets to the normal inbox. If it returns an invalid label, do not guess. If the same ticket arrives twice, preserve one canonical case ID to avoid duplicate handling. Log which rule or model version made the classification, but avoid storing raw message bodies in extra logs.
FAQ
Should auto-triage write directly into the CRM?
It can add a provisional tag if that is reversible and permissions are narrow. Let humans own irreversible customer outcomes.
What if customers write in another language?
Either test those languages explicitly or route to a qualified person; do not assume reliable multilingual performance.
References and further reading
These references provide context and official guidance. The sample workflows above are illustrative designs, not vendor performance tests.
Published by: FlowHarbor Editorial Desk. Read our sourcing and review policy.