Ticket triage automation, drafted answers from live data and a support assistant that knows its limits. The goal is a faster first response and less repetitive work for your team, with a person on every uncertain, upset or sensitive case.
The problem
A large share of support volume is the same few questions: where is my order, how do I return this, can I change my booking, what does this invoice line mean. Each one needs a lookup in another system and a reply that has been written a hundred times before. Meanwhile the genuinely difficult cases wait behind them in the same queue.
The usual answer, a chatbot that guesses, makes things worse: wrong answers, no hand-off, and customers who write “speak to a human” in capital letters.
What good looks like
- Every ticket tagged by intent, urgency and language within a minute
- Routine answers drafted from real order or account data, reviewed by an agent
- Angry, ambiguous or high-value cases escalated immediately with context
- Accuracy measured on a labelled sample every month
Scope
What we automate
Ticket triage
Drafted replies from live data
FAQ chatbot for business hours and after
Returns and case follow-up
Order-status ticket, from arrival to approved reply
A demonstration using sample data. An online shop with two support agents receives around 300 tickets a week; roughly a third ask where an order is.
- A new ticket in the helpdesk triggers the workflow.
- An AI step classifies it as “order status” with high confidence and extracts the order number or email address. Anything below the threshold is tagged “needs human” and stops here.
- The order and carrier tracking are looked up in the shop system. If no order matches, the ticket is routed to an agent with a note.
- A reply is drafted from the shop’s template with the real delivery estimate and tracking link, in the customer’s language.
- The agent sees the draft next to the ticket and sends, edits or discards it. During the first weeks every draft is reviewed.
- Sentiment is checked separately: a frustrated message about a late order is escalated to the team lead regardless of classification.
Credit classification: standard (two credits). Fits the One Workflow Pilot. See the e-commerce page for adjacent workflows.
Human escalation, designed in
- Confidence threshold. Below it, no draft is produced; the ticket goes to a person with whatever was extracted.
- Hard escalation triggers. Complaints, legal words, threats to cancel, health or safety mentions and any refund request always go to an agent.
- “Speak to a person” always works. In the chatbot, that phrase creates a ticket with the full transcript. No loops.
- Money stays manual. Refunds, credits and goodwill gestures are prepared, never executed, by the workflow.
Accuracy testing
Before launch we label a sample of real historical tickets (typically 150–300) with the correct intent and the correct answer, and test the classification and drafts against it. We report the accuracy per intent, not one blended number, and set the threshold so that the uncertain band goes to people. The same test set is re-run after every model update and monthly under a care plan, so drift is caught before customers notice.
Tools we connect: Zendesk, Freshdesk, Gorgias, HubSpot Service Hub, Intercom, Help Scout, shared Outlook or Gmail inboxes; Shopify, WooCommerce and common ERPs for lookups. Names indicate compatibility, not partnership.
What we measure
- First response time and resolution time by intent
- Share of tickets with an accepted draft (sent without edits)
- Escalation rate and reasons
- Classification accuracy on the monthly sample
- Agent hours on routine intents
Which package fits
One Workflow Pilot (from €1,250): triage plus drafted replies for one intent, such as order status. Team (€6,900): several intents, live lookups, escalation rules, a chatbot with hand-off and a monitoring view. A public-facing assistant answering from a large knowledge base is advanced scope and usually starts with the audit. Prices excl. VAT and usage; see pricing.
Questions about support automation
Can the AI support agent answer customers directly without review?
Only for intents where the monthly accuracy sample justifies it, and only if you decide so. Most clients start with every draft reviewed, then release one narrow intent at a time. Refunds and complaints never go unattended.
What happens if a customer tries to trick the chatbot?
We test for it. The assistant cannot execute refunds, change orders or reveal other customers’ data because it has no access to do so; it can only read approved content and create tickets. Adversarial prompts are part of the pre-launch test set.
Does this work in German and English?
Yes, and in other languages your templates cover. Language is detected per ticket; drafts use the template in that language. Where no template exists, the ticket is routed to a person.
How is customer data protected?
Ticket content stays in your helpdesk. Only the text needed for classification or drafting passes to the model provider you choose, with the region agreed and logged. Retention follows your helpdesk policy. Details on the security and governance page.