How Multilingual NLP Ticket Routing Is Reducing Resolution Times for a SaaS Support Team

The Challenge

Support volume had grown with the customer base, while triage still depended on a small rotation of agents reading tickets one by one and assigning them by hand.

The existing approach made it difficult to:

• Assign tickets to the right product team on the first attempt, which led to frequent re-routing between queues.
• Handle non-English tickets quickly, since only a few agents could triage in Spanish, Portuguese, French or German.
• Spot urgent issues such as outages, billing failures or data-access problems buried in long, polite messages.
• Apply the same categories consistently across shifts and regions.
• Report reliably on why customers contacted support, because manual tags were often missing or wrong.

From a technical perspective, support tickets are messy text. Customers paste error logs and stack traces, mix languages within one thread, and describe the same problem in very different words. The category list had more than forty product areas, several with only a handful of tickets per month, and the routing decision had to land inside the help desk within seconds of a ticket arriving, without sending customer data outside the company's cloud tenancy.

The Solution

InnovationM designed a routing pipeline that cleans each ticket, understands it in its original language, and writes structured fields back to the help desk where existing assignment rules take over. The team started from the company's historical tickets and their final resolved categories, then built a labeled gold set with senior support leads to measure every model version. The solution included:

• Ticket preprocessing: removal of signatures, quoted replies and boilerplate, with error logs and stack traces separated and parsed for product and error codes.
• Language detection: per-message language identification so mixed-language threads are handled correctly.
• Multilingual intent and product-area classifier: a fine-tuned multilingual transformer that predicts product area and request type directly in all five languages, without a separate translation step.
• Urgency scoring: a second model trained on escalation history that flags outage, security and billing signals for priority handling.
• Confidence thresholds and fallback: low-confidence predictions go to a smaller human triage queue, and rare categories use an LLM-assisted few-shot classifier until enough labeled examples exist.
• Help desk integration: an API and webhook connector that writes category, urgency and language fields into the AI chatbot and help desk, plus a feedback loop that captures agent corrections as new training data, built with InnovationM's data engineering practice.

The key focus was not simply translating tickets so English rules could read them, but understanding each ticket in the language the customer wrote it in and routing it correctly the first time.

The Impact

Triage moved from a manual reading step at the front of every queue to an automated decision made within seconds of a ticket arriving, with people reviewing only the uncertain cases. The solution supported:

• Shorter time from ticket creation to first response.
• Fewer tickets re-routed between product queues.
• Faster handling of non-English tickets.
• Lower overall resolution time for urgent issues.
• Consistent contact-reason data that product managers now use in roadmap reviews.

By bringing together ticket cleaning, multilingual classification, urgency scoring and a correction loop inside the help desk, the support team spends its time solving problems instead of sorting them.