Prepare the signal
Normalize free-text requests, preserve the details that distinguish categories, and turn the original helpdesk records into a consistent training set.
Machine learning product · Helpdesk operations
Routing every ticket to the team that can solve it.
The problem
A support queue is only useful when the right team sees the ticket.
Helpdesk requests arrive as inconsistent, human-written descriptions. Manual triage adds delay, and a wrong route creates another handoff. The project treats routing as a multi-output classification problem while making model confidence visible for review.
01Incoming ticket
02NLP prediction
03Support group
04Confidence check
Real-time triage
One input, three recommendations, and a confidence signal.
The workspace keeps submission and model output side by side. A user can rewrite the prompt, route the ticket, inspect the prediction, and understand how much trust to place in the recommendation without leaving the page.
helpdesk tickets30,000
IT support groups8
best benchmark accuracy81.35%
best-performing modelDistilBERT
Dataset intelligence
Analytics make the training data part of the product story.
Category volume reveals where the dataset is dense or imbalanced, while priority distribution shows the operational mix the model must learn. Keeping these views near the prediction tool makes model performance easier to interpret in context.
Model strategy
The hard part was building trust around the prediction.
Accuracy is only one part of an operational ML product. The system also needs reliable data preparation, comparable evaluation, fast serving, and a clear path for low-confidence cases.
Benchmark the models
Compare classical and transformer-based NLP approaches against the same routing task instead of choosing a model from intuition alone.
Keep people in control
Return confidence alongside each recommendation so uncertain tickets can be reviewed instead of silently routed to the wrong team.
Outcome
From raw helpdesk text to a reviewable routing recommendation.
The finished workflow connects dataset analysis, model comparison, API inference, and a human-readable dashboard. It turns an NLP experiment into a deployable tool that can shorten triage while keeping the final routing decision understandable.