Support ticket triage

Start with representative data

Historical tickets with reviewed queues, labels, priorities, and representative rare or ambiguous examples.

Choose the smallest effective approach

Compare rules, embeddings, and compact classifiers with a fine-tuned language model. A sub-1B specialist may fit a bounded label space; measure rather than assume.

Evaluate on unseen cases

Per-class precision and recall, macro F1, escalation quality, latency, and cost per accepted classification on held-out tickets.

Define the boundary

Route uncertain or sensitive cases to a person. Verify deployment compatibility; not every checkpoint can be imported into every managed service.

What the engagement delivers

An agreed baseline, data and model selection record, permitted checkpoint or adapter where training is justified, evaluation report, and a deployment recipe. Timeline, hardware, retention, and acceptance criteria are agreed during scoping.