Training Data for Customer Support Intelligence

Customer support data annotation adds intent, entity, relationship, and quality labels to support examples. Unitlab lets teams prepare CSV support records as training data for downstream routing, classification, and issue-understanding models.

Use CSV exports in tabular row mode. Each row becomes a separate annotation item with its original column names and values, such as a ticket identifier, subject, message, product, and response.

Yes. Record properties describe the complete ticket, while entity labels highlight exact text inside its fields. You can keep intent and priority alongside customer, order, product, and issue spans.

Yes. Every text column is available for entity labeling. Labels retain their column identity and character offsets so evidence from different fields stays unambiguous.

Yes. Shared relation definitions can connect labeled entities across fields within the same support record. For example, a product entity can link to the issue described in the customer message.

Reviewers can inspect the original record, labels, and properties, add contextual comments, and approve or reject the work. Configured rework paths return rejected examples for correction and another review.

A shared ontology defines entity classes, record properties, structured choices, and relationship types. Instructions and review help teams apply the same definitions across the support dataset.

JSONL exports include each record’s source values, column schema, entity spans, relationships, and supported record properties. This preserves the evidence needed for downstream training and evaluation.

This workflow prepares and reviews labeled training data. Your downstream support models use those examples for tasks such as ticket routing, issue classification, and resolution assistance.