Ticket and feedback analysis

Define a useful classification

Prepare a labeled sample of tickets or feedback and decide what you need to count: product area, complained-about entity, request type or recurring issue. Keep those dimensions separate. A single vague sentiment label rarely answers an operational question.

The deliverable is a table of classified records, reproducible aggregates and a summary linked to representative examples.

Process records in distinct steps

1. Assign a stable identifier to each record and retain the original text.
2. Ask the model to extract the defined fields. Allow unknown or multiple labels when the taxonomy requires them.
3. Validate the structure and review ambiguous or low-quality records.
4. Count and group the accepted fields in code or SQL.
5. Give the model the calculated aggregates and selected examples to draft the narrative summary.

Evaluate classification and statistics separately

Use a held-out labeled sample to compare prompts or models. Inspect categories with frequent confusion and check whether the taxonomy itself is unclear. Reconcile each aggregate with the underlying record identifiers so a reader can investigate a trend.

Do not ask a model to count thousands of tickets from memory after reading a large prompt. Larger batches may also make it harder to detect dropped records. Track expected and returned identifiers for every batch.

Connect the workflow

A script or n8n workflow can manage record iteration. Text models perform extraction and written analysis; your data tools calculate counts. SeedRouter does not automatically connect to a ticketing system.

Use current pricing with actual input sizes, output fields and rerun frequency. The cost of classification, human review and summary generation should remain distinguishable.