Example logistics company. AI agent, 2026.

An agent that closes support tickets before the team wakes up

A retrieval and action agent that resolves routine shipment questions inside Zendesk and escalates the rest with a summary.

61%of tickets resolved without a person

Illustrative case study. The numbers and names here are examples, not a client record.

The problem

Half of the support inbox was the same twenty questions about shipment status, delivery windows and address changes. The team answered them by hand, slowly, in three time zones.

What we built

An agent inside Zendesk that reads the ticket, looks up the shipment in the operations database, answers with a citation, and takes the action when it is allowed to, such as updating a delivery window. Anything outside its policy is escalated with a two-line summary and a suggested reply.

How it went

Week one was a workflow review and a read-only prototype. By week three the agent was replying to a sample of tickets with a person approving each one. By week five it was live for the routine categories, with an evaluation set of 400 real tickets run on every change.

This is an illustrative case study. Replace it with a real engagement before launch.