# How a DTC brand’s AI agent resolves 70% of tier-1 support tickets

Most of a DTC brand’s support tickets were “where is my order?”, returns and address changes. We built an AI agent that resolves those from Shopify and shipping data. It handles 70% of tier-1 tickets, first response dropped from 4 hours to under 2 minutes, and 10+ hours a week of manual work came off the team.

By the WorkflowPal team. Published 2026-10-05.

## At a glance

- **Client:** A DTC e-commerce brand (anonymized)
- **Problem:** The CX team answered the same order-status, returns and address-change questions all day
- **What we built:** An AI support agent that resolves tier-1 tickets from Shopify and shipping data and hands the rest to people
- **Results:** 70% of tier-1 tickets resolved; first response 4 hours → under 2 minutes; 10+ hours a week of manual work removed; CSAT up
- **Tools:** Shopify and shipping data

> The client is anonymized. The figures are the real before-and-after numbers from the project, reported as-is and not rounded up.

## What was the problem?

Order status, returns and address changes made up most of the ticket volume. Each one meant looking the order up across Shopify and shipping data before replying, so first response took 4 hours, and the team had little time for the conversations that drive repeat purchases.

## What did we build?

An AI agent that reads each incoming ticket, looks up the order and shipment, and resolves the routine ones: order status, returns and address changes. Everything else goes to the team.

## What changed?

Before and after:

| Measure | Before | After |
| --- | --- | --- |
| Tier-1 tickets resolved without a person | None; all handled by the team | 70% |
| First response time | 4 hours | under 2 minutes |
| Manual order lookups, returns and shipping updates | Done by hand | 10+ hours a week removed |
| CSAT | Baseline | Up |

- **70%**: of tier-1 support tickets resolved by an AI agent (DTC brand) ([case study](https://workflowpal.com/case-studies/dtc-ai-support-agent/))
- **4 hrs → under 2 min**: first response time (DTC brand) ([case study](https://workflowpal.com/case-studies/dtc-ai-support-agent/))
- **10+ hrs/week**: of order lookups, returns and shipping updates removed (DTC brand) ([case study](https://workflowpal.com/case-studies/dtc-ai-support-agent/))

## What should you know before building something similar?

- Pull a month of tickets and tag them before building. The share that is truly routine decides how much an agent can take.
- Write the policy the agent follows (returns window, address-change cutoff) as rules, not vibes.
- Give customers an obvious way to reach a person. It costs little and keeps trust high.

## Could this work for you?

If your team does similar work by hand, probably. [Request a free workflow audit](https://workflowpal.com/free-workflow-audit/) and you get a one-page plan: the 3 tasks worth automating first, hours saved, and what a build would cost. See also [AI automation for e-commerce](https://workflowpal.com/industries/ecommerce/) and [Custom AI agents](https://workflowpal.com/services/ai-agents/).

## Related

- [AI automation for e-commerce](https://workflowpal.com/industries/ecommerce/)
- [Custom AI agents](https://workflowpal.com/services/ai-agents/)
- [All case studies](https://workflowpal.com/case-studies/)

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Last updated: 2026-10-05

Next step: request a free workflow audit at https://workflowpal.com/free-workflow-audit/ or book a call at https://calendly.com/dan-workflowpal/30min.

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