AI agents · Ecommerce
AI agents for ecommerce
An ecommerce agent runs post-purchase work end to end: it checks shipping status, opens return records, catches stock mismatches and repairs product data. Zero-Shot connects these agents to your existing store and carrier integrations, and keeps human approval mandatory for anything involving a refund.
What we build
- 01An agent that answers order, shipping and delivery questions from the source system
- 02Return and exchange flow — opening records, generating labels, notifying the customer
- 03Product data cleanup: missing attributes, wrong categories, broken variants
- 04Stock and price mismatch alerts between marketplaces and your own store
- 05A mandatory human gate on refunds and cancellations
How it runs
01
Read the support history
We group the last three months of tickets by topic. The three topics that create most of the volume are picked, and the agent starts with those only.
02
Connect store and carriers
Order, shipping and return endpoints are connected read-only. In v1 the only write permission is opening a return record.
03
Load test before the sale
The agent is run against traffic that mimics a discount period, so cost and latency are known before the campaign arrives.
04
Staged rollout
Shipping questions first, then returns, then product data. At each stage the support team sees and corrects the agent's answers.
What we measure
- Share of tickets closed without reaching a human
- First response time and resolution time
- Number of incorrect return records created by the agent
- Cost and latency curve on peak sale days
- Accuracy of product data corrections
When this is the wrong fit
If order and shipping data cannot be read from one place, or every marketplace is tracked in a separate hand-maintained sheet, fix the data layer first. An agent does not clean up scattered data — it forwards the mess to customers faster.
Common questions
What happens if the agent gives a customer the wrong shipping information?
Shipping information comes from the carrier endpoint, not from the model; the agent only relays what it read. If the endpoint does not answer, the agent does not guess — it hands the ticket to a human. Every answer is traced, so you can see which data it read and what it said.
Can the agent issue refunds by itself?
No. Refunds and cancellations sit behind an approval gate. The agent prepares the record and writes its reasoning; a human approves. The gate can also be set to trigger only above a certain amount.
Do we have to change our ecommerce platform?
No. The agent connects over APIs, so Shopify, WooCommerce, a marketplace integration or your own admin panel all work. Where no endpoint exists we write a read layer, and the platform stays where it is.
Other work in this sector
Other services
What we've written on this
- Why AI agent projects failAgents that impress in a demo stall in production. Four observations on tool design, error handling, handing off to a human, and the cost ceiling.
- Where to start with enterprise AIHow to choose a first AI project: which tasks qualify, in what order to proceed, and what to expect in the first eighty days.
- Writing prompts for productionWhy a prompt that works in a demo breaks in production. Treating prompts like code: structure, versioning, boundaries and measuring every change.
Last updated: 2026-09-05
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