Every detail in its place.
The operating challenge
An online retailer’s customer team jumps between Shopify, a shipping portal, and spreadsheets to answer delivery questions and process return requests. Context is fragmented, carrier updates can arrive late, and policy exceptions need a supervisor.
An event is a signal, not necessarily the latest truth. The system must reconcile current order state, verify the customer, and apply deterministic return rules before suggesting an action.
A workflow with a clear next step
01 / Capture events
Verify webhook authenticity, store event identifiers, and acknowledge only after durable capture.
02 / Read current state
Query the order and carrier rather than trusting event arrival order. Match the request to an authenticated customer.
03 / Classify the exception
Identify delivery delays, missing items, return requests, and policy questions.
04 / Check eligibility
Apply rules to dates, excluded products, destination, fulfillment status, and approved return policy.
05 / Prepare an action packet
Draft a response with current tracking evidence and route financial or policy exceptions to an authorized person.
06 / Confirm and reconcile
Record the approved action, read back the system state, and review unresolved exceptions on a schedule.
Architecture & controls
Shopify Admin API → supported carrier or fulfillment API → help desk → workflow and policy service → Postgres job state. Language models classify and draft; code owns eligibility and action constraints.
- Refunds and inventory changes never depend solely on model output.
- Separate read permissions from financial-action permissions.
- Use idempotent action records, approval limits, and source-state revalidation.
- Verify customer identity before exposing account-specific order information.
When things do not go to plan
If carrier evidence is unclear, the workflow records uncertainty instead of inventing an ETA. Rate limits create bounded queued retries. Duplicate events cannot trigger repeat messages or financial actions. Aging exceptions are assigned to an operations owner.
The automation boundary: Refund authority and policy exceptions remain with the retailer. No delivery promise is created without current supporting evidence.
Evaluation, before expansion
Test partial fulfillments, cancelled orders, mixed returns, multiple currencies, expired return windows, delayed events, and carrier outages. Compare proposed replies and escalation decisions against approved policy before enabling customer-visible automation.
| Measure | What we measure | Proposed criterion |
|---|---|---|
| Handling time | Eligible order-status queries | Target: 30% less manual handling |
| Ownership | Exceptions assigned inside the agreed SLA | Target: at least 95% |
| Action safety | Replay financial-action events | Gate: zero duplicate actions in tests |
Targets are agreed with the process owner after measuring representative current work. A pilot also records error rates, review effort, operating cost, and exceptions. Released staff capacity is not automatically a cash saving.
Rollout & handover
Move through offline evaluation, shadow mode, and operator-assisted work before considering a limited automatic path. Each phase has a named owner, stop conditions, and a manual fallback. The approved scope determines the delivery schedule.
What the engagement would deliver
- Unified order-exception queue
- Policy rules and review permissions
- Evidence-linked communication drafts
- Reconciliation and aging alerts
- Runbook and phased rollout plan
The final handover includes client-owned access, configuration or source, known limitations, evaluation cases, and recovery instructions. Third-party subscriptions and ongoing support are agreed separately.
Technical references
These sources inform design considerations. They do not validate a deployed Tern & Loom project or establish vendor partnership.
Shopify: verify webhook deliveries ↗
Research reviewed 20 October 2026.