E-Commerce Returns

Persona

Role: E-Commerce Operations Manager at an online fashion retailer.

You oversee the returns process for an online store that ships 50,000 orders per month. Return rates hover around 25 percent -- typical for fashion -- and each return involves a return request, a refund decision, and a restocking or disposal decision. The current process relies on a ticketing system that was not designed for returns, leading to delayed refunds and inconsistent restocking decisions.

Business Problem

Customer service agents process returns manually, copying data between the ticketing system and the order management platform. Refund timelines vary from two to fourteen days because there is no structured workflow. Returned items sit in a staging area with no systematic process for evaluating condition and deciding whether to restock, refurbish, or dispose. You need:

• Structured return request intake linked to original orders.
• Refund processing with method tracking and timeline visibility.
• Restocking decisions that capture item condition and disposition.

Four-Step Application

This scenario works best as a four-step, human-in-the-loop application. The existing object model already gives this scenario a strong delivery backbone through ReturnRequest, Refund, and RestockItem.

• Mission metric focus: better conversion, lower service friction, and stronger margin protection.
• Human + AI pattern: Each step combines structured workflow data with chat assistance, background generation, document understanding, and accessible interaction patterns when they improve the experience.

Step 1. Capture demand and context

• Goal: Make it easy for the user to start the E-Commerce Returns journey with complete, trusted context.
• Required data: ReturnRequest context such as the core workflow data.
• AI support: Use chat to guide intake, generate clearer prompts, create accessible summaries, and assist with voice or vision-led capture when a form alone is not the best experience. EAI can support structured intake, chat workflows, and document-centred capture today; richer native multimodal capture may still need workflow extensions or connected services.
• Business impact: Improve completion rate, reduce first-touch effort, and raise customer or staff confidence in the UX from the very first interaction.
• EAI delivery: Model the intake as tenant-isolated object types and resources, then use actions, chat workflows, and document indexing or classification to keep the initial record complete and usable.

Step 2. Prepare the decision

• Goal: Turn the captured context into the next best action for E-Commerce Returns without forcing the human reviewer to assemble the case manually.
• Required data: ReturnRequest state and history.
• AI support: Run background summarisation, extraction, classification, recommendation drafting, and answer generation so a reviewer sees a prepared case instead of raw fragments. EAI delivers the structured records and AI workflow hooks for this today; specialised scoring engines, external rules, or advanced reasoning controls may still need integration work.
• Business impact: Reduce cycle time, improve quality and consistency, and protect the mission-critical metric before the case moves into execution.
• EAI delivery: Link records across the scenario, persist decision state as resources, and use workflow actions plus chat assistance to keep humans in control while AI prepares the work.

Step 3. Execute and collaborate

• Goal: Coordinate the actual work, handoffs, approvals, and user updates needed to deliver the service or outcome.
• Required data: Refund execution state.
• AI support: Draft replies, produce work packets, monitor exceptions in the background, and surface the next action for each operator. EAI can orchestrate tenant-isolated records, actions, chats, and document workflows today; deeper system-to-system automation may require additional connectors or workflow capability.
• Business impact: Increase operator productivity, reduce rework across handoffs, and improve service consistency across the application journey.
• EAI delivery: Use linked object types, actions, resource updates, and workflow-triggered AI assistance so the team can execute in one model instead of splitting work across disconnected tools.

Step 4. Resolve, explain, and improve

• Goal: Close the loop with a clear outcome, an understandable explanation, and feedback that improves the next case.
• Required data: final status, outcome, audit history, and follow-up signals across ReturnRequest, Refund, and RestockItem.
• AI support: Generate outcome summaries, customer-friendly answers, compliance-ready notes, management insights, and accessible follow-up content. EAI can store outcome records and support answer generation today, while richer proactive agents, advanced analytics, or channel-specific accessibility features may need additional product capability.
• Business impact: Increase trust, quality, and measurable business value through better conversion, lower service friction, and stronger margin protection.
• EAI delivery: Keep the full audit trail in structured resources, use AI workflows to explain outcomes, and feed the resulting signals into future product, service, and operational improvement work.

EAI Platform Support By Step

EAI provides the safe service boundary for E-Commerce Returns through Object Types, tenant-scoped resources, document processing, chat workflows, and CLI verification. For this scenario, the main records are ReturnRequest, Refund, and RestockItem.

Process step	What EAI provides	Calling pattern
Step 1. Capture demand and context	Tenant-scoped intake resources for ReturnRequest context such as the core workflow data. Object Type validation, starter forms, optional document intake, and chat-guided capture keep the first record complete.	Define fields in src/eai.config/object-types.ts, run eai types validate and eai types seed, create initial ReturnRequest records with useResources('ReturnRequest') or eai resources create ReturnRequest, and keep browser calls behind /api/eai/....
Step 2. Prepare the decision	Linked resource queries over ReturnRequest state and history. Search, schema checks, document classification or RAG indexing, and chat summaries turn raw context into a prepared decision.	Use useResources('ReturnRequest') list/query/search patterns, verify shape with eai resources schema, use useDocuments().upload/classify/ragIndex, eai docs upload, eai docs classify, and eai docs index where supporting material exists, and send decision-support prompts through useChat(workflowId, 'chat') or eai chat send.
Step 3. Execute and collaborate	Resource updates and actions for Refund execution state. Status changes, assignments, notes, generated work packets, and chat support keep humans in control during execution.	Model actions in the Object Type code, call client.resources.executeAction(type, id, action) or the app hook equivalent, update records through the app service layer, and verify with eai resources get/list/query.
Step 4. Resolve, explain, and improve	Outcome resources for final status, outcome, audit history, and follow-up signals across ReturnRequest, Refund, and RestockItem. Audit-friendly links, indexed final documents, reporting snapshots, and answer generation make the result explainable and reusable.	Persist outcomes as resources, index final material with eai docs index or useDocuments().ragIndex, send explanation prompts with useChat or eai chat stream, and use eai resources aggregate/search for reporting checks.

Prompt, Code, And Service Pattern Mapping

The Object Type code example on this page is the implementation contract for the EAI platform services. eai-gofer should read that code as the source of truth for which resource, document, and chat calls belong in the app.

Use this prompt shape when asking eai-gofer or another coding agent to implement the scenario:

Use the EAI App Template. Model E-Commerce Returns with Object Types for ReturnRequest, Refund, RestockItem. Use useResources for records and actions, useDocuments for uploads/classification/RAG where documents appear, useChat for workflow assistance, and verify with eai types/resources/docs/chat commands. Use eai publicapi only when no named command covers the required platform call.

Scenario artifact	How it maps to EAI service calls
Four-step process	Step 1 becomes resource creation, Step 2 becomes resource query/search plus optional document or chat preparation, Step 3 becomes resource update/action calls, and Step 4 becomes outcome persistence plus explanation/reporting calls.
Object Type definitions	eai types validate, eai types seed, and eai resources schema make the model available and checkable before UI work starts.
Properties and indexes	Fields become useResources payloads, filters, list views, and eai resources create/list/query/search checks. Indexed fields should support lookup and triage, not duplicate canonical records.
Links between Object Types	Relationships become linked-resource UI, timeline context, and audit trails that app code loads through resource queries rather than separate bespoke stores.
Actions and status fields	Workflow buttons and operator transitions call resource action/update helpers, then verify state with eai resources get/list/query.
Document and chat prompts	Prompts should call the platform documents and chat patterns: useDocuments().upload/classify/ragIndex, eai docs upload, eai docs classify, and eai docs index for documents, and useChat, eai chat send, or eai chat stream for conversational assistance.

Object Types

Name	Key Properties	Links	Actions
ReturnRequest	order, items, reason, status	has one Refund, has many RestockItems	initiate, approve, reject
Refund	return, amount, method, processedDate	belongs to ReturnRequest	process, reverse
RestockItem	return, product, condition, action	belongs to ReturnRequest	inspect, restock, dispose

CLI Workflow

1. Scaffold the app

   eai init ecommerce-returns

2. Authenticate and pull environment

   eai login
   eai env pull --include-secrets

   If you are an external developer, see [Configuration](/docs/configuration) for login and local environment setup.

3. Define Object Types

   Add ReturnRequest, Refund, and RestockItem to src/eai.config/object-types.ts.

4. Validate and seed

   eai types validate
   eai types seed

   Tenant: ecommerce-returns
   ✔ ReturnRequest — 4 props, 2 links, 3 actions
   ✔ Refund — 4 props, 1 link, 2 actions
   ✔ RestockItem — 4 props, 1 link, 3 actions
   
   ✔ All Object Types are valid

5. Create a return request

   eai resources create ReturnRequest \
     --data '{"order": "ORD-78432", "items": "Blue Denim Jacket (M)", "reason": "wrong-size", "status": "pending"}'

6. Process a refund

   eai resources create Refund \
     --data '{"return": "ret-001", "amount": 89.99, "method": "original-payment", "processedDate": "2026-03-10"}'

7. List pending returns

   eai resources list ReturnRequest --filter 'status=pending'

8. Start local development

   eai dev

Code Example

// src/eai.config/object-types.ts
export const objectTypes = {
  'ecommerce-returns': [
    {
      name: 'ReturnRequest',
      displayName: 'Return Request',
      description: 'A customer request to return one or more items from an order',
      properties: [
        { name: 'order', type: 'text' as const, required: true, indexed: true },
        { name: 'items', type: 'text' as const, required: true },
        { name: 'reason', type: 'select' as const, required: true,
          options: [
            { label: 'Wrong Size', value: 'wrong-size' },
            { label: 'Defective', value: 'defective' },
            { label: 'Not as Described', value: 'not-as-described' },
            { label: 'Changed Mind', value: 'changed-mind' },
            { label: 'Arrived Late', value: 'arrived-late' },
          ],
        },
        { name: 'status', type: 'select' as const, required: true, defaultValue: 'pending',
          options: [
            { label: 'Pending', value: 'pending' },
            { label: 'Approved', value: 'approved' },
            { label: 'Rejected', value: 'rejected' },
            { label: 'Received', value: 'received' },
            { label: 'Completed', value: 'completed' },
          ],
        },
      ],
      linkTypes: [
        { name: 'refund', target: 'Refund', cardinality: 'one' as const },
        { name: 'restockItems', target: 'RestockItem', cardinality: 'many' as const },
      ],
      actions: [
        { name: 'initiate', displayName: 'Initiate Return' },
        { name: 'approve', displayName: 'Approve Return' },
        { name: 'reject', displayName: 'Reject Return' },
      ],
      status: 'published' as const,
    },
    {
      name: 'Refund',
      displayName: 'Refund',
      description: 'A refund issued to a customer as part of a return request',
      properties: [
        { name: 'return', type: 'text' as const, required: true },
        { name: 'amount', type: 'number' as const, required: true },
        { name: 'method', type: 'select' as const, required: true,
          options: [
            { label: 'Original Payment', value: 'original-payment' },
            { label: 'Store Credit', value: 'store-credit' },
            { label: 'Gift Card', value: 'gift-card' },
          ],
        },
        { name: 'processedDate', type: 'date' as const, required: false },
      ],
      linkTypes: [
        { name: 'returnRequest', target: 'ReturnRequest', cardinality: 'one' as const },
      ],
      actions: [
        { name: 'process', displayName: 'Process Refund' },
        { name: 'reverse', displayName: 'Reverse Refund' },
      ],
      status: 'published' as const,
    },
    {
      name: 'RestockItem',
      displayName: 'Restock Item',
      description: 'A returned product evaluated for restocking, refurbishment, or disposal',
      properties: [
        { name: 'return', type: 'text' as const, required: true },
        { name: 'product', type: 'text' as const, required: true, indexed: true },
        { name: 'condition', type: 'select' as const, required: true,
          options: [
            { label: 'New', value: 'new' },
            { label: 'Like New', value: 'like-new' },
            { label: 'Worn', value: 'worn' },
            { label: 'Damaged', value: 'damaged' },
          ],
        },
        { name: 'action', type: 'select' as const, required: true,
          options: [
            { label: 'Restock', value: 'restock' },
            { label: 'Refurbish', value: 'refurbish' },
            { label: 'Donate', value: 'donate' },
            { label: 'Dispose', value: 'dispose' },
          ],
        },
      ],
      linkTypes: [
        { name: 'returnRequest', target: 'ReturnRequest', cardinality: 'one' as const },
      ],
      actions: [
        { name: 'inspect', displayName: 'Inspect Item' },
        { name: 'restock', displayName: 'Restock Item' },
        { name: 'dispose', displayName: 'Dispose Item' },
      ],
      status: 'published' as const,
    },
  ],
};

Key Takeaways

• Reason-categorized returns reveal product issues. Structured return reasons enable merchandising teams to identify quality problems and listing inaccuracies at scale.
• Refund method flexibility improves customer satisfaction. Supporting multiple refund methods -- original payment, store credit, gift card -- lets agents choose the best option for each situation.
• Condition-based restocking reduces inventory waste. The RestockItem type captures the condition of each returned product, ensuring that disposition decisions are consistent and data-driven.
• Order-linked returns close the loop. Tying each ReturnRequest to an order ID enables end-to-end visibility from purchase to return to refund.