Retail & E-commerce

E-commerce support & action

Where is my order, returns, delivery changes: actions, not answers.

The problem

Support volume multiplies in season; teams answer the same ten questions while switching screens to act in the order system. Chatbots answer, but cannot cancel.

What the agents do
  1. 01Intent and entity extraction: order number, product and request type understood in one turn.
  2. 02Catalogue and policy knowledge retrieved with RAG; sizes, compatibility and return terms answered correctly.
  3. 03Tools wired to the order system change delivery details, start cancellations and issue return labels.
  4. 04Out-of-policy requests (high-value refunds, campaign exceptions) route to human approval.
Outcomes
  • High-volume requests resolved without growing the support team
  • Order updates, cancellations and delivery changes completed inside the conversation
  • Product answers grounded in your knowledge sources, no fabrication
Capabilities used
Multi-source RAGSupervisor · sub-agentsSession memoryREST / MCP toolsGuardrailsIntent detectionEntity extraction
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Where is my order? (WISMO)

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Returns and refunds

Policy and history checked in seconds, label issued; the refund executes when the carrier scans the parcel.

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Product Q&A assistant

Size, compatibility and spec questions answered from catalogue and return data; stock and price verified.

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Review response and quality escalation

Reviews classified and answered in brand tone; recurring defects joined with return data and escalated to QA.

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Time to move from experimenting with AI to transforming with it.

In a 30-minute discovery session we take your 2–3 priority business problems, show a live demo of a similar scenario, and draft a roadmap that starts with the Value layer.

Book a 30-minute discovery session → dahi@aginies.com
  1. Your 2–3 priority problems
  2. Live demo of a similar scenario
  3. Roadmap starting with value analysis