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Conversational AI · Commerce & support

AI WhatsApp Commerce & Customer Support Agent

Project overview

Customers already message businesses on WhatsApp; most of what they ask is answerable from data the business already holds. We built an agent that lives in that thread — handling product discovery, order status, returns and refunds against live systems rather than canned replies — with an operations dashboard behind it for the queue, escalations and COD verification.

AI WhatsApp Commerce Platform demo — preview frame

A full session: order lookups returning status, courier and tracking, a returns-policy answer, a refund and cancellation flow, and the operations dashboard behind it.

The challenge

Support queues fill with questions that have a definite answer sitting in an order system — where is it, can I return it, what did I pay. Answering them by hand is slow for the customer and expensive for the business, but answering them badly with a scripted bot is worse than not answering at all.

  • Answer product, order and returns questions directly in WhatsApp
  • Read live order state rather than replying from a script
  • Escalate to a human with the conversation attached, not a ticket number
  • Verify cash-on-delivery orders before anything ships
  • Stay reliable when a user deliberately tries to break the agent

Our solution

A conversational agent on the WhatsApp Business API, grounded in the store’s own catalogue, policies and order data. It resolves what it can resolve, and routes the rest to a person with the full thread attached.

  • Product discovery and support inside the customer’s existing WhatsApp thread
  • Live order lookup returning status, items, total, courier and tracking number
  • Returns and refund answers grounded in the store’s actual published policy
  • Cancellation and refund flows handled in-conversation with confirmation steps
  • Human hand-off carrying the full conversation context
  • Operations dashboard: message queue, escalations, COD verification, security log

Technical implementation

The agent is grounded rather than free-associating: order and policy answers are retrieved from the client’s systems and passed to the model as context, so a wrong answer requires wrong data rather than a hallucination. Guardrails are enforced independently of the prompt, which is what holds up when the model is attacked directly.

  • WhatsApp Business API for messaging and delivery receipts
  • Retrieval grounded in the store catalogue, policy documents and live order records
  • Order-system integration for status, courier and tracking lookups
  • Guardrails constraining the agent to support scope, independent of user input
  • Escalation routing with conversation context preserved
  • Operations dashboard for queue state, escalations and COD verification

Technologies

  • WhatsApp Business API
  • Conversational AI
  • RAG
  • LLM Guardrails
  • Order Management Integration
  • Human-in-the-Loop
  • Operations Dashboard

Key results

  • Order, returns and refund questions resolve inside the chat, without a queue position or a ticket number
  • Answers carry real detail — order number, items, total, courier and tracking — because they are read from the order system
  • Escalation hands a human the whole conversation, so the customer never repeats themselves
  • Prompt-injection resistance is demonstrable, not asserted: in the recorded demo the agent is told to adopt an “unrestricted” persona and ignore its rules, and it declines and returns the conversation to support
  • COD orders are verified through a defined queue before dispatch rather than by ad-hoc phone calls

Why it matters

The interesting question about a support agent is not what it does on a cooperative customer — it is what it does on a hostile one, and on the edge cases where being confidently wrong is expensive. Grounding answers in live systems and enforcing scope outside the prompt is what makes the difference between a demo and something you can leave pointed at real customers.

Services behind this project

  • AI Chatbots & Customer Support

    Support agents across chat, email and messaging

  • RAG & LLM Integrations

    Retrieval grounding against real business data

  • Workflow & Process Automation

    Escalation, verification and ops tooling

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