How to automate order tracking with AI
Order-tracking automation matches a customer’s question to the store’s current order record. It can explain payment, preparation, partial shipments and cancellation as well as carrier movement. Setup requires showing the right record to the right customer, translating statuses into plain language and providing agent handoff when systems fail, reducing repetitive manual lookups.
Identify your order source
Determine which fields are accessible in Shopify, iKAS, T-Soft or another platform. Order, payment and shipping status are different concepts. Define the source and update time for each; do not expect the assistant to infer missing information.
Connect the channel and verification step
Start where customers most often ask about orders. Match enquiries to the relevant customer and order. Limit permissions in channel and store connections. When several orders exist, clarify which one the customer means.
Explain statuses in plain language
- For pending payment, explain the next step.
- For preparation, share verified processing information.
- For partial shipments, explain each parcel separately.
- For cancellation or return, distinguish completed and pending steps.
- If the source fails, explain that status could not be verified and hand over.
Do not interpret “preparing” as “will arrive today.”
Test exceptions
Test incorrect references, guest orders, multiple orders, failed payments, post-cancellation enquiries and outages. Repeated messages should not create new actions. Authorise changes such as address updates separately from lookups.
Measure and maintain the workflow
Review incorrect answers, reopened cases and handoff reasons alongside response volume. Update mappings when the store introduces a new status label. Once validated, carry the same workflow across other channels.
Evaluate your own workflow
Explore the e-commerce chatbot. Request a demo to review the flow with your own systems and customer requests.
Senior Software Developer
As a Software Developer at Palmate, Mustafa focuses on building scalable, high-performance products that turn complex AI capabilities into intuitive user experiences. He contributes to Palmate’s central AI platform, real-time embeddable chat widget, and web infrastructure. His background includes full-stack development for Canada-based DCBank.ca, covering digital identity verification, banking workflows, and card payment systems, as well as frontend development for Akinon’s marketplace platform. At Palmate, he applies this experience across React, TypeScript, Next.js, real-time web technologies, and LLM-driven product development.
Frequently Asked Questions
Answers to common questions on this topic.
Which platforms can support this?
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Can the chatbot change an order?