Resolve order requests from chat
Give support teams an AI agent for customer support that reads order requests in team chat, decides whether to look up or update the record in your ecommerce order management system, and posts the result back in the conversation.
.webp)
Ready to use
Use the ready-made workflow
Open this use case in Make with the selected apps, then customize the workflow to fit your needs.
OR
Copied
An AI agent triggered via [chat platform, e.g. Slack, Microsoft Teams] interprets natural language requests from support agents and decides whether to retrieve order information or update records, such as shipping details, cancellations, or fulfillment statuses. It then executes the action against [e-commerce/order management platform, e.g. Shopify, custom order management system] and delivers the results directly back in [chat platform, e.g. Slack, Microsoft Teams].
Guided by AI
Build your own with Maia
Start with an app-agnostic prompt, then let Maia guide you to the right apps and workflow.
Adapt at speed with visual-first automation and AI
Make drives efficiencies, solves problems, and speeds innovation by breaking down silos across your business.
%201.svg)
+2
Date
·
Duration
Problem
Customer support teams constantly switch between their chat platform and the order management system to look up order details, update records, and handle customer requests. This switching between systems slows response times, increases data entry errors, and pulls agents away from customer conversations.
.avif)
+2
Date
·
Duration
Solution
An AI agent triggered via the team's chat platform interprets natural language requests from support agents and decides whether to retrieve order information or update records, such as shipping details, cancellations, or fulfillment statuses, then executes the action against the ecommerce backend and delivers results directly in the chat conversation.
+2
Date
·
Duration
Impact
Support agents stay in the customer conversation instead of hopping between systems, so customers get faster answers and order records stay accurate.
Benefits
+2
Date
·
Duration
Faster customer response times
+2
Date
·
Duration
Reduced manual data errors
+2
Date
·
Duration
Fewer tool switches
+2
Date
·
Duration
Higher agent productivity
Who is this for?
Success stories
Adapt at speed with visual-first automation and AI
Make drives efficiencies, solves problems, and speeds innovation by breaking down silos across your business.
Similar use cases
Find inspiration by the type of content that fits your goals.
Date
·
Duration
Resolve support inquiries from knowledge base
When a customer sends a question through the support chatbot, an AI agent determines how to resolve it, querying the internal knowledge base to find relevant documentation, and deciding when the available knowledge is insufficient and a web search is needed to enrich its response. The agent adapts its research approach based on the nature and complexity of each inquiry rather than following a fixed lookup sequence, then replies to the customer in the chat.
Date
·
Duration
Route support tickets to specialists
When a new ticket arrives from any channel, the automation captures the ticket content and customer context, uses AI to categorize the issue type and complexity level, routes the ticket to the appropriate team or agent using predefined assignment rules, then uses AI to analyze the ticket against the knowledge base and previous resolutions to suggest the most relevant articles or solutions that match the issue, delivering these suggestions directly to the assigned agent.
Date
·
Duration
Collect satisfaction feedback on resolved tickets
When a support ticket's status changes to resolved in the helpdesk, the automation reads the customer's name, email, ticket ID, and assigned support agent, then emails the customer a satisfaction survey with a link tied to that ticket. It records the survey send on the ticket and adds a row to a feedback tracking sheet with the ticket details and send date.