This browser is no longer supported.
Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support.
![](data:image/svg+xml, %3Csvg xmlns='http://www.w3.org/2000/svg' height='64' class='font-weight-bold' style='font: 600 30.11764705882353px "SegoeUI", Arial' width='64'%3E%3Ccircle fill='hsl(294.40000000000003, 66%, 38%)' cx='32' cy='32' r='32' /%3E%3Ctext x='50%25' y='55%25' dominant-baseline='middle' text-anchor='middle' fill='%23FFF' %3ED%3C/text%3E%3C/svg%3E)
Hello, we're interested in the chat service on Microsoft store, it's incredible. we'd like to make a silmar one with our Shopify store. We want the AI service to help the customer to find product, place order, refund, track order, etc. and also can get the data from our Dynamics Ax 2012.
I went to the Azure portal, but there're so many things, Azure AI Search service, AI service, Bot service, etc. How do I use them? Is there any instructions for us how to use them? We have developers but need a guide what to do.
Thanks!
![](data:image/svg+xml, %3Csvg xmlns='http://www.w3.org/2000/svg' height='64' class='font-weight-bold' style='font: 600 30.11764705882353px "SegoeUI", Arial' width='64'%3E%3Ccircle fill='hsl(166.4, 22%, 26%)' cx='32' cy='32' r='32' /%3E%3Ctext x='50%25' y='55%25' dominant-baseline='middle' text-anchor='middle' fill='%23FFF' %3ESC%3C/text%3E%3C/svg%3E)
Hello @Daniel
To build a robust AI-powered chat service for your Shopify store, several key Azure components will work together.
The Azure OpenAI Service provides the core conversational intelligence, using models like GPT-4 to understand and respond to customer queries about products, orders, and more.
The Azure Bot Service acts as the chatbot framework, allowing you to build, deploy, and manage the bot across various channels such as your website or Microsoft Teams. To enable customers to search your product catalog naturally for example, “show me running shoes under $100” Azure AI Search can index your Shopify product data and support semantic, intent-based search capabilities.
Custom Integration Layer is essential to connect your chatbot with backend systems. This layer will interface with Shopify APIs for managing orders, products, and customer data, and also with Dynamics AX 2012, likely through AIF services or custom-built APIs, to access ERP data such as order status and refund details. This setup ensures a seamless and intelligent customer experience across both your e-commerce and back-office systems.
A great starting point for this is Microsoft’s Personalized Shopping Agent, which is designed to deliver conversational AI shopping experiences. This agent can be embedded into your Shopify storefront to assist customers with a variety of tasks such as product searches, order placements, refunds, and tracking inquiries.
The setup process begins by enabling natural language interaction with the agent, allowing it to understand customer intent and respond with relevant information.
The behavior of the agent can also be tailored to reflect your specific product catalog and customer service flows, ensuring a customized and engaging experience.
Please refer this Use Personalized Shopping Agent.
To connect your AI system with Dynamics AX 2012 and retrieve relevant data, you’ll need to create an API that serves as a bridge between the two. This typically involves building an Azure-hosted API service that can interface with Dynamics AX often using AIF services or custom endpoints. Once the API is in place, you can define an OpenAPI specification for it and integrate it with your AI Agent. This allows the AI to invoke the API whenever it needs to access or process data from Dynamics AX, enabling smooth interaction between the systems.
Please refer this Invoke an OpenAPI App Service web app from Azure AI Agent Service.
I Hope this helps. Do let me know if you have any further queries.
Thank you!
Hi @Daniel
You're welcome! Let me know if you have any questions or need further assistance as you proceed happy to help anytime.
Hi @Daniel,
Have you had a chance to review the response above? Thank you!
Hi @Daniel,
We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet. In case if you have any resolution please do share that same with the community as it can be helpful to others. Otherwise, will respond with more details and we will try to help.
Thank you!
Hi @SriLakshmi C ,
Not sure what's wrong, my post has been deleted. I tried the copilot studio and Azure AI Foundry these days and got some problems:
I pushed the inventory data to the AI Search, I created the semantic configurations, no vector index, I can get the answer with Chat playground, but not working with the AI Agent playground. I can't figure out what's wrong, should I create the vector profile?
and I got an issue when I add the AI search index to copilot studio knowledge.
with the copilot studio, I can add many urls to the knowledge, how can I do that with Azure AI Foundry? Should I push the data to AI Search? but I can only add one AI Search Index to the agent knowledge.
Thanks for your help!
here're the index I did.
Hi @Daniel,
Thank you for providing the details and screenshots. Based on your setup and current issues, it seems like the missing piece is the vector index. While your semantic configuration works in the Chat Playground, the AI Agent Playground relies on vector-based search for retrieval-augmented generation (RAG) scenarios. Therefore, creating a vector profile on your Azure AI Search index and linking it with an embedding model (like text-embedding-ada-002) is strongly recommended to allow the agent to retrieve and process data effectively.
Regarding your experience with Copilot Studio, it's correct that currently only one AI Search index can be added per bot, which can be limiting if you have multiple data sources. A common workaround is to combine different datasets into a single index, using metadata tags (e.g., docType: faq, docType: product) and apply filters in Copilot Studio to route queries appropriately. For additional content like PDFs or website URLs, you can upload them directly to the Copilot Studio knowledge base.
However, in Azure AI Foundry, there’s no direct equivalent to Copilot Studio's “Add multiple URLs” instead, you’ll need to extract content manually, preprocess it (e.g., using Azure AI Document Intelligence), and push it into Azure AI Search or Blob Storage as part of your custom ingestion pipeline. This means to scale your agent knowledge, especially for Shopify product catalogs and customer service content, you'll want to consolidate relevant data into a single, well-structured vector index, ensuring your AI agent has unified and efficient access to it.
Additionally, the behavior you observed when trying to integrate AI Search with Copilot Studio and AI Foundry is due to current platform limitations, such as allowing only one AI Search index per agent in Foundry, and stricter data input handling compared to Copilot Studio’s multi-source knowledge integration.
To resolve the issue, your current configuration needs to be enhanced to align with how the AI agent expects to retrieve and use data primarily through vectorized and embedding-aware search structures.
Please refer this Add Azure AI Search as a knowledge source (preview)
Azure AI Search index as knowledge source
How to build and consume vector indexes in Azure AI Foundry portal.
I hope this helps. Do let me know if you have further queries.
Thank you!
Hi SriLakshmi,
Many thanks for your reply. Really appreciate. I'll look into it. Thanks!
This answer has been deleted due to a violation of our Code of Conduct. The answer was manually reported or identified through automated detection before action was taken. Please refer to our Code of Conduct for more information.
Comments have been turned off. Learn more