This example scenario applies generally to businesses that want to integrate a conversational chatbot into applications. Specifically, this scenario shows a C# chatbot for a hotel to allow customers to check availability and book accommodation through a web or mobile application.
Beyond hotel booking, this chatbot could be used in a wide range of commerce settings. For example, the chatbot can enable customers to review the take-out menu of restaurant and place an order. Or, the chatbot can enable customers of a photography studio to search for and order prints. Traditionally, businesses hire and train customer service agents to respond to these types of customer requests. As a result, customers wait until a representative is available to provide assistance.
Architecture for hotel booking bot
This scenario covers a conversational bot that functions as a concierge for a hotel. The data flows through the scenario as follows:
- The customer accesses the chatbot with a mobile or web app.
- Using Azure Active Directory (Azure AD) B2C (business-to-consumer), the customer is authenticated.
- Interacting with the Bot Service, the customer requests information about hotel availability.
- Cognitive Services processes the natural language request to understand the customer communication.
- After the customer is happy with the results, the bot adds or updates the reservation or booking in a SQL database.
- Application Insights gathers runtime telemetry throughout the process to help the DevOps team improve bot performance and usage.
- Azure Active Directory (Azure AD) is Microsoft's multitenant cloud-based directory and identity management service. Azure AD supports a B2C connector allowing you to identify users by their external IDs, such as Google, Facebook, or a Microsoft account.
- App Service enables you to build and host web applications in the programming language of your choice without managing infrastructure.
- Bot Service provides tools to build, test, deploy, and manage intelligent bots.
- Cognitive Services lets you use intelligent algorithms to see, hear, speak, understand, and interpret your user needs through natural methods of communication.
- SQL Database is a fully managed relational cloud database service that provides SQL Server engine compatibility.
- Application Insights is an extensible Application Performance Management (APM) service that lets you monitor the performance of applications, such as your chatbot.
Other components that could be used to enhance this example scenario include:
- Speech API to change how customers interface with your bot.
- QnA Maker to quickly add knowledge to your bot from semi-structured content like an FAQ.
- Translator Text as a service to easily add multi-lingual support to your bot.
This scenario uses Azure SQL Database for storing customer reservations. SQL Database includes zone redundant databases, failover groups, and geo-replication. For more information, see Azure SQL Database availability capabilities.
This scenario uses Azure App Service. With App Service, you can automatically scale the number of instances that run your bot. This functionality lets you keep up with customer demand for your web application and chatbot. For more information on autoscale, see Autoscaling best practices in the Azure Architecture Center.
For other scalability articles, see the performance efficiency checklist in the Azure Architecture Center.
This scenario uses Azure Active Directory (Azure AD) B2C, a business-to-consumer identity management service, to authenticate users. With Azure AD B2C, your chatbot doesn't store any sensitive customer account information or credentials. For more information, see the Azure AD B2C overview.
Information stored in Azure SQL Database is encrypted at rest with transparent data encryption (TDE). SQL Database also offers Always Encrypted which encrypts data during querying and processing. For more information on SQL Database security, see Azure SQL Database security and compliance.
For general guidance on designing secure solutions, see the Azure Security Documentation.
This scenario uses Azure SQL Database for storing customer reservations. SQL Database includes zone redundant databases, failover groups, geo-replication, and automatic backups. These features allow your application to continue running if there's a maintenance event or outage. For more information, see Azure SQL Database availability capabilities.
To monitor the health of your application, this scenario uses Application Insights. With Application Insights, you can generate alerts and respond to performance issues that would affect customer experience and availability of the chatbot. For more information, see What is Application Insights?
For other resiliency articles, see Designing reliable Azure applications.
Deploy the scenario
You must have an existing Azure account. If you don't have an Azure subscription, create a free account before you begin.
This scenario is divided into three components for easier exploration:
Infrastructure components: Use an Azure Resource Manager template to deploy the core infrastructure components of an App Service, Web App, Application Insights, Storage account, and SQL Server and database. Use the following steps.
Use the link below to deploy the solution.
Wait for the template deployment to open in the Azure portal, and follow the UI instructions to create the deployment. Specify:
- A resource group name such as myCommerceChatBotInfrastructure.
- Select a region.
- Provide a username and secure password for the SQL Server administrator account.
It takes a few minutes for the deployment to complete.
Web app chatbot: To use Azure CLI to deploy a bot with the Bot Service, see Deploy your bot. To add Language Understanding and Intelligent Services (LUIS) to the bot.
Sample C# chatbot application: Use Visual Studio to review the sample C# application on GitHub. The sample application includes the Azure Active Directory authentication components and integration with the Language Understanding and Intelligent Services (LUIS) component of Cognitive Services. The application requires Visual Studio to build and deploy the scenario. Additional information on configuring Azure AD B2C and the LUIS app can be found in the GitHub repo documentation.
To explore the cost of running this scenario, use the Azure pricing calculator. To see how pricing changes for different use cases, change the service variables to match expected traffic. For example, consider three cost profiles based on the number of messages you expect your chatbot to process:
- Small, processing < 10,000 messages per month.
- Medium, processing < 500,000 messages per month.
- Large, processing < 10 million messages per month.
Azure Architecture Center articles describing chatbot architectures:
- Commerce chatbot for customer service
- Build an enterprise-grade conversational bot
- Speech-to-text conversion
Azure Bot Service product documentation:
Microsoft Learn modules: