Creating AI Chatbots in C# Using OpenAI, Azure AI, and Semantic Kernel

AI chatbots are becoming a major part of modern applications. Businesses now use AI-powered assistants for:

• Customer support

• Internal automation

• Knowledge management

• Productivity tools

• Enterprise workflows

For .NET developers, modern AI platforms and orchestration frameworks make chatbot development much easier than before.

In this article, we will learn how to create AI chatbots in C# using:

• OpenAI APIs

• Microsoft Azure AI

• Semantic Kernel

• ASP.NET Core

Why Use Semantic Kernel?

Semantic Kernel is an AI orchestration framework developed by Microsoft that helps developers integrate AI models into .NET applications.

It simplifies:

• Prompt management

• AI workflows

• Function calling

• Memory integration

• AI agent orchestration

Semantic Kernel works with multiple AI providers, including OpenAI and Azure AI.

Common AI Chatbot Use Cases

Modern AI chatbots are used in:

Use Case	Example
Customer Support	AI help desk
Enterprise Search	Internal knowledge assistant
Productivity Tools	AI workflow assistant
Developer Tools	Coding assistants
SaaS Platforms	AI-powered support systems

AI chatbots are now becoming part of enterprise software ecosystems.

Creating a New ASP.NET Core Project

Create a new Web API project:

dotnet new webapi -n AIChatbotDemo

Navigate to the project folder:

cd AIChatbotDemo

Installing Required Packages

Install Semantic Kernel and OpenAI packages.

dotnet add package Microsoft.SemanticKernel

dotnet add package Microsoft.SemanticKernel.Connectors.OpenAI

These packages allow AI integration inside ASP.NET Core applications.

Configuring API Keys

Add API settings inside appsettings.json.

{
  "OpenAI": {
    "ApiKey": "YOUR_API_KEY",
    "Model": "gpt-4o-mini"
  }
}

Store API keys securely using secret managers or environment variables.

Creating the Semantic Kernel Service

Create a chatbot service using Semantic Kernel.

using Microsoft.SemanticKernel;

public class ChatbotService
{
    private readonly Kernel _kernel;

    public ChatbotService(IConfiguration configuration)
    {
        var builder = Kernel.CreateBuilder();

        builder.AddOpenAIChatCompletion(
            modelId: configuration["OpenAI:Model"],
            apiKey: configuration["OpenAI:ApiKey"]);

        _kernel = builder.Build();
    }

    public async Task<string> AskAsync(string prompt)
    {
        var result = await _kernel.InvokePromptAsync(prompt);

        return result.ToString();
    }
}

This service sends prompts to the AI model and returns responses.

Registering the Service

Register the chatbot service in Program.cs.

builder.Services.AddSingleton<ChatbotService>();

Creating the API Controller

Create a controller to expose chatbot functionality.

[ApiController]
[Route("api/chatbot")]
public class ChatbotController : ControllerBase
{
    private readonly ChatbotService _chatbotService;

    public ChatbotController(
        ChatbotService chatbotService)
    {
        _chatbotService = chatbotService;
    }

    [HttpPost]
    public async Task<IActionResult> Chat(string prompt)
    {
        var response = await _chatbotService
            .AskAsync(prompt);

        return Ok(response);
    }
}

This creates a simple AI chatbot API endpoint.

Running the Application

Run the application:

dotnet run

Test the chatbot endpoint using:

• Swagger

• Postman

• REST clients

Example prompt:

{
  "prompt": "Explain dependency injection in ASP.NET Core"
}

Adding Azure OpenAI Support

Semantic Kernel also supports Azure OpenAI services.

Replace OpenAI configuration with Azure AI settings.

builder.AddAzureOpenAIChatCompletion(
    deploymentName: "gpt-model",
    endpoint: "https://your-endpoint.openai.azure.com/",
    apiKey: "YOUR_AZURE_KEY");

This allows enterprise applications to integrate with Azure AI infrastructure.

Enhancing the Chatbot

Modern enterprise chatbots often include:

• Conversation memory

• Document search

• RAG architecture

• AI agents

• Function calling

• Workflow automation

Semantic Kernel simplifies these advanced AI patterns.

Benefits of AI Chatbots in ASP.NET Core

Faster Development

Developers can build AI-powered applications quickly using cloud AI APIs.

Enterprise Scalability

Cloud AI services scale automatically based on workload demand.

Flexible AI Integration

Semantic Kernel supports multiple AI providers and orchestration patterns.

Better User Experience

AI chatbots improve customer interaction and automation capabilities.

Challenges of AI Chatbots

Despite their advantages, AI chatbots also introduce challenges.

AI Hallucinations

AI-generated responses may sometimes be inaccurate.

Security Risks

Applications must protect sensitive enterprise data carefully.

API Costs

High-volume AI workloads can increase operational expenses.

Context Management

Maintaining long conversations and memory requires proper architecture planning.

The Future of AI Chatbots

AI chatbots are rapidly evolving into intelligent AI agents.

Future trends may include:

• Autonomous AI assistants

• Multi-agent systems

• AI workflow orchestration

• Enterprise AI copilots

• Real-time multimodal AI

AI-powered conversational systems will continue growing across enterprise applications.

Conclusion

Creating AI chatbots in C# has become much easier with OpenAI APIs, Azure AI services, and Semantic Kernel.

By combining ASP.NET Core with modern AI orchestration frameworks, developers can build scalable and intelligent chatbot systems for enterprise and cloud-native applications.

As AI adoption continues to grow, chatbot development and AI orchestration are becoming important skills for modern .NET developers.