Build an AI-Powered Rich Text Editor on .NET MAUI with AI AssistView

Enterprise applications generate large amounts of text, including incident reports, audit logs, customer communications, internal documentation, and policy content. As this volume increases, users increasingly expect intelligent writing assistance directly in their editing experience, such as grammar correction, paraphrasing, tone enhancement, and content summarization.

However, integrating AI into a rich text editor is not just about calling large language models (LLM). The real challenge is designing an experience that feels authentic, predictable, and maintainable without breaking content flow or architectural constraints.

In this article, you’ll learn how to create a production-ready, AI-assisted rich text editor using .NET MAUI, Synchronization^® Rich Text Editor, and Syncfusion MAUI AI AssistView are powered by Azure OpenAI. This solution makes the editor the single source of truth when using AssistView as a guided, action-driven AI layer.

Why use AI AssistView and not a custom chat UI?

Calling the LLM API is relatively easy. Providing reliable in-app AI workflows is not the case.

Syncfusion AI AssistView is built specifically for contextual assistance within applications, rather than mimicking a generic chat interface. It provides:

• Seamless integration with Syncfusion controls, keeping editor content and AI state in sync.
• Structured action → response → apply workflow, not free-form chat.
• Recommendation-based interactions, presenting AI results as product features rather than raw responses.
• Customizable templates for headers, actions, and suggestion items.
• Built-in handling of real-world scenarios, such as retries, errors, and transformation history.

This model works especially well for enterprise editors who prioritize content ownership, traceability, and UX consistency.

How it works (end to end flow)

At a high level, the interaction model looks like the following sequence:

1. Users write or edit content on SfRichTextEditor.
2. The user selects the action in AI AssistView (e.g., Shorten or Paraphrase).
3. That ViewModel create AI commands using selected actions and the HTML editor.
4. The application calls Azure OpenAI through the application service layer.
5. The AI ​​response appears in AI AssistView.
6. Users apply the results back to the editor with a single click.

This approach ensures that:

• The editor remains authoritative
• AI responses are explicit, reviewable, and reversible
• The flow of user writing is never interrupted

Building an AI-powered Rich Text Editor

This section outlines how the rich text editor is extended with the help of built-in AI.

Step 1: Set Up Rich Text Editor

Start by creating a new .NET MAUI project and configuring the Rich Text Editor control following the official setup documentation.

Add SfRichTextEditor as the main editing surface and binds its HTML content to your files ViewModel:

<rte:SfRichTextEditor x:Name="richTextEditor"  
                      ShowToolbar="True"  
                      HtmlText="Binding EditorHtml, Mode=TwoWay" />

Binding editor content as HTML allows you to preserve formatting while enabling AI-based transformations.

Step 2: Add AI AssistView Interface

Next, integrate Syncfusion AI AssistView following the official documentation.

That AI Synchronization Help View components can be opened by clicking on the button located in the upper right corner Rich Text Editor. It provides intelligent suggestions, feedback, and follow-up actions to increase user engagement.

Customizable header templates display prompts such as “How can I help?” further improve usability. Additionally, you can bind collections of your view models to enable dynamic and interactive conversations driven by AI.

Here’s how you do it in code:

<aiassistview:SfAIAssistView x:Name="AssistView"
                             ShowHeader="True"
                             IsVisible="False"
                             HeaderTemplate="StaticResource headerTemplate"
                             AssistItems="Binding AssistItems"
                             Suggestions="Binding Suggestions"
                             SuggestionItemSelectedCommand="Binding SuggestionItemSelectedCommand" >
</aiassistview:SfAIAssistView>

<DataTemplate x:Key="headerTemplate">
    <StackLayout HorizontalOptions="Center"
                 Spacing="10"
                 Padding="10">
         <Label Text="&#xe7e1;"
                FontFamily="MauiSampleFontIcon"  
                FontSize="20"  
                HorizontalOptions="Center"  
                VerticalOptions="Center" />  
         <Label Text="How can I help you?"  
                FontAttributes="Bold"  
                FontSize="16"  
                VerticalOptions="Center" />  
    </StackLayout>  
</DataTemplate> 

Step 3: Set up an Azure OpenAI connection

The AI ​​layer is powered by Azure OpenAI. Initialize a secure connection using your endpoint, API key, and deployment name.

Here’s the Azure OpenAI implementation:

private const string endpoint = "YOUR_END_POINT_NAME";
internal const string deploymentName = "DEPLOYMENT_NAME";
private const string key = "API_KEY";

// Build chat client with endpoint, key, deployment
private void GetAzureOpenAIKernal()

    var client = new AzureOpenAIClient(
        new Uri(endpoint),
        new AzureKeyCredential(key))
        .AsChatClient(modelId: deploymentName);
    this.Client = client;
 

Once initialized and validated, the AI ​​engine is ready to handle transformation requests. For detailed information about service validation and implementation, see the AzureBaseService class available in the GitHub repository.

Step 4: Suggestion system and user interaction

Defining AI suggestions

The suggestion system exposes available AI actions that users can apply to editor content, such as:

• Paraphrase
• Grammar Checker
• Explain
• Shorten

Below is the code you need:

public AssistViewViewModel()

    _suggestions = new ObservableCollection<ISuggestion>
    
        new AssistSuggestion  Text = "Paraphraser" ,
        new AssistSuggestion  Text = "Grammer Checker" ,
        new AssistSuggestion  Text = "Elaborate" ,
        new AssistSuggestion  Text = "Shorten" 
    ;

    this.SuggestionItemSelectedCommand = new Command(
        obj => _ = OnSuggestionTapCommandAsync(obj));

For paraphrasing, you can enter pitch-based options dynamically:

• Humanize: Conversation and connecting
• Professional: Business-oriented tone
• Simple: Clear and easy to understand
• Academic: Structured and scientific

Handle user actions

All suggestion interactions flow through a single command handler in the ViewModel. When the user selects a suggestion, the handler identifies the requested transformation and routes it through the appropriate processing path.

Add this to your project:

private async Task OnSuggestionTapCommandAsync(object obj)
 args.SelectedItem is not ISuggestion s)
        return;
    await InputProcessingAsync(s.Text).ConfigureAwait(true);

• The act of paraphrasing results in a modified response and follow-up suggestions.
• Other actions simply return the changed content.

This keeps interaction handling centralized and predictable.

Step 5: Apply AI results to the editor

When an AI request is triggered, the system:

1. Create a prompt using the content editor
2. Call Azure OpenAI via a custom service
3. Format the response
4. Displays it as AssistView with the Apply action

private async Task GetResult(object inputQuery)

    await Task.Delay(1000).ConfigureAwait(true);
    AssistItem request = (AssistItem)inputQuery;
    if (request != null) 
    
        var userAIPrompt = GetUserAIPrompt(request.Text, EditorHtml);
        var response = await azureAIService!
            .GetResultsFromAI(userAIPrompt)
            .ConfigureAwait(true);
        response = response.Replace("\n", "<br>");
        AssistItem responseItem = new AssistItem()
        
            Text = response,
            Suggestion = GetAcceptSuggestion()
            responseItem.RequestItem = inputQuery
        ;
        this.AssistItems.Add(responseItem);
    

Nothing is committed to the editor automatically. Users explicitly apply the results, ensuring full control and transparency over content changes.

Company considerations

When moving this pattern to production, keep the following in mind:

• Show: Send selected text instead of the complete document, whenever possible.
• Scalability: Add throttling and cancellation for sequential fast requests.
• Maintenance: Isolate AI calls in a dedicated service layer.
• Reliability: Handle empty responses and transient errors gracefully.
• Cost control: Reset prompt history and limit maximum output length.

GitHub Reference

Explore a complete implementation example of the AI-assisted Rich Text Editor .NET MAUI on GitHub.

Conclusion

Thanks for reading! This article shows how AI can be seamlessly integrated into Syncfusion’s Rich Text Editor without disrupting the writing experience. By combining Syncfusion MAUI AI AssistView with a powerful editor and a clean service architecture, you can deliver intelligent authoring assistance that feels native, reliable, and enterprise-ready.

Whether you’re building a documentation platform, content management system, or collaborative editor, this approach provides a strong foundation for an AI-powered authoring workflow.

If you are a Syncfusion user, you can download the setup from the license and downloads page. Otherwise, you can download a free 30-day trial.

You can also contact us via the support forum, support portal, or feedback portal for questions. We are always happy to help you!