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

Enterprise applications generate large volumes of text, including incident reports, audit logs, customer communications, internal documentation, and policy content. As this volume grows, users increasingly expect smart writing assistance directly within their editing experience, such as grammar correction, paraphrasing, tone refinement, and content summarization.

Integrating AI into a rich text editor, however, is not just about calling a large language model (LLM). The real challenge is designing an experience that feels native, predictable, and maintainable without breaking content flow or architectural boundaries.

In this article, you’ll learn how to build a production‑ready, AI‑assisted rich text editor using .NET MAUI,  Syncfusion^® Rich Text Editor, and Syncfusion MAUI AI AssistView powered by Azure OpenAI. The solution keeps the editor as the single source of truth while using AssistView as a guided, action‑driven AI layer.

Why use AI AssistView instead of a custom chat UI?

Calling an LLM API is relatively straightforward. Delivering a reliable in‑app AI workflow is not.

Syncfusion AI AssistView is purpose‑built for contextual assistance inside applications rather than mimicking a generic chat interface. It provides:

• Seamless integration with Syncfusion controls, keeping editor content and AI state synchronized.
• A structured action → response → apply workflow, instead of free‑form chat.
• Suggestion‑driven 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 particularly well for enterprise editors where content ownership, traceability, and UX consistency matter.

How it works (end-to-end flow)

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

1. The user writes or edits content in SfRichTextEditor.
2. The user selects an action in AI AssistView (e.g., Shorten or Paraphraser).
3. The ViewModel builds an AI prompt using the selected action and editor HTML.
4. The app calls Azure OpenAI through an application service layer.
5. The AI response appears in AI AssistView.
6. The user applies the result back to the editor with a single click.

This approach ensures that:

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

Building an AI-powered Rich Text Editor

This section outlines how a rich text editor is extended with built‑in AI assistance.

Step 1: Set up the Rich Text Editor

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

Add the SfRichTextEditor as the primary editing surface and bind its HTML content to your ViewModel:

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

Binding the editor content as HTML allows you to preserve formatting while enabling AI‑driven transformations.

Step 2: Add the AI AssistView Interface

Next, integrate the Syncfusion AI AssistView by following the official documentation.

The Syncfusion AI AssistView component can be opened by clicking the button located at the top‑right corner of the Rich Text Editor. It provides intelligent suggestions, responses, and follow‑up actions to enhance user interaction.

A customizable header template featuring prompts such as “How can I help you?” further improves usability. Additionally, you can bind collections from your view model to enable dynamic, interactive AI‑driven conversations.

Here’s how you can 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 the 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 on validation and service implementation, please refer to 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:

• Paraphraser
• Grammar Checker
• Elaborate
• 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 dynamically inject tone‑based options:

• Humanize: Conversational and relatable
• Professional: Business‑oriented tone
• Simple: Clear and easy to understand
• Academic: Structured and scholarly

Handling user actions

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

Add this to your project:

private async Task OnSuggestionTapCommandAsync(object obj)
{ 
    var args = obj as SuggestionItemSelectedEventArgs;
    if (args == null || args.SelectedItem is not ISuggestion s)
        return;
    await InputProcessingAsync(s.Text).ConfigureAwait(true);
}

• Paraphrasing actions return both a transformed response and follow‑up suggestions.
• Other actions return transformed content only.

This keeps interaction handling centralized and predictable.

Step 5: Applying AI results to the editor

When an AI request is triggered, the system:

1. Builds a prompt using the editor’s content
2. Calls Azure OpenAI through a dedicated service
3. Formats the response
4. Displays it as an AssistView with an 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 result, ensuring full control and transparency over content changes.

Enterprise considerations

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

• Performance: Send selected text instead of full documents when possible.
• Scalability: Add throttling and cancellation for rapid successive requests.
• Maintainability: 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 the complete .NET MAUI AI‑assisted Rich Text Editor sample implementation on GitHub.

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Conclusion

Thank you for reading! This article demonstrated how AI can be seamlessly integrated into a rich text editor without disrupting the authoring experience. By combining a structured AssistView with a robust editor and a clean service architecture, you can deliver intelligent writing 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 solid foundation for AI‑powered authoring workflows.

If you’re 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 through our support forum, support portal, or feedback portal for queries. We are always happy to assist you!

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