Enhancing Umbraco CMS with AI Capabilities

As both an Umbraco developer and AI enthusiast, I've been exploring ways to combine these technologies to create more intelligent content management experiences. Umbraco's flexibility makes it an excellent platform for AI integration, offering opportunities to enhance both the editorial experience and end-user content delivery.

Why Integrate AI with Umbraco?

Umbraco is already a powerful, flexible CMS. Adding AI capabilities can further enhance its strengths:

1. Smarter content creation through AI-assisted writing and editing
2. Personalized user experiences based on behavior and preferences
3. Automated content organization with intelligent categorization and tagging
4. Enhanced search capabilities powered by natural language understanding
5. Content performance optimization through AI-driven insights

Practical AI Integrations for Umbraco

Let's explore practical approaches to adding AI capabilities to your Umbraco projects:

1. AI-Enhanced Rich Text Editor

Enhance Umbraco's rich text editing experience with AI capabilities:

// Create a custom rich text editor plugin
public class AiTextEditorPlugin : IRichTextEditorPlugin
{
    private readonly IOpenAiService _openAiService;

    public AiTextEditorPlugin(IOpenAiService openAiService)
    {
        _openAiService = openAiService;
    }

    public async Task<string> SuggestImprovements(string content)
    {
        // Call OpenAI or similar service to analyze and improve text
        return await _openAiService.ImproveText(content);
    }

    public async Task<string> GenerateSummary(string content)
    {
        // Generate a concise summary of longer content
        return await _openAiService.SummarizeText(content);
    }
}

This integration could provide features like:

• Grammar and style suggestions
• Content summarization
• Readability analysis
• Tone adjustment (formal, casual, persuasive)
• SEO improvement suggestions

2. Intelligent Content Classification

Automatically categorize and tag content based on its substance:

// Create a content app for AI-powered classification
public class ContentClassifierApp : IContentApp
{
    private readonly IClassificationService _classificationService;

    public ContentClassifierApp(IClassificationService classificationService)
    {
        _classificationService = classificationService;
    }

    public async Task<ClassificationResult> ClassifyContent(IContent content)
    {
        // Extract text from content properties
        var textContent = ExtractTextFromContent(content);

        // Use ML.NET or external API to classify content
        return await _classificationService.Classify(textContent);
    }

    // Implementation of IContentApp interface methods
    // ...
}

This allows editors to:

• Automatically assign categories and tags
• Ensure consistent taxonomy
• Discover related content
• Identify content gaps or overlaps

3. Personalized Content Delivery

Create a personalization service that leverages user behavior data:

// Register personalization service in startup
public void ConfigureServices(IServiceCollection services)
{
    services.AddSingleton<IPersonalizationService, AiPersonalizationService>();
    // Other service registrations...
}

// Personalization controller to deliver tailored content
public class PersonalizedContentController : RenderController
{
    private readonly IPersonalizationService _personalizationService;

    public PersonalizedContentController(
        IPersonalizationService personalizationService,
        IPublishedContentQuery contentQuery)
        : base(contentQuery)
    {
        _personalizationService = personalizationService;
    }

    public override IActionResult Index()
    {
        // Get user profile or anonymous profile from cookies/session
        var userProfile = GetUserProfile();

        // Get current content model
        var content = CurrentPage;

        // Get personalized version of the content
        var personalizedContent = _personalizationService.PersonalizeContent(content, userProfile);

        return CurrentTemplate(personalizedContent);
    }
}

This enables:

• Content recommendations based on user interests
• Dynamic content ordering by relevance
• Personalized calls-to-action
• User segment-specific messaging

4. Intelligent Search Enhancement

Improve Umbraco's search capabilities with AI:

// Custom Examine index events handler
public class AiSearchIndexer : IApplicationEventHandler
{
    private readonly IAiEnrichmentService _aiEnrichmentService;

    public AiSearchIndexer(IAiEnrichmentService aiEnrichmentService)
    {
        _aiEnrichmentService = aiEnrichmentService;
    }

    public void OnApplicationStarted(
        UmbracoApplicationBase umbracoApplication,
        ApplicationContext applicationContext)
    {
        ExamineManager.Instance.IndexProviderCollection["ExternalIndexer"]
            .GatheringNodeData += EnrichNodeDataWithAi;
    }

    private void EnrichNodeDataWithAi(object sender, IndexingNodeDataEventArgs e)
    {
        // Extract content from the node
        var content = ExtractContentFromNode(e.Node);

        // Generate AI-powered enhancements
        var enhancements = _aiEnrichmentService.EnrichContent(content);

        // Add enriched fields to the indexed data
        e.Fields.Add("aiSynonyms", enhancements.Synonyms);
        e.Fields.Add("aiEntities", enhancements.Entities);
        e.Fields.Add("aiSummary", enhancements.Summary);
    }
}

This creates a more intelligent search experience:

• Natural language query understanding
• Semantic search beyond keyword matching
• Entity recognition in content
• Synonym awareness
• Intelligent relevance ranking

5. Content Performance Optimization

Analyze content performance and suggest improvements:

// Dashboard component for content performance analysis
public class ContentPerformanceComponent : IUmbracoAuthorizedJsonController
{
    private readonly IAiAnalyticsService _aiAnalyticsService;
    private readonly IContentService _contentService;

    public ContentPerformanceComponent(
        IAiAnalyticsService aiAnalyticsService,
        IContentService contentService)
    {
        _aiAnalyticsService = aiAnalyticsService;
        _contentService = contentService;
    }

    [HttpGet]
    public async Task<ContentPerformanceResult> GetContentPerformance(int contentId)
    {
        var content = _contentService.GetById(contentId);
        if (content == null) return null;

        // Analyze content and its performance metrics
        return await _aiAnalyticsService.AnalyzeContentPerformance(content);
    }
}

This helps editors by providing:

• AI-driven content improvement suggestions
• Automated A/B test analysis
• Engagement prediction for draft content
• Content gap identification
• Competitive content analysis

Implementation Approaches

When adding AI to Umbraco, you have several integration options:

1. Third-Party AI Services

The simplest approach is to integrate with established AI services:

• OpenAI API for text generation and analysis
• Azure Cognitive Services for a range of AI capabilities
• Google Cloud AI for image recognition and natural language processing
• Amazon Comprehend for sentiment analysis and entity recognition

This approach requires minimal machine learning knowledge but depends on external services.

2. Custom ML.NET Models

For more customized solutions, develop your own models using ML.NET:

// Initialize ML.NET context
var mlContext = new MLContext();

// Load training data from Umbraco content
var trainingData = LoadContentAsTrainingData();
IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(trainingData);

// Define and train your model
var pipeline = mlContext.Transforms.Text.FeaturizeText("Features", "Text")
    .Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy());
var model = pipeline.Fit(trainingDataView);

// Save the model for use in your Umbraco application
mlContext.Model.Save(model, trainingDataView.Schema, "ContentClassifier.zip");

This approach offers more control and can operate without external dependencies, but requires more data science expertise.

3. Hybrid Approach

Often, the most effective solution combines both approaches:

• Use third-party services for general AI capabilities
• Develop custom models for Umbraco-specific needs
• Cache and store AI-generated content to optimize performance

Real-World Example: AI-Enhanced Healthcare Content Management

For a healthcare client, we implemented several AI enhancements to their Umbraco site:

1. Medical content verification: AI-assisted fact-checking against medical databases
2. Readability optimization: Automatic adjustment of medical content for different audience levels
3. Symptom-based search: Natural language processing for patient-friendly search
4. Personalized health content: Content recommendations based on user health profiles

The result was a 40% increase in user engagement and a significant reduction in content maintenance effort.

Challenges and Considerations

When implementing AI features in Umbraco, be mindful of these challenges:

1. Performance impact: AI operations can be resource-intensive
2. Data privacy: Ensure compliance with regulations when processing user data
3. Integration complexity: Plan for proper separation of concerns
4. Editorial control: AI should assist editors, not replace their judgment
5. Cost management: External AI services can become expensive at scale

Conclusion

Integrating AI capabilities into Umbraco opens up exciting possibilities for creating more intelligent, personalized, and effective content experiences. Whether you're looking to enhance the editorial workflow or create dynamic user experiences, the combination of Umbraco's flexibility and modern AI tools provides a powerful foundation.

Have you implemented AI features in your Umbraco projects? I'd love to hear about your experiences in the comments below.