Building an AI-Powered Personal Companion Mobile App - Gerald Versluis - NDC Copenhagen 2026
The talk demonstrates how to integrate advanced AI functionalities into cross-platform mobile applications using .NET MAUI. The session emphasizes building robust, context-aware companion apps by leveraging abstraction layers and standardized APIs (like the Essentials API). Key architectural patterns include supporting multimodal input (voice, image), utilizing cloud services like Azure AI Foundry for LLMs, and implementing local model support via Onyx for sensitive data scenarios.
Key takeaways
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AI Integration is Cross-Platform
17:22
The implementation of AI features can be abstracted across platforms (iOS, Android, Windows) using .NET MAUI's abstraction layer, ensuring a consistent user experience while allowing platform-specific native behavior.
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Context Awareness and Memory
23:50
AI applications can be significantly enhanced by incorporating context from various sources—including location (geolocation), calendar data, biometrics, and conversation history (memory)—to provide highly personalized user experiences.
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Multimodal Input Handling
30:05
The system supports multiple input types beyond text, including voice recording and image analysis. This requires transforming raw data (audio/image) into a format consumable by Large Language Models (LLMs), such as using speech-to-text transcription.
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Model Agnosticism via Interfaces
40:05
By relying on interfaces (e.g., `I ChatClient`), developers can easily swap out concrete AI implementations—such as those from Microsoft Foundry, OpenAI, or local models like Apple Intelligence/Onyx—without changing core application logic.