Privacy-conscious users demand AI features without cloud dependency. Learn how to build intelligent Flutter apps that run entirely on-device using small language models like Gemma. This hands-on session demonstrates implementing a financial assistant with sub-100ms response times, covering model selection, optimization techniques, and real-world performance trade-offs. Perfect for developers wanting to add AI capabilities while keeping user data private. Walk away with practical code examples and a working offline AI assistant. Attendees will leave with a production-ready AI assistant that runs entirely on-device, eliminating monthly API costs while ensuring complete user privacy. They'll master practical skills including model selection (choosing between Gemma, SmolLM2, and Phi-3), implementing 4-bit quantization for 87.5% size reduction, and achieving sub-50ms response times. The session provides copy-paste ready code for model downloading, caching, and memory-efficient loading that you can implement immediately. By the end, they'll be able to build GDPR-compliant AI features for any Flutter app, working perfectly offline with zero operational costs. Perfect for developers who want to add AI capabilities to banking, healthcare, or any privacy-sensitive applications.