Everyone loves the idea of running AI locally on a phone. Until they actually try shipping it. When I decided to add an AI-powered mock interview coach to SSB Prep, a Flutter app used by more than 7,000 students preparing for India's Services Selection Board (SSB) interviews, I assumed model integration would be the hardest part. It wasn't. The real challenges appeared after the model worked. How do users download a 1.4 GB model? Which devices can realistically run it? How do you prevent crashes on low-memory phones? What happens when your AI feature is larger than your app itself? And how do you build a voice-first experience around a local model that feels natural to users? In this talk, I'll share the architecture, Flutter implementation, deployment strategy, and lessons learned while rolling out an on-device AI experience to thousands of real users. We'll cover model selection, download management, device compatibility checks, speech-to-text integration, performance optimization, and the tradeoffs that come with shipping AI directly to user devices. You'll leave with practical patterns, implementation strategies, and a realistic understanding of what it takes to bring private, offline, and low-latency AI experiences to production Flutter applications.