Fordel StudiosCross-platform mobile with on-device AI — where latency meets privacy.
The on-device/cloud split for mobile AI is the architecture decision that determines latency, privacy posture, and battery life. Apple Neural Engine, Core ML, TFLite, and MediaPipe make on-device inference viable for a wider class of tasks than ever. We build Flutter applications that use the right approach for each feature — and handle the engineering complexity of both.
Mobile AI applications face constraints that web applications do not: a 400ms cloud round-trip feels instant on the web and slow on mobile for real-time features. Battery consumption limits how aggressively on-device inference can run. App Store review policies have specific requirements for AI-generated content disclosure. Device capability varies significantly across the installed base — a model that runs well on a flagship device may be prohibitively slow on a mid-range device two years old.
The on-device AI ecosystem has matured significantly. Apple's Core ML and Neural Engine support transformer-based models natively — including smaller LLMs and vision transformers. TFLite and MediaPipe provide cross-platform on-device inference with optimization tooling. Google ML Kit bundles pre-trained models for common tasks (text recognition, translation, face detection) without custom training. For voice AI, ElevenLabs and PlayHT provide streaming TTS APIs with low enough latency for conversational mobile experiences. These tools change the viable on-device/cloud architecture surface.
- On-device vs. cloud inference per feature — driven by latency, privacy, and capability requirements
- Device capability tiers — minimum hardware for on-device features, graceful degradation below threshold
- Battery impact of background inference vs. foreground-only processing
- Offline capability scope — which AI features degrade gracefully without connectivity
- Voice agent integration — ElevenLabs/PlayHT for TTS, Whisper or cloud ASR for STT
- App Store compliance — AI content disclosure requirements and content filtering expectations
We build Flutter applications because the cross-platform model (single codebase for iOS and Android) is the right economic choice for most products. Flutter's widget rendering engine produces consistent visual behavior across platforms — important for custom AI feature UX components that standard widget libraries do not provide.
For AI integration, we evaluate each feature against the on-device/cloud matrix: latency requirements, privacy constraints, model capability requirements, and offline behavior expectations. On-device inference is implemented via TFLite bindings, Core ML via platform channels, or MediaPipe Flutter packages. Cloud AI is integrated with streaming support where the API supports it. Voice agents use ElevenLabs or PlayHT for TTS with Whisper or platform ASR for STT.
Mobile AI integration architecture
Inventory target device capability tiers. Establish minimum hardware requirements for on-device features. Design degraded experience for devices below threshold — cloud fallback, reduced functionality, or feature unavailability with clear messaging.
TFLite model packaging and loading via Flutter bindings, Core ML packaging for Apple Neural Engine optimization on iOS, MediaPipe Flutter packages for real-time camera or audio processing. Quantized models for memory-constrained devices.
Typed API clients for cloud LLM endpoints with retry logic, timeout handling, and streaming response consumption. Offline detection with graceful degradation to on-device fallback where available.
ElevenLabs or PlayHT streaming TTS for low-latency voice output. Whisper (on-device or cloud) or platform ASR for voice input. Conversation state management for multi-turn voice interactions.
Riverpod for feature-level state, Bloc for complex multi-step AI workflow state with defined transitions. AI feature states — loading, streaming, error, complete — handled through typed state classes, not ad hoc boolean flags.
Flutter cross-platform development
Single codebase for iOS and Android with native performance characteristics. Flutter's rendering engine provides consistent visual behavior across platforms — important for custom AI feature UX components not available in standard widget libraries.
On-device inference with TFLite and Core ML
We integrate TFLite models, Core ML models, and MediaPipe solutions into Flutter applications via platform channels. On-device inference provides sub-100ms response times for real-time features without network dependency or privacy exposure.
Voice agent integration
ElevenLabs and PlayHT provide streaming TTS APIs with low enough latency for conversational voice experiences on mobile. We integrate these with Whisper or platform ASR for full voice agent pipelines — input, processing, and output — within Flutter.
Streaming cloud LLM responses on mobile
Cloud LLM responses streamed to the mobile client over SSE or WebSocket with incremental rendering. Streaming eliminates the perceived latency of waiting for a complete response and is essential for conversational AI features on mobile where perceived speed matters more than on web.
Privacy-preserving AI patterns
On-device inference for sensitive data that should not leave the device. Audio and image processing in local memory with no cloud upload. Privacy nutrition labels accurately reflecting AI data handling for App Store compliance.
- Flutter application (iOS and Android) with AI feature integration
- On-device model integration (TFLite, Core ML, or MediaPipe) where applicable
- Cloud AI API integration with streaming support and offline fallback
- Voice agent integration with ElevenLabs/PlayHT TTS and Whisper STT (if in scope)
- State management architecture with typed AI feature states
- App Store and Google Play submission with privacy declarations and AI content disclosures
Cross-platform Flutter development reduces the engineering cost of separate iOS and Android codebases. On-device AI for latency-sensitive features provides user experiences that cloud-only approaches cannot match — real-time camera processing, offline voice transcription, and instant UI feedback.
Common questions about this service.
Flutter or React Native?
Flutter is our current recommendation for new cross-platform mobile projects. Flutter's own rendering engine produces more consistent behavior and better performance for custom UI. React Native's bridged native component model has improved with the new architecture, but Flutter has a stronger track record for complex, custom-designed applications. If your team has deep React Native expertise, that outweighs framework preference.
On-device AI vs. cloud AI — when does each apply?
On-device: real-time features where <100ms response is required, sensitive data that cannot leave the device, offline functionality requirements. Cloud: LLM-class reasoning tasks, models too large for mobile hardware, features that can tolerate 1-3 second response times. Many applications use both — on-device for real-time preprocessing and privacy-sensitive processing, cloud for deeper reasoning and analysis.
How do ElevenLabs and PlayHT integrate into mobile voice agents?
Both provide streaming TTS APIs where audio chunks are streamed back as the text is processed — the first audio chunk arrives in ~300-500ms rather than waiting for the full response to be synthesized. We integrate streaming audio playback in Flutter with proper buffer management so voice responses feel conversational rather than delayed. STT uses Whisper (on-device for privacy, cloud for accuracy on mobile hardware) or platform ASR.
How do AI features affect App Store approval?
Apple and Google have guidelines specific to AI-generated content: disclosure requirements, content filtering expectations, and restrictions on specific use cases. We review App Store and Play Store guidelines for your specific use case during architecture design, before implementation. Approval surprises are avoidable with upfront review of the guidelines.
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