Cross-Platform Apps

Benched.ai Editorial Team

Cross-platform AI apps deliver the same assistant experience across web, iOS, Android, and desktop with a single backend. The challenge lies in harmonizing model APIs, auth flows, and UI patterns under varied OS constraints.

Platform Capability Matrix

Feature	Web (PWA)	iOS	Android	Desktop (Electron)
Real-time speech input	MediaRecorder + WebRTC	AVAudioEngine	SpeechRecognizer API	Node audio modules
Local caching	IndexedDB	CoreData	Room DB	SQLite
Background tasks	Service Workers	BGProcessing	WorkManager	Main/Renderer split
On-device model	WebGPU, WebNN (early)	Core ML	TensorFlow Lite	ONNX Runtime

Design Trade-offs

Native SDKs expose advanced sensors (camera depth) but fragment codebase.
PWAs simplify deployment yet face stricter background execution limits on iOS.
Electron bundles full Chromium, increasing installer size (>100 MB).

Current Trends (2025)

Shared Rust + WASM inference engines compiled for mobile and web.
React Native Turbo modules enable streaming TTS with sub-50 ms latency.
Edge-sync state stores (Replicache, ElectricSQL) provide offline chat continuity.

Implementation Tips

Abstract model calls behind a thick client library; swap between on-device and cloud backends per platform.
Use platform-specific push mechanisms (APNs, FCM) to wake suspended apps for streaming.
Ship small quantized models (INT4) for on-device fallback when offline.