Model is ready in app custom model but needs more refinment, in sense of more sever expen I’m ready to build a fully featured Arabic ↔ English translator that runs on both iOS and Android. The goal is real-time interaction anywhere, even without an internet connection, so every core capability must function 100 % offline. Core functionality • Voice recognition: detect spoken Arabic or English instantly and return the translated speech and text in the other language. • Text translation: type or paste phrases and receive immediate output. • Camera translation: point the device at signage, menus, etc., and overlay the live translated text. Key requirements – Native performance on current iOS and Android versions (Swift/Objective-C, Kotlin/Java, or an efficient cross-platform framework such as Flutter or React Native—your recommendation is welcome). – On-device language models so no data connection is needed; please outline which open-source or licensed engines you will integrate (e.g., TensorFlow Lite, ONNX, Apple Core ML, Google ML Kit). – Clean, bilingual UI/UX that switches automatically based on device language. – Latency for voice and camera translation under two seconds on mid-range hardware. – Modular architecture that lets me add new language pairs later. Deliverables 1. Source code with clear documentation and build instructions. 2. Compilable app packages ready for TestFlight and Google Play internal testing. 3. Brief user guide and a one-page technical overview of the offline translation pipeline. 4. Two weeks of post-handover support for bug fixes. If you’ve previously shipped offline translation or speech apps, please share a link or short demo; that practical experience will be a big plus.