Intelligent Mobile Apps
Build mobile applications with on-device machine learning, computer vision, natural language processing, and context-aware intelligence that works even offline.
import 'package:tflite_flutter/tflite.dart';
class SmartCamera {
late Interpreter _model;
Future<void> init() async {
_model = await Interpreter
.fromAsset('model.tflite');
}
List<Detection> detect(CameraImage img) {
var input = preprocess(img);
_model.run(input, output);
return postprocess(output);
}
}
On-Device ML with Flutter
We integrate TensorFlow Lite models directly into Flutter applications for real-time object detection, image classification, and pose estimation — all running on-device with zero latency and full offline capability.
- TensorFlow Lite for on-device inference
- Real-time camera-based ML processing
- Model optimization with quantization
- Cross-platform iOS and Android support
import { useTensorflow } from
'react-native-tensorflow';
const SmartScanner = () => {
const { model, predict } =
useTensorflow('detect.tflite');
const onFrame = async (frame) => {
const results = await predict({
input: frame.data,
threshold: 0.8,
});
setDetections(results);
};
};
React Native AI Integration
Bring TensorFlow Lite models into React Native apps for intelligent barcode scanning, document recognition, and augmented reality experiences. Native bridge modules ensure GPU-accelerated inference at 60fps.
- Native module bridges for GPU acceleration
- Camera frame processing in real time
- Custom hooks for ML model lifecycle
- Hot-updatable model weights via OTA
Mobile AI Ecosystem
Ready to Build an Intelligent Mobile App?
From on-device ML to AR-powered experiences, our team builds mobile apps with intelligence baked into every interaction.