Android Apps with On-Device ML
Real-time intelligence on the phone — not on a server
Android apps with on-device machine learning: real-time object detection, image classification, and custom models running entirely on the device.
- Inference fully on-device — zero per-call cloud cost
- Camera-stream pipelines tuned to 30+ FPS on mid-range Android
- Custom-model training on your dataset, not just stock models
On-device ML changes what mobile apps can do. We build Android apps that run computer-vision and classification models on the phone itself — no round-trip to a cloud API, no per-call inference cost, no privacy compromise. We integrate TensorFlow Lite models into Flutter and native Android, optimize them for mobile, and handle the camera-stream and inference pipelines that make the experience feel instant.
Built for
Teams shipping computer-vision, AR, and intelligent-camera features on Android — retail scanning, field inspection, accessibility, and consumer creative tools.
Inside Android Apps with On-Device ML
Production-grade work, end-to-end — same engineers from scope to ship.
Real-time object detection
Live camera streams processed on-device with TensorFlow Lite — bounding boxes, classes, and confidence in milliseconds.
Image classification
Pre-trained or custom models for product recognition, defect detection, document classification, and more.
On-device inference
Models run on the phone's CPU, GPU, or NNAPI — no cloud calls, no inference bills, full offline support.
Custom model training
Transfer learning on your dataset to train models for the exact thing your product needs to recognize.
Performance optimization
Quantization, delegate selection, and pipeline tuning to hit 30+ FPS even on mid-range devices.
Privacy by default
Inference happens on-device. Camera frames never leave the phone unless you explicitly send them.
A delivery model that stays out of your way
Weekly demos. Shared roadmaps. Open Slack. Shipping code, not status decks.
- 01
Discover
Define the detection task, target device profile, and the latency / accuracy bar that has to be met.
- 02
Model
Pick or train a model — pre-trained MobileNet/EfficientNet, or transfer-learn on your dataset.
- 03
Integrate
Camera pipeline, frame preprocessing, inference loop, and overlay UI — built into the Flutter app.
- 04
Optimize
Quantize, choose delegates, profile on real devices — until the experience feels instant.
- 05
Ship
Play Store release, in-app analytics for inference metrics, and a plan for retraining as data grows.
Built with production-grade tooling
Same stack we use across client work — battle-tested, easy to extend, no surprises in production.
- Flutter
- Dart
- Kotlin
- TensorFlow Lite
- Camera plugin
- TFLite Flutter plugin
- Android NNAPI
- Firebase ML
Building something with computer vision?
Tell us what the phone needs to see — we'll come back with a model plan and a working prototype timeline.