Android with ML

Intelligent mobile apps powered by on-device AI

Our Process

Step-by-Step Workflow

Every project follows this proven 10-step process to ensure quality, accuracy and on-time delivery.

Step 1

App Concept & Feature Planning

Define Android app purpose and identify which features will use ML (vision, NLP, etc.).

Step 2

UI/UX Wireframing

Design app screens using Material Design principals for intuitive user experience.

Step 3

Android App Development

Build the full Android app in Java/Kotlin with clean architecture (MVVM).

Step 4

ML Model Training

Train model for face recognition, object detection, sentiment analysis or gesture recognition.

Step 5

TensorFlow Lite Conversion

Convert trained model to .tflite format for efficient on-device inference.

Step 6

On-Device Model Integration

Integrate TFLite model using ML Kit or TFLite Android library for real-time use.

Step 7

Camera / Sensor Integration

Connect camera, mic, or other sensors to ML pipeline for live predictions.

Step 8

Multi-Device Testing

Test on multiple Android versions and screen sizes for compatibility.

Step 9

Performance Optimization

Optimize app size, inference speed, and battery usage for smooth UX.

Step 10

APK Build & Delivery

Final APK with source code, documentation and installation guide.

📱

Get in touch and our team will reach out within 2 hours with a customised plan and quote.