Android Architecture & Gemini AI Integration

I architect production Android applications using multi-module clean architecture with Jetpack Compose, Hilt, Room, and Gemini AI integration.

My approach uses feature-based modularization with strict layer separation:

app/ — Application entry point and navigation

feature/ — Self-contained feature modules (each with its own UI, ViewModel, and domain logic)

core/data — Repository implementations and data sources

core/model — Domain models shared across features

core/network — API clients and network configuration

core/database — Room entities, DAOs, and database setup

core/ui — Shared Compose components and theming

This structure enables teams to scale without stepping on each other. Each feature module depends only on core modules, never on other features.

I have integrated Gemini AI using two different approaches depending on project requirements:

Official SDK — Using com.google.ai.client.generativeai for standard chat, content generation, and multimodal features. Best for straightforward integration with structured JSON output via responseMimeType.

Custom REST Wrapper — Using HttpURLConnection with kotlinx.serialization when features like Google Search Grounding, custom timeout controls, or response schema enforcement are needed beyond what the SDK offers.

Both approaches include production guardrails: gibberish input detection, dynamic context injection, and structured JSON output with validation.

I have built and sold 3 products to major technology companies using this architecture. Each project ships with comprehensive testing (unit, integration, and Maestro E2E), CI/CD pipelines, and code quality gates.