Build intelligent Android apps: Introduction to Jetpacker

Posted by Jolanda Verhoef, Senior Developer Relations Engineer,  Android Developer Relations Building GenAI features in your app usually means navigating through various models, APIs and architecture choices:  Execution location: Where does your model run? On device, in the cloud, or both? Complexity: How complex is your setup?

Are you doing a single inference call or do you need a more agentic flow? In-app or Android System: Should your feature be built into your Android app or does it fit better as an Android system integration? In this blog post series we'll navigate these choices with you.

We will take you along on a journey, starting with a basic mobile app and transforming it into a personalized , intelligent , and agentic experience. Jetpacker: a demo travel app Jetpacker is a technical showcase app that our team built from the ground up for this year's Google I/O (built using Antigravity). At its core, Jetpacker helps users plan, explore, and enjoy their next big adventure.

It shows an overview of your trips, the itinerary of each trip, and details of each event on that trip. Of course following all best practices of Android development, including a beautifully expressive Material UI design. And best of all?

It's fully open source ! Today we are publishing a series of technical blog posts diving deep into each of these features. We’ll provide detailed implementation steps, code snippets, and architectural insights to help you build your own intelligent Android applications.

On-device intelligence On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes Using an on-device model comes with no additional cloud inference costs, means you don't have to worry about internet connectivity , and lets users be confident that private information will be processed locally , on the device, without any of their data being sent to the cloud. In Jetpacker, we chose on-device inference for three of our features: The trip overview feature transforms a messy, multi-day itinerary into a concise, actionable summary. It leverages Gemini Nano through the ML Kit GenAI APIs to process data locally on the device.

We consider this a nice-to-have feature where we don't want to incur extra cloud costs, making on-device inference the right choice. The expense tracker automatically extracts structured data from receipt images to help users track their travel spending. It uses the multimodal capabilities of Gemini Nano 4 through the ML Kit GenAI APIs.

We choose an on-device solution so that any privacy-sensitive information on the receipt images never leaves the user's device. The audio diary records, transcribes, and categorizes voice notes into relevant trip activities. It is powered by the ML Kit Speech Recognition and GenAI Prompt APIs .