AI inside your Android app
An assistant that lives in your Android app.
We build AI features into Android apps — assistants, summaries, smart capture, suggestions — natively in Kotlin and Jetpack Compose, running on the device, on your servers, or both.
An example Android app: a field engineer asks the assistant to summarise the day and book the follow-up. The assistant replies with three issues and suggests a follow-up time, which the engineer taps to schedule.
What it is
On a phone, AI has to be quick, cope with a patchy connection and respect the battery. We decide with you what runs on the device and what runs on your servers, design the feature around how people already use your app, and ship it through Google Play like any other release.
This page is one part of AI inside your product. The same service covers web and iOS apps.
The spec
- What you get
- AI features built natively into your Android app, in Kotlin and Jetpack Compose
- A split that fits: on-device models where speed or privacy matter, server models where quality does
- Offline behaviour and fallbacks, so the app still works without the AI
- Monitoring for quality, response time and cost across devices once it is live
- How we measure it
- Task success for real users, response time on the devices your users actually have, and cost per active user — measured in production, not only on a flagship phone.
- A good first step
- Name the moment in your app where people get stuck. We prototype the AI feature right there and test it on real devices.
What we build with
Our usual Android stack. We work inside your codebase and conventions.
- Kotlin
- Jetpack Compose
- Material Design 3
- Coroutines
- Hilt
- Room
- Retrofit
- ML Kit
- On-device models
- Firebase
- Your backend APIs
Where it fits
Examples of what this work looks like — not client stories.
- Example
Smart capture
Point the camera at a receipt, a label or a form and get the fields back, ready to check and save.
- Example
Summaries on the go
Long threads, notes or reports turned into a short summary with the next action, readable in a few seconds.
- Example
An in-app assistant
Answers questions about the user’s own account, orders or history, and hands off to support when it cannot.
- Example
Voice to action
Speak a note in the field; the app files it, tags it and suggests the follow-up.
Common questions
Often, for tasks that suit small models — classification, extraction, short summaries. Larger tasks usually run on your servers. We measure both on your data before choosing.
We test on a spread of devices that matches your users, not only recent flagships, and fall back to a server model or a simpler path where a device cannot keep up.
Yes. We work inside your codebase and release process, and your team reviews and owns everything we ship.
On-device features keep the data on the phone. Server features go through your backend, under the provider terms you choose — including no training on your data.
What should AI do in your Android app?
Tell us the feature and who uses it. We will come back with how we would build it, where it should run, and how we would measure it.