01 / Overview
AI product
A mobile AI application
Chat Bubbles is an Android AI assistant with a floating chat bubble on the screen.
People can ask questions and receive answers while using other applications, without moving back and forth between multiple screens.
02 / Challenge
How can people talk to AI from anywhere without interrupting the task they are already performing?
The application must keep its floating window stable, respond quickly, and preserve the conversation even as the user switches between multiple applications.
03 / Goals
- Access AI quickly from any application
- Reduce switching between applications
- Keep the floating interface compact and easy to use
04 / Technical challenges
Keep the Android Overlay stable
Preserve conversations while switching between applications
Respond quickly while conserving device resources
05 / Research
We studied how people use AI applications on their phones and found that repeatedly opening and closing an application breaks concentration.
The floating chat bubble was therefore selected as the primary access point, keeping AI available without disrupting the wider phone experience.
06 / Architecture
The application architecture consists of three main parts:
- A floating bubble that manages Overlay permissions
- A conversation screen that manages history and state
- An AI service that processes requests and language-model responses
The reference architecture includes:
- Android Client: Interface · Overlay · Voice
- AI Service: Conversation management · Streaming · GPT
- Data: Conversation history · User settings
- Operations: Secure connections · Permission management · Error and performance monitoring
Each layer is separated so the application remains maintainable and ready to expand.
Operational priorities:
- Secure connections
- Controlled permissions
- Error and performance monitoring
Reference architecture
Safe and stable operations
Android Client
Interface · Overlay · Voice
AI Service
Conversation management · Streaming · GPT
Data
Conversation history · User settings
Operations
Secure connections · Permissions · Error and performance monitoring
07 / Design
The design focuses on reducing taps and keeping AI ready whenever it is needed.
The primary screens are:
- Conversation list
- Chat screen
- Floating bubble
Each interaction is kept compact so the assistant supports the current task instead of replacing it.
Interface gallery
Explore the primary Chat Bubbles interface states on Android. Select an image to view it at high resolution.
4 images
Mobile application
04 screens08 / Development
The application is developed as a native Android experience connected to an AI model. Its primary capabilities include:
- Floating bubble
- Realtime conversations
- Conversation-history synchronization
- Voice input
- Multiple conversations
Native Android behavior and Material Design patterns keep the experience familiar and responsive.
09 / Optimization
- Fast bubble initialization
- Conversation state survives app switching
- Conversation data synchronizes across devices
- Responses stream in real time
10 / Deployment
The interface, conversation, and AI service layers are separated for easier maintenance and expansion. Releases are:
- Delivered in small versions
- Validated for stability
- Designed to reduce operational risk
11 / Scaling
The architecture supports additional AI models and new capabilities without disrupting the existing experience.
- Support multiple AI models
- Synchronize multiple devices
- Add new AI capabilities
New services can be introduced behind stable application interfaces.
12 / Monitoring
The application monitors:
- Overlay errors
- AI response performance
- Conversation state
- Network connection quality
This visibility helps maintain a fast and dependable mobile experience.
13 / Result
AI anywhere
Available in every application
Realtime
Responses stream in real time
Synchronized
Conversations across multiple devices
Voice
Supports voice input
14 / Lessons learned
A powerful feature does not necessarily create a good experience if users must take too many steps to reach it.
The project demonstrates that optimizing everyday user flows can be just as important as improving the capabilities of the AI model.
Our responsibilities
Duration
4 months
Deployment date