TL;DR
- Most of the AI features developers add to chat apps today (writing tools, transcription, image editing, reply suggestions) can run as small, on-device models instead of cloud-hosted large language models.
- On-device models process audio, text, and images locally, using platform frameworks like Apple's Foundation Models and Core AI, so user content never has to leave the device.
- The seven features in this post cover the most common use cases: background removal, text refinement, voice-to-text, a general assistant, a vision agent, AI-drafted replies, and media generation.
- Every demo here is built on Stream Chat, and Stream Agent Skills let a coding agent wire any of these features into an existing Stream-powered app in minutes.
AI features can make messaging and chat apps faster to use and easier to build. Think AI-suggested replies, on-device transcription, or background removal on an uploaded photo.
This post walks through seven of these features, the model behind each one, and how to add them to your app.
Each feature relies on a different form of AI, from text, music, video, speech, and image generation, to content improvement, searchability, translation, and more. What they have in common is where they run.
AI can run on large, cloud-hosted models or small, on-device models. Large models need GPUs and send data to a third-party AI provider. Small, on-device models trade some raw capability for privacy and cost. Nothing leaves the device, and there's no per-token bill. Every demo in this post uses an on-device model for exactly that reason.
App Experiences You Can Build
Not every app requires AI integrations, but identifying where it could add value can help your product scale.
Doing a product assessment for AI features requires listing all current non-AI features and investigating how they can be improved or replaced with AI.
Here is a highlight of some app categories and their AI use cases.
- Video Conferencing: Using AI for live and translated captions, meeting summaries, sentiment analysis, talk metrics, video background blur, replacement, and generation.
- Live Activity Streaming: In-app AI that moderates viewers' comments and removes them for toxic behaviors.
- Community and Social Platforms: Use AI moderation to safeguard in-app communities and groups.
- Online Marketplaces: An AI-powered recommendation system for displaying curated content to buyers.
The 7 Best AI Features For Messaging Apps
Messaging and work collaboration platforms like WhatsApp and Slack have regular features such as channels, threads, replies, emoji reactions, media uploads, offline support, Giphy, and more.
While keeping these existing features, AI ones can be added in so many ways.
This section will guide you through the top seven AI support features, models you can use for each, as well as how teams can build them for mobile and desktop use cases.
Although you can build these seven features into any project, we will focus our integration on using Stream Chat. Why? The Stream Chat SDKs (Android, Flutter, iOS, React, React Native) have ready-to-use chat components for developers to use for AI integrations. Depending on the platform of your choice, you can utilize Stream Agent Skills and instruct a coding agent like Opus 5, Fable 5, GPT-5.6, or Kimi K3 to modify any of these messaging components within Stream Chat to insert or swap a whole component with an AI feature.
Let's discuss the 7 most useful and likely required AI features for your apps.
1. AI Background Image Removal
Many apps support image uploads for use cases such as updating a user profile or adding a photo to a user's post or gallery.
One useful AI feature for the attached/uploaded images in an app is the ability to remove their backgrounds before using them. For background image removal, you need a vision or segmentation AI model. The demo in this section uses Meta's Segment Anything Model 3 (SAM 3) to remove the background from images. When a user attaches an image in Stream Chat, SAM 3 is used to remove its background before sending it in chat.
SAM 3 is an open-source object masking and segmentation model that runs on edge devices (laptops and mobile phones). Check out the Hugging Face community for other image manipulation models, such as those for editing and face swapping.
2. AI Text Refinement and Writing Tools
In any app that requires users to write to perform a specific task, AI refinement tools can be implemented to improve the writing. This can take many forms, such as:
- Summarize: Make a long text concise by omitting unnecessary portions.
- Proofread: Use AI to check out the correctness of text.
- Refine or Rewrite: Change the tone and style of text.
On platforms like SwiftUI, text fields have built-in support for writing tools. Developers can leverage this support to help users enhance their text-writing experience in their apps. As demonstrated in the preview above, writing tools are enabled in the Stream Chat message composer, allowing people to rewrite and style draft messages before sending them to others. By default, writing tools on Apple platforms use system-provided AI models (Foundation Models) and Apple Intelligence for operation. Any multi-purpose text-generation model from Hugging Face can be used for this purpose.
3. Voice Writing With AI
The primary way to enter text in nearly all apps is through the device's keyboard. However, using speech as input is faster than typing on a keyboard. Depending on your app's platform, there are several APIs, frameworks, and automatic speech recognition (ASR) models for low-latency speech-to-text or transcription.
The Stream Chat demo for this section integrates the Apple Speech Analyzer API and its companion SpeechTranscriber model for all users to quickly write text messages with their voices and send them to others in chat.
The automatic speech recognition model category on Hugging Face gives a wide range of transcription models for any use case.
4. General-Purpose AI Assistant
Stream Chat UI components provide a great place for general-purpose agents to live. With these reusable components, developers can build AI assistants for a wide range of purposes across multiple application areas, such as coding, research, writing, healthcare, and finance.
A general-purpose assistant can be built using commercial, open-source, and open-weight always-on local and cloud models. The any-to-any models on Hugging Face help you bring general intelligence to products and services, making it easier to privately run these models on edge devices like phones, tablets, and desktops.
In a Stream Chat-powered app, excellent components to house all-purpose AI generation features are the outgoing and incoming chat bubbles. These components are customizable and help integrate multi-purpose assistants that respond to user queries in real time.
5. Vision Agent
Vision is an essential AI service for our industries. It helps applications to analyze, classify, segment, and see.
In Stream Chat, for example, you can add vision functionality that lets users ask about attached/uploaded images/files, and documents in chat. There are specialized AI models for performing specific computer vision tasks, such as the following.
- Image and Video Classification: Models for classifying images and videos. Some of these models can, for example, recognize participants' facial expressions in video conferencing.
- Object and Keypoint Detection: You can integrate these models for detection, pose estimation, and segmentation, using them to generate bounding boxes and pose skeletons for the face, hands, and torso.
- Image/Video Segmentation: The models in this category enable the use of text and visual prompts to precisely identify, segment, and track objects in images and videos, processing them in real-time.
- Depth Estimation: These models are designed to generate high-resolution depth maps for images and videos, including fine-grained details.
The video preview in this section uses the MiniCPM 4.5 model to analyze and answer queries about images in Stream Chat. Chat participants can upload images from their device's photo galleries, use MiniCPM to communicate about whatever is in the image.
6. Reply With AI
This in-app feature allows users to get AI-suggested answers tailored to their specific needs. You can implement it so that people can select a topic category and receive AI-generated solutions.
For our chat messaging application, the implementation allows chat participants to tap and hold incoming messages, then select "Reply with AI" to obtain drafted replies to edit and send in chat. The default integration uses Apple Intelligence and Foundation Models. However, the official and open-source Core AI model repos are great resources for integrating custom local and on-device Core AI models.
7. Media Generation
Media upload (images, photos, short clips, and videos) is a ubiquitous feature in most mobile and web applications. On-device AI and local models can now make it easy to generate and enhance such media in different output formats.
- Text-to-Image: Use a model like Flux 1 Dev to create images from text descriptions.
- Image-to-Image: Use image input/output models for various generations. For example, Best Face Swap for swapping faces, heads, and the whole body with natural tone blending and consistent lighting.
- Text-to-Video: Render video avatars and short clips with a model like MiniMax H3 Realism People LoRA.
With media generation, you can build a demo that helps chat users be creative and generate images and short video clips (up to 8 seconds) in Stream Chat to share with others.
Build the 7 AI Features Into Stream Chat
Implementing the seven AI features above in your app may require specific models (local/cloud-based), depending on the task and platform (web/mobile).
You could begin by manually creating a Stream-powered chat app and adding the AI features. This approach can take you hours to complete.
However, you can use Stream Skills to accelerate the building process to have a fully working chat messaging and all the AI features in minutes with your favorite coding agent. Just pick your platform and install the required skills.
- AI Framework: All the example demos in the various sections of this article were created using the Apple Core AI framework and its official and community-built local models. For Android, Flutter, and React Native, you will need a similar framework.
- Pick AI Models: Decide whether to use local or cloud-based AI models from any provider. For the sake of simplicity, privacy, and to avoid token costs, all the demos in this article use free and open-source local models.
- Select an SDK: Choose a Stream Chat SDK you want to integrate the AI features, Android, Flutter, iOS, React, or React Native.
- Install Stream Skills: CLI, Android, Flutter, iOS, React, and React Native.
Extend Your App With Other AI Integrations
All the AI models used in the example demos in the various sections of this article can run successfully on iPhone, iPad, or macOS without issues. Most Hugging Face models can handle the feature discussed without issue. However, if your app requires complex computer vision tasks and image/video processing, the model you select to run on-device can cause issues such as overheating the iPhone, making your device unresponsive, and high response latency. Experiment carefully to see what works for your use case.
We covered the seven best out of many AI features you can build into any app by using local on-device models. Other features, such as music and video generation, were not covered in detail, but on Hugging Face, you can find a wide range of models in these categories for experimentation and building AI features for production.
In addition to building these AI integrations into Stream Chat, other AI features can be implemented in a similar way in Stream Video components for real-time communication and face-to-face interactions.