AISTORS ServicesAI mobile apps for iOS and Android

Service 11

AI apps for iOS and Android that people keep.

On the phone where it can run, on your backend where it cannot.

For teams who need an app with AI in it, not a chat window bolted onto one. We design, build and release iOS and Android apps where the AI does a real job: reading a photo, listening to a request, finding the right record, drafting the next step. Each feature runs on the device when the device can do it well, and on a backend in your own cloud account when it cannot.

Book a 30-minute call

In short

How the AI in your app works, in four steps

What the phone can do runs on the phone.
Text recognition, image labelling, speech to text and short summaries run on the phone where supported, with a cloud fallback for phones that cannot.
Cloud models are reached only through your backend.
No provider key ships in the app. Keys stay in your vault, usage is metered per signed-in account, and providers change without a store release.
Every AI feature is scored on real examples.
Real photos, requests and documents are collected first, and each feature is scored on them before release and whenever a model or prompt changes.
Store disclosures match what the app really does.
Privacy labels, data disclosures, permission prompts and any sharing with an AI provider are written during the build, not the night before submission.

Who this is for

If the left column is not you, say so on the call and we will tell you what would serve you better.

This is for you if

There is a task people do on their phone already. Field staff photographing a job, customers checking an order, a team logging work on the move. The app makes that task faster, not a new habit nobody asked for.
AI does one clear job in it. Reading a document from the camera, answering from your own records, turning speech into a filled form. A named job can be tested; a general assistant cannot.
You need both iOS and Android. Your users carry both, so the app has to behave the same on each, pass both stores' review and keep passing it as the platforms change.
Your data has rules attached. Photos, voice and personal details that should stay on the device where possible, and go only to providers you have approved where not.
You want to own it outright. The code in your repository, the apps in your own developer accounts, the backend in your own cloud. Nothing that stops working if we stop working with you.

This is not for you yet if

You want an app because competitors have one. Without a task people will open it for, an app is a cost with an icon. We would rather help you find the task first, or tell you a website will do.
The AI is the whole product and nobody has tested it. If the core idea depends on a model doing something it has never been shown to do, the first job is a short proof on real examples, not a full build.
You need a game or heavy 3D. That is a specialist field with its own engines and its own studios, and we will point you to one.
It must ship in two weeks. Store review, real devices and a test set of real cases take time. A date that leaves none for them produces an app that gets rejected or gets deleted.
You want the cheapest possible build. We spend time on testing, privacy and the release process because those are what keep an app in the stores. A quote that skips them will be lower.

What we fix, and how

Four things go wrong with AI in mobile apps. Each one is known in advance and each has a plain answer.

PROBLEM 01

Every feature waits on the network

What we do. Features that the phone can run, such as text recognition, image labelling, speech to text and short summaries on supported devices, run on the phone. The app stays useful on a poor connection, and the photo or recording never has to leave the device.

PROBLEM 02

A provider key is shipped inside the app

What we do. Cloud model calls go through a backend in your own cloud account, never straight from the app with a key inside it. Keys stay in your vault, usage is metered for each signed-in account, and changing provider is a server change rather than a store release.

PROBLEM 03

Nobody knows if the AI is any good

What we do. Before the build we collect real examples of the job: the photos, the requests, the documents. Each AI feature is scored against that set before release and again whenever a model or prompt changes, so a quiet regression is caught before your users find it.

PROBLEM 04

Store review blocks the launch

What we do. Privacy labels, data disclosures, permission prompts and any sharing with an AI provider are written up during the build, not the night before submission. Both stores see an app whose stated behaviour matches what it actually does.

What you get

Six deliverables, released under your accounts, with nothing that depends on ours.

What we build

Design for the task, on both platforms. Flows tested with people who will use the app, following each platform's own conventions so it feels native on an iPhone and on an Android phone.
The apps, native or cross-platform. Swift and SwiftUI for iOS and Kotlin with Jetpack Compose for Android, or one Flutter or React Native codebase where the product suits it. The choice is argued on the call, not assumed.
AI features, on the device and behind it. On-device models for camera, vision, voice and short text tasks, and cloud models for the work a phone cannot do, each with a fallback when the other is unavailable.
The backend in your cloud. The API, sign-in, storage, push notifications and the model gateway, deployed as infrastructure as code in your Azure, AWS or Google Cloud account.
Release to the App Store and Google Play. Test builds through TestFlight and Play testing tracks, store listings, privacy labels and data disclosures, and submission under your own developer accounts.
Maintenance after launch. Updates for each new iOS and Android release, crash monitoring, dependency and security patches, and the AI test set re-run whenever a model or prompt changes.

What this is not

Not a chat window in an app shell. If the only AI feature is a general chatbot, your users can already open one. Each feature here does a named job inside a task they came to do.
Not a template app. No white-label kit with your logo swapped in. The flows are designed around your task and your users.
Not published under our accounts. The apps sit in your Apple and Google developer accounts from the first test build, so the listing, the reviews and the users are yours.
Not your data sent where you have not agreed. Every model provider the app uses is named before it is used, and anything that can stay on the device stays there.
Not a hand-off at launch. Apps break when the platforms change underneath them. Maintenance is scoped from the start, or handed to your own team with runbooks if you would rather run it.

How it runs

Five stages, and the app is in real hands before it is in the stores.

Name the task and the AI's job in it

Who opens the app, for what, and which step the AI takes off them. We collect real examples of that step, because they become the test set every AI feature is scored against.

One task, written down

Design and prove the hard part

Clickable designs for the main flows, and a small proof of the riskiest AI feature on real devices, so the uncertain part is settled before the full build is committed to.

Riskiest feature first

Build both apps and the backend

Built in short cycles with a test build on your phones every week. The backend goes into your cloud account as code, with the model gateway and its usage metering in place from the start.

Weekly test builds

Test with real users, then release

A closed test with the people the app is for, the AI features scored against the test set, and both store submissions prepared with the privacy and data disclosures complete.

Both stores, your accounts

Maintain, measure, improve

Crash and usage reporting, updates for each platform release, and the test set re-run on every model or prompt change. You decide what is built next from what people actually use.

Monthly report

Platform native

Each platform's own AI tooling where it does the job well, and your cloud behind it for the rest.

iOS

Swift and SwiftUI, Core ML with the Vision and Speech frameworks, and Apple's Foundation Models framework for on-device language features on supported devices. Distributed through TestFlight and App Store Connect under your developer account.

Android

Kotlin and Jetpack Compose, LiteRT (formerly TensorFlow Lite) and ML Kit, and Gemini Nano through ML Kit's GenAI APIs on supported devices. Distributed through Play Console testing tracks under your developer account.

Flutter and React Native

One codebase for both stores where the product suits it, with native modules wherever the camera, on-device models or background work need platform code to perform properly.

Your cloud behind the app

The API, sign-in, storage, push and the model gateway on Azure, AWS or Google Cloud, in your account, under your provider contracts, as infrastructure as code in your repository.

On-device features keep photos, voice and personal details on the phone wherever the model can run there. Any cloud model path, including a platform provider's own cloud processing, is a sub-processor, and it is named and agreed with you before the app uses it.

Questions we are actually asked

Native or cross-platform?

It depends on what the app does most. Heavy camera work, on-device models and background tasks usually favour native Swift and Kotlin. Forms, lists and account screens usually suit Flutter or React Native, at lower build and maintenance effort. We will recommend one on the call and say why, and some apps are best as cross-platform with a few native parts.

Does the AI work offline?

The features that run on the device do: text recognition, image labelling, speech to text and, on supported devices, short summaries and rewrites. Anything that needs a large model or your own records needs a connection, and the app is designed to say so plainly rather than spin.

Which phones support the on-device language models?

Apple's and Google's on-device language models run only on recent devices, and the supported list changes with each release. We check it against the phones your users actually carry, and every feature built on those models has a fallback for the phones that cannot run them.

Who owns the app and the store listing?

You do. The apps are published under your Apple Developer and Google Play developer accounts, the code is in your repository and the backend is in your cloud account. If we stop working together, nothing needs transferring.

Can you add AI to an app we already have?

Yes, and it is often the better starting point. We review the existing codebase first and tell you whether the feature can be added cleanly, or whether part of the app needs rework before it can carry it.

What happens when Apple or Google changes something?

Something changes every year, from operating system releases to store rules. Maintenance covers testing on each new release and keeping the app compliant, and if a change affects an AI feature we tell you what it means before it reaches your users.

Next step

Bring the task your users do on their phones.

Thirty minutes, no obligation. Tell us who would open the app, what for, and which step you want the AI to take off them. We will tell you whether it should be native or cross-platform, which parts can run on the phone, and whether it needs to be an app at all.

Starts with: The task, the people who do it and a handful of real examples of the step the AI would take on. An existing app's repository helps if there is one.
Investment: Scoped on the first call, once the platforms, the AI features and the maintenance you need are known.
If an app is the wrong answer: We will say so. Sometimes a mobile website or a feature in an app you already run serves the task better, and you keep the reasoning either way.

Book a 30-minute call

Or write to [email protected].

Related work

Service 01

AI agents and assistants

When the app is the front end to work an agent does behind it, inside your own systems.

Service 03

Data and retrieval for AI

When the app has to answer from your own documents and records, this is the retrieval layer it relies on.

Service 09

Managed AI and cloud operations

If you would rather we keep the backend and the AI features running after launch, with an exit designed in.