AI-Powered Mobile App Development for
iOS & Android
Cross-platform and native mobile apps built with Flutter, React Native, Swift, and Kotlin, with on-device AI features where they genuinely help. One team, one codebase where it makes sense, the full backend included.
Getting your product into people's pockets, on both platforms, without maintaining two apps forever.
The real decision most founders and operators face is not whether to build a mobile app. It is whether to build it twice, once in Swift for iOS and once in Kotlin for Android, or once in a shared codebase that ships to both.
Building twice means every bug gets fixed twice, every feature gets shipped twice, and every future hire needs to know two separate stacks. For the large majority of apps, that cost is not buying you anything the user can see. We default to one shared codebase unless there is a specific, real reason your app needs to be fully native.
The same team that builds your web AI features builds your mobile app, so the backend, the AI, and the app itself are one coherent system rather than three vendors who do not talk to each other.
What you actually get
- ✓ One codebase powering both iOS and Android, unless native is genuinely justified for your case
- ✓ On-device AI features where they actually help: privacy, offline use, or speed
- ✓ The backend, sync, and push notification infrastructure included, not just the screens
- ✓ App Store and Play Store submission handled for you
- ✓ Code and documentation fully handed over, plus 2 to 6 months of free post-launch support
Everything below this point is the technical detail: the frameworks, models, and architecture we use. If that is not your area, skip it and send us a note instead.
Six mobile development capabilities.
From choosing the right framework to on-device AI and store submission, each capability maps to getting your app shipped and adopted, not a technology showcase.
Cross-Platform Development with Flutter
For most apps, one codebase serving iOS, Android, web, and desktop is the right call. We build on Flutter, now on version 3.41 with the Impeller rendering engine, which delivers roughly 50% faster frame rasterization than its predecessor. Flutter leads on raw rendering performance and hot reload speed, which is why it now accounts for around 46% of cross-platform mobile development.
- ✓ Flutter 3.x with Impeller engine
- ✓ Single codebase, iOS + Android + web
- ✓ Consistent brand and design system
React Native with the New Architecture
When your team already knows JavaScript or TypeScript, or you need the widest possible library ecosystem, we build on React Native 0.84 and its New Architecture, Fabric, JSI, and TurboModules are now the default. React Native powers apps serving billions of users, and gives you access to the npm ecosystem alongside a large pool of engineers who already know the stack.
- ✓ React Native 0.84, New Architecture
- ✓ Fabric, JSI, and TurboModules
- ✓ Access to the npm package ecosystem
Native iOS Development (Swift & SwiftUI)
Cross-platform is the right default for most products, but not all. When an app needs the deepest possible OS integration, the best performance for compute-heavy features, or on-device inference through Apple Silicon's Neural Engine, we build native in Swift and SwiftUI, with Core ML for AI features that run entirely on the device.
- ✓ Swift & SwiftUI
- ✓ Core ML for on-device inference
- ✓ Full platform-native feature access
Native Android Development (Kotlin & Jetpack Compose)
The Android equivalent of the same principle: Kotlin and Jetpack Compose for apps that need to be fully native, plus Android's ML Kit GenAI APIs for on-device AI features that run through the AICore system service rather than a round trip to a server.
- ✓ Kotlin & Jetpack Compose
- ✓ ML Kit GenAI APIs
- ✓ AICore on-device integration
On-Device AI Features (Apple Intelligence & Gemini Nano)
Where privacy, offline reliability, or latency genuinely matter, we build features that run the model on the user's device instead of a server. On iOS, that means Apple's Foundation Models framework running on the Neural Engine. On Android, that means Gemini Nano, which ships on Pixel 9 and newer and Galaxy S25 and newer. Modern compressed on-device models run in 1 to 4GB of storage and 1 to 2GB of RAM, small enough to ship inside a normal consumer app.
- ✓ Apple Foundation Models framework
- ✓ Gemini Nano via ML Kit
- ✓ Fully offline-capable AI features
Backend, Sync, and App Store Deployment
A mobile app is not just a UI. We build the same production backend patterns we use for web AI applications, FastAPI, LangChain, and real-time sync, adapted for offline-first mobile use, push notifications, and background jobs. We also handle App Store and Play Store submission, and the DevOps to ship updates safely. Monitoring, support, and maintenance after launch are covered inside your free maintenance period, not billed as a separate line item.
- ✓ Offline-first sync & push notifications
- ✓ App Store & Play Store submission handled
- ✓ Maintenance included after launch
Mobile apps that ship to both stores and stay maintained.
We build mobile apps engineered for production and store approval from the first commit, with the backend and AI infrastructure to match, not a UI shell bolted onto someone else's API.
Frequently asked questions.
Should our app be native or cross-platform?
For most products, cross-platform (Flutter or React Native) is the right default: one codebase, one team, and a bug fixed once instead of twice. We recommend native Swift or Kotlin only when the app genuinely needs deep OS integration, compute-heavy performance, or on-device AI capabilities that are easier to reach natively. We will tell you honestly which one your app actually needs before we start building.
Do you build for both iOS and Android?
Yes. Most engagements ship both platforms from a single Flutter or React Native codebase. When a client needs one platform built natively for a specific reason, we build in Swift and SwiftUI for iOS or Kotlin and Jetpack Compose for Android.
Can you add on-device AI features to our app?
Yes. On iOS, we build on Apple's Foundation Models framework, which runs on the device's Neural Engine. On Android, we use Gemini Nano through Android's ML Kit GenAI APIs. Both let an app offer AI features like summarization, smart replies, or on-device search that work offline and never send the underlying data to a server, which matters for privacy-sensitive use cases.
How much does mobile app development cost?
The same entry point as every service we offer: a $5,000 pilot that proves out one fixed-scope piece of the app before you commit further. A focused cross-platform MVP typically runs $20,000 to $40,000, and a full native build across iOS and Android, or an app with on-device AI and backend infrastructure, ranges from $50,000 to $150,000 or more. You get a fixed price and a realistic timeline before committing to the full build.
What happens after the app is submitted to the App Store and Play Store?
We handle the submission process itself, including the review requirements both stores enforce. After launch, every engagement is backed by 2 to 6 months of free post-launch support and maintenance, scaled to the size of the build, which covers small bugs, store-review follow-ups, and monitoring on live features.
Can I just build the app myself with an AI coding assistant?
You can get further than most people expect, and it is genuinely worth trying for a prototype. AI coding assistants from Anthropic and OpenAI are very good at scaffolding screens, wiring navigation, and generating boilerplate for React Native or Flutter, and they will get a demo running fast. Where they consistently struggle is the part that decides whether an app survives: state management across screens, offline sync and conflict resolution, background jobs, push notification infrastructure, platform permission handling, and the store review requirements both Apple and Google enforce. Those are architecture decisions, not code generation problems, and an assistant will happily produce something that runs on your laptop and falls over with real users on real networks. Our honest advice: use AI to prototype and prove the idea, then bring in engineering before you commit to the architecture you will live with for years.
What goes wrong most often when a mobile app is vibe coded?
Four failures come up repeatedly. First, architecture debt: the assistant generates working screens with no separation between UI, business logic, and data, so by screen fifteen every change breaks something unrelated and the app cannot be extended without a rewrite. Second, the wrong toolset for the requirement, most often reaching for a cross-platform framework when the app needs deep native capability, or the reverse, building twice natively when one shared codebase would have done. Third, imprecise requirements: asking for a "login screen" and getting exactly that, with no token refresh, no secure credential storage in Keychain or Keystore, no session expiry, and no biometric path, because none of it was specified. Fourth, dependency sprawl, where a package gets added for each small problem until the bundle is oversized, the build is fragile, and half the dependencies are unmaintained. None of these show up in the demo. All of them show up in month three.
How do I keep AI token costs under control while building a mobile app?
Token consumption is the cost line people underestimate, and it applies both to building the app and to running AI features inside it. On the build side, the expensive pattern is pasting an entire codebase into context on every request; the fix is scoping each request to the specific files involved and keeping the assistant working in focused sessions rather than one sprawling thread. On the runtime side, if your app calls a hosted model for every user interaction, cost scales linearly with usage and a popular app becomes an expensive app very quickly. This is the main practical argument for on-device inference through Apple Foundation Models or Gemini Nano, where the per-request cost is zero because the model runs on the user hardware. Our usual pattern is a hybrid: run routine work such as summarization, classification, and smart replies on-device, and reserve hosted frontier model calls for the genuinely hard requests. We model this cost per active user before the build, not after launch.
What should I know about setting up the mobile development environment?
The environment is where first-time mobile projects lose the most time, and it is worth budgeting for honestly. Android requires Android Studio with the correct SDK and build-tools versions, a configured emulator or physical device with USB debugging, and a Gradle setup that matches your framework version. iOS requires Xcode on macOS specifically, which means you cannot build or submit an iOS app from Windows or Linux, plus CocoaPods or Swift Package Manager, a provisioning profile, signing certificates, and an Apple Developer account. The most common pitfalls we see are version mismatches between the framework, the platform SDK, and the native toolchain, code signing failures that only surface at submission, and Gradle or Pod caches that need clearing after a dependency change. For teams new to mobile, this setup alone regularly consumes the first week. We hand over a documented, reproducible environment so your team is not rediscovering it later.
Why not just hire a mobile developer instead of working with an agency?
Because one app is almost never one skillset. A production mobile app needs the app layer itself, a backend and API, data modelling, offline sync, push notification infrastructure, AI integration if the product uses it, QA across a wide range of devices and OS versions, and the DevOps to build, sign, and ship to two separate stores. Then it needs monitoring, support, and maintenance once it is live. In practice engineers specialise and mostly prefer to stay in their lane, so a single hire is strong in one area and thin everywhere else, and covering the full surface properly means several hires. One engagement with us gives you that whole spread without assembling and managing the team, and the operational tail that people forget to budget for, monitoring, support, and maintenance, sits inside your free post-launch maintenance period rather than being another role to fill.
Not ready to book a call?
Tell us the one manual process eating the most time in your business. We will reply with whether it is automatable, roughly what it would take, and what it would be worth. No deck, no pitch.
Thirty minutes.
We'll tell you exactly
where your ROI is.
No sales deck. No 50-page report you have to pay for before anything gets built. Just a direct conversation about which of your workflows are costing the most and whether AI can fix them. If there's no compelling answer, we'll say so. And it's a conversation with Kash, our founder, not a rep reading from a script, because the person who built this business is the one who should understand yours.