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Specialized Service

iOS Development

Native iPhone & iPad apps

Overview

Built to work in the real world

On iOS, users notice the details you skip — a janky animation, a layout that breaks on a smaller iPhone, a permission prompt that appears at the wrong moment. We build native iOS apps in Swift and SwiftUI that hold up to that scrutiny, because Apple's users expect it and Apple's review process enforces it. You get an app that feels like it belongs on the platform: fluid, considered and consistent with the conventions people already know.

We build with modern SwiftUI and a clean architecture that keeps the codebase maintainable as features accumulate. That covers proper state management, sensible use of the Apple frameworks that fit your app, and a release process tuned to App Store review rather than fighting it. When on-device intelligence adds value, we use Core ML to run models locally for speed and privacy. What you receive is a shipped, App Store-ready app and a codebase your team can confidently extend.

Use cases

Where it delivers

A few of the ways teams put this to work — each one something we can scope and ship.

Consumer apps

Refined iPhone and iPad apps where interaction quality and Apple-native feel are the whole point.

Apple ecosystem

Experiences that span iPhone, iPad, Apple Watch and widgets with shared logic and platform-appropriate UI.

On-device ML

Vision, language and personalization features running locally with Core ML for privacy and instant response.

Secure & regulated apps

Finance, health and identity apps using Keychain, biometrics and Apple's privacy frameworks correctly.

Capabilities

What's included

Swift & SwiftUI
Human Interface guidelines
App Store launch
Core ML

Tech we build with

SwiftSwiftUICombineSwift ConcurrencyCore MLCore DataCloudKitWidgetKitXCTestXcode Cloud
How we deliver

A path from idea to production

The disciplined path we follow on every engagement of this kind.

01

Scope & design fit

We align the app to Apple's Human Interface Guidelines and define target devices and OS support before building.

02

Build in SwiftUI

We develop features in SwiftUI with modern concurrency and a testable architecture that scales cleanly.

03

Test & profile

We run XCTest suites and profile with Instruments to keep performance, memory and battery usage in check.

04

App Store launch

We handle provisioning, App Store review, privacy labels and phased release so submission goes smoothly.

Deliverables

Everything you walk away with

Native iOS app submitted to the App Store
Clean Swift codebase built with SwiftUI
Automated XCTest unit and UI test suite
Xcode Cloud or CI pipeline for signed builds
Crash reporting and analytics integration
Technical documentation and handover
FAQ

Questions, answered

SwiftUI or UIKit?

We build new apps in SwiftUI by default — it's Apple's direction and it keeps the codebase concise and maintainable. Where a specific control or legacy integration still needs UIKit, we bridge to it cleanly rather than forcing the whole app one way. The choice follows what your app actually needs, not dogma.

How do you avoid App Store rejection?

We design to Apple's guidelines from the start — privacy declarations, permission usage strings, in-app purchase rules and content policies are handled as we build, not scrambled at submission. We've been through review many times and flag likely friction points early. It doesn't guarantee a first-pass approval, but it makes surprises rare.

Can you build for iPhone, iPad and Apple Watch together?

Yes — we share business logic across targets and design platform-appropriate UI for each, rather than stretching one layout everywhere. Watch apps, widgets and iPad-specific interactions get the attention they need to feel native. We scope which surfaces are in and out up front so the effort is clear.

Does on-device ML work well on iPhones?

Very well — Apple silicon runs Core ML models efficiently, so vision, language and personalization features can execute locally with low latency and no data leaving the device. We convert and optimize models for the Neural Engine and fall back to cloud inference only when a model is too large to run on-device. Keeping inference local is usually the better call for both speed and privacy.

Ready to build with iOS Development?

Tell us what you're building and we'll map the fastest reliable path to production.