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AI Integration · 3–5 Weeks

On-Device AI Integration for iOS

Add private, on-device intelligence to your iOS app using Core ML and Foundation Models. Models run locally — fast responses, no cloud cost, and user data never leaves the device.

Timeline

3–5 weeks

Data to server

0 bytes

Collaboration

Async-first

Investment

From €5,400

Start Integration →Full service details

What on-device AI integration means in practice

Most iOS apps that add AI today route user data to an external API. The app sends text, images, or audio to a cloud server, waits for a response, and shows the result. Every call adds latency, costs money per request, and sends user data outside the device — which creates privacy obligations that are difficult to satisfy under App Privacy Details and user expectations.

On-device AI runs entirely inside the device. The model lives in the app bundle or is downloaded once. Inference happens on the Neural Engine — Apple's dedicated hardware for machine learning — with no network hop. The result is sub-100ms response time on supported hardware, zero cloud cost per inference, and a provably private feature that your users can verify in your App Privacy Details label.

This service adds that capability to an existing iOS app. It covers the full integration path: architecture planning, model selection, implementation, performance profiling, and production rollout strategy with staged deployment controls.

What the integration covers

Architecture and capability baseline

Review of your existing app architecture to identify integration points, data flow requirements, and any structural changes needed before implementing on-device inference.

Core ML integration

Implementation of task-specific inference using Core ML — image classification, natural language processing, regression, or custom models you provide or source. Includes model storage, versioning, and update strategy.

Foundation Models integration

Generative AI features using Apple Foundation Models for conversational interfaces, text summarization, or structured output generation — running entirely on device without API keys or server costs.

Inference performance profiling

Xcode Instruments profiling to measure inference latency, memory footprint, and Neural Engine utilization. Bottlenecks are identified and optimized before the feature ships.

Privacy boundary audit

Verification that no inference input, output, or intermediate state is transmitted outside the device. This directly informs your App Privacy Details disclosure and complies with Apple privacy expectations.

Production rollout playbook

Staged rollout strategy with feature flags, observability hooks, and documented rollback criteria. AI features are shipped incrementally — not all at once to all users.

Deliverables

  • ✓Production AI feature integrated into your existing iOS codebase
  • ✓Inference performance report from Xcode Instruments profiling
  • ✓Privacy boundary audit document — no data leaves the device
  • ✓Model update strategy and versioning plan
  • ✓Production rollout playbook with feature flags and rollback rules
  • ✓Post-integration handoff notes with architectural context

How it works — 3 to 5 weeks

Week 1

Architecture baseline and scope lock

Review of your existing codebase to identify integration points. Scope document finalized covering the AI feature, model requirements, and integration boundaries.

Week 2

Model integration and core implementation

Core ML model integrated or Foundation Models interface implemented. Data flow from the app to inference engine connected and tested in isolation.

Week 3

Performance profiling and optimization

Instruments profiling for inference latency, memory pressure, and Neural Engine utilization. Optimizations applied to meet performance targets.

Week 4

Privacy audit and rollout controls

Privacy boundary verified — no user data transmitted outside the device. Feature flags and staged rollout controls implemented.

Week 5

Handoff and rollout playbook

Final build, performance report, privacy audit document, and rollout playbook delivered. Post-integration notes cover model update strategy and future feature expansion.

Who this is for

The On-Device AI Integration service is built for teams with an existing iOS app that want to add AI features without a cloud dependency. The typical client has already shipped a version of their app and is now ready to differentiate with intelligence that works faster, costs less to run, and is verifiably private.

Good fit

  • ✓Existing iOS apps adding AI features for the first time
  • ✓Privacy-first products where user data must not leave the device
  • ✓Teams moving AI inference from cloud APIs to on-device processing
  • ✓Health, finance, or productivity apps with sensitive user data
  • ✓Products where inference latency is a user experience bottleneck

Not a fit

  • —Apps with no existing iOS codebase — start with the MVP Sprint
  • —AI features that require server-side compute by design
  • —React Native or cross-platform projects
  • —Projects where Apple Intelligence hardware availability is a blocker

Pricing

€5,400starting price · fixed scope

Base price covers integration of a single AI feature using Core ML or Foundation Models in an existing iOS app. Multiple AI features, custom model training pipelines, or Apple Watch extensions are scoped separately. Apply with a description of your app and the AI feature you want to ship.

50% upfront, 50% on feature delivery. Fixed scope — no hourly billing.

Common questions

Can on-device AI be added to an existing iOS app?↓
Yes. The service is specifically designed for existing iOS apps. Integration uses architecture-safe insertion points so existing features are not disrupted. Fallback paths are included so the app degrades gracefully on devices that do not meet the hardware requirement.
What is the difference between Core ML and Foundation Models?↓
Core ML handles deterministic prediction: image classification, text analysis, regression, and custom models you provide. Apple Foundation Models is Apple's on-device large language model framework for generative text — available on Apple Intelligence capable devices. Most integrations use both: Core ML for task inference and Foundation Models for conversational or generative flows.
Does on-device AI work on all iPhones?↓
Core ML runs on all modern iOS devices. Heavier Core ML models benefit from the Neural Engine, available from Apple A12 Bionic onwards. Apple Foundation Models requires Apple Intelligence hardware — iPhone 15 Pro and later, or M-series iPad and Mac. The integration includes fallback logic so the app works for all users, with AI features activated only on capable hardware.
How does on-device AI protect user privacy?↓
On-device inference processes everything inside device memory. No data is uploaded for analysis. The integration deliverables include a privacy boundary audit verifying that no inference input, output, or intermediate state is transmitted outside the device. This directly informs your App Privacy Details disclosure.
What if I need AI features that require server-side compute?↓
Some AI tasks genuinely require server infrastructure — real-time data, large model capacity, or multi-user shared state. If your use case requires a hybrid approach (on-device for some tasks, cloud for others), the architecture baseline phase identifies the right split and the implementation covers both sides with appropriate privacy controls.

Other services

iOS Architecture Audit

AI-native readiness review in 5 days · from €1,440

Apple Platform MVP Sprint

Production iOS app from zero in 6–8 weeks · from €8,400

Shipping iOS AI this quarter?

Apply with a description of your app and the AI feature you want to add. A scoped plan and fixed quote is returned within 1 business day.

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