Core ML · Private
On-device AI, where the data never leaves.
Inference on the phone: no round trip, no server bill per request, and no privacy policy explaining where your users' data went.
Stack
01Scope
What on-device intelligence covers.
Apple's frameworks do more than most teams realise before they check.
- Apple Intelligence and Foundation Models
- The on-device model available through Apple's frameworks, for summarisation, classification and structured generation at no per-token cost.
- Core ML model integration
- Converting, quantising and profiling models so they fit the memory and thermal budget of the devices you actually support.
- Vision and natural language
- Text recognition, document scanning, image classification and entity extraction, running offline and instantly.
- Hybrid routing
- On-device for the common path, server for the hard path, with an explicit rule for which runs when.
02Why it wins
The three arguments for on-device.
Any one of them is usually enough to justify the approach.
- Privacy as a feature
- Health, finance, journaling and legal products can state that data never leaves the device — and pass the review questions that follow.
- Zero marginal cost
- No per-request bill. A feature used a hundred times a day by every user does not change your unit economics.
- Offline and instant
- No network dependency and no round-trip latency, which is the difference between a feature people use and one they wait for.
03FAQ
On-device AI: common questions.
Which devices support on-device AI?
Core ML runs across the supported iPhone range, with performance scaling by chip generation. Apple Intelligence features require the newer devices specifically, so a product depending on them needs a stated minimum device and a fallback for everything below it.
On-device or cloud AI?
On-device when the data is sensitive, the feature runs constantly, or it must work offline. Cloud when the task needs a frontier model's reasoning. Most products are best served by both, with an explicit routing rule.
Can you convert our existing model to Core ML?
Usually — conversion, quantisation and on-device profiling against a real device set. We check size, memory, thermals and accuracy loss before committing to it in a build.
04Related
Related capabilities.
Most engagements touch more than one of these.
AI app development
Assistants, personalisation, vision and workflows — built as product features with real failure states, not a demo behind a text box.
Apple Watch app development
The Watch is a glance and a tap. Products that respect that constraint get worn; products that port a phone screen get uninstalled.
iOS app development
Swift and SwiftUI, current-generation Apple frameworks, and a release you own end to end. No wrappers, no WebView shortcuts.
Tell us what you're building.
Send the rough version. A senior engineer reads every brief and replies within one business day.