Mobile AI apps
Mobile AI app development for iOS and Android
AI on mobile has constraints the web doesn't. Battery, memory, flaky networks, app review, and users who expect the camera or microphone to respond instantly. A cloud-only design feels slow on a train. An on-device-only design runs out of memory on an older phone.
- Native iOS and Android
- On-device inference
- Cloud inference
- Camera, voice and sensors
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.
Signs you need this
We build native apps that split the work sensibly. Small models on device for speed and privacy, larger models in the cloud for reasoning, with offline behaviour designed in. Then we take the app through App Store and Google Play review and keep it there.
- An AI feature that works on Wi-Fi and stalls on cellular
- App Store rejection over AI content or data use
- On-device model crashes on older phones
- Separate web and mobile teams building the same AI logic twice
- No crash or latency data from real devices
- API keys shipped inside the app binary
What you get
What is included
Native iOS and Android
Swift and SwiftUI, Kotlin and Jetpack Compose, or React Native where shared code makes sense. Chosen per product, not by habit.
On-device inference
Core ML, Apple Foundation Models and on-device runtimes on Android for transcription, vision and classification without a round trip.
Cloud inference
A secure backend for frontier model calls, so keys never ship in the binary and costs are metered per user.
Camera, voice and sensors
Real-time camera, speech-to-text, text-to-speech and wearable inputs wired to the model with latency budgets.
Store submission
App Store and Google Play submission, privacy labels, AI content disclosures, review responses and phased release.
Device telemetry
Crash reporting, latency and model performance by device class, so regressions show up before reviews do.
How it runs
How we deliver Mobile AI apps
- 01
Device and model plan
Decide what runs on device and what runs in the cloud, by feature, with latency and cost targets.
- 02
Prototype on hardware
Test the AI path on real phones, including older ones, before building the rest of the app.
- 03
Build and beta
Full app build with TestFlight and Play internal testing on real users.
- 04
Store release
Submission, review handling and phased rollout, then ongoing releases on your developer accounts.
Industries we build this for
Mobile AI apps questions
Something else? Email t@moher.ai and an engineer will answer it.
Native or cross-platform?
Native when the app depends on camera, audio, on-device models or wearables. React Native when the product is mostly screens and forms and a shared codebase saves real time.
Can AI run fully on the phone?
For transcription, classification, vision and small language tasks, yes, on recent hardware. Longer reasoning still runs better in the cloud. Most apps use both.
Do you handle App Store and Google Play submission?
Yes, on your developer accounts, including privacy labels, AI disclosures, review responses and phased releases. We have shipped to both stores.
Whose name is on the store listing?
Yours. Apps are published under your developer accounts and your company name from the first build.
Can the mobile app share a backend with our web app?
Yes. We usually build one typed backend and API for both, so AI logic, auth and billing live in one place.
Tell us what you need built
Four quick questions and an email. An engineer reads every brief and replies within one business day with next steps, and a written scope follows within a week.
- Written scope and price within a week
- Production deploy by week three
- Your repo and cloud from the first commit
- Senior engineers only, no account managers
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.