AI Mobile App Development Services for iOS & Android

It depends on the trade-off you want. On-device inference is fast, works offline, and keeps data on the phone, but the model must be small enough to fit, which usually costs some accuracy. Cloud inference runs larger models but adds latency, network dependence, and per-request cost. We pick per feature, and often use both in one app.

Native, with Swift and Kotlin, gives the tightest performance and access to platform AI frameworks. Cross-platform, with React Native or Flutter, ships one codebase to both stores faster and cheaper. We choose by your performance needs, budget, and how much the app leans on platform-specific AI features, and we are honest when native is worth the extra cost.

We design the interface around it. Streaming responses so the user sees output as it generates, on-device models for instant features, and clear loading states for slower cloud calls. Latency is a design problem as much as an engineering one; an app that shows progress feels fast even when a model is thinking, and one that freezes feels broken.

For on-device features, yes. A quantized model bundled with the app runs without a network, which matters for privacy and for users with poor connectivity. Features that need a large cloud model require a connection, so we design a sensible offline mode and clear behavior when connectivity drops, rather than letting the app simply fail.

On-device inference is the strongest option, because the data never leaves the phone. Where cloud is needed, we minimize what is sent, use private or zero-data-retention endpoints, and are clear in the app about what leaves the device. Privacy expectations on mobile are high, and app stores enforce them, so we design for that from the start.

A lot. The model must fit in the app and run within the phone's memory and battery limits, so we quantize and sometimes distill to a smaller model. This trades some accuracy for the ability to run at all on a device. We measure that trade-off on real hardware rather than assuming a model that works on a server will work on a phone.

Yes. We build to Apple and Google guidelines, prepare the submission, and handle review, including the AI-specific disclosures both stores now expect. App review can reject apps that mishandle AI-generated content or data, so we design for those rules rather than discovering them at submission and losing a release cycle.

Yes. The codebase, the models you own, and the app store listings are yours, under clear IP terms. We can hand over to your team with documentation or continue under a maintenance arrangement. There is no lock-in to us in the delivered app.