AI-Powered Mobile Apps

It means adding AI features to an iOS or Android app, such as assistants, smart search, camera and voice understanding or recommendations. The work covers choosing the use case, the model, privacy, testing and cost control, not just calling an API.

On-device AI is private and works offline, but suits smaller tasks. Cloud AI is more capable but needs a connection and careful data handling. Many apps combine both. We choose per feature.

We send the minimum data needed, avoid sending sensitive data to outside models unless you agree, explain sharing clearly to users and follow store privacy rules. For sensitive apps, we favour on-device processing.

Yes, AI can be wrong. We reduce this by grounding answers in your own content, testing on real examples, adding human review where mistakes matter and showing users when they are talking to AI.

Cost depends on the model, how often people use the feature and how much text or imagery is processed. We estimate the cost per user up front and use caching, smaller models and limits to keep it predictable.

Yes. Most AI features can be added to an existing iOS, Android, Flutter or React Native app without a rewrite. We start with one feature, measure it and build on it.

We choose models for each feature based on quality, cost, speed and data rules, and design the app so that the model can be changed later without rebuilding.

Usually not. Most business features work well with existing models plus your own content and good evaluation. Custom training is considered only when there is a clear need and enough data.

We build a test set of real examples, score the answers, test for off-topic or unsafe output, and monitor quality after launch. Quality is a number we track, not a feeling.

We monitor quality and cost, update prompts and models, and improve features based on real usage, under a plan with agreed response times.