Questions about AI app development
(01)Progressive web app or native app – what do you recommend?
For most AI applications we recommend progressive web apps (PWAs) built with Next.js: they run on all devices, can be installed and don't need an app store review process. Native apps make sense if you need hardware access (for example the camera for computer vision) or the highest possible performance. We advise you transparently on the best strategy.
(02)Can AI run directly on the device, without the cloud?
Yes, with on-device ML models. TensorFlow Lite and Core ML make it possible to run compact AI models directly on the smartphone – ideal for image classification, text recognition or simple NLP tasks. More complex tasks such as longer text generation or in-depth analysis still require cloud APIs such as Claude or GPT.
(03)How do you integrate AI streaming into mobile apps?
We use server-sent events (SSE) and WebSocket connections for real-time streaming of AI responses. The Vercel AI SDK abstracts the complexity and delivers token-by-token streaming with careful battery usage on mobile devices. The result is a natural, chat-like experience without noticeable waiting, even for longer answers.
(04)What does developing an AI app cost?
Costs depend on the platform (PWA, native or cross-platform), feature scope and AI integration. Fixed price after scoping, proposal within 48 hours. A good starting point is a Prototyping Sprint (2 days) at a fixed price of €4,500, in which we test your app idea with a clickable prototype and real AI functions.
(05)How fast can AI features in the app respond?
With streaming, intelligent prefetching, caching of frequent requests and optimised prompt design, we keep latency low; on-device models usually respond particularly fast. Streaming responses ensure that users get visual feedback immediately, and loading states make longer analyses understandable for users.
(06)Can you add AI features to an existing app?
Yes, we extend existing apps with AI functionality without completely rewriting the existing codebase. Through API integrations and modular components we add AI features such as intelligent search, chatbots or recommendation systems. The integration happens step by step and is tested at every stage.
(07)How do you ensure good app performance with AI?
AI models can increase response times. We counter this with several strategies: lazy loading of AI features, background processing for time-consuming analyses, optimised model choice (smaller models for simple tasks), edge computing for low latency and intelligent caching. The result: AI features that make the app feel faster, not slower.
(08)Do you also support app store submission?
Yes, we handle the entire app store submission process: creating store listings, screenshots and descriptions, complying with Apple and Google review guidelines (especially for AI features), and communicating with the review teams if questions arise. For enterprise customers we also set up MDM deployment.
(09)How is user data protected in the AI app?
Data protection is a core part of our app architecture: end-to-end encryption for sensitive data, local processing where possible, anonymised analytics and transparent consent. We choose API terms under which your data is not used for model training. Data is stored on European servers and processed in line with the GDPR.
(10)Do you also offer app updates and maintenance after launch?
Yes. We fix defects free of charge for 30 days from final delivery. After that we offer maintenance by agreement: regular updates for operating system compatibility and frameworks, AI model updates, bug fixes and new features. Fixed update cycles, for example monthly, have proven effective for keeping the app up to date and performing well.