Question 1

How much does a Kotlin Multiplatform project cost?

Accepted Answer

Kotlin Multiplatform projects start at €15,000 for a functional MVP covering iOS and Android with shared business logic. The final price depends on the number of platforms, backend complexity, and integrations such as payments, maps, or biometric authentication. We provide a fixed-scope proposal after the discovery call with no hidden costs.

Question 2

What is Kotlin Multiplatform and how does it reduce development costs?

Accepted Answer

Kotlin Multiplatform is a cross-platform development framework by JetBrains that lets you share approximately 70% of your code between iOS, Android, and desktop while keeping the UI fully native. You maintain one codebase for business logic, networking, and data layers instead of two separate teams. The result is faster delivery, lower maintenance overhead, and consistent behavior across platforms.

Question 3

Why choose Kotlin Multiplatform over Flutter or React Native?

Accepted Answer

Flutter and React Native render UI through a single non-native layer, which creates performance limitations and breaks platform-specific behavior. Kotlin Multiplatform shares only the business logic — the UI stays 100% native using Jetpack Compose on Android and SwiftUI on iOS. You get better performance, no UI compromises, full access to native SDKs, and easier long-term maintenance without rewriting your app.

Question 4

Is Kotlin Multiplatform stable enough for enterprise and production use?

Accepted Answer

Yes. Kotlin Multiplatform is developed by JetBrains, recommended by Google for Android development, and used in production by Netflix, Cash App, Philips, Square, and Booking.com. We use it for enterprise apps that require biometric authentication, encrypted local storage, offline support, and real-time data processing.

Question 5

How long does a Kotlin Multiplatform project take from start to production?

Accepted Answer

A typical Kotlin Multiplatform MVP ships within ~12 weeks. We start with a 45-minute discovery call to define scope, followed by a fixed-scope architecture proposal. Development runs in weekly agile sprints with live demos every Friday. After launch, we include 90 days of post-launch support at no extra cost.

Question 6

What is on-premise AI deployment and why is it important for GDPR compliance?

Accepted Answer

On-premise AI deployment means running large language models (LLMs) inside your own servers, data center, or private cloud instead of sending data to OpenAI, Claude, or other US-based cloud APIs. Your data never leaves your infrastructure, which eliminates Standard Contractual Clauses (SCCs), Transfer Impact Assessments, and most GDPR and EU AI Act compliance risks. It is the only legally viable option for many regulated European industries.

Question 7

Which AI and LLM models can you deploy on-premise?

Accepted Answer

We deploy Llama 3, Mistral, Qwen2.5, DeepSeek Coder, and custom fine-tuned models depending on your hardware, use case, and latency requirements. We also build private Retrieval-Augmented Generation (RAG) pipelines and agent workflows that connect your internal documents, databases, and knowledge bases to the model.

Question 8

Why deploy AI on-premise instead of using OpenAI, ChatGPT, or Claude?

Accepted Answer

Cloud AI providers like OpenAI and Anthropic process your data on US servers, creating GDPR and EU AI Act compliance risks. Every prompt, document, and internal conversation is sent outside your network. On-premise deployment keeps all data inside your infrastructure, reduces latency by avoiding internet round-trips, gives you full audit control, and eliminates dependency on external providers. For finance, healthcare, and government organizations, on-premise AI is often the only compliant option.

Question 9

How much does on-premise AI and LLM deployment cost?

Accepted Answer

Our on-premise AI assessment and architecture roadmap starts at €2,500. Full deployment projects range from €15,000 for a single-model setup with private RAG to €50,000+ for multi-model enterprise configurations with custom agents and GPU infrastructure. We also offer monthly maintenance plans starting at €500 to handle model updates, security patches, and monitoring.

Question 10

What happens after the app or AI system goes live?

Accepted Answer

Every project includes 90 days of post-launch support at no extra cost. We monitor performance, fix bugs, and provide full technical documentation so your team can operate the system independently. After that period, you can choose a monthly maintenance plan or reach out on demand. For on-premise AI deployments, we also handle model updates, security patches, and performance optimization.

One codebase. Every platform. Your data, your rules.