AI integration that saves work.

Yes. STUDIO X has rebuilt its entire development process around AI, and AI writes much of the code. Experienced developers decide what to build, how it fits with the systems you already have, and check that it holds up. We pass the gain on as speed and a fixed price, see the fixed price page.

No, not as a rule. The risk of poor quality arises when AI makes the choices without anyone with development expertise in charge. With us, decisions on architecture, database, integrations and security are made by experienced developers, and what we develop goes through automated tests, code review and security checks.

For a prototype or a simple MVP that is often a good choice, and an AI prototype is a good starting point for a requirements specification. It is different when the system goes into operation: data migration, integrations with systems already in use, security and privacy requirements, and an architecture that holds up for many years. Then you need someone who is responsible for it working.

Only if it is safe and you want that. For sensitive data we use business agreements with Anthropic (Claude) or OpenAI, where the data is not used to train the models. The solution only sends what is needed to answer, and we decide what can be sent based on how sensitive the data is.

For simple, rule-based decisions, yes. For complex or critical choices we always keep a human in the loop.

RAG (Retrieval-Augmented Generation) means the solution first retrieves relevant documents from your own knowledge base and sends them to the language model together with the question. It works with the common models, such as Claude and GPT. Answers are grounded in your own data, the risk of made-up answers goes down, and you can see which sources were used. You need it when AI should answer questions about internal matters, such as routines, agreements or professional content.

We use Claude (Anthropic) and GPT (OpenAI). Claude is strong on complex reasoning and Norwegian text, GPT on high volume and a broad developer ecosystem. We choose based on the task, and develop the solution so the model can be swapped out later.

It depends entirely on the task. For text-based tasks such as report generation and supplier checks, time spent can often be reduced significantly. As a rule of thumb: if a task involves reading and synthesising text, there is usually a lot to gain with the right setup.

AI systems that process personal data must have a clear legal basis and comply with GDPR requirements, including on automated decisions. We take privacy and security seriously from the start and tailor the measures to how sensitive the data is.