Vibe Coding: From Prototype to Production Software

With Claude or ChatGPT, you can now build your own AI tool in just a few weeks, something that used to take months. This approach is called Vibe Coding: You describe to the AI what the application should do, and it writes the code for it. After three months, you realize you need professional support. Because as soon as real users, real data, or external partners come into the picture, “it works” is no longer enough.

Our take: An AI prototype and production-ready software are two different things. The prototype proves that the idea works. What the company brings to the table is valuable: a clear vision of what the solution should do and how it should feel. What’s missing is the technical foundation to ensure it does so reliably.

The production version must handle high traffic, clearly separate roles and permissions, be monitored, and handle data in a legally compliant manner. We guide you through this transition.

When it comes to AI applications, it’s not enough to simply check the visible features. What matters most is what data is being processed, where that data flows, and how the AI is technically integrated. This is especially true when personal data, customer data, or business-critical information is involved.

Four questions that must be clarified before going live:

• Is data transferred to external AI services, and if so, which ones?
• Is it contractually and technically prevented that business data is incorporated into model training (Data Processing Agreement pursuant to Art. 28 GDPR or Art. 9 nDSG)?
• Are API keys managed securely, and is access traceable?
• Are there clear roles and permissions?

Not every in-house AI tool needs to be developed from scratch. Often, an existing prototype can be stabilized, secured, and refined in a targeted manner. Sometimes, a clean rebuild makes more economic sense if the architecture or security would otherwise become too costly in the long run.

5 Questions to Ask Before Investing in Your Prototype

• How clean is the existing code?
• How sensitive is the data being processed?
• How many users and what kind of external access are planned?
• What interfaces are required?
• How well is the solution documented?

We’re familiar with both sides of the spectrum: the rapid AI prototype and the requirements for a production system that meets GDPR/nDSG compliance, includes monitoring, and adheres to SLAs. We’ve done exactly that before: analyzed existing, mature solutions, stabilized them, and brought them into production. We now bring this experience with code migrations to AI prototypes as well. That’s why a technical analysis lasting just a few days is all it takes for us to determine whether your prototype can be salvaged or what it would cost to rebuild it from scratch.

Turning an AI prototype into a production-ready application requires more than just working code. Architecture, security, data flows, operations, and clear lines of responsibility are crucial. We explain how we generally use AI in development in our article on AI-assisted software development.

1. AI-Assisted Code Analysis:
   We use AI-powered tools ourselves to quickly assess existing code and realistically estimate the effort required to incorporate it. This allows us to determine early on whether reusing the code is worthwhile and to make targeted modifications before it goes into production.
2. Security and Data Protection Check:
   We analyze data flows, API connections, roles, permissions, and potential vulnerabilities. The goal is to protect sensitive data and strictly limit access.
3. Decision-Making Framework:
   We determine whether stabilization, targeted further development, or a complete rebuild makes sense. Our recommendation is based on the technical condition, effort required, risks, and future benefits.
4. Go-Live Preparation:
   We define the cloud architecture, deployment, monitoring, backups, and operating environment. The goal is a setup that not only launches but also runs reliably under real-world conditions.
5. Operation and Further Development:
   We support the application after go-live with maintenance, support, and targeted further development. To ensure that the transition poses no risk to your company, we safeguard operations through an SLA. This means you know from the start what response times and support you can expect.

It’s not a mistake for companies to start with AI on their own. This often leads to a better understanding of their own processes and the desired solution.  

The key is to recognize the right time to take the next step: As soon as an application starts working with real data, is made available to customers or partners, or supports business-critical processes,it needs a professional technical foundation.