From Vibe-Coded Prototype to Production

It runs perfectly on localhost:3000. That is exactly the problem.

Something new keeps happening in the companies we work with. A sales manager, a product owner or an operations lead sits down with Claude, Cursor, Lovable or Bolt on a Friday afternoon and by Monday has a working tool. A quoting calculator. A booking overview. An internal app that finally connects three systems nobody had connected before. It works, and it solves a real problem.

Then comes the question: how does the rest of the team get to use it?

Right now the app lives on one laptop. It opens at localhost, which means only the person who built it can use it, and only while their computer is switched on. The API keys are probably sitting in a file inside the project folder. There is no login. The data lives in a local database or a spreadsheet. If that laptop is replaced, the app goes with it.

None of this is a reason to throw the work away. The hard part, figuring out what the tool should actually do, is already done. What is missing is the boring, important layer that turns a prototype into something a company can rely on. That is where Tietotalo comes in.

Why "just put it online" is not enough

Uploading the project to a hosting service will make it reachable. It will not make it safe or maintainable. These are the issues we see most often in AI-generated prototypes:

• Secrets in the code. AI coding tools often place API keys and database passwords directly in the source files. The moment that code lands in a shared or public repository, those keys are exposed.
• No real login. Anyone with the link can open the app, see the data and in many cases change it.
• Data without a plan. Where is the data stored, who can access it, is it backed up, and does it stay inside the EU? For anything touching customer or staff data, GDPR applies from day one.
• Code nobody fully understands. Generated code tends to handle the happy path well and the edge cases poorly. When something breaks six months from now, someone has to be able to read it.
• Running costs nobody is watching. An app that calls an AI model on every click can quietly become expensive once fifty people use it instead of one.

How we take it from localhost to production

We keep what works, fix what has to be fixed, and put the whole thing on a foundation your IT team can live with.

• Code and security review. We read what was built, look for exposed secrets, outdated or vulnerable dependencies and missing input validation, and tell you honestly which parts can stay and which parts are worth rebuilding.
• A proper home in the cloud. We set the app up on Microsoft Azure or another platform that fits your situation, with separate test and production environments so changes can be tried before they reach users.
• The login your staff already use. With Microsoft Entra ID single sign-on, people open the app with their existing work account. You decide who gets access and what each role can see.
• Secrets and data handled properly. Keys move out of the code into a secure vault. The data moves into a managed database with backups, hosted in the EU.
• Version control and automated deployment. The code goes into a managed repository with a deployment pipeline, so updates are tested and released the same way every time instead of copied over by hand.
• Monitoring and support. Logging, alerts and cost tracking for AI usage, so problems and surprise bills get spotted early. If you want, our continuous services team keeps an eye on it after launch.
• A handover in plain language. Clear documentation of how the app is built and run. You own the code and the environment.

You stay in control

Moving pilots and proofs of concept into production is a large part of what we do, and it works best when the person who had the idea stays involved. We do not take your app away from you. The aim is a setup where the original builder can keep improving the tool, still with AI if they like, while the security, hosting and deployment are handled in a way that will not break the moment the app gets popular.

This is a good fit if:

• your team built an internal tool with AI and colleagues are already asking for access
• you are a founder with a working proof of concept that now needs to face real customers or investors
• you are an IT manager who has discovered a few of these apps in the organisation and would rather make them safe than ban them