AI challenge

Our vibe-coded app needs to become professional

With Claude Code, Codex, Cursor, Lovable, Bolt or another AI tool you can get very far very fast. But as soon as real users, sensitive data or paying customers come in, technical choices suddenly matter. We help you determine what is fine, where the risks are and what is needed to move forward professionally.

Does this sound familiar?

The tipping point usually comes when an experiment slowly becomes a real software product.

  1. New changes feel riskier and riskier

    A small feature can suddenly affect authentication, the database, deployment or other parts of the app.

  2. The codebase gets harder to understand

    There are more and more files, dependencies and solutions and nobody knows exactly why they were built that way.

  3. Production is getting nerve-racking

    You doubt whether data, permissions, backups, error handling and deployment are reliable enough for real users.

  4. You want to move on, but not blindly

    Rebuilding everything feels excessive, but carrying on as now no longer feels responsible either.

Where do you want to be?

You want to keep the speed of AI-assisted development without your product becoming more and more fragile technically.

A reliable technical foundation

Architecture, data, authentication, deployment and error handling are set up sensibly enough for serious use.

Able to keep changing safely

New features no longer unexpectedly break existing functionality every time.

Able to keep building yourself

You or your team understand the most important technical choices and can then carry on independently with AI tools again.

Why does this happen?

Vibe coding isn't the problem. Treating a prototype as if it will become a production platform by itself usually is.

See how we approach this

The software grew faster than the architecture

Local solutions work individually, but together they no longer always form a logical system.

Technical guardrails are missing

Tests, clear boundaries, security checks and deployment feedback were often added later, or not at all.

The requirements have changed

What was fine for an experiment isn't automatically suitable for real users, sensitive data and continuous development.

Why 9to5 for this?

A code quality score won't get you far here. You need senior engineers who can understand existing software, prioritise risks and fix the hard part themselves.

View cases

Software engineering and AI

Besides the generated code, we look at the complete product around it.

We work in what's already there

Existing codebases, integrations and technical debt are no reason for us to start over.

Handover is part of the result

The goal is that after the rescue you can carry on independently and responsibly again.

What else fits your situation?