Trust AI-Generated Code
Without Compromising Quality or Security

Ensure your codebase is ready for AI – and that AI‑generated code maintains high quality

AI‑assisted development can accelerate delivery and drive innovation, but only if the underlying codebase is healthy. With Quality Insights, we objectively assess your AI‑readiness and the quality of AI‑generated code, giving you a clear view of risks, improvement areas, and how to use AI responsibly in your development workflow.

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One service – two use cases
For teams preparing to adopt AI and for teams already using AI for code generation

1. Preparing to adopt AI

Is your codebase AI‑ready?

Before introducing AI-assisted development, your codebase needs a solid foundation of structure, consistency and quality. Without it, inefficient patterns risk scaling rapidly once AI enters the toolchain.

AI-generated code can only be as good as the codebase it builds on.
Our AI-Readiness Analysis helps you understand:

  • How predictable and consistent the codebase is
  • Whether existing patterns are suitable for AI-generation
  • Where technical debt may intensify when AI is introduced
  • Which architectural areas need stabilisation
  • What to improve before automating development tasks


YOU RECEIVE:

  • AI-readiness score
  • Software health assessment
  • Prioritized recommendations for improvements
  • Input for modernization or refactoring initiatives

2. Already using AI

Does the AI‑generated code maintain quality?

Once AI is part of the development workflow, the codebase needs continuous oversight to stay healthy. Without it, AI-generated code risks introducing issues that quietly accumulate over time.

The quality of AI output depends on prompts, context and codebase quality.
Our AI-Generated Code Analysis helps you understand:

  • Where security vulnerabilities may have been introduced
  • Whether code style remains consistent across the codebase
  • If unnecessary complexity or duplicated logic has crept in
  • How testability is affected by AI-generated additions
  • Where edge case handling is incomplete or missing


YOU RECEIVE:

  • Review of AI-generated code segments
  • Identification of quality and security risks
  • Assessment of impact on overall software health
  • Recommendations for sustainable AI usage
  • Guidance for prompt strategy and QA alignment

WHY THIS MATTERS

AI increases development speed, but also the need for strong quality assurance.

Three key insights:

  1. AI amplifies existing patterns – strong code becomes stronger; weak code spreads faster.
  2. Technical debt can grow rapidly when inefficient patterns are scaled.
  3. Codebase health is essential for long‑term value from AI initiatives.


Organisations that prepare their codebase before and during AI adoption gain stability, lower risk and higher return on investment.

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What the analysis includes

Our assessment combines automated tools with senior QA expertise:

  • Code quality and maintainability
  • Architectural structure and modularity
  • Testability and automation potential
  • Style, structure and consistency
  • Security patterns and vulnerabilities
  • Identification of technical debt
  • AI‑generated code assessment
  • AI‑readiness evaluation

You receive a clear picture of the current state, a prioritized improvement roadmap and practical recommendations.

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WHAT CAN YOU EXPECT

  1. Clear understanding of codebase health
  2. AI‑readiness score and identified improvement areas
  3. Quality assessment of existing AI‑generated code
  4. Risk and impact analysis
  5. Short- and mid‑term improvement recommendations
  6. Input for planning, budgeting and modernization
  7. Guidance for responsible and effective AI usage
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Want to know if your codebase is ready for AI?

Request an AI‑Readiness or an AI‑Generated Code Quality Analysis by filling out the form below and one of our experts will contact you as soon as possible.