AI-Powered Software Development

Your team uses AI for coding – but are they using it effectively? We make the difference.

Your engineering team is using GitHub Copilot, Cursor, or similar AI coding tools. Maybe they have been for months. But honestly: do you really feel the difference in productivity, code quality, and development speed? In many organizations, the potential of AI in the development process falls far short of expectations. The tools are there, but clear standards for their safe and effective use are missing. Every developer uses them differently, while internal knowledge such as architecture principles, coding guidelines, or API specifications is not systematically incorporated. The result: AI-generated code varies significantly in quality, style, and maintainability. Instead of reducing workload, it creates additional effort for review, correction, and rework. The gap is clear: there is a disconnect between the technical availability of AI coding tools and their effective, controlled use in developers’ day-to-day work. Without guardrails, AI creates not only productivity gains but also inconsistencies, technical debt, and new quality risks.

Duration
6 - 10 weeks
Format
Project
Target audience
CTO, Engineering, IT

Solution Approach

Introducing AI tools into software development is easy. Using them in a way that creates real impact is the real challenge. That is exactly where our AI-Powered Software Development approach comes in. We help you integrate AI-assisted development in a structured, secure, and measurable way into your existing engineering processes. Not a one-size-fits-all tool rollout, but a robust enablement approach tailored to your organization. Based on an analysis of your current development environment, we evaluate suitable AI coding tools, define governance and quality guardrails, and identify concrete use cases along your development process. We also develop proven best practices for prompting, reviewing, and validating AI-generated results.
By piloting the approach in selected teams and integrating your internal knowledge sources, we create a scalable modelthat establishes AI-assisted development in a transparent, verifiable way and in line with your engineering standards.

Benefits

  • Controlled adoption instead of uncontrolled proliferation: AI coding tools are systematically integrated into development processes, standards, and toolchains rather than left to chance.
  • Noticeably higher engineering productivity: Recurring tasks such as boilerplate code, refactoring, or test generation are completed faster and more reliably.
  • Better code quality and consistency: Clear quality guardrails and review mechanisms prevent unsuitable AI-generated results from entering the codebase unchecked.
  • Scalability from day one: The approach grows with your organization and can be gradually expanded to additional teams, projects, and technical domains.

Approach

  1. Analyze your engineering environment: We assess the current developer experience, tooling landscape, and existing development processes to identify maturity levels and the biggest levers.
  2. Define the target state for AI in development: Together, we define strategic goals, relevant use cases, and target groups so there is a clear direction.
  3. Develop governance and quality guardrails: We define guidelines for permitted use as well as security and validation requirements that build trust.
  4. Evaluate and integrate AI coding tools: The right tools (e.g. Claude Code) are assessed and embedded into IDE, repository, and CI/CD environments.
  5. Pilot in selected engineering teams: In real development environments, we measure productivity, quality, and risks and refine the approach accordingly.
  6. Integrate internal knowledge sources: Documentation, coding guidelines, and API specifications are incorporated directly into the AI-assisted development process.
  7. Derive best practices and build an operating model: Insights from the pilot are translated into a scalable model for organization-wide adoption.

Deliverables

  • Maturity Assessment of your engineering team for AI-assisted development as a solid baseline assessment
  • Defined Target State and Roadmap for AI Coding Enablement with clear milestones
  • Prioritized Use Cases along your development process with the greatest productivity potential
  • Tooling and Integration Strategy for AI coding assistants, tailored to your tech stack
  • Governance and Usage Guidelines for the safe use of AI in engineering
  • Quality and Validation Guardrails for AI-generated results to ensure trust and consistency
  • Pilot Setup with Measurable Success Criteria for the first real-world validation
  • Best Practices and a Scalable Operating Model for productive use across additional teams

Speak with our experts!

Let’s work together to identify where the greatest potential lies and which measures will deliver the greatest added value for you.

Alexander Piehl
Senior Software Engineer
Contact us!
Frequently Asked Questions

Do you have questions? We have the answers.

Which AI coding tools are considered?

We are tool-agnostic and evaluate the solutions that best fit your tech stack. These may include Claude Code, GitHub Copilot, Cursor, Tabnine, and others. We select the right tools together based on your existing IDE landscape, security requirements, and licensing models.

Do my developers need prior experience with AI coding tools?

No. The enablement meets your team exactly where they are. Whether they have only had initial exposure or are already advanced users, we adapt the depth and focus to their actual maturity level and ensure everyone reaches a shared level of understanding.

How do you ensure that AI-generated code meets our quality standards?

A core element is the governance and quality guardrails we define together. These include review processes for AI-generated code, validation guidelines, and the integration of your internal coding guidelines directly into the tooling workflow. This ensures that generated code aligns with your standards.

What happens to sensitive code and internal knowledge when using AI tools?

Data protection and IP protection are integral parts of our governance guardrails. Together, we assess which data may be shared with external models, define clear usage boundaries, and evaluate options such as self-hosted models or enterprise licenses with appropriate data protection guarantees.

How do we measure the success of the enablement?

We define measurable success criteria during the pilot phase, such as changes in cycle times, code review effort, test coverage, or developer satisfaction. This allows you to see within a few weeks whether and where AI creates a real productivity gain in engineering.