AI-Assisted Software Development

AI agents can now take on a large share of development work. Whether dependable software comes out of it is decided by rules, guardrails and review gates. We introduce these in your team and know from daily work on large codebases what holds up and what does not.

Introducing Coding Agents in Your Team

Licenses are bought quickly. The benefit only arises once the agents know your codebase and your rules:

• Rules and working instructions written for your codebase
• Consistent guidance across several tools, such as GitHub Copilot, Claude Code, Codex and OpenCode
• Clear division of tasks: what an agent decides on its own and what a human approves
• Guardrails against risky actions on repository and infrastructure

Review Gates for AI-Generated Code

Whether AI-generated code can be trusted is decided by the checks it passes:

• Automated security review for every change
• Cross-check by a second, independent model
• Static analysis, architecture and duplication checks as a precondition for merging
• Claims in pull requests are measured before they count

Orchestration and Connecting Your Systems

For recurring tasks we define the workflow as code; the model only does the actual work.

• From ticket through implementation and review rounds to the pull request
• Every task in its own isolated working copy
• MCP servers that connect AI assistants to internal systems, such as ticketing, support or deployment

Local AI as an Option

Where source code and data must not leave the premises, open language models run on your own hardware. A local model can, for example, work as a second reviewer in development.

• Selection and operation of open language models on your hardware
• Tuning throughput, context size and memory use based on measurements
• Integration into existing development workflows