Teams with solid quality gates get better with AI. Teams without them get worse faster. That is why my projects start with a baseline of your delivery pipeline, not with a tool selection.
Director of AI Engineering at SHI GmbH in Augsburg
Three steps, always in this order.
01
Measure before anything gets introduced. Without numbers from before the rollout, every later efficiency claim is an assertion — mine included.
02
Tests, linting, security analysis and review rules run automatically in the pipeline and apply to human and machine-written code alike. That makes the origin of the code a side issue.
03
We work in your repositories, not in sample projects. What is left at the end is a working setup in your code that your team can run without me.
Where you can see the material in person over the coming weeks.
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Tools and libraries I maintain.
Native device features for Cordova and Capacitor apps, with TypeScript support.
MCP server that lets AI agents run web performance audits with Google Lighthouse.
A shared library of AI coding rules that keeps a team's assistants on the same conventions.
Two ways in: a short call about where your team stands, or an email — a speaking enquiry, a question about a talk, or anything else.
No strings attached. We talk about where your team stands.