Long-form notes from the build.

This journal covers the systems we actually ship for mid-market companies in DACH — server-side tracking architectures, AI and RAG systems in production, EU AI Act readiness, EU-sovereign cloud platforms, and big data pipelines on AWS, GCP, and bare-metal EU infrastructure. Most posts are engineering deep-dives where we work through a real problem end-to-end. Some are plain-language guides for the people who have to sign off on the risk — managing directors, and regulated professions handling confidential data — because the decision and the architecture are two halves of the same problem.

We don't run a content calendar. Posts ship when a lesson is durable enough to be worth your reading time — usually after we've solved the same problem more than once for different clients and a pattern is stable. The format favours depth over breadth: long-form deep-dives, concrete numbers, and the trade-offs we made on real engagements.

A few recurring threads run through the archive. The data-loss math behind client-side GA4 in DACH (typically 25-40% of pageviews missing on a standard setup) and what server-side tracking with GTM Server-Side and proper consent management actually recovers. MLOps and observability for LLM-backed systems — model evaluation, drift detection, agent architectures, and the discipline that separates a Jupyter notebook from a production AI agent that runs reliably without you in the room. The EU AI Act compliance picture for mid-market deployers under the August 2026 deadline: what's actually on the technical-controls list, what's optional, and how to evidence it during an audit.

We also write about EU-sovereign architecture — Hetzner and bare-metal EU hosting choices, BigQuery and Snowflake alternatives that keep data residency clean, and the practical Terraform and Kubernetes patterns we use to run production data systems on infrastructure that holds up legally and operationally. And the unglamorous parts: data quality in production, deployment automation, monitoring that pages the right person, and the cost trade-offs of distributed processing with Spark and Hadoop versus simpler architectures.

If you want new posts in your reader instead of checking back, the RSS feed is linked at the top of this page. For consulting on any of these topics for a DACH mid-market company, the contact form on this site goes straight to us — there are no account managers in the middle.