Practical guides on building production AI systems — agents, LLMs, RAG, and MLOps.
“Saves 20 minutes a day × 12 people = €30,000” is the business case everyone half-knows is fiction — there is no half-person to remove. What a number that survives your CFO actually needs, and the four channels that turn saved time into money.
Almost none of them fail because the model wasn't good enough. They fail on a week-one decision, then look like progress for five months. The recurring patterns — and the five questions that expose them while the budget is still intact.
Data readiness, not the model, is usually the bottleneck. Where company data hides, how to inventory, clean, structure and permission it — and why “good enough” beats a two-year data project.
Prompt injection, data leakage, insecure output handling and over-privileged agents — the new attack surface of LLM apps, and the defensive playbook to build systems you can trust.
Data sovereignty for mid-sized companies: what self-hosted LLMs really involve, the GDPR drivers, the honest trade-offs in cost and capability, and when each is the right call.
Open-source or proprietary? Cost, latency, data privacy, licensing and lock-in — the criteria that actually matter, and why the top benchmark model is the wrong question.
Custom system or off-the-shelf tool? When buying is enough, when building pays off, the hidden costs of each, and a clear decision framework for mid-sized companies.
A pilot should produce a decision, not a demo. Scope one use case, use real data, define what “working” means, and timebox it — so you get a clear go/no-go instead of a project that never ends.
The hard part of AI is deciding what to point it at. Almost every worthwhile use case falls into four buckets — reading documents, answering questions, automating steps, forecasting — with an honest guide to where to start.
For a German mid-sized company, the first question about AI is “are we even allowed to do this?” A practical guide to the GDPR duties that touch an AI system, where projects go wrong, and how to make compliance a byproduct of good architecture.
Running a language model on your own hardware keeps data fully in-house. This guide demystifies the hardware question — model size, GPU memory, quantization, and when on-premise beats the cloud.
A straight answer on AI project costs: what drives the price, what you can realistically build at each budget, the hidden running costs, and how to de-risk the spend before you commit.
The EU AI Act explained without the panic: which of your AI uses are even in scope, what you actually have to do, the timeline, and how it relates to GDPR.
AI agents perceive their environment, decide on actions, and execute them — repeatedly, without human input. Learn how they work and when to use them.
Enterprise AI does not start with models. It starts with process, data, control, and rollout. Learn how to deploy AI where it creates measurable business impact.
RAG systems do not just search and paste. They retrieve, rank, ground, and answer. Learn how a production RAG pipeline works and where teams break it.
Fine-tuning does not just adapt tone. It shapes behavior, narrows outputs, and can make a model fit a task or fail it. Learn when to fine-tune, when not to, and how teams ship it safely.
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