Writing
Notes on building AI products
Lessons from taking AI from prototype to production — engineering, architecture, and the decisions that decide whether an AI feature ships.
Your customers don't google anymore — they askAI assistants are replacing classic search for buying research. What the 2025–26 data really says about AI Overviews traffic and click-through — and an honest generative engine optimization (GEO) playbook for AI search visibility.Agentic software in the Mittelstand: a practical, GDPR-aware guideAI agents are moving from vendor demos into real Mittelstand operations. What agentic AI actually is, where AI agents create value in mid-sized companies, when a deterministic workflow beats an agent — and how to adopt agents without violating GDPR or the EU AI Act.RAG vs fine-tuning for SMEs: an engineering-economics guideRAG vs fine-tuning is the wrong fight — they solve different problems. What retrieval augmented generation actually costs, when fine-tuning an LLM earns its keep, and a worked cost example for a 10,000-document knowledge base, with current provider pricing.Shipping AI that survives productionWhy most AI features die between demo and production — and the engineering discipline that gets them across: LLM evaluation harnesses, LLM-as-judge calibration, observability and tracing, guardrails, and cost budgets. A field guide to eval-driven development and AI reliability.
What work could agents take off your team?
Bring one recurring workflow or a product idea. Thirty minutes, an honest assessment, a clear next step.