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An AI agent is an LLM that plans multi-step work and uses tools — search, CRM, email. Working in production today: triage and drafting, research, cross-system data entry, all with review gates. Our rule: give an agent the autonomy you'd give a new intern in week one.

You don't need to rebuild your product to add AI. An API layer beside your existing stack, one high-value workflow, and evaluation before rollout — that's the pattern. Most first integrations ship in 4-6 weeks. Here's the playbook.

Most teams overpay for LLM APIs by 5-10x. Model routing, prompt caching, and batching cut the bill by 60-90% in the projects we've optimized — usually in under two weeks of work. Here are the six levers, ranked by effort.

A RAG chatbot on your own docs costs €8,000–€25,000 to build and €50–€500/month to run. A fully integrated support agent runs €25,000–€60,000. Here's the breakdown — and when you shouldn't build one at all.

We built an unofficial MCP server and CLI for mein-aldi.de, ALDI Süd's grocery delivery. Ask your AI assistant what's in stock and what it costs over the public, read-only API — plus an opt-in prefill of your own cart that stops before checkout. Not affiliated with ALDI.

Article 4 has applied since February 2025 — even if your team just uses ChatGPT. What 'AI literacy' concretely means, why no certificates are needed, which penalties are real (spoiler: not €35M fines), and the 5-step program that satisfies the duty in an afternoon.

Your AI chatbot processes personal data every time a user sends a message. Here's how to build LLM features that satisfy GDPR, the EU AI Act, and your legal team.

July 2026 update: the Digital Omnibus pushed high-risk obligations to December 2027 — but transparency duties and market surveillance start as planned in August 2026. What your engineering team needs to build and when, in code instead of legalese.

RAG and fine-tuning solve different problems. RAG gives the model new knowledge. Fine-tuning changes how the model behaves. Here's a practical guide to choosing — and when to combine both.

A practical engineering guide to adding AI features to products already in production. Model selection, architecture patterns, cost management, and the RAG vs. fine-tuning decision.

92% of developers use AI coding tools. But 45% of AI-generated code fails security tests. Here's our actual workflow for shipping AI-assisted code without shipping vulnerabilities.

LLMs are everywhere. We use them daily — for code generation, reviews, and documentation. Here's what actually works, what doesn't, and where human judgment is still non-negotiable.