Most AI projects stop at the prototype. Not because the technology fails — but because there is no systematic design behind it. The DAI framework changes that. It classifies every production-ready AI system into eight functional element classes — from language processing and knowledge stores to autonomous agents, data pipelines, and quality assurance. Not as a tutorial for one specific framework. As a reusable design methodology for any automation case in the SME context. What you will be able to do after this guide: Design AI systems from method, not guesswork — the eight DAI element classes (E1–E8) fully described: what each element delivers, which technology options exist, what it costs, and when it is needed - Apply combination logic to any automation case — the complete combination matrix across twelve application classes and four minimal configurations: which elements belong together, and why - Learn from real cases — five case studies from development and consulting contexts: document audit, HR onboarding automation, client analysis, contract analysis, ISO compliance — with technical architecture and cost estimates - Turn production capability into a viable product — six monetisation models with decision matrix: which model fits which starting situation, from subscription products to outcome-based pricing - Implement directly — executable code for all eight elements: LangGraph workflows, RAG pipelines, ReAct agents, FastAPI integration, quality assurance - Operate from day one — Docker Compose setup, phase transitions, common implementation errors, and a cost reality check for the path from local deployment to first production environment Who this guide is for: Developers, system architects, and technical consultants who build AI-driven process automation for SMEs in the DACH region and beyond — with GDPR-compliant local model operation as an architectural component, not an afterthought. What this guide is not: No tutorial for a specific framework. No market overview. No introduction to large language models. Volume 1 of the DAI series — Practitioner's Guide. Volume 2 (Decision-Maker's Guide) covers the same framework from the perspective of those who commission and govern AI investments.