Building AI Copilots That Are Highly Reliable
How do you create copilots that act like dependable domain experts rather than creative chatbots? We present a technical and methodological framework with concrete patterns.
Read moreHow do you create copilots that act like dependable domain experts rather than creative chatbots? We present a technical and methodological framework with concrete patterns.
Read morePractical guide to secure, audit-proof AI systems in enterprises: Tenant Isolation, data classification, audit trails, model versioning, and our architecture with Hetzner, Coolify, Postgres and an internal AI proxy.
Read moreHow companies can build their own LLM clusters on Hetzner: step-by-step architecture, SaaS vs. self-host cost comparison, deployment with Coolify and best practices for security and auditability.
Read moreA single internal LLM can be more than a tool: it becomes the central infrastructure for knowledge bots, process copilots, autonomous workflows and decision engines. We outline architecture, security and change management.
Read moreIn 2025, more and more SMEs choose their own domain-specific LLMs instead of external APIs. Why this is more secure, cheaper and more business-relevant — with architecture, tools and a practical roadmap.
Read moreDashboards provide data. We need systems that decide, act, and automate work. This post shows how <strong>LLM-based workflows</strong> steer decisions — with architecture, observability and risk management.
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