11 posts
AI agents: systems that do the work with tools — safely, behind an allow-list and a human-gate. From chatbot to an agent that acts.
How AI in logistics reduces warehousing costs, optimizes delivery routes, and predicts demand. Architecture, patterns, and limitations.
How to implement AI personalization and recommendations in a company: architecture, models, GDPR and AI Act, guardrails, costs, and when ROI begins.
AI for translations in companies reduces the time for localizing documents, contracts, and marketing content. Architecture, quality, GDPR, and AI Act in one guide.
How to implement AI in corporate training: personalized learning paths, knowledge agents, RAG on materials, GDPR and AI Act in practice.
How to connect n8n with an AI model and build real end-to-end automation. Patterns, pitfalls, and secure integration principles.
AI agent maintenance costs in TCO terms: infrastructure, tokens, monitoring, knowledge base updates, and human oversight. What does an agent really cost after deployment?
MCP (Model Context Protocol) is an open standard for connecting AI models to external tools and data. How it works, what it offers businesses, and what security risks it entails.
How to monitor an AI agent, which KPIs make business sense, and how to build a quality dashboard before deployment spirals out of control.
When Make and Zapier are enough, and when do you need a custom AI agent? Comparison of capabilities, costs, and limitations of no-code vs dedicated architecture.
AI multi-agent systems 2026: when orchestrating multiple specialized agents outperforms a single overloaded one and how to avoid loops, costs, and chaos.
A multilingual AI assistant serves customers in their own language without separate bots per language. Architecture, language detection, guardrails, and GDPR in practice.