Notes from the lab: agents, sovereign infrastructure, data, visibility and PropTech.
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How to connect AI to ERP, CRM, and other corporate operational systems. Integration patterns, data security, and the real cost of implementation in 2026.
How to connect n8n with an AI model and build real end-to-end automation. Patterns, pitfalls, and secure integration principles.
There’s no single best model. There’s the right model for the job—chosen by measurement, not by name. A practical guide to selection.
Models can confidently fabricate information. Here’s how to ensure your AI assistant responds based on facts and says 'I don’t know' instead of making things up.
Preparing data for AI is the foundation of every deployment: without clean, structured data, even the best model will respond poorly or hallucinate.
How to choose a vector database in 2026: pgvector vs Qdrant, criteria for scale, filtering, self-hosting, and GDPR compliance. Practical decision table.
How to measure AI ROI: concrete metrics, formulas, and pitfalls in measuring return on AI implementation for Polish companies in 2026.
When fine-tuning makes sense: selection criteria, costs, and pitfalls. When RAG solves the problem cheaper, and when model training is the only way.
How AI classifies tickets by category, urgency, and sentiment and routes them to the right queue in 2026. No misprioritization of urgent cases.
LLM token cost is growing faster than the planned AI budget. How to measure usage, where hidden costs lurk, and which optimization patterns actually work in production.
AI agent maintenance costs in TCO terms: infrastructure, tokens, monitoring, knowledge base updates, and human oversight. What does an agent really cost after deployment?
LLMs detect patterns humans wouldn’t spot in a month. But without guardrails, explainability, and human-gate, hypotheses instead of accelerating work generate verification debt.
Concrete takeaways on AI agents, sovereign infrastructure and visibility in AI models — no spam.
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