// 00Category · AI Infrastructure

AI Infrastructure

01/06/2026

AI Act and GDPR in 2026: What a Company Implementing AI Must Do

From August 2026, the AI Act is enforceable. What this means in practice: transparency, human oversight, DPIA, and how to design compliance from the first line of code—not after an incident.

01/06/2026

AI Act: high-risk systems in practice (HR, finance, scoring)

AI Act high risk in 2026: which systems are subject to strict regulation, what obligations do HR tools, credit scoring, and customer assessment entail, and how to design compliance.

01/06/2026

AI governance in a company: policies, roles, control

AI governance in a company is a set of policies, roles, and control mechanisms that enable responsible AI deployment, compliant with the AI Act and RODO. A practical guide.

01/06/2026

Knowledge Updates and Versioning in RAG

How to maintain the relevance of a RAG knowledge base: strategies for incremental reindexing, document versioning, and knowledge drift detection in production environments.

01/06/2026

Anonymization and PII masking before sending to AI

How to protect personal data before sending it to AI models. PII masking patterns, pseudonymization, GDPR, and practical architecture for businesses.

01/06/2026

AI assistant security audit: pre-deployment checklist

AI assistant security audit 2026: checklist covers prompt injection, PII leakage, tool permissions, rate-limiting, and RAG database vulnerabilities.

01/06/2026

LLM Security: OWASP Top 10 in Practice

OWASP LLM Top 10 outlines 10 vulnerability classes in large language models. How each manifests in production systems and how to build layered defenses.

01/06/2026

LLM semantic cache: how to cut costs and latency for repeat queries

LLM semantic cache in 2026: how the embedding similarity threshold works, when it reduces costs by 40-60%, what risks it carries, and how to manage invalidation.

01/06/2026

Document chunking for RAG: how to split for retrieval to work

How to choose a document chunking strategy for RAG in 2026: fixed size, recursive, semantic, tables, and code. Concrete sizes and overlap.

01/06/2026

DeepSeek vs Mistral vs Qwen: which AI model for what

Three leading model families, three distinct profiles. Head-to-head based on measured parameters—and when to choose which.

01/06/2026

Embeddings for Polish: How to Choose a Model for RAG

How to select an embedding model for RAG with Polish documents in 2026: criteria, comparison of multilingual and monolingual models, evaluation on your own data.

01/06/2026

AI Agent Evaluation: Pre-Production Testing, Golden Sets, and Benchmarks

How to test an AI agent before deployment in 2026: golden sets, faithfulness, tool accuracy, regression tests, and the limits of LLM-as-judge.

01/06/2026

Corporate GPT: AI assistant on your knowledge base

A corporate GPT on a knowledge base is an RAG assistant that responds using your documents. How to build it, what to ensure in the security layer, and when it pays off.

01/06/2026

Hybrid search: when to combine BM25 with vectors

Hybrid search BM25 + vectors 2026: when semantics fails with SKU, how RRF fusion works, and how to configure hybrid search in a RAG system.

01/06/2026

AI Integration with ERP and Corporate Systems

How to connect AI to ERP, CRM, and other corporate operational systems. Integration patterns, data security, and the real cost of implementation in 2026.

01/06/2026

n8n and AI: end-to-end automations without coding

How to connect n8n with an AI model and build real end-to-end automation. Patterns, pitfalls, and secure integration principles.

01/06/2026

How to choose an AI model for the task (not the hype)

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.

01/06/2026

How to choose a vector database: Qdrant, pgvector, and decision criteria

How to choose a vector database in 2026: pgvector vs Qdrant, criteria for scale, filtering, self-hosting, and GDPR compliance. Practical decision table.

01/06/2026

When fine-tuning makes sense (and when RAG is enough)

When fine-tuning makes sense: selection criteria, costs, and pitfalls. When RAG solves the problem cheaper, and when model training is the only way.

01/06/2026

LLM token cost: how to measure and optimize it

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.

01/06/2026

AI Agent Maintenance Costs: TCO and Operations

AI agent maintenance costs in TCO terms: infrastructure, tokens, monitoring, knowledge base updates, and human oversight. What does an agent really cost after deployment?

01/06/2026

Local LLMs: What Hardware and GPU Do You Really Need

Which GPU and hardware to choose for local LLMs in your company? Comparison of VRAM, memory bandwidth, models, and costs for self-hosted deployments in 2026.

01/06/2026

Small specialized AI model vs large LLM

Small AI model vs large LLM: when a specialized 7B outperforms a general GPT-4-class model, the cost difference, and how to choose correctly for your business.

01/06/2026

MCP: how AI securely connects to tools

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.

01/06/2026

Migrating from API to your own AI model: when and how

Migrating from OpenAI API to your own AI model: when self-hosting LLM pays off, how the process works, and what to keep from your existing architecture.

01/06/2026

Reasoning vs instruct models: when AI should think

Reasoning models excel at tough decisions—but they're slow, expensive, and empty when forced into simple tasks. When thinking pays off.

01/06/2026

Monitoring and KPIs for AI Agents: How to Measure Effectiveness

How to monitor an AI agent, which KPIs make business sense, and how to build a quality dashboard before deployment spirals out of control.

01/06/2026

No-code (Make, Zapier) vs custom AI agent

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.

01/06/2026

Prompt caching in LLMs: how a cheaper static prefix cuts bills

LLM prompt caching in 2026: what is a static prefix cache, how it differs from semantic cache, and how to structure your prompt to hit the cache.

01/06/2026

Prompt engineering for businesses: what works and what doesn’t

Prompt engineering for businesses in 2026: techniques that improve LLM quality, mistakes that waste tokens and time, guardrails, GDPR and AI Act in prompt design.

01/06/2026

RAG: how to evaluate response quality (golden set)

RAG evaluation step by step: golden set, faithfulness and relevance metrics, LLM-as-judge, regression tests, and AI Act audit trail for RAG systems.

01/06/2026

Reranking: how to improve search quality in RAG

What is reranking in RAG, when a cross-encoder beats ANN, and how to build a search pipeline that returns relevant chunks instead of just similar ones.

01/06/2026

Company data and AI: data processing agreement and legal basis

When using AI requires a data processing agreement (DPA), what it must include, and how to avoid legal gaps when implementing an assistant or automation.

28/05/2026

Sovereign AI infrastructure: own models instead of a surprise bill

Why self-hosted LLM and RAG on your own knowledge give you control over cost, privacy, and provider.

22/05/2026

LLM Cost: Local vs Cloud API - When Each Pays Off

The cost intersection point between self-hosted models and cloud APIs. How to calculate when self-hosting starts to win.

20/05/2026

Ollama Cloud in the enterprise: power of large models without your own server room

How to use Ollama Cloud in an organization responsibly: via a router, with PII masking and cost control - instead of direct calls.

18/05/2026

Self-hosted LLM and GDPR: How to use AI without sending data outside

Why a self-hosted language model simplifies GDPR compliance and what exactly changes in the flow of personal data.

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