The Complexity We Choose
AI can help us write more software, but our real opportunity is to make software simpler to understand, verify, and own.
Personal expertise · practical AI services
Useful AI, built around the way your business works.
From phrases to tokens. From tokens to useful work.
Frazik combines hands-on engineering with a personal, practical approach to help Slovak companies run private AI and automate meaningful work.
The person behind Frazik
Technology should make work clearer, not more complicated.
Frazik is both a personal technical journal and a small AI engineering practice. The blog captures years of working with software; the services turn that experience into dependable systems for real teams.
What I can help with
Two practical ways to start
01
Local LLM hardware that stays under your control.
Custom workstations and servers for teams that need capable AI without sending sensitive information to a public model provider.
Discuss your setup →02
Turn repetitive work into a system your team can trust.
A practical path from scattered AI experiments to secure assistants, connected workflows, and measurable time saved.
Plan an automation pilot →A focused process
From a useful question to a working system.
01
Understand
We map the work, constraints, data, and the result that would actually matter.
02
Design
We choose the simplest secure architecture, whether that means local hardware, cloud services, or both.
03
Deliver
You get a working system, documentation, and a clear path for support and improvement.
Writing
Ideas, experiments, and lessons from building software.
Amplifier vs Claude Code — where Microsoft’s Agent Framework fits in
A concise comparison of Microsoft Amplifier and Anthropic's Claude Code, and how Microsoft's new Agent Framework complements both for production agent development.
Optimizing Large Language Models A Look at Key Techniques
An introduction to key techniques for optimizing Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG), Chaining Transformers, Prompt Engineering, and Transfer Learning and Fine-tuning.
Have a process worth improving?
Start with a conversation about your data, your team, and what you want AI to do.
Start a conversation