Applied AI for business
Practical notes on where AI pays off — agents, LLM, RAG, and forecasting, from the AKVANT team in Prague.
How to Choose Which Processes to Automate with AI
Not every process should be automated. A practical framework for choosing the ones where AI returns more than it costs — by volume, structure, and tolerance for error.
RAG vs Fine-Tuning: What Your Business Actually Needs
Retrieval changes what a model knows; fine-tuning changes how it behaves. When to use each — and why most businesses start with one.
AI Agents in Operations: Real Use Cases
Where AI agents earn their place in day-to-day operations — triage, reconciliation, support routing, document handling — and what separates a useful agent from a liability.
What an AI Project Actually Costs — and How to Budget It
The real cost of an AI project is rarely the model. Where the money actually goes — data, integration, evaluation, maintenance — and how to budget without surprises.
RAG for Business, Explained: Answers Over Your Own Documents
Retrieval-augmented generation connects a language model to your actual documents — accurate, sourced answers over corporate data, without retraining.
AI Agents vs Chatbots: What Actually Changes in Production
An AI agent is not a smarter chatbot. It's software that takes actions inside your systems — and the difference decides whether it creates value or risk.
Where AI Actually Pays Off in a Business
Most AI projects start with the technology and lose money. The ones that pay off start with a single question — where does AI return more than it costs?