The AKVANT Journal
We write about how companies actually use AI: what works, what quietly loses money, and how to tell one from the other before you spend.
Automation
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, rules, and tolerance for error.
Retrieval
RAG vs Fine-Tuning: What Your Business Actually Needs
Two ways to make a language model useful on your data — retrieval and fine-tuning solve different problems. When to use each, and why most businesses start with one.
Agents
AI Agents in Operations: Real Use Cases
Where AI agents earn their place in day-to-day operations — order triage, reconciliation, support routing and document handling — and what separates a useful agent from a liability.
Economics
What an AI Project Actually Costs — and How to Budget It
The real cost of an AI project is rarely the model. A breakdown of where the money goes — data, integration, evaluation, maintenance — and how to budget without surprises.
Retrieval
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.
Agents
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.
Strategy
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?