AI deployments, process automation and engineering — from the team that runs Brokik, CheaperForDrug and Qkwit every day.
AI deployments
AI implementation ROI comes down to four numbers: hours of manual work, the cost of errors, cycle time, and the cost of maintenance. Skip a rigorous count of those four, and the result is a number on a slide, not a decision you can build a budget on.
AI deployments
An AI agent is not a chatbot with a longer memory. It is a system that carries out steps on its own: it reads a document, checks data in another system, prepares a decision. We build it under one rule: the agent proposes and code calculates and records, while a person approves the exceptions.
AI deployments
AI implementation that ends the day it goes live is really just getting started. Our own products have taught us a simple rule of thumb: building the system is about 40% of the work — keeping it running well is the other 60%.
AI deployments
The question of pilot versus full rollout comes up in every conversation about AI in a business — and it's usually framed wrong. The real question isn't which option is safer in general, but how big the first step should be for a specific process.
AI deployments
Before asking which AI model to use, it's worth answering questions that have nothing to do with technology: is the process ready, can the data be processed, who owns the result. This checklist lets you check that yourself.
AI deployments
In a law firm, an accounting office, or a consultancy, not every process is ready for automation at the same pace. There's a clear line between what AI can take over almost immediately and what it shouldn't touch on its own.
AI deployments
Before any process at our company goes near AI, it goes through an audit — a concrete, multi-step review, not a conversation about AI potential in general. The audit decides whether an implementation makes sense, what it costs, and where to start.
AI deployments
A demo that impresses in a meeting and an implementation that runs in daily use month after month are two different things — and most AI projects get stuck exactly at that boundary. The same five mistakes show up almost every time, regardless of industry.
AI deployments
Every system we implement follows one rule: AI reads, classifies, and proposes, while deterministic code handles critical decisions and every calculation. Wherever money is involved, that's the only responsible division of roles we know.
AI deployments
AI implementation isn't one line item — it's three separate budgets: pilot, production, and maintenance. Count only the first one, and you'll get a quote that looks cheap, and a bill that comes back later.
AI deployments
Most conversations about AI in a company end with a list of ideas that never make it past the slide deck. Here's how we pick the first process to automate and how we measure whether an implementation is even worth it.
AI deployments
An AI implementation that actually works rarely starts with picking a model. It starts with understanding the process it's meant to support. Here's the framework we follow: audit, pilot, production, support — and what happens at each stage.
