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.
Collaboration
A contract for an AI implementation differs from a typical implementation contract in a few places that are easy to miss in the excitement after a good demo. Here's a checklist of what to check before you sign — from the buyer's side, not the vendor's.
Finance
Automating banking processes doesn't mean handing AI control over financial decisions. It comes down to choosing the right processes to automate, and making sure any decision with a real financial consequence stays with a human or deterministic code.
Automotive
A dealer management system, a service system, and a parts warehouse rarely talk to each other the way they should. In automotive businesses, IT integration usually comes down to groundwork: making the same data look the same everywhere.
AI accounting
A sole proprietor's monthly bookkeeping cycle comes down to four recurring tasks: documents, expense categorization, deadlines, and preparing data for filings. Here's where the line sits today between what AI handles and what still needs a person.
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%.
Collaboration
Brokik now runs in 25 countries, but the real scaling lesson wasn't interface language — it was rental law, which looks different in every market. Here's what building one SaaS product for that many markets taught us.
Process automation
Every back office is full of repetitive work, but not every process has the same potential for AI automation. We ranked candidates by where rules are clear and volume is high — not by whatever is currently annoying the team most.
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.
Finance
Banking is one of the industries where an automation mistake costs the most — which is why AI implementation there doesn't start with what can we automate, but with how do we document and oversee it. That difference decides whether a project survives an audit.
AI accounting
Reading invoices and receipts with AI looks like magic today: a phone photo turns into a ready bookkeeping entry. In practice it has real limits — what happens after the reading step decides whether the right number ends up in the books.
