AI deployments, process automation and engineering — from the team that runs Brokik, CheaperForDrug and Qkwit every day.
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.
Automotive
Automation in automotive businesses rarely starts with flashy AI — it starts with documents: parts orders, vehicle handover protocols, service invoices. Here's where AI genuinely helps.
Process automation
AI rollouts rarely fail because of the model. They fail on integration — data sitting in three systems that were never built to talk to each other. That's the most common reason a promising project stalls for months before anyone sees a real result.
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 accounting
A language model can read an invoice brilliantly, but it doesn't guarantee it will calculate the same tax the same way twice. That's why, in AI-assisted accounting, the calculating stays with a deterministic engine, not the model.
Collaboration
Plenty of IT companies now say they "do AI." It's harder to verify — because the best proof isn't a slide deck, it's a product the company runs itself, on its own risk, for real users.
Process automation
The first time a company gives AI access to its data, the security question always comes up — and it should. This isn't about being afraid of it; it's about setting a few concrete rules before AI gets access to anything.
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.
Finance
AI is good at reading documents and classifying data, but in regulated financial services, decisions need to stay auditable and reproducible. We look at where the model's role ends and deterministic code has to take over.
Process automation
A public chatbot and an assistant built on your company data are different tools. We look at context, data security, permissions, and how current the knowledge is — and why that decides whether AI is safe for daily work.
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 accounting
AI doesn't replace the accountant at an accounting firm — it speeds up entering data from documents, checking that a client's paperwork is complete, and handling client communication. Here's where that time saving is real today, and where it's still too early.
