AI deployments, process automation and engineering — from the team that runs Brokik, CheaperForDrug and Qkwit every day.
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.
Collaboration
Choosing a partner for an AI implementation follows different rules than choosing a team for a regular application. A polished demo in a sales meeting tells you nothing about whether the system will survive its first month running on real data.
Process automation
Automating decisions doesn't have to mean AI decides everything on its own. The safer model works differently — AI acts automatically where it's confident, and routes the rest to a human, with a clear trail of why.
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 accounting
In Qkwit, AI reads invoices and receipts for sole proprietors, while a deterministic engine calculates tax and social security contributions. Here's how that compresses the monthly bookkeeping cycle — and where the mechanism ends and marketing gloss begins.
Process automation
Every year brings a new wave of tools promising to change how a company works. Some of them actually do. Most end up as a conference demo. Here's a sober map of the technologies worth adopting in 2026, and the ones worth skipping.
Process automation
Document workflow automation with AI isn't one magic step — it's a concrete pipeline: intake, extraction, validation, exception escalation, and writing the result into your systems. Here's what that looks like in practice, without the marketing shortcut.
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.
AI deployments
Building an impressive AI demo takes an afternoon. Keeping a system running every day, on real data, taking responsibility for the outcome, is a different league entirely. Here's what that daily-use gap actually looks like, and why we see it so rarely closed.
