On custom software, data, and AI applied to business — no hype, no fluff. The same honest way we work.
Every software project starts out exciting. What separates the teams that deliver from the ones that disappoint is almost never the price on the proposal — it's six questions you can ask before signing.
Pasting customer data into a public chatbot is a leak nobody sees. The answer isn't banning AI — it's changing where it runs.
The per-seat model charges you more precisely when you do the right thing: getting more people to look at the data. A tax on your data culture.
The hype sells autonomous agents. The AI that delivers has a human in command: it does the work, but someone approves between phases.
The token math almost nobody does. Why estimating isn't enough — and what changes when you measure (with real numbers).
The legacy-system dilemma is a false one. How to decide — and why replacing it piece by piece, with the system running, almost always wins.
And it's almost never the devs' fault. The six real causes of delay — all method choices made before the first line of code — and the antidote to each one.
Every company says it has AI; almost none has AI actually running. Six signs you were sold theater — and the question that unmasks each one. A buyer's guide, no hype.
The price on the proposal is the smallest part of the bill. The five hidden costs — maintenance, lock-in, rework — that decide the total cost of your software. And why the "expensive" way of doing it right is almost always the real bargain.
Strange advice coming from a company that sells AI? The market pushes LLMs at everything, but plenty of good automation is a deterministic rule: cheap, predictable, auditable. The simplest-thing-that-solves-it test — and the 5 questions to ask before hiring an LLM.
It's the way we work, too. Ask for a free diagnosis of your case — no sales pressure.
Request a free diagnosis →