In 2025, only 17% of small enterprises in Europe had adopted AI, compared to 55% of large ones. The gap is real, but so is the potential. European SMBs that move now have a huge opportunity to see results.
Pair Building, the done-with-you track. You drive, she navigates.
AI orchestration, knowledge systems, strategy, and ongoing partnership, an independent AI consultancy in Luxembourg
Multiple AI agents, one coherent workflow. From document processing to client communications, we connect the dots with AI: scoped in extraordinary detail because your data is focused, not sprawling.
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Both can work. The deciding factor is whether you have an in-house technical person with a few hours a week to track an AI tooling space that changes weekly.
Both can work. The real question is whether you have time to learn the field while running everything else. The AI tooling space changes weekly. What's best practice in March is obsolete by July. If you have an in-house technical person who can dedicate a few hours a week to staying current, you can absolutely do this yourself. If you don't, you'll either pick the wrong tools, build something fragile, or spend so long evaluating options that nothing ships. We help organizations skip the evaluation tax and get to a working system faster.
When you have a specific, recurring task that eats hours every week and feels mechanical. Or when you've tried AI tools yourself and hit a ceiling you can't get past.
The clearest signal is when you have a specific, recurring task that eats hours every week and feels mechanical when you do it. Not "we should probably use AI somehow." That's too early. "We spend six hours a week routing inbound emails to the right team, and it's always the same patterns." That's exactly the right time. The second signal is when you've tried AI tools yourself and hit a ceiling: the chatbot gets you partway, but the last twenty percent is too fiddly to maintain by hand.
Four matter most: comparable past work, ownership of the final code, where your data sits and which jurisdiction can access it, and what ongoing support costs after delivery.
Four matter most. First: can they show you something they built for an organization roughly your size? AI work for a Fortune 500 looks nothing like AI work for a four-person NGO. Second: who owns the code and the system at the end? You should own it, full stop. Third: where does your data live during and after the project, and which jurisdiction can compel access to it? Fourth: what does ongoing support look like, and what does it cost? The system will need adjustments. If they vanish after delivery, the system goes stale within a year.
In hours and outcomes, not technology. Agree what the current state costs before you start, measure the same thing after the system is live, and compare.
In hours and outcomes, not in technology. Before we start, we agree on what the current state costs you. Usually that's time: hours per week spent on the task, or backlog depth, or response time to a customer. After the system is live, we measure the same thing again. A successful project moves the number meaningfully and the system keeps working without constant intervention. If the AI is impressive but you still spend the same hours on the task, the project failed. We'd rather build something modest that you actually use than something ambitious that needs babysitting.
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