AI that works in daily operations.
Our approach
Value over hype.
Most AI projects fail on the missing use case, not on the technology. We start where AI measurably saves time or money, and leave out the rest.
Use case first
We start from a process that costs you time today, not from a model. Only once the value is clear does the technology follow.
Your data stays yours
We choose the setup so data protection and data sovereignty are settled from day one, whether that means the EU cloud, an API with a data processing agreement, or your own server.
Control & approvals
Wherever it matters, the AI only suggests and a human decides. Every step is logged and traceable.
Services
Three ways to put AI to work.
From automated workflows to models on your own data to assistants for customers and your team. Each works on its own or combined.
How we work
From use case to running system.
Start small, show value fast, then expand. That keeps the risk manageable.
Find the use case
We look at your processes and identify where AI measurably saves time and where it doesn't (yet) add value.
Build a pilot
Within a few weeks, a working pilot runs on your real data, tested by your team in daily work.
Add guardrails
Approvals, limits, and data protection are locked in: what the AI may do alone, where a human decides, and where the data lives.
Operate & expand
After go-live we monitor quality and costs, adjust, and expand where the value is proven.
You don't need an in-house AI team for this. We handle the technology and operations; your team brings the domain knowledge.
Common questions
What businesses ask us about AI.
With a clearly scoped, routine-heavy process: sorting emails, reading documents, answering standard questions. That's where the value shows up in weeks, not months.
We settle this before the start. Depending on your requirements, models run in the EU cloud, via APIs with a data processing agreement, or on your own infrastructure. Your data is never used to train third-party models.
A first pilot is usually in the range of a small software project. After that, you decide based on real results whether and how to continue. We calculate ongoing costs for model usage and operations transparently.
Nobody can rule out errors entirely. That's why we build workflows so the AI hands over to a human when uncertain, cites its sources, and every step is logged. Critical actions never happen without approval.
No. We build so the model stays interchangeable. If a better or cheaper model becomes available, switching takes little effort.
From the blog
- AI with Human Approvals: Automation You Can Actually Control
How to build AI automation so that people stay in control: confidence thresholds, tiered approvals, audit logs, and the underrated problem of rubber-stamping.
- Taming the Email Flood with AI: Sorting, Routing, Drafting
How AI triage for a shared mailbox works in practice, why draft replies should not be sent automatically, and where these projects tend to fail.
- Invoices, Delivery Notes, Contracts: Extracting Document Data Automatically
Why language models are replacing template-based OCR for document extraction, where the real error sources are, and what a workflow your accounting team can trust looks like.
Next step
Which of your processes would benefit most from AI?
Tell us briefly what your team spends the most time on. We'll also tell you if AI isn't the right fit there.