Automate processes.
Free up resources.

Rule-based, recurring processes with high volume and clearly defined steps are the best starting point — for example invoice processing, data entry or approval workflows. During the process mining step, we identify exactly these "quick wins" first, so you see a measurable ROI early on. More complex, highly variable processes follow later, once the foundation is proven.

Purely UI-based RPA bots are indeed sensitive to interface changes. That's why we favour API-based automation wherever possible, which is more stable and requires far less maintenance than pure UI scripting. Where no APIs exist, we combine robust selector strategies with regular bot maintenance, so interface changes don't lead to a full outage.

For typical RPA and document processing use cases, we frequently see a break-even within 6 to 12 months, depending on process volume and the manual effort involved today. During the concept phase, we calculate the business case using your actual figures, not industry averages. That way you know before the project starts when it will pay off.

In our projects, automation rarely eliminates entire roles — instead, it removes routine work, which is especially valuable when facing staff shortages or unfilled positions. Freed-up capacity flows into higher-value, customer-facing or strategic tasks. Teams can be restructured deliberately this way, without quality or workload getting out of balance.

Yes, when implemented correctly this is standard practice in our projects. We build data minimisation, purpose limitation and deletion concepts into the automation design from the start and document processing steps transparently. For sensitive processes, we complement the automation with a data protection impact assessment before it goes live.