Straight into productive processes. Short projects. Measurable benefit. No 6-month strategy exercises with uncertain outcomes — instead, MVPs that go live after 8 weeks and expand step by step from there.
Classic AI projects fail because they start too big. We go the opposite way: small, productive MVPs — from which organic growth emerges.
Three principles that decide between success and failure — and that we apply consistently.
Transparent process analysis reveals where AI creates the greatest value. A clear cost-benefit assessment forms the foundation for lasting project success — not technical possibilities, but economic impact.
No prototypes, but productive solutions. A business-oriented MVP demonstrates feasibility with minimal effort — and delivers real operational benefit from week 1, not just PowerPoint slides.
Step-by-step learning reduces risk. Manageable MVP investments minimize time and cost risk. Successful MVPs build the foundation for scaled automation — with evidence, not assumptions.
Three indicators that let us assess in 10 minutes whether automation is worthwhile.
Multiple input steps, data from various sources, validations against master data — the more complex the manual work, the greater the leverage through automation.
High number of recurring inputs, many manual data checks, regular pressure peaks. Automation typically pays off from around 500 transactions per month.
Input data is digital but unstructured (PDF, email, Office). Back-end systems can be connected via interfaces. You don't need a perfect data landscape — just an accessible one.
A proven approach we follow with every customer — transparent, with clear milestones and no hidden scope creep.
30 minutes are enough to see whether there's a meaningful entry point in your processes — or whether it doesn't make sense yet. We tell you both, honestly.