Our method for AI projects in companies

We start with the process, not the model, and stay with you until the hand-over to your team. Simon Micheler describes the three stages in his book "Digital Translator" (Linde Verlag).

Stage 1Read the map

Read processes as they really run.

Expert interviews, on site with the team, watching over their shoulder. We understand your workflows the way they happen day to day, not the way they look on the org chart.

OutcomeImpact Effort matrixUse cases prioritised by effort and impact. A clear picture of where to start first.
Stage 2The first ascent

Start with the highest ROI.

We begin with the use cases that have a noticeable effect. From experience: small, visible wins build trust. The people who will work with it later need to be behind the solution.

OutcomePilot in useA first working solution, tested by the team and with measurable impact.
Stage 3Securing the summit

Embed, train, hand over.

Close support, continuous feedback, joint adjustment. After a successful test we integrate the processes into everyday work and train users until the team can carry on independently.

OutcomeChange managementTraining, adoption and hand-over. The team carries on without us; ownership stays in-house.
N° 01 Stance
Most AI projects don't fail on the technology. They fail because no one read the process properly beforehand. And no one supported adoption afterwards.
01 We start with the process, not the model. Only once the work is clear do we decide where AI actually adds value.
02 We say no when AI isn't the right tool. Sometimes the honest answer is a better template or a clear workflow.
03 We measure in business outcomes, not tokens. Success is a process your team actually uses.