OneKapisch · Privacy-first software studio built in Germany.
SELECTED WORK
Evidence, with the boundary left intact.
Public proof for confidential enterprise work should be specific about the outcome and equally specific about what it does not claim.
Confidentiality by design
Useful proof without borrowed authority.
Enterprise transformation work often carries confidential operational context. Public evidence therefore focuses on the problem class, my contribution, the verified outcome, and the boundary of the claim.
01
Procurement workflow
From negotiation preparation to decision-ready intelligence
Context
A defined procurement negotiation workflow depended on repetitive manual preparation across fragmented inputs.
My contribution
Workflow diagnosis, AI-assisted operating design, prompt and evidence structure, and adoption guidance.
Verified outcome
60 to 75% less preparation time within the defined workflow.
Claim boundary
The figure applies to the documented workflow, not to procurement as a whole.
02
Capability building
From general AI awareness to role-specific practice
Context
Professionals needed practical confidence to apply AI inside real work, not only conceptual training.
My contribution
Applied learning architecture, workflow demonstrations, adoption formats, and champion enablement.
Verified outcome
500+ professionals enabled through applied AI learning and adoption.
Claim boundary
Enabled describes participation in structured capability-building activity, not a universal productivity claim.
03
Workflow portfolio
From scattered ideas to working AI workflows
Context
AI opportunities needed to move from isolated ideas into a coherent, testable portfolio.
My contribution
Use-case prioritisation, workflow architecture, prototyping, and operating guidance.
Verified outcome
10+ AI workflows designed and delivered across real work contexts.
Claim boundary
Public descriptions stay abstract where the underlying work is confidential.
PRIVATE WORKING SESSION
Bring the real operating problem.
For leaders who need to move from AI activity to evidence, adoption, and repeatable capability.