Real outcomes, from real client data.
A selection of engagements, anonymized by sector and outcome. The numbers are documented, and the capability stayed with the client.
Challenge
The company wanted AI to show up where it counts, in the financials, not in a pilot that never pays back. Machine downtime and manual effort were eating into margin.
What we did
Through the Acceleration Program, the company’s own engineers built and validated AI use cases on their operational data, targeting downtime and the most time-consuming manual work.
Outcome
A recurring EBITDA gain of EUR 1.02 million, around 1.5 percent of EBITDA, with machine downtime down 15 percent. Capability the company kept and keeps building on.
Challenge
A digital-transformation consultancy in the pharma space wanted to move beyond advice and put AI to work inside its own delivery, while bringing its experts up the AI learning curve quickly.
What we did
Through the Acceleration Program, the team built and validated AI use cases on their own data, learning by doing rather than by slideware, and identifying the work worth scaling.
Outcome
A 50 percent productivity uplift, a new AI-native service line, and a shift in the company’s business model. The capability kept paying off well after the engagement ended.
Challenge
A manufacturer of equipment for highly regulated pharma environments carried a heavy documentation and compliance workload. Compliance consumed a large share of expert time, and document rework was frequent and slow.
What we did
Through the Acceleration Program, the team built AI use cases on their own compliance and quality documentation, targeting the most time-consuming and error-prone steps in the process.
Outcome
The share of time spent on compliance fell from 35 to 27 percent, and document rework dropped by 25 percent, freeing experts to focus on higher-value engineering work.
Challenge
A real-estate fund held data across many portfolio companies, but each asset learned in isolation. Physical property inspections were frequent, costly and slow, and no single company had enough data to model the problem well on its own.
What we did
We ran the hybrid approach across the portfolio and applied federated learning, so insights were shared securely across companies without exposing raw data.
Outcome
Physical property inspections fell by 15 to 18 percent as the shared model guided where attention was actually needed. The kind of cross-company value only common ownership makes possible.
These are four of a wider set of engagements. Further anonymized case studies are available under NDA on request.
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Get in touch with our founder. We will talk through the case most relevant to your sector and how the approach could apply to you.