The Challenge
Kurtz Ersa — mechanical engineering and electronics manufacturing, internationally set up — faced a typical mid-market problem: a fragmented AI landscape. Different tools without a shared entry point, without consistent security standards, and with growing sprawl.
What was missing was a single point of entry: anyone who wants to do something with AI should know exactly where to go.
Why meinGPT
meinGPT became the central platform — the one entry for every AI topic in the company. The rollout was deliberately staged: first 25–30 multipliers who “learn to crawl,” then expansion to 200 users. Concrete use cases arrived early: creating specifications, translations, and OCR to digitize handwritten notes.
Our vision was a single point of entry: anyone at Kurtz Ersa who wants to do something with AI goes into this one platform.
Solution
Instead of more point tools, IT got a governed surface — and the business units a clear path into usage. The two waves kept introduction and enablement from drifting apart. Use cases stayed close to daily work: specs that used to need multiple meetings, translations, and digitizing notes.
Results
- 200 users on the platform
- Specification creation in about 1.5 hours instead of several 2-hour meetings
- Rollout in two waves — from multipliers to breadth
The outcome is less “yet another tool” and more steerable AI practice in mechanical engineering.
Conclusion
Kurtz Ersa shows how industrial companies scale AI in a controlled way: first clarity on the entry point, then multipliers, then breadth — with use cases that actually save time and coordination.