Prove first, then build
With AI that is not a matter of style but a necessity: whether a model is reliable enough in your situation, you only know once it has been let loose on your own data.
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01
Use case and yardstick
One process, with agreement up front on when it is good enough. These documents, this margin of error, this turnaround time.
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02
Proof of concept on your own data
The hardest part first, measured against a set of examples with the right answer.
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03
Hardening and integration
Error handling, a cost ceiling, logging of every decision, approval steps and a fallback for when the model is down.
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04
Phased rollout
First one team or one site, then the rest. Never one moment where everything has to go right.
What happens to your data
Your data trains nobody's model
Business contracts that rule it out — or fully on-premise.
Personal data filtered
Taken out before anything goes to a model.
Every decision traceable
What went in, what came out, which source. No black box.
No lock-in to one supplier
Built supplier-independent; switching doesn't take a rebuild.
Half an hour costs you nothing
Tell us which process takes the most manual work. We will tell you honestly whether AI is the right answer — and sometimes the answer is: fix your integration first. Then you have that too, without an invoice.