A useful AI POC starts with a decision: what gain must it prove, and at what price?
Robinswood frames AI POCs to avoid attractive but non-industrializable prototypes: measurable objective, available data, sponsor, risks and minimum budget.
- measurable success criterion, not a vanity metric;
- data, tools and responsibility perimeter;
- security, compliance, adoption and maintenance risks;
30 minutes
First decision: POC, diagnosis or no-go.
In 30 minutes, we check whether the POC is fundable, measurable and industrializable. If the topic is first a flow or framing issue, we redirect.
Fast qualification
- Human reply within 24–48 business hours
- No tool selling before diagnosis
- Short form, enough context
Fit filter
This page is intentionally filtering: an AI POC must be sponsored, measurable and fundable.
Qualify an enterprise AI POC
Describe the use case, sponsor, data and expected budget: we reply within 24–48 business hours with a first go/no-go view.
Request an AI POC opinion
5 fields. Goal: avoid gadget POCs and check whether the 30k€ minimum is justified.
POC framing is useful if
- an AI prototype is requested by leadership or a business team;
- POC success is not tied to an operational metric;
- required data is not clearly accessible;
- budget is discussed but scope keeps moving;
- you want to avoid a demo that will never go to production.
What we frame before prototype
- measurable success criterion, not a vanity metric;
- data, tools and responsibility perimeter;
- security, compliance, adoption and maintenance risks;
- 30–90 day POC plan with go/no-go decision.
Best fit
- SMEs and mid-market companies with sponsor and 30k€ minimum POC budget;
- leaders who want to prove a gain before industrialization;
- IT/business teams that must frame risks before prototyping.
Preuve terrain
A POC without success criteria becomes a demo
On a business assistance topic, the POC was launched only after clarifying target flow, allowed data and delay reduction target. The prototype tested an operational decision, not an abstract model performance.