AI governance · Real usage

Useful AI governance starts from real uses, not from a generic policy.

Robinswood structures governance around your flows, risks and responsibilities so AI remains controllable and maintainable.

  • AI use register and risk level;
  • decision and escalation roles;
  • simple rules for data, tools and validation;

30 minutes

First conversation: maturity level and priority angle.

In 30 minutes, we identify whether your urgency is use inventory, risk register, policy or operational steering.

Fast qualification

  • Human reply within 24–48 business hours
  • No tool selling before diagnosis
  • Short form, enough context
Initial response within 24–48 business hours
No automated sales follow-up after the form
Not an ERP, RPA or n8n reseller

Free conversation · 30 minutes

Let’s talk about your project

Tell us what is slowing you down. We’ll see whether we can help. This conversation does not commit you to the €5,000 audit, excluding tax.

What problem would you like to solve?

Two steps: your needs, then your contact details.

1. Your needs

2. Your company

Initial response within 24 to 48 business hours
This form requests a free 30-minute call. It does not commit you to buying the €5,000 audit.
Data is used to process the request. The four notes are only sent with explicit opt-in.

Your governance is fragile if

  • AI uses already exist but are not inventoried;
  • no one knows who validates sensitive use cases;
  • data rules vary across teams;
  • expected gains are not monitored;
  • compliance, IT and business teams move separately.

What the framing produces

  • AI use register and risk level;
  • decision and escalation roles;
  • simple rules for data, tools and validation;
  • 30 to 90 day governable adoption plan.

Best fit

  • leadership teams that want to industrialize without chaos;
  • IT, business and compliance functions;
  • organizations moving from informal AI tests to durable uses.

Preuve terrain

The frame must survive daily work

In a mid-market company, AI governance became operational by connecting each use case to an owner, allowed data, validation level and gain indicator. The topic moved from theory to management.

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