One-off experiments
AI initiatives are emerging everywhere, without an overview or any coordination.
AI & Governance
AI governance for business
We help organizations stay in control of AI use. Ownership, policies, approvals, a model register, risk monitoring, and auditability—all in one place, so policies don’t just sit in a folder while people do their own thing in practice.
Get a handle on AI risksLess risk, more confidence.
Clear ownershipEvery AI has an owner.
Demonstrably in controlReady for audits and due diligence.
AI register and oversight
exampleAI systems34+3 this month
Awaiting approval74 days on average
High risk515% of the total
Incidents (30 days)2both handled
Recent approvals
approvedKlantchat AI v1.3today
approvedFraud detection v2.12 days
under reviewContract analysis v1.05 days
Control today,
trust tomorrow.
trust tomorrow.
Does this sound familiar?
No one knows what's running
No complete picture of models, data, suppliers and usage.
No ownership
It’s unclear who is responsible for results and risks.
Policy is disconnected from practice
Guidelines exist, but they are not translated into practice.
Risks are not monitored
Without monitoring and logging, you can't get timely insight into anomalies.
What you need
Six components. Together, they make the difference between policy on paper and policy that works.
AI register
Central overview of all AI systems, models, and suppliers — including those that haven’t been officially registered.
Roles and responsibilities
Clear who owns it, who reviews it, and who can approve it.
Approval flow
A consistent process for review and decision-making, with a trail that can be traced back.
Usage guidelines
Practical guidelines for responsible design and use, in plain language.
Monitoring and logging
Ongoing monitoring of performance, risks, and deviations.
Periodic reviews
Ongoing reassessment of systems and risks over time.
What governance looks like
From policy to use to oversight — and back again.
1PolicySet frameworks and guidelines for responsible AI use.
2Use caseDefine the application’s purpose, value, and risks.
3ApprovalReview by the right roles through a clear workflow.
4UseOnce approved, the model is put into use in a controlled way.
5MonitoringContinuous monitoring of performance, risks and data quality.
6ReviewPeriodic review and adjustments based on risks and impact.
Documented, demonstrable and auditable
Four things you need to be able to show when someone asks: a regulator, an auditor, an investor or your biggest customer.
AI policy and guidelines
Documented, dated and assigned a version number — so you can show what was in effect and when.
Approvals
Which use case was assessed by whom, with what outcome and risk level.
Exceptions
What’s temporarily allowed, why, until when and who assessed it.
Audit trail
Every change, approval, and risk update, in chronological order rather than reconstructed after the fact.
Why this belongs with us. Governance is knowledge management by another name: recording who decided what, why, and where that information is stored. We build it into the portal you already have — not a separate system nobody opens.
For executives, risk, IT and business
Executive team
Manage value and risks with clear reports and KPIs.
Risk and compliance
Meet standards with demonstrable control and traceability.
IT and data
Manage models and data responsibly, with technical and organizational controls.
Business
Develop and use AI solutions quickly, within clear boundaries and with confidence.
Put AI governance into practice.
One overview, one truth, one team that knows where it stands. Start with the free online scan to see where you are now.
Free online scanNo obligationsIn your own portal
Ask this site
Ask your question about AI, the AI Act, integrations or digitalization. The answer comes from the pages of this site, with references to where it can be found.
Governance is one of the nine layers. In the AI capability model it runs like a rail through the other eight — ten capabilities, from AI register to cost ceiling, with a scan that shows where you stand.
The way it works now
- The appointment is in one colleague's mailbox
- Data is manually retyped from one system to another
- The person who knows is on vacation
- No one can see afterwards why this decision was made
As it is
- The agreement is stored with the client, findable for everyone
- Once entered, information flows through to the rest
- Work continues, even if someone is absent
- Every decision has a source, an owner, and a date
drag with your finger
What if Peter leaves?
Two out of five rules on this page disappear. That's how it feels when knowledge isn't part of the company.
What is your next step?
Free self-assessmentTwelve click questions, three minutes, immediate score and your biggest risk.
Fresh LookBusiness case, benchmark and priority. Settled once we build after this.
What it costsFixed price per package, no price per user, no hourly billing.
From the fieldWhat changed at companies that started with this.
Also good to know: frequently asked questions · security and data · terms and conditions · partners · research and figures · templates · what's changing · where you start
Where does the process break down in practice?
Employees use AI, but not everyone knows the risks, permissions and review processes. Who is allowed to use what, and how do you demonstrate compliance?
Direct answer
Start with a concrete business problem and a measurable outcome, not with an AI tool. Arrange data, ownership, risk and human control before you scale up.
What we do
We connect analysis, business intelligence, data and execution so you don't get stuck in advice or loose tooling.
How it works
- We clarify the current situation, data and the real bottleneck.
- We determine the smallest working solution, owner and measurable outcome.
- We implement or prioritize the next step and verify the result.
- We secure what works and stop or fix what doesn't work.
What you get
A concrete analysis, priorities, decision criteria, an executable next step and visibility into what needs to be built, integrated or secured next.
For whom and whom not
Right for Dutch SMB organizations that want to solve a concrete process, knowledge, data or AI problem. Not for organizations looking only for a generic tool demo or unproven AI promise.
Costs and choice
Price and scope depend on the chosen next step. View the current fixed packages and pricing logic at pricing for digitization.
Risks and concerns
We work step by step, make assumptions and ownership explicit, use only the minimum necessary data, and maintain human control over decisions and execution.
Frequently asked questions before you decide
Does everything have to be done at once?
No. The preference is one clearly defined problem with a measurable outcome, then scale up.
What does it cost?
The current pricing and package logic is on the pricing page; custom work is only chosen if the scope really requires it.
How do you prevent a trajectory without results?
By defining the problem, owner, success metric and stop criteria in advance, and doing readback and verification after each step.
How we assess your situation
We map your current process, systems, exceptions and costs. Only then do we advise whether an off-the-shelf solution, a custom integration or another approach makes sense.
From insight to execution
This page supports the following concrete solution.
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