AI consulting in the Netherlands — your questions, answered

Start narrow: pick one process where a clear, measurable outcome would justify the effort, rather than trying to “add AI” everywhere. Our first step is a fixed-scope AI Audit & Strategy (€2,500) that maps your data, the highest-value use cases and a realistic roadmap. From there you decide whether to build a proof of concept.

In almost every project the AI does not replace your systems — it plugs into them. We connect to your CRM, ERP, databases and document stores through their APIs, and where needed we build the data pipelines that move and clean the data in between. The aim is AI that fits your current stack, not a rip-and-replace.

A Proof of Concept delivers a working prototype on your own data in a matter of weeks, not months. It is deliberately fixed-scope at €20,000 so we can move quickly and you can see real results before committing to a full production launch. Read how we keep that scope tight in scoping an AI proof of concept.

Yes — we work in fixed-scope engagements rather than open-ended hourly billing. The tiers are an AI Audit & Strategy at €2,500, a Proof of Concept at €20,000 and a Production Launch from €50,000; ad-hoc work is €150 per hour. See the full breakdown on our pricing page, or read what AI implementation actually costs.

Usually yes — your own operational data (transactions, documents, logs, support tickets) is often your single biggest AI advantage. The first job is to confirm it is accessible and meaningful for the use case, which the audit checks. We cover this in using existing data to train AI.

“AI-ready” mostly means the data exists, is accessible, and is consistent enough for the task — perfect data is rare and rarely required. We assess this honestly in the audit and tell you if a use case is not yet viable. Our guide on whether your data is AI-ready walks through the checks.

Some cleaning is almost always needed, but you do not have to fix everything before starting — that is part of the work. We handle the preparation through proper data engineering, building repeatable pipelines rather than one-off manual fixes. More on the why in data engineering for AI.

In most cases yes, provided you have a lawful basis, minimise personal data and document the processing — your own business data is generally fair to use for your own purposes. We design projects GDPR/AVG-first and keep data within agreed boundaries. See training AI on company data under GDPR for the practical detail.

AI is the broad goal of getting software to do tasks that normally need human judgement; machine learning is one technique to get there, where a model learns patterns from data instead of being explicitly programmed. Not every AI solution needs machine learning. We unpack this in machine learning vs AI.

Often something simpler will do — rules, search or a well-prompted language model can solve a problem without training a custom model. We recommend machine learning only when the pattern is genuinely hard to express by hand and you have the data to support it. We weigh this up in machine learning for business.

A trained model is a snapshot of the patterns in the data it saw, so as the real world changes its predictions drift — this is model drift, not a malfunction. The fix is monitoring and periodic retraining, which is why models need maintenance, not just a launch. We explain it in why ML models stop after training.

Yes — building the model is only the start; running it reliably is the harder part. We cover deployment, monitoring, retraining and the surrounding data pipelines so the model keeps performing after launch. See machine learning in production.

Yes — we build generative-AI assistants grounded in your own content so they answer from your documents rather than making things up. They are typically built with retrieval-augmented generation and kept human-in-the-loop where the stakes are high. This sits within our AI services; tell us your use case via the free consultation.

RAG (retrieval-augmented generation) lets a language model look up your own documents at answer time, so responses are grounded in your content and stay current without retraining. You need it whenever a chatbot or assistant must answer from your specific knowledge base. Read what RAG is and how it compares in RAG vs fine-tuning.

A growing number of Dutch consultancies and software teams do — and Crux Digits is one of them. We build RAG-based assistants and search on clients’ own data as part of our AI consulting work. If you want a candid view of whether RAG fits your case, start with a free consultation.

Yes — alongside SMEs we act as a hands-on AI implementation partner for larger organisations, delivering against a fixed scope and clear milestones. We deliberately stay a boutique, senior team rather than a body shop, so you get focused delivery rather than headcount. Learn more on about us and our services.

Compliance depends on the risk category of your specific use case, which the Act defines — most business automation sits in the lower-risk tiers but still needs documentation and human oversight. We design EU AI Act-first, with transparency and human-in-the-loop built in from the start. See EU AI Act compliance in the Netherlands.

We work GDPR/AVG-first: data minimisation, clear processing agreements, keeping data inside agreed boundaries, and avoiding unnecessary copies. Where sensitive data is involved we favour architectures that keep it under your control. This runs through every engagement — see how we approach it across our services.

Our fixed tiers are an AI Audit & Strategy at €2,500, a Proof of Concept at €20,000 and a Production Launch from €50,000, with ad-hoc work at €150 per hour. Fixed scope means no open-ended invoices. See the detail on pricing and the reasoning in what AI implementation costs.

Three clear stages: an AI Audit & Strategy to find the right use case, a Proof of Concept to prove it works on your data in weeks, then a Production Launch to deploy and maintain it. Each is fixed-scope, so you can stop or continue with full visibility. The stages map directly to our pricing.

You get a senior, boutique team, fixed-scope pricing, EU AI Act- and GDPR-first delivery, and full ownership of the code and models we build — no lock-in. We do real custom AI projects, not staff augmentation, BI tooling or marketing. We have also benchmarked our AI against human experts to keep ourselves honest.

Yes — we work fully bilingually in English and Dutch. Crux Digits B.V. is based in Nieuwegein in the Utrecht region of the Netherlands, led by managing director Tom Joseph. You can reach us any time via the contact page or read more about us.