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About CixMindHow

A small implementation studio. Founded in 2022. Based in Breda, Netherlands.

What we do and why we're structured the way we are

CixMindHow started because both founders had worked inside larger consultancies long enough to see the pattern: clients got sold on AI potential, received a proof-of-concept, and were then handed a multi-year services agreement to "operationalise" the thing that never quite made it to production.

We set up differently. Every engagement has a defined deliverable, a defined timeline, and a defined end. You can run the system yourself after handover, or you can come back for more — but neither is required.

We work with operations leads and CTOs at European SMBs — typically companies between 20 and 200 people — who have a concrete workflow problem and want a working solution, not a strategy session.

The work itself is LLM integration and agent orchestration: wiring models to your existing tools, data sources, and APIs so that repetitive decisions get made automatically and exceptions reach humans with enough context to act on them.

Two professionals reviewing workflow diagrams in a modern office

The people who do the work

Christopher Vreugdenhil Co-founder, Automation Engineering

Previously a backend engineer at a Rotterdam logistics software company for seven years, where she built internal tooling and later led a small team working on API integrations with logistics carriers. She joined CixMindHow in 2022 to focus on the kind of workflow problems she'd been solving informally at her previous employer — agent design, orchestration logic, and system integration. She covers topics on agent architecture, LLM selection, and integration patterns.

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Ruben Klarenbeek Co-founder, Implementation & Client Work

Worked as a product manager and later as a technical consultant at two mid-sized Dutch software consultancies, mostly in the legal and professional services verticals. He came to CixMindHow from the client side — having commissioned automation projects that underdelivered, he wanted to run engagements the way he would have wanted to receive them. He handles scoping, client communication, and the practical side of getting agents deployed into production environments. He writes about implementation decisions, scoping, and when automation makes sense.

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A few things we're deliberate about

If a workflow could be handled better with a simpler tool — a no-code form, a standard CRM automation, a well-written Zapier flow — we say so. We're not going to sell you an AI agent when the problem doesn't require one. It makes the project slower, more expensive, and harder to maintain.
Every agent we build logs its decisions in a format a non-technical person can read. "Agent routed this to finance because the invoice amount exceeded the threshold" rather than a token trace. If something goes wrong, your team should be able to understand what happened without calling us.
We design with an explicit model of what the agent handles autonomously and what it routes to a human for review. High-value, irreversible, or ambiguous decisions get flagged, not auto-executed. We document this boundary in the scope agreement so it's not implicit.