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AI assistants often invent non-existent ECLIs. We built rechtspraak-mcp: an open-source connector that pulls real Dutch rulings from the official Rechtspraak Open Data into any AI assistant.

Vector search alone fails on complex technical queries. I break down why we integrated BM25 and Cohere Rerank to improve context precision for our RAG agents.

On April 2, 2026, the NIST public comment period for agent identity and authorization guidelines closed. This milestone marks the definitive shift toward regulated non-human identity management. What does this mean for the security and auditability of enterprise AI?

The logistics disruptions of March 2026 proved that static planning fails under pressure. Discover how autonomous supply chain agents are now executing logistics pivots independently, without manual intervention.

As the 2026 deadlines for high-risk AI systems (Annex III) relentlessly approach, focus is shifting from policy to the hard reality of EU AI Act compliance audits. What does this mean for your organization and how should you prepare?

New data from March 2026 shows that agent-to-agent negotiations are reducing procurement cycles in the manufacturing sector from weeks to hours. Discover how autonomous procurement agents are redefining the buyer's role.

With the March 2026 update to the AI Liability Directive, clear European regulations have emerged surrounding 'autonomous harm'. For executives, this means that demonstrable control over AI agents is no longer a luxury, but a strict legal requirement.

Most organizations have seen enough pilots by now. The next question is more grounded and more important: where does AI create measurable value, how should that value be tracked, and how do you avoid confusing a strong demo with structural return?

The AI-enabled organization is not just about tools. It requires new work design, new team rhythms, and leadership that intentionally combines human expertise with agentic systems.

Public organizations want to use AI to make services faster, more consistent, and more accessible. The challenge is doing that without weakening explainability, human accountability, or data protection.

In manufacturing, engineering, and construction, AI is moving from isolated analysis to direct support for planning, quality control, and operational decisions. Digital twins are increasingly becoming the layer that ties that together.

Private AI is no longer only about security. For European organizations, the choice increasingly revolves around continuity, data control, explainability, and the ability to deploy AI without becoming dependent on one public environment.

Many organizations are experimenting with AI, but far fewer are turning that activity into scalable process improvement. That requires different design choices in workflows, governance, integration, and leadership.

AI literacy is no longer a soft side topic. Organizations that deploy AI need to show that employees, managers, and administrators understand how the system works, where the risks are, and how human oversight should operate.

At PrudAI, we develop intelligent AI solutions that make organizations smarter and more effective. Together with Symbol, an authority in the field of Lean Six Sigma, we have developed a new product: the Lean Six Sigma AI Consultant.

As an AI coach and AI implementation partner, I see organizations working with AI along two tracks. On one hand, copilots help people within a task step through a prompt (writing, summarizing, classifying). On the other hand, agentic AI: software 'colleagues' that independently translate goals into plans and actions, with quality controls and integrations. The second category provides the greatest added value.

Since beginning this year at PrudAI, much has happened. What began as a compact initiative with a clear mission – to make AI accessible and responsibly used for businesses and governments – has now grown into an organization with powerful products, deep expertise, and a vision that aligns with the challenges of our time.

Large Language Models (LLMs) are revolutionizing how we interact with information, but they have inherent limitations. To address these challenges, two powerful methodologies have emerged: Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP). While distinct, their true potential is unlocked when used in tandem, creating AI systems that are not only knowledgeable but also capable of taking meaningful action.

AI agents are emerging as the digital colleagues of the future. They independently perform tasks, communicate with systems and people, and are available 24/7. But what exactly are agents? And how do they truly help organizations – and you as an employee – move forward? As an AI coach at PrudAI, I often explain it like this: "An agent is like a regular employee, but virtual." In this blog, I explain how agents work, what they can do, and how multiple agents can function together as a virtual organization with one digital leader: the Chief Executive Agent (CEA).

AI is no longer a thing of the future. More and more organizations are using it to speed up work, reduce errors, and support employees. In this blog, I will show where AI truly adds value today: from support departments to policy development, and how the latest generation of agentic AI is reshaping our way of working.

Generative AI has quickly become a key concept. From automatically writing reports to generating images, code, or even policy texts: this technology seems capable of anything. But how does it actually work? What happens under the hood of tools like ChatGPT or Claude? As a senior AI coach at PrudAI, I help organizations apply generative AI responsibly and effectively on a daily basis. In this blog, I explain in an understandable way what generative AI is, how Large Language Models (LLMs) work, and why it's important to know what you're doing.

The rise of generative AI offers enormous opportunities. But many organizations – especially in the public sector, healthcare, and finance – rightly ask the question: "Is AI safe enough for our sensitive data?" The short answer: yes, provided you maintain control over your data and infrastructure. In this blog, I explain how to do that with so-called Private AI solutions, where the LLM (Large Language Model) acts purely as a processor – not as the owner of your data. As an AI coach at PrudAI, I guide organizations that want to use AI without compromising on privacy, security, or compliance. The key? Understanding where the risk lies and how to make it manageable.