Making AI
Understandable.
We help organisations understand, audit, and explain their AI systems, from model-level explanations to EU AI Act compliance.
Explainable AI, in Practice
NoEx.ai is an Explainable AI (XAI) consultancy based in Utrecht, the Netherlands. We help organisations understand how their AI systems make decisions, and make those decisions clear to the people who depend on them.
Understandable models are easier to debug, easier to trust, and easier to defend in front of regulators, customers, and your own team. We build the tools and methods that get you there.
We keep one foot in the academic XAI community and one in industry, so the methods we apply are current and the results hold up in practice.
Our Services
Three ways we help: audit what you have, build explainability in, and train your team to keep it that way.
AI Auditing & Assessment
We evaluate your AI systems for explainability gaps, bias risks, and regulatory exposure. You get a clear picture of where you stand and a concrete plan for what to change.
- Model transparency assessments
- Bias detection & fairness analysis
- EU AI Act readiness evaluation
- Risk classification & documentation
XAI Implementation
We build explainability into your AI systems, from integrating interpretation methods to creating dashboards that let non-experts see why a model decided what it did.
- SHAP, LIME & attention-based explanations
- Explainability dashboards & interfaces
- Interpretable model architecture design
- Model cards & automated documentation
Training & Governance
We train your teams to maintain and extend explainable AI on their own, from hands-on workshops for data scientists to governance frameworks for leadership.
- XAI methods workshops for technical teams
- AI governance framework design
- Executive briefings on AI regulation
- Ongoing advisory & support
The Case for Explainable AI
Regulatory Compliance
The EU AI Act requires transparency and human oversight for high-risk AI systems, with penalties for non-compliance of up to 7% of global revenue. Explainability is how you meet those obligations.
Stakeholder Trust
Customers, investors, and partners increasingly demand to know how AI decisions are made. Explainability builds confidence and opens doors that black-box models close.
Better Models
Understanding your models means catching errors, reducing bias, and improving performance. Explanations often show a model leaning on the wrong features before your users find out.
Research & Collaboration
We're open to collaborations with universities, research institutes, and companies: joint research, student projects, grant proposals, and applied pilots.
Universities & Research
Joint research projects, co-supervision of student projects and theses, guest lectures, and collaboration on publications in explainable and responsible AI.
Grants & Consortia
Preparing a funding application? We can join your consortium and take responsibility for the explainability, transparency, and AI-governance tasks in programmes such as Horizon Europe and NWO.
Industry Pilots
Pilot studies, proofs of concept, and audits for companies and public organisations, applying current XAI methods to real systems and real data.
Have a project, proposal, or idea in mind?
Get in TouchLet's Talk
Tell us about your project, proposal, or question, and we'll get back to you soon.