This interview was conducted with Jean-Philippe Schepens van Thiel, Founder and CEO of AxonJay who has completed a sustainability assessment and sustainable digitalization workshop with sustAIn.brussels.
Q: What is your role or position within the organization?
J-Ph: I am the founder and CEO of the company. Together with my co-founder, I shaped the vision to make AI accessible, transparent, and sustainable. We both have backgrounds in data, deep-tech and innovation and wanted to do more than just collect ‘big data’. We aim for AI to provide concrete value to companies, with attention to people and the environment.
Q: Can you tell us something about the company?
J-Ph: We started AxonJay in December 2021 and launched our Self-ML Platform™ as a service in September 2024. In the meantime, we have signed major clients all over the world who work with our two main products: AI-Sales Agent and AI-Fund Agent. Our core revolves around real-time data processing and machine learning: we capture signals from news, social media, websites, databases, ... and translate those into Real-Time Signals. A cocktail of Signals is predicting a behaviour of a company. For example, we predict if the company will buy your product, service, will be in need of cash, will move, will be at more risk etc. We cover more then 80% of all companies in the world and we can rank them in Real-Time according your cocktail of signals! We are full transparent so you see which signal has what weight in the total cocktail and the cocktails are dynamic as you can give instant feedback, this reinforcement learning generate new adapted cocktails, this to always have the best real-time suggestions.
Companies quickly gain insight into which customers or prospects are potentially interesting and who may be less relevant. We also do this in an energy-efficient way, thanks to our unique technology that consumes less computing power than traditional AI solutions. This is our famous DataPeeling™ technique!
Q: Why did you start AxonJay?
J-Ph: Before this, I sold a company to a large global B2B data provider. There, I saw how customers struggled to leverage AI and big data. They often received black-box solutions, with data that was already outdated by the time analyses were completed. Together with my co-founder, I started with this idea:
- Real-time AI: so organizations continuously have the most up-to-date insights.
- No black box: the user sees which ‘signals’ count, and can give feedback to train the algorithms. (full Transparency)
- Sustainable: our technology consumes less energy and the data remains owned by the customer, instead of us. We are building an AI model that serves people and companies, not the other way around.
Sustainability really runs as a guiding principle through your activities. Can you tell us more about that?
J-Ph: Sustainability but also transparency and innovation are deeply embedded in our DNA. This manifests itself in various aspects:
- Manifesto & tribe culture: All our employees – or ‘tribe members’ – sign our Manifesto. It contains agreements on how we collaborate with our values, handle resources, and continue to challenge each other to work more sustainably and more aware.
- Net zero & circular choices: Our office is fully furnished with second-hand or recycled materials. We offset our travel movements, consciously choose bicycles or public transport, and use green energy. Sustainability is not ‘a must’, but a logical part of our way of doing business.
- Full transparency & social responsibility: We openly share how our algorithms work and who owns the data (always the customer). Additionally, our internal operations are based on honesty,awareness, involvement, and the engagement of our employees: everyone can, for example, invest in the company, regardless of their position or seniority.
Q: You have a machine learning product. How do you integrate sustainability into it?
J-Ph: We weave sustainability into our technology in various ways:
- Energy-efficient model: By intelligently compressing signals and data, you don’t need massive computing power. This significantly reduces CO₂ emissions. We ban as much as possible deep-learning models.
- Transparency about algorithms: Instead of a ‘black box’ with only a result, we show which signals contribute to the prediction. Users can thus adjust, this with direct feedbacks and receive honest insights.
- No data-owner: We do not sell or reuse customer data. All generated insights remain 100% theirs.
Q: You followed the Sustainable Digitalization Workshop with experts from sustAIn.brussels. How did you experience that?
J-Ph: That workshop was partly a confirmation for us that we were already doing a lot right. At the same time, it provided us with new ideas and a concrete action plan. It was enlightening to sit around the table with experts and see if there were aspects we hadn’t thought of ourselves. For example, we received extra input on building social and ecological impact in the long term.
Q: How has this workshop concretely helped AxonJay?
J-Ph: We received detailed reporting and a kind of ‘digital transformation canvas’ that further exposed our strengths and opportunities. We were already quite advanced in our sustainability policy, but through the workshop, we focused even more on optimizing our cloud infrastructure, cybersecurity, and the social side of digitalization. The report also prompted us to update our internal Manifesto with additional practical objectives. We had already tackled a lot of low-hanging fruit. Through this workshop, we were able to map out and address that extra mile.
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Ready for your next step?
This interview illustrates how AxonJay combines real-time machine learning with sustainability, with transparency as the key word. Want to know how you can digitalize more sustainably yourself or are curious about expert guidance tailored to your company? Contact us to discover how our services and workshops can help you.
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