Dayrize was recently awarded €2.7M funding from the RVO Innovation Credit program, given to groundbreaking innovation projects with significant technical challenges and exceptional market potential. This funding will drive the development of DaySI – Actionable Insights, an advanced AI solution designed to revolutionize supply-chain transparency and sustainability analytics for businesses in the consumer goods sector.
In this interview, we spoke with Bart Nollen, Dayrize Co-Founder, and Manos Riglis, Chief Product & Technology Officer, to gain first-hand insights into the team’s reaction to this milestone achievement. They shared their initial thoughts on the funding, the ambitious vision behind DaySI, and how it is set to help businesses achieve measurable sustainability improvements while increasing operational efficiency and reducing costs.
Could you share your first reaction to receiving €2.7M in funding from the RVO Innovation Credit program? What does this mean for Dayrize and the DaySI project?
Bart Nollen: The RVO funding validation is a crucial milestone for Dayrize. Having worked with RVO previously, we’ve demonstrated our ability to develop and deploy advanced AI solutions that deliver measurable sustainability improvements. This €2.7M will accelerate our development of DaySI, allowing us to help businesses reduce their environmental impact while improving operational efficiency. We’re already seeing pilot implementations achieve 20% reductions in material waste and 15% improvements in sustainability compliance within six months.
Manos Riglis: It was an exciting moment. The application process was rigorous—over 60 pages of detailed submission—so securing this funding was a testament to our vision. It recognizes both the technical and market potential of DaySI and accelerates our work towards actionable sustainability insights.
The RVO program supports projects with high technical risk and market potential. What aspects of DaySI made it stand out?
Bart Nollen: What sets DaySI apart is our holistic approach to sustainability optimization. Think of it as having multiple specialized sustainability experts working in perfect coordination – one focused on carbon emissions, another on material usage, another on regulatory compliance. Our AI system works the same way, with multiple AI agents analyzing different aspects of sustainability simultaneously. This ensures that when a business makes improvements in one area, like reducing packaging waste, they’re not unknowingly creating problems in another area, like increasing carbon emissions from alternative materials.
For example, when a consumer goods company wanted to improve their packaging sustainability, DaySI didn’t just suggest switching to recycled materials. It analyzed the entire value chain, identifying opportunities to reduce material usage by 25% while simultaneously optimizing shipping efficiency, resulting in both environmental and cost benefits.
Manos Riglis: Unlike traditional analytics tools that provide only descriptive insights, DaySI focuses on actionable recommendations. It doesn’t just tell businesses where they stand on sustainability—it guides them on how to improve through AI-driven optimizations.
What are the immediate next steps for Dayrize and the DaySI project now that you’ve secured this funding?
Bart Nollen: We’re currently working on two key areas: First, integrating thousands of datasets to train our models, and second, developing our first proof-of-concept AI agent to test real-world applications.
Manos Riglis: We’re focusing on automating data collection and refining AI agents for product-level analytics. These steps will validate our approach and allow us to engage customers early for feedback.
Do you see potential for expanding DaySI beyond the consumer goods sector into other industries? If so, how?
Bart Nollen: While we’re starting with consumer goods, our roadmap includes expanding into finance and asset management. We’re already developing specific modules to help financial institutions assess the sustainability impact of their investment portfolios. This isn’t just about measuring carbon footprints – it’s about providing actionable insights that help asset managers make better investment decisions. For instance, our system can analyze a portfolio company’s entire supply chain, identifying both sustainability risks and opportunities for improvement that could affect future valuations.
We’re seeing increasing demand from financial institutions who need to comply with new ESG regulations while also identifying sustainable investment opportunities. DaySI can help them evaluate companies based on comprehensive sustainability metrics, going beyond surface-level ESG scores to understand real impact potential.
As Co-Founder and Chief Innovation Officer, what excites you most about DaySI’s potential impact on the industry?
Bart Nollen: What drives me personally is seeing how DaySI can transform abstract sustainability goals into concrete actions. When businesses can clearly see how specific decisions affect their environmental impact and bottom line, sustainability becomes a practical business imperative rather than just a compliance exercise.
For example, one of our early adopters used DaySI to analyze their product line and discovered that by making three specific changes to their manufacturing process, they could reduce their carbon emissions by 30% while actually lowering production costs by 12%. This is the kind of actionable insight that can drive real change at scale.
For businesses considering adopting AI-driven sustainability tools, what advice would you give to ensure they maximize their results?
Bart Nollen: One of the most common challenges I encounter when discussing AI is skepticism, which often stems from misunderstandings about its capabilities. Many people engage with AI through tools like ChatGPT, where they can verify incorrect responses, leading to doubts about AI’s reliability. However, AI extends far beyond large language models—it can be trained on constrained, highly relevant datasets to perform specific, reliable tasks.
Businesses should recognize that AI is a powerful tool when used correctly. It can analyze vast datasets, automate sustainability reporting, and generate actionable insights. The key is to ensure that AI is applied with a clear strategy, leveraging high-quality data, and maintaining human oversight. AI should be seen as an enabler, augmenting decision-making rather than replacing expertise.
DaySI has been described as a next-generation AI-driven sustainability tool. Can you explain the core idea behind DaySI and how it differs from existing sustainability analytics tools?
Manos Riglis: Most sustainability tools today provide descriptive analytics, giving businesses an overview of their current impact metrics. While this is useful, it does not offer actionable steps to improve sustainability performance. DaySI aims to go beyond just reporting impact—it actively recommends actions that businesses can take to enhance sustainability, whether through material choices, emissions reduction, or supply chain optimizations.
A key differentiator is that DaySI doesn’t just highlight sustainability risks or gaps; it provides targeted interventions. Instead of simply stating where a company stands, it suggests specific strategies, such as switching to recycled materials in packaging or optimizing energy use.
The second core aspect of DaySI is its commitment to explainable AI (XAI). Many AI-driven tools operate with opaque decision-making processes, making it difficult for users—especially in regulatory or compliance settings—to understand how conclusions are reached. Our approach ensures transparency by clearly outlining how insights and recommendations are derived. Customers will not only receive AI-generated advice but will also have visibility into the reasoning behind it, ensuring the tool’s trustworthiness while generating actionable data points.
You mention that DaySI uses technologies like ReACT and MARLSO. How do these advanced AI components enhance DaySI’s ability to deliver actionable insights?
Manos Riglis: The ReACT (Reasoning and Action) component allows us to use a large language model to break down complex sustainability challenges. It provides structured reasoning to understand the problem and generates a strategic plan. When a user queries the system, ReACT determines the necessary steps, retrieves relevant data, and validates the approach to ensure accuracy. For example, it might pull additional supply chain data or call an API to rescore a product’s sustainability metrics, ensuring the insights remain dynamic and up to date.
Meanwhile, MARLSO (Multi-Agent Reinforcement Learning for Sustainability Optimization) focuses on optimizing sustainability decisions. This framework consists of specialized AI agents that independently analyze different dimensions of sustainability, such as recyclability, emissions, and circularity. These agents collaborate and compete through reinforcement learning, continuously refining their recommendations based on reward functions that measure sustainability improvements. This ensures a balanced approach—avoiding trade-offs where improving one metric harms another.
Explainable AI (XAI) plays a role in DaySI to make impact data accessible to non-technical users. Why is this transparency critical, and how does it set DaySI apart from competitors?
Manos Riglis: Transparency is critical for two key reasons. First, it builds trust. Instead of simply presenting AI-generated recommendations, DaySI provides clear, data-backed explanations of how each insight was derived. This gives businesses the confidence to act on AI-driven sustainability guidance.
Second, it ensures compliance. Many sustainability regulations require companies to substantiate their claims with verifiable primary data. DaySI’s explainable AI (XAI) framework ensures that all insights are not only actionable but also fully traceable.
Beyond transparency, we emphasize auditability and continuous improvement. Every insight generated by DaySI can be traced back to its source data and reasoning process, allowing for periodic validation and refinement. Our ‘human-in-the-loop’ approach ensures AI-driven recommendations remain accurate, reliable, and aligned with evolving sustainability goals.
Lastly, how do you see the relationship between innovation funding, like RVO’s support, and the future of sustainable technologies?
Bart Nollen: When businesses consider adopting AI-driven sustainability tools, it’s important to understand that AI is most powerful when focused on specific, well-defined problems. Unlike general-purpose AI tools that many people are familiar with, DaySI is trained on extensive sustainability data and industry-specific metrics. This means it can provide highly accurate, actionable recommendations rather than general suggestions.
We’ve found that the most successful implementations start with a clear focus area – like packaging optimization or supply chain emissions – and then expand based on measured success. For instance, one client started by focusing just on their packaging sustainability and, after achieving a 25% reduction in material usage within three months, expanded to analyze their entire product lifecycle.
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