The Thinking Behind Greeneye Solutions

Investors looking to identify companies that contribute to addressing environmental and social challenges face a seemingly simple question: Which companies produce products and services that offer solutions to these challenges, and how significant are these activities to their businesses?
In practice, the answer is far from simple. Traditional ESG assessments focus primarily on how companies manage their operations and the associated risks. They do not necessarily tell us what companies produce, which problems their products are designed to solve, or how much these activities contribute to their business.
We set out to address this gap by developing Greeneye Solutions, a system that identifies and maps publicly traded companies' exposure to products and services that make positive environmental and social contributions.
This information can be valuable to investment managers, thematic and impact fund developers, analysts, and consultants. It can help identify investment opportunities, compare companies, and explore business activities that are not always visible through traditional sector classifications.

What Actually Makes a Product a Solution?
One of the central questions we faced was where to draw the line between a product that provides a solution to an environmental or social challenge and one that can simply be used for a positive purpose.
We chose to focus on what a product was designed to do, rather than every possible benefit that might result from its use.
A search engine, for example, can help people learn and expand their knowledge, but it can also serve countless other purposes. The fact that it can be used for education does not make it an educational product. By contrast, a platform specifically developed for teaching and learning has education at the heart of its design.
Another important distinction is between a product that is made more sustainably and one that is intended to address a broader challenge. A product manufactured through a cleaner production process, or one whose components have been replaced with recycled materials, is not necessarily a solution in the sense we are looking for. These are important improvements, but they largely belong to the realm of ESG management. A drinking water purification system, on the other hand, is designed to address a problem beyond the product itself: access to safe drinking water.
Design intent, however, does not always translate into actual impact. Our aim is to identify products designed to address environmental and social challenges, rather than measure their full real-world impact.
In other words, with Greeneye Solutions we are not trying to assess companies' overall sustainability performance, but rather which solutions they offer to the world. A company may offer products that make a meaningful positive contribution while also facing significant ESG management challenges. These are two different questions, and both deserve attention.

When Computers Think Differently
Artificial intelligence allowed us to approach research on a scale that would have been difficult to achieve through manual work alone. But along the way, we discovered something interesting: computers and humans approach the same questions differently.
As humans, we bring intuitions, preconceptions, and sometimes emotional associations about what is considered positive or sustainable. AI does not approach the task with the same emotional baggage, but neither does it automatically understand distinctions that seem obvious to us. It can, of course, reflect other biases inherited from its training data.
Much of our work therefore involved repeatedly refining our prompts: defining principles, examining borderline cases, identifying inconsistencies, and making the instructions more precise.
This process revealed an additional benefit. Having to explain to a computer exactly why one product qualifies as a solution while another does not forced us to sharpen our own reasoning. Intuitions became explicit rules, and underlying assumptions were put to the test.
In this way, working with AI did more than expand our research capabilities. It also helped refine the methodology itself, making it more consistent, transparent, and less dependent on the subjective judgment of any individual analyst.

From Concept to System
Based on these principles, we developed a classification framework covering seven major groups and more than 60 detailed product and service classifications. We built a process that combines AI-powered research, business activity classification, revenue exposure estimates, and professional quality control.
The result is a system that allows users to search and filter companies by the types of solutions they offer, examine the estimated share of revenue associated with these activities, and explore the explanations and sources supporting each assessment.
The work is still evolving. We continue to review our classifications, improve our research processes, refine the results, and steadily expand the number of companies covered by the system.

We invite investors, analysts, and other professionals interested in this field to explore Greeneye Solutions, share their perspectives, and discuss potential applications and opportunities for collaboration.

Contact us at: [email protected]