Helios is an artificial intelligence-based platform that is aggregating billions of data points to provide a global, real-time view of the climate and economic risks affecting agricultural commodities.
Helios was co-founded by Francisco Martin-Rayo and Eden Canlilar, two food and technology entrepreneurs from different backgrounds. Canlilar previously worked as a Senior AI and machine learning engineer at Google and owned a restaurant. Martin-Rayo worked on avocado trade from Mexico, served as the Chief Commercial Officer at an AI start-up in financial services, and served as a Principal at the Boston Consulting Group.
Canlilar and Martin-Rayo “saw the impacts of climate change accelerate…but were not seeing progress on dealing with the impacts on agriculture,” Martin-Rayo recalls. Meanwhile, they also noticed “a revolution of extraordinary advances in AI and technology, and more climate information than ever before from more commercial satellites, making it cheaper to get that data.” This led to the realization that they could use these advances to build a platform to better understand and predict agricultural disruptions.
Helios was born to develop ways to use AI to analyze climate data and predict potential disruptions in crop supply chains. Over time, the company has added different product lines, Martin-Rayo explains. They primarily assist producers of consumer-packaged goods (CPGs) to forecast where crops including mangoes, tomatoes, potatoes, and raspberries can be reliably grown in the coming years. This service shows importers and exporters whether they can rely on a region to yield a specific crop or need to pivot to another crop or region.
Helios has also assisted hedge funds to predict prices of soft commodities such as corn, wheat, soy, and cocoa. “The types of crops that hedge funds are interested in differ widely from CPG interests,” says Martin-Rayo.
And most recently, the platform also “expanded its technology to work with non-governmental and nonprofit organizations such as Save the Children and the World Food Programme to predict factors for food insecurity around the world and in the United States,” saysMartin-Rayo.
Helios has “divided the world into 14 million hexagons to get daily climate information, and built custom machine learning models for each crop,” Martin-Rayo tells Food Tank. The data reflects how crops have reacted to various environmental conditions in the last 20 years. When inserted into the generative AI model, it can regularly create hundreds of customized reports for different categories of customers, commodities, and stages of the supply chain.
Helios also uses the technology to notice patterns and cycles of climate and crop interactions. Martin-Rayo and Canlilar noticed that some small farms are experiencing challenges adapting to climate volatility, higher input prices, and scarce resources.
Climate factors are making it harder for farms to access water, pushing them farther away from urban centers, and adding higher transportation costs. And because the margins for small-scale farms are already low, farmers are feeling a tighter squeeze. Martin-Rayo hopes the technology can be used to support small farmers that are close to urban centers so they will be able to continue growing diverse crops.
Martin-Rayo attributes much of Helios’s success to the company’s global approach, which allows it to provide broader predictions and solutions, and sets it apart from other agricultural AI companies. He also points to Helios’s compliance with legal regulations and requirements by not sharing customer data or using any customer data to train AI models.
This practice and transparency have allowed the platform to build trust and gain engagement, according to Martin-Rayo. He notes that there is still a “gap in regulators and law makers’ understanding about AI technology,” as they don’t always realize how fast-moving the technology is and how quickly it can change.
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Photo courtesy of Mika Korhonen, Unsplash
