Cordulus

About Cordulus

Based in Aarhus, Denmark, Cordulus operates as a key player in the AgTech and hyperlocal weather data market. The company was established in 2014, initially as Ceptu, by founders including John Smedegaard, Morten Birk, and Andreas Troelsen. The venture originated from a study group at Aarhus University, with an initial focus on using satellite imagery for agricultural benefits, an idea that won the ESA Space App Camp award in 2015. A realization that refining their satellite technology required precise local weather data led the company to develop its own weather stations. This pivot proved successful, leading to the company's evolution into FieldSense and eventually rebranding to Cordulus in 2023.

Cordulus has secured a total of $4.79M in funding over five rounds, culminating in a Series A round of $3.3M in February 2022. Key investors include Danish Agro, Rockstart, and the Pajbjerg Foundation. The business model is centered on a subscription service. Clients, primarily farmers, pay a start-up fee and an annual subscription, which covers the rental of a proprietary weather station, maintenance, service, and data access, eliminating unexpected costs for the user. This approach provides a steady, recurring revenue stream for the company.

The core of Cordulus's offering is a comprehensive weather monitoring system designed for the agricultural sector. This system includes a robust, solar-powered weather station that transmits data over mobile networks every 10 minutes. The station is equipped with sensors to measure air temperature, humidity, pressure, soil temperature, wind speed at crop-level, and rainfall via a patented self-emptying bucket system. The data collected populates the Cordulus Farm app, which provides farmers with real-time and historical weather data, AI-enhanced forecasts, a rain radar, and tailored dashboards for activities like spraying, sowing, and harvesting. The platform leverages AI and a network of over 4,000 weather stations to deliver hyperlocal forecasts that are demonstrably more accurate than traditional models, helping farmers optimize operations, increase yields, and mitigate risks from pests and fungal threats.