Side-by-side comparison of AI visibility scores, market position, and capabilities
IoT soil monitoring sensors for precision irrigation in orchards and vineyards. Based in Aarhus, Denmark. Provides real-time soil moisture data for water-efficient growing.
SoilSense is a Danish agricultural technology company headquartered in Aarhus that develops IoT-based soil moisture monitoring solutions for orchards, vineyards, and vegetable farms. The company's wireless sensors measure soil water content at multiple depths in real time, delivering the field-level data growers need to implement deficit irrigation strategies and maximize water use efficiency.\n\nSoilSense's platform is designed for ease of use and deployment — sensors are installed quickly without specialist equipment, and data flows directly to a web and mobile dashboard where growers and their advisers can monitor soil conditions and receive irrigation alerts. The system integrates with weather data and evapotranspiration models to provide context for sensor readings and help growers calibrate their irrigation timing.\n\nThe company serves specialty crop producers across Europe, with particular traction in wine grape and apple growing regions where precise water management improves both fruit quality and yield. SoilSense has received support from Innovation Fund Denmark and continues to develop its platform capabilities, including integrations with commercial irrigation control systems that enable automated irrigation triggering based on soil moisture thresholds.
a2z Radiology AI raised $20M in 2025 for its whole-body AI that simultaneously screens for 24+ conditions across CT scans — from incidental cancers to cardiovascular risk — in a single automated read.
a2z Radiology AI has developed a whole-body CT analysis platform that simultaneously screens for over 24 medical conditions across a single CT scan, including incidental cancers, coronary artery disease, aortic aneurysm, bone density loss, and organ abnormalities. The AI acts as a second reader that radiologists can use to catch incidental findings that fall outside the primary reason for a scan — a major source of missed diagnoses.
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