Osirix
Turning existing farm machinery into precision-ag tools.
Company Overview
Snapshot
Founded in October 2022 by Ido Shekel and Tomer Peretz, Osirix operates with 1–10 employees. The company is currently in the Pre-Funding stage, developing its precision agriculture technology.
Business overview
Osirix develops precision agriculture tools that transform existing farm machinery into smart spraying systems. The company's core technology utilizes aerial imagery and an AI-powered dashboard to analyze fields, pinpointing problem areas that require treatment. This generates prescription maps that integrate directly with farmers' existing sprayers, enabling targeted application of chemicals. Osirix addresses the challenge of constantly changing agricultural imagery by providing agronomists and farmers with a no-code platform to train and adapt image recognition algorithms for specific field conditions, ensuring accurate and precise spray passes. The company operates within the Agriculture & Food Technologies sector, focusing on sustainable farming, smart farming, and precision agriculture.
Log in to access full profile ›Company Intelligence Q&A
- What is Osirix's primary focus in agriculture?
- Osirix focuses on transforming existing farm machinery into precision-ag tools by using aerial imagery and AI to enable targeted chemical application, reducing waste and improving efficiency in farming operations.
- Who founded Osirix?
- Osirix was co-founded by Ido Shekel, who serves as CEO, and Tomer Peretz, who is the CTO.
- When was Osirix founded?
- Osirix was founded in October 2022.
- What problem does Osirix aim to solve for farmers?
- Osirix aims to solve the problem of excessive chemical use in farming by enabling precision spraying. It addresses the issue that typically only 30% of a field requires treatment, yet entire fields are often sprayed, leading to waste and environmental impact.
- How does Osirix's technology adapt to changing field conditions?
- Osirix's dashboard allows agronomists and farmers to train and adapt image recognition algorithms without coding or machine learning expertise. This enables them to quickly adjust to changes in weeds, crop stress, growth stages, and regional variations, ensuring accurate prescription maps.