Nando Drones Business
Business models
B2B, B2B2C
Sector
Aerospace, Defense & HLSDefense & HLS SolutionsUnmanned Systems
Target Customer
Industrial ManufacturingHybrid IndustriesMiningAgriculture & FoodCropsDefense, Safety & SecuritySecurityHomeland Security
Core Technology
Artificial IntelligenceMachinery & RoboticsDrones
Tags (19)
charging-stationsagriculturedronessecurityautonomous-vehiclesmachine-learningborder-securityartificial-intelligencesolar-panelsmobile-applicationssensorsservice-providershomeland-securitycommercial-sitescomputer-visionautomotivemonitoringperimeter-securitydefense
Nando Drones Financials
Undisclosed
Capital raised
Cumulative Funding Raised Over Time ($)
Nando Drones Lifecycle
Cumulative Funding Raised Over Time
Nando Drones News
2 articles
iHLS Accelerator Startup: The Future of Site Security is Here and It is Right Above You - iHLS
Israeli startup Nando Drones has developed an autonomous drone-based security solution with a flight time of up to 70 minutes, longer than current solutions on the market. The company offers a complete drone-in-a-box system and participates in the iHLS Security Accelerator. Nandos platform includes a docking station, automation module, drone, day and thermal-based night camera, and control software. The platform performs image processing to identify humans and vehicles, transmitting live video to the control center and response team. Nando has successfully piloted the system in a cannabis farm and solar farm, and is now operating in Oranit, Israel. The company aims to advance the drone industry and make manual tasks autonomous.
CustomersExpand
Nando-Drone PR Milipol Paris, Homeland Security & Safety
Nando Drones Team
Dvir Simhi
Co- founder & CEO
Founder
Nir Shaharabani
Co-founder (No longer with the company)
Founder
Guy Tamir
Senior Mechanical Engineer
Employee Info
| Employees (range) | 1-10 |
| Exact count | 6 |
| Team members | 3 |
Similar Companies
Similar companies data is computed dynamically and not stored in the entity profile. This section requires implementing a similarity query (e.g. by classification overlap, sector, district).
This entity has 14 classification IDs that could be used for matching.