Artificial Intelligence | Machine Learning | Natural Language Processing Seadronix | Deep-Tech Solutin For Marine Industry


Enterprise, Autonomous Naviation, Vision Analysis, Berthing Monitoring, Obstacle Recognition, Smart Port Seoul , South Korea

Seadronix

Artificial Intelligence | Machine Learning | Natural Language Processing


Seadronix | Deep-Tech Solutin For Marine Industry

Seadronix

Autonomous Naviation, Vision Analysis, Berthing Monitoring, Obstacle Recognition, Smart Port


Seoul , South Korea

Seadronix's AI technology-based ship control system allows ships to safely follow their desired destinations and routes, taking into account the condition of the vessel as well as the sea obstacles. Completed with a variety of learning data, the technology makes both cost-efficient and safe. 

When a marine accident occurs, its result is brutal. The repairing fee is big and even costs life. The collision accident that happened in Yeosu port, Korea, in 2014 resulted in $ 3500 M. of repairing costs. The accident in the Arab Emirates in 2017 made 10 injuries. According to a statistical study from the British insurance company, three-quarters of marine accidents are caused by human error. Marine accidents are not only a matter of safety but also a matter of our employees' life qualities. These brutal results raise labor costs due to the avoidance phenomenon which makes it harder to hire new workers and burdens the existing employees. As a solution, the unmanned ship and the technological aids to prevent those human errors are necessary.

Seadronix aims to develop an AI-powered autonomous navigation system.  As the first step to our vision, we conducted the Berthing monitoring system, which reduces the risk when the ship is approaching the port. It is considered the critical interaction point where the ship encounters the most obstacles. The judgment on the distance between land and the traffic density is the most crucial for the process. This system remedies the existing obstacle sensors and reduces the tiredness of the pilot and human error. And as a result, it reduces the possibility of collision during the birthing process. In the process of developing the product, our team collaborates with the pilot and the Port Authority staff.  Based on the obstacle recognition technology development, we plan to mount our product directly onto the ship as the aro

 

B2B

1 to 25

N/A

N/A

Scaling Up

2015

 
 

Marine Ports & Services

N/A

Increase Efficiency

 
 

Analytics
Service

Yes

Active

 
 

   Machine Learning
   Natural Language Processing
   Automated Planning and Search


Transportation

Transportation


Image

Image

Video

Video

Audio

Audio

Text

Text

Structured

Structured

 

   Software


N/A


Machine Learning Algorithm

Machine Learning Algorithm

Deep Learning Algorithm

Deep Learning Algorithm

 
 

1

1

$5.8M

Company was founded 2015 and it took almost 7 years (May 2022) to raise first external round

 
 

Date

Round

$ Raised

Investors

05/09/2022

Series A

$5.8M

SoftBank Vision Fund 1

Date : 05/09/2022

Round: Series A

$ Raised: $5.8M

Investors: SoftBank Vision Fund 1

 

Investors

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Byeolteo Park

Byeolteo Park
CEO

Hanguen Kim

Hanguen Kim
CTO, Co-Founder

Kyongsoo Noh

Kyongsoo Noh
Chief Operating Officer

 
 

Potential Customers

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