Shinkei Systems utilizes sensor fusion, edge computing, and machine learning to automate fish processing, significantly reducing labor costs by up to 85% while enhancing product quality and shelf-life. The technology addresses the issue of high fish waste, ensuring that a greater percentage of the catch reaches consumers with improved freshness and reduced drip loss.
Funding
$6.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
- VC website (opens in a new tab)
- Startup funding source · Source unavailable
Founders
Product
Problem
The seafood industry faces significant challenges with high labor costs, substantial product waste, and inconsistencies in processing that affect product quality and shelf life. A large percentage of harvested fish never reaches consumers due to spoilage and inefficient handling.
Solution
Shinkei Systems offers an automation suite that integrates sensor fusion, edge computing, and machine learning to optimize fish processing. Their technology aims to reduce labor expenses, minimize waste, and improve the overall quality and freshness of seafood products. By automating key steps in the processing chain, Shinkei Systems ensures a greater percentage of the catch is suitable for consumption, while also enhancing its market value. The system provides analytics and record-keeping automation, enabling fishers and farmers to optimize their operations and scale efficiently.
Target Audience
Shinkei Systems targets seafood processors, fish farmers, and wholesale distributors seeking to reduce labor costs, minimize waste, and improve the quality and shelf life of their products.
Features
- Sensor fusion technology combines data from multiple sensors to provide a comprehensive understanding of the fish processing environment.
- Edge computing enables real-time data processing and decision-making directly at the processing site, reducing latency and improving responsiveness.
- Machine learning algorithms optimize processing parameters to minimize waste and maximize product quality.
- Automated analytics and record-keeping provide insights into processing efficiency and product yields.
- Robotics-as-a-Service model offers a complete harvest suite that can be scaled to fit operations of any size.
- Scientifically validated to improve drip loss by up to 20%.