Aquaticode develops an AI platform that utilizes machine learning to predict fish characteristics and optimize aquaculture operations. Their solutions, including SORTpro and SORTmini, enhance growth efficiency and breeding outcomes by automating the sorting of salmon and broodstock based on gender and other traits, ultimately increasing seafood production and reducing costs.
Funding
$6M 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.
Founders
Product
Problem
Traditional aquaculture practices often lack efficient methods for sorting fish based on key characteristics like gender and growth potential, leading to suboptimal resource allocation and reduced yields. Manual sorting is labor-intensive, slow, and prone to errors, hindering the ability to optimize breeding programs and growth efficiency.
Solution
Aquaticode offers AI-powered solutions for aquaculture that automate the sorting of fish, improving operational and biological efficiency. Their technology uses machine learning to analyze visual data and predict fish characteristics, enabling precise sorting based on gender, size, and other relevant traits. By automating the sorting process, Aquaticode helps aquaculture farms optimize resource allocation, improve breeding outcomes, and increase overall seafood production. The company's products, including SORTpro and SORTmini, are designed to address specific needs in salmon and broodstock management.
Target Audience
Aquaticode's primary customers are aquaculture farms, particularly those involved in salmon and shrimp production, seeking to improve efficiency and optimize breeding programs.
Features
- SORTpro: High-speed, automated sorting of salmon based on gender and other traits to increase growth efficiency.
- SORTmini: Semi-automated gender sorting of broodstock to improve breeding outcomes without requiring specialized expertise.
- AI-driven image analysis: Utilizes machine learning to detect, identify, and predict relevant traits from visual data.
- Trait prediction: Ongoing research to decode the relationship between genotype (DNA) and phenotype (visual expression of the DNA) in fish and shrimp.
- Integration with existing infrastructure: Designed for seamless integration into existing aquaculture operations.