Gigablue
Gigablue utilizes nature-driven carbon capture and removal techniques, enhanced by an AI prediction engine, to optimize the efficiency of carbon sequestration in ocean environments. The company addresses the urgent need for effective and affordable solutions to help corporations and governments achieve their negative emission targets.
- Artificial Intelligence
- Clean Technology
Funding
Raised to date
$20MRaised 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.
across 2 rounds
KOPO$20M · Series A Tranche
Announced Jan 2026 · from article date
Disclosed cumulative: $20M
Founders
Product
Problem
Corporations and governments face increasing pressure to meet negative emission targets, but current carbon capture and removal (CDR) solutions are often expensive, inefficient, and difficult to scale. There is a need for affordable and effective methods to sequester carbon dioxide from the atmosphere and oceans.
Solution
Gigablue employs nature-driven carbon capture and removal techniques, enhanced by an AI prediction engine, to optimize carbon sequestration in ocean environments. By mimicking natural processes, Gigablue accelerates the removal of carbon dioxide, offering a scalable and cost-effective solution. The AI engine analyzes oceanographic data to determine the optimal deployment locations and product types, maximizing the efficiency of carbon capture. This approach helps organizations achieve their emission reduction goals while restoring balance to the planet's carbon cycle.
Target Audience
Gigablue's primary customers are major corporations and governments seeking to meet their negative emission goals through affordable and scalable carbon capture and removal solutions.
Features
- Nature-driven carbon capture and removal processes that mimic the planet's natural CDR mechanisms.
- AI prediction engine that learns from deployments and oceanography studies to optimize product selection and placement.
- Maximization of the ratio between deployed particles and tons of carbon captured through AI-driven optimization.