Loamin provides an Earth intelligence platform that uses geospatial AI and a Large Earth Model to deliver high-resolution insights on soil carbon, biodiversity, and land-based carbon removals. The platform enables users to upload or draw land parcels, query pre-trained algorithms, and generate interactive maps and summary reports in real time via an API. It serves food and beverage companies, natural capital markets, financial institutions, and landowners seeking remote, data-driven environmental monitoring.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations across food supply chains, natural capital projects, and agricultural finance lack cost-effective, scalable ways to monitor soil carbon, biodiversity, and environmental impacts across large or remote land areas. Traditional soil sampling and field assessments are expensive, time-consuming, and often insufficient for verifying carbon removals or managing portfolio-level risk exposure.
Solution
Loamin provides an Earth intelligence platform that combines remote sensing, spatial statistics, and machine learning to deliver high-resolution insights on soil and biodiversity. Users can upload or draw their land parcels, select from pre-trained algorithms or request bespoke metrics, and generate interactive maps and summary reports powered by Loamin's Large Earth Model. All results are available in real time through the Loamin API, enabling seamless integration into decision-making workflows for supply chain monitoring, carbon project verification, and agricultural portfolio analysis.
Target Audience
Primary customers include food and beverage companies, natural capital market project developers, financial institutions managing agricultural portfolios, and landowners seeking high-resolution soil and biodiversity insights for sustainable land management.
Features
- Large Earth Model integrating remote sensing, spatial statistics, and machine learning for land-based intelligence
- Pre-trained algorithms for soil carbon, biodiversity, and carbon removal estimation, with options for custom metrics
- Interactive high-resolution maps and summary reports generated from user-defined land parcels
- Real-time API access for embedding geospatial AI into external systems and decision workflows
- Remote monitoring capabilities that reduce the need for costly physical soil sampling