Airtotree is a digital platform that connects companies looking to offset their carbon footprint with rural landowners and cooperatives who grow and manage new forests. These forests remove carbon dioxide from the atmosphere, which gets measured, verified, and sold to buyers as high-quality carbon removal units (CRU’s). The platform supports the full lifecycle of this interaction—from project onboarding and review to discovery and engagement—while providing transparency into environmental impact and project quality. By combining a clear marketplace model with strong foundations for data integrity, compliance, and future monitoring technologies, Airtotree aims to make nature-based carbon removal more trustworthy, scalable, and accessible.
The MVP of our product would be: - Updated marketing website, including lead-generation forms - Authentication/authorization (RBAC) of sellers (landowners), buyers (companies), supporters (donators), and administrators (Airtotree users) - Interactive user interfaces - both web pages and AI agent - exposing data and insights on projects to the right sellers, buyers and supporters - Loosely coupled data analytics component transforming satellite and human inspection time series data into insights for the aforementioned dashboards, all of which is auditable for third-party verification
The Completed Version would be (MVP plus the following): - Project library available on marketing website for browsing - Extending marketing website with e.g. testimonials, nature-based carbon removal knowledge hub, carbon capture leaderboard - Automated onboarding flows for sellers, buyers and supporters - Self-service environment for sellers, buyers and supporters (users, projects, financial), enabling direct interaction between buyers/supporters and sellers - Administrator portal with RBAC for functional application management (users, projects, financial) Integration of payment service provider for automated payment collection from buyers and payouts to sellers, using an escrow account - LiDAR drone data incorporated with satellite and human inspection time series data to extend raw data to extract insights from