Making every carbon credit trustworthy
ForestTwin is the carbon asset intelligence platform that turns satellite imagery and IoT sensor data into living 3D replicas of forest assets, so sustainability teams can verify, report, and grow with confidence.
Our Mission
The voluntary carbon market is projected to reach $50 billion by 2030, but confidence in carbon credits has never been lower. Buyers cannot verify what they are purchasing. Sellers cannot prove what they are delivering. Auditors spend months cross-referencing spreadsheets against satellite passes.
ForestTwin exists to close that gap. We combine high-resolution remote sensing, ground-truth IoT sensors, and AI-driven biomass models into a single digital twin that updates continuously. The result: carbon assets you can see, measure, and defend under audit in real time.
What We Stand For
Six principles that shape every feature, every integration, and every line of code we ship.
Transparency First
Every carbon credit should be traceable to a real tree, a real sensor, a real timestamp. We build systems that make greenwashing impossible.
Science-Backed
Our digital twins fuse satellite imagery, LiDAR, and IoT sensor networks into models validated against peer-reviewed forestry science.
Audit-Ready Always
From Verra to Gold Standard, our reports are built to withstand the toughest third-party audits without a single manual spreadsheet.
Data Sovereignty
Your forest data stays yours. We process it to power your digital twin, never to train models or sell to third parties.
Global Scale, Local Precision
Whether it is a 200-hectare reforestation project or a million-acre concession, ForestTwin adapts to your geography and regulatory context.
Built for Teams
Sustainability leads, forestry engineers, compliance officers, and C-suite all share a single pane of glass with role-based access.
Built by TechTide AI
ForestTwin is a product of TechTide AI, a venture studio that ships AI-powered products for real-world industries. Founded by Alex Cinovoj, TechTide builds at the intersection of machine learning, domain expertise, and production-grade engineering.