Kodama raises $7 million for forestry physical AI, bringing total to $14 million

Kodama Systems announced Oct. 19 that it raised $7 million for its physical AI business serving the forestry industry. The latest funding brings its total to $14 million. The round was led by Konvoy Ventures and Breakwater Ventures, with participation from Blue Forest Asset Management, NYA Ventures, Breakthrough Energy Ventures, Congruent Ventures, Dangerous Ventures and M1C. The company said it will use the funds to expand commercial deployments, deepen OEM partnerships and develop autonomous capabilities for complex off-road environments. Its technology is already deployed in commercial logging in the southeastern United States, where it has moved more than 100,000 tons of timber across multiple states over the past six months.

Kodama’s approach, which adds supervised autonomy and remote operation to existing forestry machinery, directly addresses the challenge of maintaining equipment utilization in sparsely populated and remote areas. The company says it moved more than 100,000 tons of timber across multiple states over the past six months, indicating that its commercial deployments have moved beyond testing and into operational use.

The news is notable because Kodama has positioned its physical AI for forestry not as a research and development project, but as a business entering a commercial expansion phase. The underlying approach, however, continues along an established path. The company is sticking with a design that combines supervised autonomy and remote operation, and operates on existing forestry machinery in demanding off-road environments. The use of funds—commercial expansion, OEM partnerships and stronger autonomous capabilities—also builds on that approach. Our previous report, Vecna Robotics raises $31 million, pointed to investment in flexible automation. Kodama differs in that its target is not warehouses or general automation, but forestry sites that are demanding, remote and highly variable. The question, then, is not whether AI will be adopted, but how much operational improvement can be achieved while retaining existing machinery. The company’s work also connects with the heavy equipment and physical AI intersection covered in Caterpillar expands partnership with NVIDIA on Jan. 7, 2026, to transform industry with physical AI and robotics. But Kodama is adding capabilities closer to on-site operations, rather than pursuing the higher-level strategy of a major machinery manufacturer. From an industry perspective, the focus is less on the funding amount than on the data feedback loop that develops after deployment. The reported movement of more than 100,000 tons of timber suggests that a learning loop spanning equipment, operators, sites and workflows is beginning to take shape, rather than this being a one-off demonstration. The plan to deepen OEM partnerships suggests a strategy of adding an intelligence layer to existing machinery rather than replacing entire fleets. This could make operational software, remote support, safety features and management of updated data increasingly important competitive factors in the forestry machinery supply chain, alongside hardware sales. The pace of operational expansion and the economics remain unresolved. Key points to watch include the number of machines deployed, which models are covered by OEM partnerships, the geographic scope of commercial expansion, and the operating conditions under which the company can replicate its record of moving more than 100,000 tons. So far, the company has disclosed its intended uses of the funds and its existing deployments, but not production volumes, customer composition or unit economics.

Kodama combines supervised autonomy and remote operation, assigning repetitive tasks to machines while experienced operators handle complex work and exceptions remotely. The system is designed to work within the existing forestry machinery ecosystem, allowing new operational capabilities to be introduced while preserving the value of existing fleets. The company says that as deployments grow, a data advantage will build through a continuous learning loop spanning equipment, operators, sites and forestry workflows. CEO Merritt Jenkins said that maintaining high utilization in harsh environments requires durable hardware, production-ready software, safety systems and close feedback loops with customers in the field.

Improving the efficiency of forestry machinery and remote operations is also a challenge in Japan. The announcement specifically describes a system that adds supervised autonomy to existing fleets and feeds field data into continuous learning. For Japan’s forestry machinery, remote operation, safety systems and field communications industries, competition may increasingly depend on offerings that include operational data and software updates, not just the machines themselves.