Destro, a startup that emerged from stealth mode Tuesday with an $8 million seed round, is betting that the most effective way to deploy robots in logistics is to stop building them. Instead of developing new hardware, the company has created an AI software layer that orchestrates existing robots and human workers, a model that has already secured deployments at Yusen Logistics facilities.
The approach addresses a persistent friction in warehouse operations: the gap between expensive, specialized hardware and the messy reality of daily logistics. While many robotics startups focus on novel form factors or general-purpose AI models, Destro concentrates on specific, high-value workflows like cross-docking, where goods are unloaded from one truck and sorted for another. By acting as the "intelligence layer" for fixed automation and mobile robots, Destro claims to solve operational bottlenecks that pure hardware providers often miss.
Orchestrating humans and machines
Destro's system relies on two core components: "Vision," an operating system based on open-weight vision-language-action models, and "Mothership," which directs the entire workflow. In a recent pilot at a Yusen Logistics facility in the Pacific Northwest, the software coordinated three cart-moving robots from Miva Robotics alongside human workers. As employees unloaded goods from incoming trucks into carts, the AI directed the robots to retrieve full loads and transport them to designated sorting areas.
Richard Brunelle, director of automation for the American logistics group at Yusen Logistics, said the key advantage was Destro's ability to manage the whole process rather than just one part of it. "We make that operation less labor intensive, and we get away from the paper," Brunelle said. "Everything is now systematic."
Yusen had previously evaluated two other major robotics startups but found their solutions insufficient for their cross-docking needs. One provider offered robots that could move carts point-to-point but lacked the capability to manage loading or unloading logistics. The other provided fleet management tools but still required a human operator to handle orchestration. Destro won the contract by applying autonomous direction to the entire workflow, integrating the human, the cart, and the truck into a single systematic process.
From pilot to full deployment
The startup's pivot to cross-docking began when Brunelle met with Destro founder Manthan Pawar. Destro was originally focused on picking and packing, but Brunelle recognized that his challenge-unloading goods from one truck and sorting them into mixed loads for final delivery-was fundamentally similar. He asked if Destro would adapt its solution to fit the cross-dock environment. "They went away, thought it through, and came back excited," Brunelle said. "They agreed nobody was addressing that space and that they could modify their tool to support it. Since then, they've gone all in."
Initial success has led to rapid expansion. Destro is moving from its three-robot pilot to a full deployment of 26 robots at the Pacific Northwest site and is launching another pilot with 17 robots at a Yusen facility in Southern California. Pawar, a former supply chain and robotics industry veteran, said the company is on a path to cash-flow positivity by the end of this year.
The funding, led by Base10 Partners and Bonfire Ventures with additional investment from CoFound Partners, reflects investor confidence in Destro's ability to replicate this workflow across thousands of warehouses. Pawar argues that the value in logistics automation is accumulating in the software layer that connects disparate hardware components. "Robots are a platform. Every layer model is a platform," Pawar said. "But we build a harness around it. That harness is just so complex and value-added that without that harness, these workflows are completely impossible."
Why this matters for operations and management
For operations managers and human resources leaders, Destro's model highlights a shift in how workforce automation is deployed. The technology is not replacing human labor with autonomous units but rather augmenting it with AI-driven coordination. This reduces the cognitive load on workers by removing manual scheduling and paper-based tracking, potentially lowering training time and error rates.
It also suggests that companies should look for software integrators who understand specific operational workflows rather than just hardware vendors. When evaluating automation tools, managers should ask whether the solution orchestrates the entire process-from unloading to sorting to loading-or if it leaves gaps that require additional human management. Tools that integrate with existing AI for Operations Courses can help teams assess whether their current infrastructure supports such layered automation strategies.
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