RedCloud Deploys Agentic AI Across Trade Network
Global trade infrastructure company RedCloud has surpassed 100,000 customers and activated its agentic AI layer across its network. The milestone marks a significant real-world deployment of agentic AI infrastructure at scale for retailers, wholesalers, and distributors. It's a key proof point for AI moving from pilot projects to core business operations.
London-based RedCloud was founded in 2014 by CEO Justin Floyd, a serial entrepreneur whose previous ventures include Vecta Software and Cambridge Medical Robotics. Floyd's mission to fix systemic gaps in global trade is inspired by the inventory and supply struggles he witnessed firsthand at his mother's small shop in North London. The company's "Open Commerce" platform was built to address the estimated $19 trillion in non-digital payments and $1 trillion in annual sales lost to out-of-stock inventory that plague global supply chains. RedCloud's model digitizes the process, directly connecting brands, distributors, and local merchants to reduce these inefficiencies. Unlike traditional automation that follows pre-defined rules, agentic AI systems operate autonomously to achieve goals. In logistics, this means AI agents can independently manage inventory cycles, reroute shipments based on real-time sales data, and dynamically adjust pricing without waiting for human approval. These systems move from decision-support to decision-execution. For distributors on the network, this technology can reduce manual lookup and reconciliation workloads by up to 50% and cut expedite costs by 3-5%. For retailers, AI agents can predict demand at the individual store level, automate reordering, and ensure shelves are stocked based on sales velocity and even external factors like weather or local events. This same agentic framework has parallels in quantitative finance, where autonomous systems execute trades based on real-time market data and macroeconomic signals. In fintech, agentic AI is already being deployed to automate the entire loan origination process, perform KYC compliance checks, and detect novel fraud patterns as they emerge. Building these systems requires a tech stack that goes beyond a single large language model. A production-grade agentic AI architecture includes layers for orchestration (breaking tasks into steps), tool integration (interacting with APIs and databases), persistent memory, and governance to ensure the agents operate safely and reliably.
