RedCloud Activates Agentic AI for Global Trade

London-based RedCloud, founded in 2014 by Justin Floyd and Soumaya Hamzaoui, went public on Nasdaq (RCT) in March 2025. The company's platform has already facilitated over $6.91 billion in trade value for fast-moving consumer goods (FMCG), connecting more than 6,700 brands with its network. RedCloud's "RedAI Trading Co-Pilot" was developed with partners including Amazon Web Services and NVIDIA. The system is designed to embed intelligence directly into the transaction layer, moving beyond analytics to autonomously optimize inventory, allocate working capital, and forecast demand for its users. This use of agentic AI differs from traditional automation, which follows predefined rules. Agentic systems are designed for autonomous decision-making and adaptive planning, creating self-optimizing ecosystems that can proactively manage supply chain risks like supplier failures or geopolitical events by analyzing diverse data sources in real time. The underlying principle mirrors AI's application in insurance risk assessment, where machine learning models ingest vast datasets from telematics, weather patterns, and other sources to improve predictive accuracy for pricing and underwriting. This shift from static analysis to continuous intelligence allows for a more dynamic understanding of a risk portfolio. Similarly, the retail sector uses AI to analyze customer browsing history, purchase data, and even social media sentiment to power hyper-personalized recommendations and dynamic pricing. This allows companies to anticipate future customer needs and optimize stock levels, a core function RedCloud aims to automate for B2B trade. Deploying such systems at enterprise scale requires a robust MLOps architecture to manage the entire machine learning lifecycle. Best practices include building modular, reusable data pipelines and leveraging cloud-native platforms to ensure that models are reproducible, scalable, and auditable—key challenges in regulated industries like finance and insurance. The technology is evolving rapidly, with Google recently upgrading its Gemini models and OpenAI launching GPT-5.3 Instant. Major players are also securing the necessary infrastructure for these large-scale systems, exemplified by Meta's multi-year, $100B+ deal with AMD to secure Instinct GPUs for its AI buildout. This broader trend is fueling hiring in tech hubs like New York City, which has become a center for enterprise AI in finance, media, and healthcare. In-demand roles include Machine Learning Engineers and AI Product Managers with experience in ML workflows and deploying models at scale.