Strategic partnership with a globally recognized apparel brand operating more than 2,000 retail locations worldwide and serving customers through owned retail, wholesale, and direct-to-consumer channels.

By combining advanced machine learning, planner-centric workflows, and intelligent AI systems capable of monitoring, reasoning, and recommending actions, the initiative aims to create a scalable foundation for the future of AI-operated planning.

As consumer demand becomes increasingly dynamic across channels, regions, and product categories, retailers face growing pressure to anticipate demand shifts, optimize inventory, and respond quickly to market changes. Fusemachines brings deep expertise in AI-powered demand prediction, helping enterprises leverage machine learning, probabilistic forecasting, and operational AI to improve planning accuracy and business performance at scale. The company's forecasting solutions integrate historical sales data, seasonality, product attributes, market signals, and external factors to generate risk-aware forecasts that are designed to continuously adapt as conditions change.

Fusemachines will partner closely with the client's Data Science team in an effort to transform demand planning into an AI-powered operating capability by scaling forecast coverage, improving prediction accuracy, and embedding AI directly into planning workflows. The engagement intends to deliver risk-aware demand forecasts with built-in uncertainty metrics, broader forecasting coverage across products and channels, planner-centric decision-support tools, and continuous monitoring to ensure trusted performance at enterprise scale. In addition, the initiative is meant to establish the foundation for agentic AI systems capable of proactively identifying forecast anomalies, monitoring demand signals, surfacing planning risks and opportunities, recommending inventory and allocation actions, and automating routine planning workflows while keeping human planners in control of critical business decisions.

With more accurate and scalable demand forecasts, the goal is for the company to be better positioned to optimize inventory across its global catalog, reduce excess stock and markdowns, improve product availability, and make more informed capital allocation decisions earlier in the planning cycle.