ChAI, a London-based startup that uses artificial intelligence (AI) and machine learning to predict commodity prices, has secured a seed round to help expand its industry-leading services into new markets, including raw-material businesses and key supply chain providers. The funding will enable them to invest in developing commodity risk insurance products that will enable smaller companies that are underserved by existing risk transfer solutions, such as options or futures markets, to transfer commodity price risks and derivatives. It will also enable larger companies and material providers to transfer financial risk for a variety of raw materials, including plastic and steel. ChAI employs unique data sets, such as satellite imagery and maritime transport data, to provide unprecedented visibility and confidence in price fluctuations and movements of the world's key raw materials, such as oil, aluminium, copper, and others. Founded by Marcus Dixon, Michael Button, Silvi Wompa, Stephen Butler, Tristan Fletcher in 2019, ChAI is democratising tools, techniques, and data while combining cutting-edge AI techniques with new alternative data sources to eliminate risk in physical supply chains.

Commodities AI Ltd (ChAI), an AI-driven commodity intelligence company, has completed a seed round to expand its industry-leading services into new markets and provide real-time commodity price forecasts to new global audiences, including commodity companies and key supply chain providers. The funding announcement marks an important milestone in ChAI's product development and ability to provide important information to a range of stakeholders as commodity markets continue to experience significant volatility. Under current market conditions, supply chain providers need long-term forecasts to address global food security challenges. ChAI uses purpose-built AI technology to analyze thousands of data sets to determine what variables are driving market prices. At the same time, through the use of AI technology, the company is able to predict raw material costs, identify risks and help its customers to effectively purchase the necessary raw material products.