On January 24, 2023, causaLens, the London-based deep tech firm and a leader in Causal AI, announced the release of decisionOS, the first operating system to leverage cause-and-effect reasoning for all aspects of an enterprise's decision-making.
Causal AI is a technology that recognizes the underlying web of causes in order to provide essential decision-making insights that current machine learning fails to deliver. The technology is rapidly gaining traction, with 'big tech' businesses such as Amazon, Microsoft, Meta, Google, Spotify, Netflix, and Uber investing extensively in its research. It was recently highlighted in multiple Gartner hype cycles.
decisionOS optimizes business decisions by incorporating Causal AI models into decision workflows at any organizational level. Now, enterprise users across all industrial sectors will be able to comprehend cause and effect relationships to generate actionable insights that take consider company objectives and resource constraints instead of just relying on past patterns and correlations to make predictions. Retailers, for example, can utilize decisionOS' recommendations and insights to determine the optimum pricing for individual products across specific locations while keeping the current business environment in mind.
decisionOS can integrate all of an enterprise's decision workflows serving as a unique one-stop system, including product pricing and promotion, customer acquisition, retention, optimizing manufacturing processes, marketing spending optimization, demand forecasting, supply chain risk management, inventory and order management, and many more.
About causaLens

Founded in 2017, causaLens builds Causal AI powered products that empower organizations to make superior decisions. Leading enterprises across a wide spectrum of sectors rely on its Causal AI-powered technologies. Through an intuitive user interface, its no-code Causal AI platform enables all kinds of individuals to make better decisions. It is working towards the goal of building a world in which people can trust machines with the most challenging problems in the economy, society, and healthcare.