causaLens has also partnered with the Safe4Play project, funded by the Erasmus+ Programme, which is using its Causal AI models to improve sexual health education.
The Innovate UK competition ‘Business-led innovation in response to global disruption’ attracted over 20,000 applications, with causaLens winning a grant for its application that uses its proprietary Causal AI platform to help retailers to quickly adapt their business to the rapidly changing commercial environment. In times of crisis, the demand for products changes wildly, as we have seen with toilet paper, pasta and mask shortages.
One of the innovations delivered by causaLens is the ability of the deployed machine learning models to identify the causal drivers behind demand trends and model for changing circumstances such as the eventuality of another lockdown or a change in government policy. This will help retailers minimise product shortages and maintain adequate volumes of stock, resulting in a drastic, positive impact on their customers and helping retailers maintain their financial robustness.
causaLens has also partnered with the Safe4Play project, which is creating a mobile application that utilises innovations such as augmented reality to improve communication around sexual and reproductive education. causaLens is leveraging the unique capabilities of Causal AI to develop adaptive learning features that tailor the material delivered to the user based on their prior behaviour. The explainability of their models and ability to simulate counterfactuals helps to understand which early interventions should be made to ensure the users have the most positive outcome.
“We are very proud of causaLens’ involvement in these projects, which demonstrate the diversity of use cases for Causal AI and the opportunity to use AI for good. While each project is aimed towards vastly different objectives, both projects share commonality in that they require functionality beyond the capabilities of current machine learning approaches” – Darko Matovski, CEO and Cofounder of causaLens