Emotiva, the Milan-based deeptech AI startup focusing on emotion recognition, closed a seed investment round of €610k led by business angels and LVenture Group.

Emotiva was accelerated in 2020 by LVenture Group’s Acceleration Program LUISS EnLabs. The startup develops computer vision and machine learning algorithms to analyse people’s emotional responses in real-time, measuring facial micro-expressions through a standard webcam, to collect data useful to understand human behaviours.

Thanks to its proprietary technology, the Italian startup has launched the SaaS platform EmPower, a media analytics tool that measures audience attention and engagement, obtaining in a few clicks valuable insights on their unconscious response. Through EmPower, companies can make accurate decisions about marketing campaigns, using attention and emotional engagement as new metrics to evaluate content performance, correct targeting and avoid waste of budget.

Emotiva fulfils the need for companies to understand more deeply the consumers’ decision-making process, making affordable complex and not easily accessible data. A growing trend confirmed by the startup’s results, which in the first half of 2021 has more than quadrupled the turnover.

Emotiva partners actively with the PHuSE Lab, the Computer Science department of the University of Milan, and with the main Italian universities in the field of computer vision and robotics for the research and development of new algorithmic models also applicable in sectors such as HRtech and Fintech.

Luigi Mastromonaco, Head of Investments & Growth of LVenture Group stated: “Deep tech is today the new global frontier of innovation, with investments exceeding $ 60 billion in 2020 and estimated to triple by 2025. Emotiva has developed a cutting-edge technology based on artificial intelligence that recognises human emotions, a crucial driver in the decision-making process, considering how more than 90% of our behaviours are determined by them. Nowadays, it is crucial for companies to measure and analyse these high added-value data, to gain a deep understanding of consumers, thus optimizing processes and avoiding the waste of economic resources”