Nailed it! Tom Sherwood🤩 Koios closed £440,000 in a fantastic pre-seed funding round.
Employee disengagement is a major issue in the global economy, costing a staggering $7.8 trillion annually. Outdated practices and a lack of understanding of employees are contributing to this problem. However, Koios, a voice-driven AI platform, aims to revolutionize the personality and management insights market by reducing costs and friction in the process. Unlike traditional companies in this space that can charge up to £250 and take half an hour to complete assessments, Koios offers a monthly subscription starting at £3 per employee and can generate insights from just 90 seconds of active speech. This inclusive approach allows leaders and organizations to have a personalized approach to managing and developing their talent based on their unique personalities.
Koios recently announced its Pre-Seed funding round, led by Seedcamp along with support from Evolvient Capital and industry angels. The London-based company is focused on improving how organizations work with their talent by providing low-friction and cost-efficient personality insights through its proprietary voice-driven AI algorithms. The platform requires a minimum of 90 seconds of active speech from candidates or employees to generate a set of customized personality and management insights. These insights enable managers and organizations to communicate, motivate, and develop each employee in a way that suits their unique needs and background.
The founders of Koios, Tom Sherwood and Alex Lewis, experienced firsthand the pain of the lack of technology available to support managers and teams. They were joined by Martin Lukac as the third co-founder and Chief Data Scientist. The team recognized the opportunity to address the issue of "culture fit" in hiring processes and make robust personality assessments more accessible and cost-effective. They developed state-of-the-art audio deep learning models using original data collection and a conscientious approach. Koios aims to eliminate bias in its algorithms, ensuring fair and inclusive insights that can be used to lead, manage, and develop employees.
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