Mental health is costing the UK economy over £117.9bn per year. Staff absenteeism continues to rise at 6.2% per year with 45% being attributed to stress-related absence. 1 in 4 people will experience a mental health problem of some kind each year and 1 in 6 people report experiencing a common mental health problems (like anxiety and depression) in any given week in the UK. Mental health is now a priority for businesses with 91% of employees believing that their employer should care about their mental health. Businesses are responding as 77% are already promoting good mental health and well-being in the workplace and 88% are expecting to provide access to online mental health in the next 12 months. HeadClear is a workplace mental health and well-being platform, using leading-edge AI technology to track and monitor stress levels and well-being scores. HeadClear equips companies and employees with the right tools to increase productivity, reduce workplace stress and empower them to take control of their own health and well-being. This project focuses on the well-being score using clinical psychology and scientific algorithms to determine an Employee's wellness. These questions are focused on emotional characteristics like self-worth, resilience, positivity and others. The questions are then combined and processed through our AI algorithm. Resulting in a well-being score provided to the employee. The project will develop emotional characteristic algorithms to provide detailed scoring against a framework. This framework will detail a score for each element such as self-worth, resilience and others. The employee will then be able to determine which areas they wish to understand, control and/or improve. For example, a low score for resilience indicates that coping with change may be difficult. The framework will connect to a knowledge base providing useful well-being items, information, videos and guides to the employee. Using AI this delivery mechanism will scale to large datasets and produce high-quality well-being recommendations in real-time. This type of filtering will match the employee's scores and previously viewed (and rated) items to similar items and make recommendations into a list viewed by the employee.