People working on laptops


The new social well-being platform built for individuals and organisations to help strengthen resilience, create a sense of team wellbeing, better connect employees, increase engagement and team feeling.

Predict and warn against potential burn-outs works by allowing the user to connect various devices and data sources such as their calendar and health data, it uses machine learning to understand patterns in their data to predict and warn against potential burn-outs and intervene and help before it’s too late. As it gives the user the option to share concerns with help groups within the organisation and get support. is designed with data privacy at its core, as users have full control of what to share and who to share it with.

People working on laptops

Create opportunities to connect team members uses matching algorithms on calendar data and input from users such as (desire to help others, social appetite) to create opportunities to connect team members as often as possible and help them socialize. It also helps better manage workload of employees under pressure through suggestions of increased collaborations between co-workers based on their capacity.

Customer profile user interface
People working on laptops

Valuable Metrics

How we built it

We Use AI/ML to predict when pressure of work is high and the employee's work pressure is trending badly through a "Burn-out Risk" Score/Indicator. The user can then confirm this prediction by simply moving a slider on the page, this is used to further optimise the machine learning model.

It takes input from the user on his/her appetite to help others or to socialise with others, This is then fed to a matching algorithm that uses this information and calendar information to match events with immediate team members such as common free lunch slots and common locations where they can potentially meet and socialise. as well as potential colleagues that may be able to help with workload

We then used React to build a front-end web app to deliver the outcome in way of recommendations and also take input from the user on preferences of type of recommendation. User can choose to receive social recommendations or work related support as well as exercise and activities

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