We do not just follow standard black-box approaches. We start by reviewing published scientific literature that are rigorous and peer-reviewed, in the relevant application domain. Based on this evidence, we build explainable belief models that maps sparse input data to thousands of related human constructs.
We know that a huge volume of relevant past data reside in many various parts of an organization. This can be in the form of paper-work, databases, or even in intangible forms, like past experience gathered from years of work. We can incorporate these past data into our computational models.
Our key strength is in helping your organization understand people deeply, at scale, even with sparse data, so that you can make better decisions for your business. This could apply equally to your clients, customers, or even your internal staff. From HR needs, such as job placement and team-fit, to retail consumer segmentation, to finance KYC, our methods translate equally well.
We help you see patterns by turning data into actionable outcomes.
One example is hiring in HR. Should you hire a candidate? Our engine turns data, even sparse data, into clear, understandable metrics that you can use.
Result: transparent decision-making, greater certainty backed by data.
A platform for anyone to build profiles backed by research data for better opportunities at work, or simply for discovering oneself. It serves employees or job hunters who wish to display their compatibility and talents to specific job roles to employers, or students who want to find out what roles they might find interesting based on their personalities, and many more.