Pradjna™

Pradjna™ is a cloud based platform which uses a revolutionary machine learning engine to evaluate human motor skills. Currently we are prioritizing driving skills; but the engine can handle other motor skills like welding, industrial painting, machining etc...

Pradjna is a unique platform that has multiple applications across the Blue Collar segment. They have use cases in Insurance, Mobility, Enterprise Resource Management, and Skill Development. Pradjna offers employers and potential employers a unique perspective on the Behavioural Biometrics of the Blue Collar employees. The current method of employee evaluations has limitations of subjectivity, bias, and unpredictable outcomes. Pradjna platform offers data-driven, objective, unbiased assessments of skilled resources. Employees are evaluated continuously but unobtrusively. Better evaluations are correlated with better motivation, productivity, workplace safety, and reduced material wastage. Pradjna’s machine learning platform is unique as they use an Artificial General Intelligence framework. This framework is generic and more flexible than industry-leading deep learning networks. These deep learning networks like Google Tensor Flow or IBM Watson, require a separate training mode to learn the specific context and to build internal logic. However, the Pradjna platform requires no separate training and testing mode as it uses the same data stream to first learn and subsequently detect anomalies. This is clearly a unique advantage in many real-life situations. For instance, driving - where each driving context is dissimilar to previous contexts. Also, their data stream is generic time-series data that makes their system more adaptable. Equally unique is the fact that their framework closely follows the way the human neocortex works. It learns by building a neural network on the fly by strengthening and weakening the network learning nodes the same way a biological neural network learns its context or environment. Technology Explainer : https://youtu.be/Y2BRwG1W90c