Sourcing, in today’s highly volatile employee-centric recruitment landscape, has become a cornerstone to the success of any organization as it redefines the cost structure and enhances their competitive ability by reducing the time to hire. Every day the HR department is inundated with job applications, for which the department hire multiple personnel to source the expectant “fit for the job” candidate in the least possible turnaround time. The multiple hiring to fulfill a repetitive process of CV sifting, increases the operational and sourcing cost, eventually affecting the bottom line.

Sniper AI, through machine learning algorithms, predicts the success of a prospective candidate by comparing their metrics with the qualification to success ratio pattern model, built from a large amount of sourced data of successful employees, and grades the candidates who match the criteria. The result is 53% internal manpower reduction, allowing the sourcing managers to spend considerably less time searching and far more time interviewing.

Sniper AI, driven by machine learning algorithms taps into candidate market forecasts, and sources data from various job searching portals and employment-oriented services like LinkedIn through integrated API’s. The sourcing capability of SniperAI enables 37% sourcing cost reduction with smarter application management, effectively identifying potential candidates across existing data sources and reducing reliance on external clients.

AI-powered HR processes will free up a lot of time for the HR employees which hitherto consumed by a more administrative task. The capabilities of SniperAI have resulted in a taut hiring strategy, driven by real-time data and predictive models based on machine learning algorithms. The accuracy of hiring has helped in mitigating the cost associated with high turnover and rehiring.