Gem To Advance Its Candidate Recruitment Platform, Secures USD9 Million In Funding

Artificial Intelligence News

Gem_To-Advance-Its-Candidate-Recruitment-Platform-Secures-USD9-Million-In-Funding Gem To Advance Its Candidate Recruitment Platform, Secures USD9 Million In FundingGem, formerly known as ZenSourcer, a San Francisco-based startup has raised USD9 million in a series A funding round for candidate recruitment platform. The financing round was led by Accel, which brings the company’s total venture capital to USD11 million. According to the Gem Co-founder and CEO Steven Bartel, the fresh fund will help stimulate the rollout of the company’s newly launched Candidate Relationship Management platform (CRM).

Confounded in the year 2017 by former Facebook engineer Nick Bushak and Bartel, Gem provides a suite of tools aimed at recruitment teams and hiring managers. It combines with platforms such as LinkedIn, Gmail, and Outlook and facilitates tracking of potential employees with due date selectors, stages, custom fields, and other constructive status indicators. Moreover, it is capable of automating follow-ups; surface job applicants’ email addresses; and track metrics, including email clicks, positive email replies, and LinkedIn InMail response rates. Bratel stated that he developed the company with collaboration in mind and to that end, it can automatically sync possible employees’ profiles and activities for all hiring managers to observe and enable those managers to share talent maps, analysis matches together, and contribute to lists through a custom-built Chrome extension. Afterward, the whole of that data feeds into an analytics dashboard which estimates the return on investment (ROI) for sourcing, campus recruiting, ads, and more.

The hiring and recruitment industry amounted with USD200 billion is crowded with vying Software-as-a-Service providers, like Pymetrics, which recently hoisted USD40 million to spread out its intelligent hiring merchandise to new marketplaces, and another company Plum that utilizes Machine Learning to surface employees on the basis of their raw talent, as opposed to specific skills.