Fetcher, a New York-based startup that combines Machine Learning and human intuition to automate the process of headhunting the best candidates, has bagged USD 5.4 million in a seed funding round. Accomplice and Slow ventures led the funding round, with additional participation from Revel Partners, Picus, and a host of angel investors, in addition to Kayak founder Paul English. According to the reports, venture capitalists have been pouring millions of dollars into recruitment-centric startups to curb the workforce crisis. Moreover, with several industries, Artificial Intelligence is playing a significant role in space.
Keeping in mind to address against this backdrop, Fetcher assists companies to find potential candidates by proactively combing diverse online channels like LinkedIn, GitHub, Stack Overflow, and Dribble and then support in finding out the best way to contact qualified individuals. Fetcher sorts out a wide range of problems, such as highly skilled employees tend to be in demand and may be less inclined to look for a new job if they are already well compensated and appreciated in their current position. According to analysts, recruiting is an age-old tactic, but it is also extremely time-consuming, which is where Fetcher steps in. Solutions what Fetcher offers is capable to correlate keywords and skills to work out whether a potential candidate has the experience required for a position, even if they haven’t listed a specific skill.
Besides, Fetcher can also automatically compose emails personalized for these candidates, though it is worth noting that its in-house team of humans works with clients to establish a template outreach message. Once this has been set up, the system can be synchronized with the customer’s email account and automate everything from that point on, including follow-up emails.