Seattle-based TalentSpring, Inc. announced that its open funding round, where it secured $1.6 Million in investment from Second Avenue Partners and private investors, is now closed.
TalentSpring said the funding enabled them to launch and promote their semantic-search technology to the talent acquisition industry.
In addition, the TS team has been working on an SaaS talent sourcing service that they will be piloting to more than 50 SMBs. The software, which they plan on releasing to the public in May 2009, enables recruiters and hiring managers to search social networking sites, job boards, and corporate applicant tracking systems to find candidates.
“Today, recruiters have to read through an average of 200 resumes to find one good candidate,” said Bryan Starbuck, CEO and Founder of TalentSpring. “Not to mention, it is not uncommon for a recruiter to read 2,000 resumes to actually fill a position. The task is so time consuming that the best candidates for a position are often overlooked because recruiters simply don’t have time to find them. Or by the time they’re found, they’ve been hired by a competitor.
“We’re solving a huge problem for recruiters by automating the candidate sourcing process of manually reading and ranking resumes. TalentSpring’s technology will allow every recruiter to become an expert candidate sourcer and will save organizations substantial amounts of money.”
So how do they plan on doing this? Starbuck said that by employing semantic-search technology to candidate sourcing, TalentSpring finds resumes that are similarly matched to the attributes listed on the job requisition, thereby increasing the pool of potential high-quality candidates.
He added that the new methods are a major innovation over today’s Boolean search method, which only finds candidates whose resumes contain the exact Boolean logic and keyword(s) the recruiter used. If a candidate’s resume doesn’t have the exact keywords used in the search, it is discarded before ever being read by a recruiter. The result is that organizations are not able to take advantage of the large available labor pool to find the best matched candidates for their positions.
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