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Artificial intelligence impacts our daily lives in the form of smart assistants, internet of things devices, and popular AI use cases targeting consumers with ads, recommendations, and personalized messaging. AI can also make a big difference in a range of B2B applications, including how startups find and hire the best candidates.
Today, AI-driven recruiting technology company Hiretual has announced a $5 million funding round led by Northern Light Venture Capital, which brings the company’s total raised to $6.5 million. Founded in April 2015, Hiretual has been stealthily building and scaling its talent recruitment platform, which is used by more than 80,000 recruiters from more than 200 companies.
Glassdoor reports that the biggest obstacle in the hiring process is a shortage of candidates, and GetAppLab’s 2017 survey revealed that more than 40 percent of recruiters report a shortage of candidates is their biggest challenge.
Hiretual‘s idea is a simple one. It scours unstructured internet content to create a candidate sourcing base that is much larger than current sourcing platforms. It says its database currently has over 700 million candidates, which is significantly higher than the likes of LinkedIn (at 562 million) or Indeed (which has 200 million).
That’s a huge advantage because the platform doesn’t have to wait for potential candidates to enter their data.
“Hiretual gathers professional data from more than 30 social platforms, such as Angel List, Indeed, GitHub, Stack Overflow, and others, as well as the entire open web, turning the internet into a recruiter’s database,” Hireutal CEO and cofounder Steven Jiang told me. “We specialize in turning large volumes of loosely structured and unstructured data into more complete professional profiles. As a result, candidates don’t need to submit any data directly to us to be seen by recruiters using our platform.”
This is where the AI kicks in. Hiretual’s search functionality goes way beyond standard keyword matches and Boolean logic and instead uses machine learning to uncover appropriate candidates from the internet at large.
“Recruiters provide job titles, skills, locations, and other search factors to focus on,” Jiang said. “Hiretual helps them build out these sections by using Natural Language Processing (NLP) to recommend related skills or to parse an existing job description. When running a search, Hiretual uses NLP to expand the search to include semantically similar terms that may not be part of the keyword set. So searching for an ‘inside sales rep’ will still pick up a person with a sales skillset that states their title as ‘revenue growth superstar’.”
The AI ranks candidates by how well they match the requirements of a job and then automatically adjusts rankings for the next round of candidates based on user interactions. As with most AI technologies, the system becomes more accurate over time.
Once suitable candidates are uncovered, Hiretual provides contact information, email automation, and tracking to make the job of reaching out to these potential hires easier. Recruiters can keep tabs on their talent pipeline on a project-by-project basis, tracking hiring stages along the way, and they can invite team members or hiring managers to participate in the candidate selection process.
In the real world, of course, people have multiple skills and can do more than one job. How does the system handle this, weight each ability, and ensure there is a strong alignment?
“Hiretual focuses on proactively building talent pipelines, so searches are initiated by recruiters who designate what skills and work history matter for a given position,” Jiang said. “As we gather information on a given candidate, we build a knowledge graph of their professional profile and use that to determine how relevant they are to a particular recruiter’s job.”
AI plays a part in honing the system’s initial results, learning what recruiters want as the process moves along.
“We return a list of candidates to recruiters in blocks of 50, ordered by relevance, but the process doesn’t end there,” Jiang said. “As recruiters go through the list, they give feedback to our system by picking candidates who fit best and removing candidates that don’t fit as well. This tunes our AI-sourcing engine, updating what factors matter most when we deliver the next block of candidates.”
Interestingly, Hiretual also offers a diversity filter, which is particularly useful for companies that want to, for example, expand their organization’s ranks to include more women or hire veterans. And for recruiters who already have active applicant tracking systems (ATS), Hiretual integrates with their systems to refresh, enrich, and re-discover candidate data.
Hiretual is also smart enough to be able to deal with project-based employment, which is common in many industries and suits short-term contract workers more than permanent employees.
“Our software is chiefly concerned with finding and engaging candidates who fit a position, so the lifespan of the position doesn’t influence our ability to find strong candidates,” Jiang said. “However, a recruiter may utilize terminology like ‘contractor, temp, or consultant’ to seek out people who are more likely to be open to short-term opportunities.”
Hiretual will leverage the new funding to accelerate growth and scale its operations and AI-powered HR technology, focusing on intelligence rather than simply data-gathering and matching.
“Our vision is to move recruiting from a data-driven process to an intelligence-driven one,” Jiang said. “For us, that means building a proactive talent pipeline that can anticipate hiring needs, make candidate engagement more effective and more personalized, and deliver useful insights, rather than just raw data.”
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