Today we announced that Woo closed a $7M round in Series A funding. We think it’s a validation of the incredible work our team put together to create the market’s first 2-sided AI-powered virtual headhunter. Her name is “Helena” and she acts as both candidate agent and company headhunter, sparing both sides the hassle of needing to actively search for each other.
I don’t need to tell you how taxing it is to upgrade yourself to a new job: it’s time consuming, erratic, stressful and imprecise. So it’s not surprising that most people out there, searching for something new, are only doing so, because they’ve reached a point where they can no longer stand to be at their current job.
What’s surprising is the amount of people out there, 60%, who are entirely open to but not actively seeking a new job opportunity. Today they are limited to on the one hand being able to remain a ‘passive employee,’ and on the other hand get interesting opportunities they would be inclined to check out.
On the other side of the fence are all these employers and recruiters laboriously searching to find that one perfect hire. The immense amount of time spent using an outdated “spray and pray” technique to source the right candidates, with the hope that a handful will at least make it to an interview, is standard for most recruiters.
However you look at it, the recruiting process is broken and needs a better solution.
Our goal at Woo has alway been to help developers get the right opportunity that matches their preferences and desires. We also want to help companies make the perfect hire.
To accomplish this, we embarked on a 2 year journey to create a scalable virtual robot, powered by AI, that acts as an agent to candidates and a headhunter to companies. When Helena is given the opportunity to single-handedly do the matchmaking, the end result actually yields more powerful matches than if both parties (employers and candidates) are involved trying to search for each other and find a good fit.
How we built Helena
We started by putting together a dream team of the best recruiters and data scientists in the market. They taught the headhunter robot how recruiters think and make decisions. We also built a framework to model the labyrinth of technologies and underlying technologies along with their characteristics and the relationships between them. The team then tracked performance, enabling Helena to get smarter over time through feedback and machine learning.
Helena surprises all
Helena outperforms human recruiters in the quality of her match-making, speed, and overall performance. Unlike her human counterparts, she is infinitely scaleable, with the capacity to handle an unlimited amount of candidates.
Fifty-two percent of candidates that Helena sourced initiated interviews. This performance is 3x that of recruiting agencies, for whom about 20% of sourced candidates move on to interviews, and 20x better than online job boards.
Helena is more precise, reliable and faster than a human headhunter. Her matches are unbiased against: age, gender, education, or previous employers. This makes for hires that would have never occurred, even by the best headhunters out there — and there’s some really good ones.
Today Woo’s platform houses a unique pool of candidates, 80% of whom are open to but not actively seeking a new job — a.k.a. “passive employees.” Thousands of passive developers discreetly join Woo every month, input their experience, and share what their ideal dream job would be, then they are served up only opportunities that match their preferences and desires.
On the employer front, Helena delivers unique insights. She provides real-time visibility into how job seekers react to job opportunities, including if they passed on a position and the reason why. She also helps companies remain competitive by showing how they perform relative to similar companies.
Looking ahead we see Helena getting smarter as a headhunter and as an agent. Ultimately we believe that as long as candidates and employers are passive in the process and Helena takes full responsibility of matching them, the shorter the road is to the perfect match and the perfect hire.
