BEN KAMINSKY Chief Executive Officer
10+ years in innovations in organization, business intelligence, semantic, NLP
CHARLIE MARKHAM Chief Technical Officer
15+ years in technology, big data, high performance trading systems
MAX KNUPFER Chief Marketing Office
20+ years in hospitality, recruitment and operations
The Pub and a Napkin
The office sofa was uncomfortable, so we went to a pub with beer mats we could write this stuff down on.
Charlie thought recruitment could and should be automated end to end.
Ben wanted everyone to be matched instantly to their perfect employee by simply voicing their desires to a Siri like bot.
Max wanted to see a machine do the drudge work so he could spend more time talking to great people that wanted to talk to him
A jaded recruiter told us once that everyone gets a job, the question he asked, is did you get a fee? Recruiters…
But seriously recruitment has issues, chief among them is that almost everyone hates actually doing the recruiting or having to go through the process.
The point is that recruitment is a process and although the product is unlike any other both complex and emotional, it is still a process in which if run enough times everyone gets a prize, prizes means jobs.
SO how do we make this brutal darwinian process better for those in it and tasked with running it?
We started by building our own recruitment agency for the purpose of deconstructing the process and testing our theories on real people.
The principle we followed was simply this find a human and ask them to perform a process full manual, then we refine or eliminate the process, automate processes and try again to eliminate, automate, eliminate, automate, you get the picture.
We started at the top of the funnel and have been steadily working our way through the process.
Our guiding metric is winning – by that we mean have we have been able to select and shepherd the candidate that ultimately gets and accepts the job through the process.
As we tackled the challenges of sourcing, selecting and shepherding the winning candidate we realised the only way to win consistently was to work with big pools of candidates, really really big pools of candidates, but without employing 5000 recruiters… how could we even keep a medium sized database up to date and relevant?
Along came EVA a bot… Chatbots are not new or particularly innovative, or are they?
We took a typical bot and flipped it on its head – EVA drives conversation and seeks information in an intelligent way, rather like a human recruiter would except EVA can have an unlimited number of conversations in parallel where on a good day a human has maybe 2 at a time.
Now we could begin to address very large candidate pools at low costs.
Our goal is always to find the person that gets the job
And then to try and understand why
In understanding why we can add tremendous value
The quest for why leads right to AI, natural language processing, machine learning and importantly supervised machine learning, prediction engines powered by neural networks, and owning every interaction point along the entire process.