DEATH MATCH GRAND CHAMPION!
Andreea Wade and Opening.io
Welcome to Death Match Europe - part four of four - the GRAND CHAMPION EDITION. This Chad and Cheese Death Match episode features Andreea Wade, CEO of Opening.io. Death Match took place at TA Tech on May 9th in Lisbon Portugal at 5pm with a room full of TA Tech practitioners. The bar was open and Chad and Cheese snark was flowin'.
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<<< MUSIC >>>
Chad: All right Lisbon. I need a beer.
Announcer: Me too.
Music: Ladies and gentlemen please. Would you bring your attention to me. For a feast for your eyes to see...
Chad: Hello everybody, hopefully everybody had drinks, I mean the bar is open, hopefully your there, hopefully you’re on your second one or third at this point. Who's ever seen the Chad and Cheese death match, anyone? Anyone? Alright excellent so this is...
Joel: Bad ass right?
Chad: ...not first, not first for some of you. What about Firing Squad? Anybody listen to Firing Squad? Alright, Alright. So today...
Joel: What happened to the last death match winner?
Chad: They were acquired.
Joel: There's big money in potential winnings.
Chad: You have a mic? Yeah, you have a mic right there.
Joel: Oh there's a mic.
Chad: There's a mic. Yeah, so today we have four, count them, four startups coming up to death match. They’re going to have an opportunity to have a two minute pitch, no demos, no robots.
Chad: Their going to come up, they’re going to pitch 2 minutes, then with the balance of the fifteen minutes that they have, were going to belt the hell out of them with Q & A. Okay?
Chad: Were ready for Andreea Wade from Opening.io.
Chad: CEO. Product. Genius
Andreea: What the song said. I'm kidding. I think the best way to tell you about our company is to give you some insight into how the idea started and why. Two frustrated candidates with zero recruitment in HR background that got sick of sending off their CV into oblivion into the black hole that is legacy ATS and platform, who kind of started thinking and said, "What if?"
Andreea: What if this job seeking process actually worked for us, the candidates? And we were data people and we were product people and we looked at this data heavy document heavy industry that were in and we said, "Probably great for automation. What if in one click I can upload my CV or send off my CV in a match to meaningful jobs and what if we build this technology, we don't keep it to ourselves, we give it to everyone, we build something robust enough, intelligent enough, and scalable enough, that can work for everyone? And so today, ladies and gentlemen, globally, were competing with the likes of Google and IBM Watson.
Andreea: And I'm telling you this because the last two contracts we won against Google and IBM Watson. In fact, we replaced them. Today we powered the recruitment processes of really large global employers and we have managed to partner with the company that bought LinkedIn and get help to be their preferred job matching vendor within the media market.
Andreea: Revenue wise; 300 percent up Q1 of this year compared to all of last year. Were growing fast. And because of our bias to research were continuously innovating. Just between ourselves here, were getting really interesting, potentially a world first results with cross lingual matching.
Chad: Nice! Okay. All right, so you didn't mention their name. The company that bought LinkedIn, so tell us about that partnership because it sounds like your trying to hide it. Why are you trying to hide this Microsoft partnership?
Andreea: I was just being cute.
Chad: Well tell us about the partnership. What is it? How does it help? Not just from... how does it help, what does it do?
Andreea: Microsoft put out this call because they wanted to position as an AI player and they said, "Hey world, and EMEA markets, what if technology could solve, what if AI could solve your wish?"
Andreea: So they put this question out. They got about 130 million visits to the site and they gathered 2000 something wishes and then they brought them to 63 and then to 10 and job matching was one of them. And they looked around, they looked at their partners. We have a really really good relationship with them, they really like our attack, so they selected us to be their job matching partner.
Andreea: Some of the things that we are doing is going into houses that are traditionally IBM and replacing them with our tech and Microsoft tech. Were also doing a European trip around... Microsoft is bringing HR leaders and were presenting our technology to them and Microsoft is talking about their digital transformation and so on. So, were partners with Microsoft.
Tanya: Hi Andreea.
Tanya: In this era of tech, are the main challenges you've had to overcome in bringing your product to market and how would you, quickly describe if I had no idea about the volume that you could bring to my organization, how would you, quickly describe the value, or why do I need to use your product?
Andreea: Imagine if you're a salesperson and you have all these sales leads and at the end of every month or at the end of the day you throw them away. You put them somewhere, and you start again, and you start again, and you start again. What we do is, we tap into your existing data. We tap into your existing CV databases. We work with really large entities that might have millions of CVs, and we get the value out of the data.
Isabelle: Explain exactly how you're better and different than Watson and Google.
Andreea: Well, we hear, and this is something that we did not know about our attack. We hear a lot, obviously, about the matching technology itself, about the use cases that we have developed. We hear a lot about the robustness of our APIs. In fact, the contract that we just won, they used to have IBM Watson in their BETA. They replaced them with us because of, again, the robustness of our APIs and what we could do at scale. As well as the matching technology. We have the ability to match people to jobs, jobs to people, people to people, jobs to jobs and we have a fifth API for tech only the rest are all industry agnostic. Our models are continuously being improved.
Joel: Aside from the names you have mentioned who are your competitors? What's the greatest threat to your business and how are you preparing for that?
Andreea: Our competitors, initially we thought that the likes of Restless Bandit or Ideal or whatever, our competitors. Realistically, we compete with Elastic Search with Google, Sovereign or Text Kernel. That's what we hear. The thing with Europe is that Text Kernel has been bought fully by Career Builder. So what we hear today, how do I say this? We hear some, there is some apprehensiveness from potential buyers to go to Text Kernel and then they go, “Well, we don't know what's going to happen to them so can we talk to you?"
Andreea: So in Europe, we see that we could make a solid move. Our big problem is multilingual matching, which is something that we are working on right now. So were working with Microsoft Machine Translation. We’re updating the engine in about 120 languages. We’re hoping to have that this year, but languages, that's definitely one of the big problems that were having in terms of threat.
Andreea: For a startup, I have about 50 jobs. We’re not that many people in the team. We’re growing in the states so were trying to manage that growth in a way that doesn't kill us.
Chad: So, today, again with algorithms, were going to beat this like a dead horse. Amazon had this algorithm. It became bias, right, so how... I'm going to go away from how do you do it, but how do you ensure, especially if you are going to compete in the U.S. with the OFCCP and many of the regulations that are out there. How do you ensure that there is transparency with your algorithm? It's not black box. It's white box. So that those companies who hopefully will spend many dollars with you can defend how your system works? Do you have Black Box? Do you have White Box? How are you ensuring those companies can defend those decisions by your algorithm?
Andreea: Thank you. Going from Black Box to White Box in any industry with deep learning models, I am not sure how possible that is. I watched a chief decision scientist in Google talk exactly about this, and their view is not, "Make Black Box, White Box. Make Black box testable and testable and testable and build models that look for that bias.
Andreea: You know, decision making of the algorithms, you can't just throw that out. But for sure you can test and retest and make sure that you do build some models that look at those attributes that you use when you build your algos. That's something that we've done from day one.