Boom! The name is STELLARES – S T E L L A R E S
STELLARES takes about 1 hour to setup and 1 hour to train recruiters. That’s it. You can expect the first matching talent within 24 hours of setup.
While attending Hiring Success 19 in SF, I was fortunate to run into, what turned out to be, something really really exciting. I meet with a lot of founders on an everyday basis and although solutions are impressive, this one is taking it into a whole different level. I saw, what I believe, is one of the more innovative, holistic, automated systems I have seen to date. Since I was not aware of them I though they were perhaps under the radar…So no…to my surprise, their platform is already helping nearly 100 bay area tech companies, size of 25-2500 employees to hire stellar technical talent (software engineers, data scientists, product managers, and designers). STELLARES automates the entire stack of sourcing & initial engaging, from “reading” a JD, through crawling the web to finding a potentially fit stellar talent, to engaging and activating them, to filtering them based on fit, to then getting them excited about your opportunity. All automated. They are almost 3 years old, and a team of 20 very talented individuals, VC backed by JVP.
How are they different? Join me in my interview with Roi Chobadi, Founder of Stellars.
Stellars filters people based on caliber requires for the job, and only presents talent that are stellar (top 10% in what they do).
First, there’s a huge controversy today about the usage of the term “AI” in recruiting. Can you please elaborate as to what exactly you mean?
Our AI is written in Python and written by a team of former 8200 who’s done similar things there. (KA – 8200 is an elite intelligence unit in the Israeli army. Many of former unit soldiers have, after completing their military service, gone on to founding and occupying top positions in many international IT companies in Silicon Valley). We have ML, NLP, deep learning, NLG. AI 🙂
I myself have previously co-founded and exited another AI/ML company, LiquidM.
So we do have AI – In any technical possible way available today. Have we built a 2100 futuristic AI with emotions and political opinions? Of course not.
Help me understand how is STELLARES different than other platforms who claim “recruiting” and “AI”?
In several ways:
- STELLARES isn’t a marketplace, where you can reach out to candidates who signed up. It’s a SaaS platform powered by AI that acts as a really good sourcer. It crawls the web to find people who could be a fit and engages them. It then introduces to you only those that indeed seems like a great-fit candidate.
- STELLARES is full-stack. We don’t just give you a lead list and let you waste hours trying to engage the people on the lead list (the same one the platform gave to your competitor). STELLARES automates not only the sourcing and matching, but also engagement, filtering and story-telling. The bottom line: you got a warm interested fit talent, and it was 100% automated behind the scenes for you.
- STELLARES goes deeper than any other platform on “fit”. It doesn’t only match based on skills, but also holistically – would the talent be happy and productive in your role? Are you the company they are searching for? Would this be progress in their career? Learning environment? Great team to enjoy working with and learn from? Overlapping with their passion? Etc
Bottom line: STELLARES does all the heavy lifting at the backend to introduce you to stellar, tight-fit engaged and interested talent. You just take it from there and start the relationship.
So basically you’re doing everything ! :)..What’s left for us to do?..
We’re doing everything at the top of the funnel, the part that happens before the first connection. Today there’s so much pressure on recruiters to ever increase the top of the funnel and source more and more candidates for each open position. That activity is really time consuming, and doesn’t really use the recruiter’s best skills, their people skills and their judgement. It’s a mechanic work that can be automated. The advantage of automating the top of the funnel is: (1) you free up the recruiter to spend more time bottom of the funnel – working with candidates, connecting with them, evaluating them, and having a larger bottom of funnel. (2) the bottom of funnel will be much more relevant, as AI can do more and deeper filtering at scale.(3) When a recruiter is reaching out to dozens and hundreds of candidates, they can’t truly personalize their reach out to each individual, so many will not respond, even if they could have been great fit. AI can go both very deep and personalized and at scale. Our Storytelling Engine when reaching out to candidates who chatted with our virtual agent (chatbot), gets 42% of active talent it reaches out to and 25% of passive candidates to connect with the company.
We’re doing everything at the top of the funnel, part that happens before the first connection
Tell me more about this Storytelling Engine!
Our system learns a lot about the talent, both from crawling the web to learn about them from different sources and digital professional and social profiles, and by chatting with them over SMSs via our virtual agent (chatbot), a conversation that takes at least 15 min (and has over 75% completion rate). As the AI knows so much about the person, it can then go and tailor the opportunity to each talent, based on who they are, their style, and what they care for. We do super personalized initial engagement, at scale.
Sounds amazing. Could we influence the stories the platform tells the candidates?
Absolutely. First, you can see in advance what stories the system will tell, and you can choose to remove a few if you don’t like them. More than that – you can work together with your dedicated Customer Success Manager to add more stories. We have one requirement – everything must be factual and anchored in real hard data.
You mentioned earlier your system finds people who fit the job in terms of their skill. How are you evaluating talent’s skills?
First, the platform crawls the web for different places where it can find publicly available info about candidates (github, stackoverflow, kaggle, etc). That’s where it gets input as to what the talent can do. Then, it uses advanced algorithms to measure the distance in weeks of learning between everything a person knows and what the job requires, to show you people who might not be 100.00% perfect, but can do the job hitting the ground running. The AI doesn’t keyword-match, but measure skills distances.
The AI doesn’t keyword-match, but measure skills distances.
Do you integrate with other existing platforms?
We’re integrated with almost all ATSs, so we’re a plug & play solution.
We’ve seen cases where using AI leads to diversity issues. What’s the case at STELLARES?
It really depends how you build your technology. If you throw many parameters into a model and run ML/regression on it, you basically get a mirror to your past behavior which is human and biased. That’s not how we’ve built our technology. With diversity in mind, we’ve built the matching engine such that it’s impossible for diversity issues to leak in. Moreover, the platform itself is built such that it strips identifying details, asking recruiters to decide who to have a call with and whom not to, without knowing their gender, age, ethnicity, etc. I’m proud to say on average 59% of the talent our platform introduces are either women or people of color, and that’s without lowering the bar – they are all stellar. Curious to see what that looks like? Try our platform out 🙂
We’ve built the matching engine such that it’s impossible for diversity issues to leak in.
Many sourcing platforms require users to write emails and manage campaigns. That’s not required?
Not at all. That’s a huge time sink we want to help our customers avoid. Also, when you’re running templating campaigns, even if you have some variations, you can’t really get to super-personalization in 1-1 ratio. You’ll hardly ever get to 42% interest rate (talent getting an opportunity and scheduling a screening call with recruiter) and you end up spending so much time on it… We relieve our customers form all ongoing tiresome manual work, so they can focus on connecting with the candidates.
What does onboarding look like?
It takes about an hour to setup and an hour to train recruiters. That’s it. You can expect the first matching talent within 24 hours of setup.
Tell me more about you. How did you come about to found Stellars? Where is your passion to recruiting coming from?
I’m not an industry guy. I have a lot of respect for the industry, so we have great people like Jeff Diana (previously Chief People Officer at Atlassian, Success Factors, and Expedia, amongst other roles) guiding us. I am a deep-tech matching guy. Before building STELLARES, who’s matching people to jobs, I’ve built and exited LiquidM, matching people to products. I’ve been a deep-tech/ML engineer myself most of my life, starting with 8200, the Israeli NSA. In fact most of our tech team (based in Tel Aviv, where HQ is in San Francisco) is 8200 alumni. Crawling the web, making sense of that data, and pattern-recognition is our expertise. Also, I am the founder and president of the 8200 Alumni in Silicon Valley Association, counting several hundreds of members. Through that position over the past 6 years I advised probably more than 100 stellar talents on their careers. I realized I was uniquely positioned to start STELLARES. I’ve hired dozens of people as a hiring manager, I’ve coached dozens of people about their career, and I know how to build deep-tech matching engines, as I’ve done it before successfully.
“STELLLARES” – What does the name mean “STELLARES”?
It’s the latin plural of “stellar” as our platform also filters people based on caliber requires for the job, and only presents talent that are stellar (top 10% in what they do).
Last question – As a recruiting & AI expert – how would you advise our audience to pick the best talent acquisition/sourcing platform for them?
There are too many platforms that solve just part of the problem, and then you end up with many platforms that don’t really work well together, or having to do much of the work manually. Too many platforms help with “matching” but they don’t automate engaging, so it’s up to you to manually send dozens and hundreds of reach out messages, to hear back from just a few, wasting hours of your precious time where you could have been closing engaged candidates.
I’d advise to find a platform that is fullstack, and fully own a piece of the recruiting funnel (such as end-to-end sourcing->engaging->filtering->exciting brining a person to your door. Also a platform that integrates well with your current IT (ATS), so it’s all seamless.
Last, I think that a great hire is much more than skills keywords match. As Lou Adler just said on the last SmartRecruiting conference in SF “A great hire is someone that if you ask him/her after a year – are you happy they say Yes! And if you ask their hiring manager they say “bring me more like him/her”. That’s a fit that goes much deeper than skills into passion, learning opportunities, right environment and team, etc. You want a platform that can help you source talent like that.
And as founder/CEO I must be always selling so I have to add: “and that’s STELLARES”.
Samuel Adayemo, the COO of a the leading AI renewable energy startup Aurora Solar:
“STELLARES filled our first Machine Learning Engineer role in only a few weeks. The process was seamless — they took care of all the sourcing and engagement. I only had to review a few candidate profiles (invested time: 45 minutes) and were very impressed with the candidates they matched us with. They also helped us close the candidate we made an offer to, by highlighting how our opportunity was the perfect fit for achieving his learning goals. This was a key hire who has since become an integral part of our team. STELLARES is the future of recruiting! I can’t recommend working with them enough.”
Thank you SO much Roi. Loved to talk to you and even more to have seen the demo and the back end, which was…simply mind blowing!