Nikhil Chainani: How AI Interviews Talent Humans Can't Even Screen

Full transcript

Welcome back to another episode of the Agentee Digital Podcast. Today I'm joined by Nikhil Chainani, a co founder and CEO of a company called Suitable AI. Nikhil, it's great to have you here.

Thank you, Mikulas. It's a pleasure and thank you for the warm welcome.

So for people that are hurting about hearing about suitable for the first time, how would you explain it to people that never heard about what you guys do?

Definitely. So suitable AI is powering Indian manufacturing companies in aerospace, automotive, semiconductors, the next set of you know, manufacturing giants in India, enterprises in India, by enabling them to hire topped talent from our platform. we use AI agents which run autonomously to do sourcing, screening.

coordination, technical interviews of talent, which become part of our data set. And these enterprises can come and hire contractual as well as full time talent from our platform. so we are driving the growth in engineering talent in India right now through autonomous AI agents and that's what we are building right.

Uh-huh. That's awesome. Nikhil, I'm correct me if I'm wrong, but Suitable is your fourth company, right? After Perspectico, Content Grow and then you were in investment banking at Nomura. So I you definitely had a lot of experience prior to starting Suitable. do you just when you look back at kind of your career arc?

Yes.

What did the earlier companies teach you that you applied from day one at Suitable AI?

well, out of the four experiences that I've had in my life, three of them actually have been my own companies or startups or organizations that have started with my with my friends. very proud to say that my current co founder has been with me in both Perspectico and Quantangaro and Mool. so one one of the co founders and he has been with me in both the journeys and this is our third.

Mm-hmm.

As far as Nomura is concerned, when I started my career, I was very passionate in finance. And I started with Nomura as an investment banker, and there I got a chance to closely look at mergers and sectors which included TMT. So technology, media, telecom in EMT, in EMIA region, Europe, Middle East, and Africa is what I used to look at as an analyst. And I was mostly on the buy side. So I used to analyze companies, and that's where my interest of

technology became in came into the picture. And I learned hiring and I understood hiring as a pain point. I understood process automations in my first two companies as well. So I think in retrospect, a mix of entrepreneurial or problem solving mindset with an interest in technology and then interest in automation and AI, I think created a perfect base for for me to start suitable and

And a a good part is I did my co founder whose learnings also compounded. So so that's that's the that's what my experiences have taught.

Yeah, that's that's great. I love AI. You must also love it. just about your core product. You have some AI agents that are actually interview candidates over voice and video. So how do you get like company comfortable letting an AI run the first interview instead of a human being? Because I

i it's a new way of approaching the hiring process, isn't it?

Absolutely. And when we started Suitable, we were initially also hesitant that whether or not companies will accept it, whether or not candidates will accept it. So the the benefits outweigh the doubts. That's the answer. And what are those? The clear benefit for an organization are two. One is there is evidence in whatever decision you are taking. With an AI agent interviewing a candidate on phone or on video.

You have the recording, you have the transcript, you have logic on why the candidate is good, bad, what's happening, why the AI has given a particular score. So it removes you know subjectivity out of the equation. It makes things very objective. If you get if you're a Microsoft and you get 800 applications in a day, you know, you know what who are the top 10 that you should spend time on. So from a company lens, projects are delivered faster, teams are rammed up faster.

Engineering managers who used to waste 40% of their week talking to underqualified talent now have you know better things to do and now can save a lot of time. The second big benefit that AI builds is compounding intelligence. For a big organization where decision making is very preferential, hiring managers have their own set of preferences. The moment you change the recruiter on the other side, if it's a human, the suddenly the learning goes to zero. Then another person has to learn, you know.

what is the preference, who the hiring manager likes, what kind of target companies they like the talent from, while an AI is kind of building and compounding intelligence of the preferences at the back end. So every next recommendation becomes powerful. It's like watching a YouTube video. It understands your recommendations. Similarly, AI understands your preferences. So you might require a let's say Gen AI developer, I might require, but your preference might be different. What projects they have worked on and all. So slowly, slowly AI gets into that.

From a candidate side also we believe that our being an intermediator, being a platform, we have a opportunity to tell candidates that hey, if these are the things that where you are lacking from, it's okay, it's not the end of the road, improve them, give the interview again, and then try and apply to a different job. So we give multiple opportunities to the supply, that is the candidate as well. So that eleventh candidate who is not in the top ten is rejected.

Can leverage AI to get a selection in the next job. So it is democratizing and giving a fair chance to the talent as well. So therefore the benefits outweigh the doubts and candidates are adopting it, companies are adopting it, and we are seeing now people are actually dependent. Like if you if you just send them a resume, they don't consider it. They are like, where is the Alex that's our video interviewing agent? Where is Alex report? Or where is Nancy, who is our voice agent, where is her analysis?

So slowly, slowly it's an i inevitability that we are catering in.

Interesting. So from my understanding it not only qualifies the prospect and it skips the people that aren't qualified, but it also gives them feedback to the people that are rejected so that they can take that feedback and apply it to another job. Is that what you're saying? That's great.

Absolute. Absolutely. Yes.

Okay. And you're you're focused heavily on global capability centers, right? You're you're you're building engineer teams in India and other hub. Why do you think that's the right veg versus going more abroad on all hiring?

Absolutely. That's a fair question. See, as a growing company, we realize that we should start catering our services or our product to areas where the pain point is the hardest. it is very hard to hire an aerospace engineer. It is very hard to hire an engineer who understands radio frequency in satellites. It is very hard to hire a marine scientist. And these are the typical roles that enterprises hire from us. Why?

Because as a as a normal human being, I can't I can't interview an expert in this domain. I don't know, you know, aeronautical sciences to that depth. And even if I can ask certain questions, I can't dw dwell deep, right? For example, one of the biggest areas where we cater our clients, let's say in automotive, is calibration engineering, engine calibration. So we have we have engineer so if a human would have interviewed the candidate, they would have asked, Do you understand engine calibration? the candidate would have said yes and they would have moved on.

Mm-hmm.

But an AI can dw drill deep. What type of engines do you calibrate? Do you are you into training? Are you into validation? Are you into testing? What are the difficult projects that you have done? So that level of depth which an AI interview provides in certain jobs gives us that kind of pool. So suitable kind of has one of the largest pools of interviewed expert talent in these domains right now. And we are slowly building it up, right? So once you start with the hard problem.

Then slowly, slowly these enterprises get the real value out of it and they start throwing easy or plain vanilla roles to you. Also, we believe that in the long run, expert jobs will remain. Maybe the mediocre jobs or the easy jobs will anyway be taken by AI. So we are wanting to go for jobs which are difficult to perform, which require high expertise, and create a create a stem pool out of that. and India right now, by the way, has the

largest population of English speaking STEM talent. So if we are able to crack crack that wedge, probably in the future, you know, and you know Japan average age is declining, China's average age is declining, India's average age of demographics is still rising and you know we still have young population. So we believe India will be at a forefront of supplying this expert talent to globally to the world in the future.

And therefore we are going after the going after that right now. This was the logic behind not going broad and going specific.

Mm-hmm. Got it. And where do you see kind of suitable going? What's what's the goal here for this year, for example?

this year we will want to achieve a dominance in a particular sector. And we are going after a few sectors like automotive, aerospace, defense, manufacturing of semiconductors. There are certain sectors we are going after. By the end of the year, we want to cater to maybe the top ten enterprises in that sector together. For example, in automotive, we are currently working with perhaps the top companies.

Mm-hmm.

Right now. We are working with maybe five out of the ten. By the end of the year we want to work with eight on ten or ten on ten. So the idea is to build dominance and you know dominate one particular sector where you have the largest pool of experts and the biggest companies in that particular sector which can hire from that. So that's how we want to go sector by sector, build dominance, and by the end of the year we we aim we aim that we will be able to do that in one of the sectors.

Mm-hmm.

Mm-hmm.

And we are going after automotive as of right now.

Mm-hmm.

Mm-hmm. And how do new customers find suitable today? Are you mainly going after referrals or maybe you're running some kind of ads? What has worked the most for you?

I think it is a mix of referrals. we get a lot of customers from referrals and sin since these hiring managers do talk to each other, someone in BMW does talk to someone in Squadar and they are also fighting for the same talent. So we get a lot of leads, we get a lot of inquiries inbound via referrals. That is w one of the biggest sources of getting demand for for us.

Mm-hmm.

and the second kind of is reach outs through expos, reach outs through you know phone calls, meeting these people face to face. So we attend and we can arrange a lot of meetups in person and in these hubs like in Bangalore, Pune, Hyderabad, Chennai, all these GCC hubs where they are. We go physically and we, you know, meet these guys face to face. So that's how we c

Yeah.

kind of get the demand, but the biggest source is referral because the biggest pain point for an engineering manager or the biggest nightmare is when you have a big project from your client and you have to deliver and you just don't have the time to ramp up a team and you need the best people for it. So that is where they do contact us. And that's the story. Like we have helped Spiro, which is Africa's largest EV company, set up their base in India and in Pune. And even today

Mm-hmm.

Mm-hmm.

they are one of the one of the biggest clients that we have. So we really help and achieve help them achieve the goals and then they refer to different companies as well.

Mm-hmm. Mm-hmm. And have you have you tried some sort of cult outbound before? Or has it not worked?

We do cold outbound, but we don't just rely on emails. majority of our c outbound is complemented with phone calls, actual phone calls, like calling a person and that. So cold outbound is also yeah, one of one of the biggest sources for us to generate leads.

Mm-hmm.

Mm-hmm.

Mm-hmm. Mm-hmm. That's smart.

Mm-hmm. Mm-hmm. Mm-hmm. Now I have a special question for you. If you could fix one issue right now at Suitable AI, what would you what comes to your mind? Because I I get that building such a company, there it's not all rainbows and sunshines, right? It there are a lot of challenges that you have to overcome.

so what's what's kind of one thing that if you could just fix right now, what what would it be?

I think we need to do some work with regard to branding. that's one area where, you know, because we are now positioning ourselves both for the candidates and the demand. So our entire focus till now was clients and you know getting their trust. But when a person is applying on Suitable, the benefits that they are getting or the fact that they are able to get to multiple opportunities.

And how Suitable AI is actually helping them improve their chances is something that we need to build out in a story, need to be more vocal about, need to tell the candidates more. So I think the next quarter's targets that I personally will sit on is how to position suitable better in terms of the supply, in terms of getting to the right people, in terms of making the biggest pool and leveraging that. So that is going to be one thing that we need to put in motion.

Interesting, interesting. Okay, well Nikhil for people that want to check you out or suitable where where where should they go?

Well, suitable AI, we are very active on our socials. We are very active on LinkedIn. You can check out our website. You can book a demo there in case you want to. And with for I am very reachable, they can reach out to me at nikhil at suitable.ai. That's my email and my LinkedIn will always like I can share with you and you can probably put it in the description. So they can reach out to me on my LinkedIn as well.

For sure. Okay. Well I will leave a link down below and thank you for watching and Nikhil thank you for joining.

Thank you, thank you, Mikulas. It was a pleasure. Thank you.