Chidvi Pemula Podcast Transcript
Chidvi Pemula joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to The Digital Executive. Today’s guest is Chidvi Pemula. Chidvi Pemula is the founder and CEO of Mirror AI that launched publicly this year. Mirror AI is an AI-powered interview preparation platform helping job seekers land their next role with confidence. The platform combines personalized AI mock interviews, expert coaching, peer practice, and real interview analysis to create a comprehensive interview preparation experience tailored for each candidate.
Well, good afternoon, Chidvi. Welcome to the show
Chidvi Pemula: Good afternoon, Brian. Nice to meet you.
Brian Thomas: Absolutely, my friend. I appreciate that. I just love talking to people from all around the world, and I know you’re in Seattle, Washington. I’m in Kansas City, so just two hours. So, two hours apart anyway. I just appreciate you making this time at this time of day.
So Chidvi, let’s jump into your first question. Your background is impressive. Indian Institute of Technology in Madras, Indian Institute of Management in Calcutta, product leadership at Amazon, AWS, and an earlier startup before this one. But you’ve said Mirror AI came directly out of your own job search, where preparing for one of the most competitive interview processes in tech took four months of study and eight mock interviews.
Walk us through that experience. What exactly felt so broken that you decided to build a company around fixing it?
Chidvi Pemula: Mm-hmm. Thanks for the question, Brian. Yeah, Mirror A came out of my own frustration with, interview preparation and I was preparing for a high stakes interview at a big tech, and I want to give myself the best possible chance.
That means like, having prepared enough and walking into that interview confidently. And it took me like really four months to get there because I found two problems in my preparation. One is in terms of arranging for practice is what took most of my effort. Especially finding people who are at the same level, preparing for same role, and who are giving useful feedback.
Finding those people was really, really hard. And I have seen people do 80 mock interviews, spend more than six months in preparation for high stakes interviews. And it’s– One of the challenging things I found out is like people cancel in the last minute. Sometimes people don’t even show up. And there are times where I’ve been in conversation with the other person is very early in their career, and I was actually helping them rather than me getting anything out of that practice session.
And there’s another option, which is using career coaches. We have excellent career coaches, but the challenge with them is the cost adds up very quickly, and getting a slot on the, on their calendar is really tough because the good coaches get booked in advance. So that’s the bigger problem that I found is like the logistics around interview preparation.
And the second problem is in terms of all these sessions or practice sessions that I’m doing are not really adding on to each other. Like I was manually Looking at the feedback, trying to assess what the areas that I want to improve on, and I’m also like identifying which area to focus and whether I’m really getting better or not.
Had to deal with like a lot of documents, Excel sheets recordings, which was complete mess. And those two problems are the biggest challenge, and I’m putting a lot of effort in arranging for preparation rather than actually preparing for the interviews. And having built AI products before when I saw these AI models are becoming capable of reasoning, I thought I could bring all of this together into one single place and build an AI companion that actually manages all these logistical operations for you, and you can ac- just focus on the interview preparation.
And that’s why I built Mirror AI. And our vision is simple: help people pre-prepare for interviews whenever and however they want and make sure every session adds on and people get better, so when they enter the interview room, they enter with confidence that they have earned.
Brian Thomas: Thank you. Chidvi, I appreciate that.
Just going back, talk about the genesis of Mirror AI, you know, your experience, and you highlighted some things. The problem you ran to, again, is arranging for that practice, right? Finding people with similar backgrounds or as much as of experience as you was always hard. And then, of course, career coaches are very costly.
We’ve all done that. But the other thing was, those practice sessions, right? Very overwhelming. With the feedback loop doc- documentation, the notes, how do you compile that and focus on the areas that you need the most help with? So I really appreciate you unpacking that. And Chidvi, your compare interview skills to athletic skills, something that improves with repetition rather than a fixed trait you either have or don’t.
Professional athletes spend far more time practicing than competing, yet most candidates have never had real access to that kind of practice. Unpack that analogy for us. Why has interview preparation stayed stuck in the you either have it or you don’t mindset for so long?
Chidvi Pemula: Mm-hmm. Sure, Brian. Like, this is something I learned the hard way.
Especially when we are doing an interview, we are actually doing two jobs at the same time. First, we are actually gathering our thought, thoughts and trying to explain our experience to someone who wasn’t there. And at the same time, we are trying to read the room and get an assessment and look at all the non-verbal cues that we’re getting so that we can actually guide the conversation.
And for someone who has not practiced What most of the brain capacity is end up in actually like creating a response to the question, forming the answer, and they’re left with very limited capacity to actually get a sense of what’s happening in the room. And that’s where exactly the sports analogy fits in.
If you look at a football player, they actually study the playbook. They practice the plays on a field before the match. And when the match begins they actually read the play, and they adjust in real time, and then they go and execute the play where they don’t have to think about each and every basic movement.
I think that’s how the interview prep is also, where if you practice enough all the gathering thoughts, picking examples becomes very natural to you. So you are left with more capacity to actually read the room, read the non-verbal cues, and actually guide the conversation in your own favor. I think the meaning– for many years, what we thought is like being good at our job makes us good at interviewing.
And when we actually go and do an interview and if it goes badly, it is easy to conclude that I’m not good at interview. But interviewing is a skill, and a skill can be improved upon. So the way we’ll to look at is like experience gives you the materials and the topics that you can talk about, where practice helps you create a clear story that anyone can understand, and then you can deliver that in under pressure in a short period of time.
So that’s why like practice is really important to actually do better at interviews.
Brian Thomas: Amazing. Thank you so much. I, I appreciate that. And of course, you speak from experience. You talked about learning the hard way, and interviewing is really like having two jobs at the same time. AI might be able to do it, but, but it’s hard as a human, right?
You talked about reflecting and gaining your thoughts in the moment, but also you got to read the room, and you’ve been in those daunting panels where there’s sometimes six to twelve people in a room that are on the panel, and it’s challenging. I get that. But I love the analogy with athletes was, was great.
Very helpful for our audience. And of course, I took away, practice does help make perfect, and I think that’s so, so important. So thank you And should be Mirror AI deliberately blends AI mock interviews with expert coaching and real peer practice rather than being purely automated. Why did you decide the human elements were essential, and what does practicing with real peer give someone that even a great AI interviewer can’t?
Chidvi Pemula: No, great question, Brian. Especially around there’s a lot of debate going around humans versus AI. Our view is AI is great for repetition, and humans are great for the reality. And the reason why we strongly believe the human element and have it in our product is because of three reasons. I think the first one is, in any interview or any job it is always the person who’s actually judging you on the other end.
And that means, like, there’s a lot of emotions that get exchanged in a conversation body language cues that the person is reading and making an assessment of you. I know, like, there’s a famous research where you actually like a person or not in the first 30 seconds of your conversation, right?
And AI is completely can’t do that. So that’s one of the reasons why we wanted humans because we want to replicate the real interview the feeling. And the second reason is The interviewing with a human and interviewing with AI is inherently so different on many fronts. For– The first one is in terms of the unpredictability of how another person would respond in an interview, right?
Sometimes they could be kind, sometimes they could be nice, sometimes they are, like, over-enthusiastic, sometimes they could be, like, nonchalant or even, like, they might give you a hard time. So that has a real impact on how you conduct yourself in an interview, and that is something that AI cannot do it, and probably not sooner.
The second dimension is in terms of, of all the human flaws that we have and how an interviewer would conduct in an interview. They might yawn, they might look away they might m-show, like, different emotions. They might even, like, say that, “Hey, my kid called me, so give me a moment to talk.” So those all human flaws which AI cannot replicate will di- indirectly have an impact on the person who’s actually doing the interview.
And we want to give a real simulation of interview for people. And the third reason why we wanted all of this is because people prefer different modes of preparation at different times, right? So we wanted to give them ev- all of it in one single place, so they don’t have to go be outside our platform.
And also, we can actually use each and every session and build confidence for the people. So those are the main three reasons why we strongly believe in the human component of the interviews. What really our mode is the combination is what gets someone really be successful. AI actually helps people to do repetitions in a consistent way, and humans will give them the reality check.
Brian Thomas: I love that. That’s amazing. We talked about humans and AI, right? They’re so different, but at the end of the day, and you just mentioned, the reality check is with a human. You went into some of those examples, again, AI can be somewhat predictable. Humans, on the other hand, are not.
And people go through so much during an interview. That emotional conversation they’re having and it, it all comes out in their body language as, as you talked about. But again, at the end of the day, AI can help, but humans are definitely gonna be the game changer and they bring that reality to it.
So thank you. And Chidvi, the last question of the day, if you could briefly share your bigger vision goes beyond interview prep. You’ve talked about how a resume reduces a career to titles, dates, and a short list of accomplishments, et cetera, and many never even survive the automated six-second scan. You see a future where candidates and companies match on evidence of real demonstrated ability.
Paint that picture for us, if you don’t mind. How does hiring actually change, let’s say, over the next five years, maybe 10? And what has to happen for Mirror AI to help replace the resume rather than just sit alongside it?
Chidvi Pemula: No, sure. Happy to share our vision and paint the picture of how hiring is going to be from our lens.
So resume I would say is a very flat representation of a person in terms of list of skill sets, list of jobs that they have done. It doesn’t actually give the depth of the skill sets and the depth of experience in terms of what challenges they faced, how they overcame them. And the bigger component is the resume completely misses out on the personality traits in terms of what makes this person tick, like what motivates them?
Where would they succeed? What are they looking for in their career? And that’s where AI can actually play a much bigger role in terms of not only having those list of skills, but the depth of skills and the skill sets in their experiences at a much deeper level, as well as their personality traits.
And the future that we are envisioning is people will have personal agents which actually knows a lot more about them in a professional setting. And on the other side, we will also have the HR agents which will not only just know the job description, but they would know, like what does the team need?
What is the skill gap that the team have, and at what level of skill that this person needs to be? And also in terms of what are the personality traits of the team and where would– which personality trait would actually fit into this whole team and actually uplift the whole team rather than create challenges within the team.
And those two agents, like personal agent and HR agent, are going to talk twenty-four by seven all the time, and that would make right person for the right job a reality. And also people don’t have to apply for jobs, and these agents are gonna go find the jobs when people need. I think that’s the future we are going to see.
And for Mirror AI to reach there the first step for us is to actually help people build those personal agents. And interview prep is the first step where we can actually learn more about their professional and build that interview prep buddy which knows everything about them. And the second step for us is to actually build a native hiring platform for teams so that we can build those HR agents for them.
And eventually connecting both of them is where we see the future of there’s not going to be any resumes anymore. People just talk, and we know their experiences, and we can get jobs for people and find them the right teams. And eventually, we can say that we may be able to reach a stage where may- most of the people will have more satisfying careers than what we have right now.
Brian Thomas: Amazing. Thank you so much. I appreciate that ’cause at the end of the day, you’re really– you see a lot of great candidates that just, on the surface, maybe a resume as you talked about, which is a very flat representation of a candidate, right? But with your tool, using AI to augment, dig in, and, and really find the different facets of the experience, the skill set of that candidate is going to really help bring that out initially and match the right candidate with the right employer.
And I think that’s just awesome. You’re taking a lot of the heavy lifting out and, of course, a lot of anxiety. We’ve all been there, so thank you. And Chidvi, it was such a pleasure having you on today, and I look forward to speaking with you real soon.
Chidvi Pemula: Thanks, Brian. It was pleasure being here, and I’m looking forward to speaking to you again.
Brian Thomas: Bye for now.
Chidvi Pemula Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
