Listening To 4000 AI Phone Calls with Monik Pamecha

Paul J Daly: 0:00And you guys know good riddle. No,Unknown: 0:09

this is Auto Collabs. Remember

Paul J Daly: 0:11

the riddles you try to solve in like middle school?

Kyle Mountsier: 0:14

My dad, my dad literally. And this is one time he came to our house and we were just sitting on the couches, hanging out, talking, and he just started telling riddles to my kids, to, like, discover and it was an endless Trevor treasure trove of riddles over and over and over. And I was like, where do you store

Paul J Daly: 0:34

right, right, where's all this coming from? It was wild.

Michael Cirillo: 0:37

I grew I feel like I grew up in a riddle. You know what I mean? It's like, like, like, my dad, I get it now. I get the whole thing. Yeah, it's like, dad walks in and says, You have four sisters, and that's the that's it. It's like, figure that

Paul J Daly: 0:53

out. Well, then, then you're like, what? I only know three of them. Wait a minute. All right, here I have a riddle for you. I'm built in lines, but sold in rows. I carry people where they go. My heart is fire. My breath is there. I sleep in lots, but roam everywhere with four feet turning. I dash with my What am I shining in the light? This

Michael Cirillo: 1:16

is Michael Cirillo at 3am with ferocious heartburn.

Paul J Daly: 1:21

It's a car to car. Yeah, I don't know that was pretty lame. GPT,

Unknown: 1:27

US GPT for that. Well, well, yeah, it's built

Kyle Mountsier: 1:30

in assembly lines. And, yeah, it

Paul J Daly: 1:35

was real easy. It's it wasn't that. It definitely

Unknown: 1:39

wasn't hard cars. I know. I know. Well, today we're get to, get to meet a new person. Monik from Toma, talk about solving riddles. What do you do with so much information, especially with phone calls and all the data being produced? How do you make something useful out of it? I mean, more useful than the riddle I just read. We hope you enjoyed this conversation we have. Was you gonna say something. Who two said something? No, I thought I had chair. Was that? What it was, we'll blame it on Kyle's chair, either way, we hope you enjoyed this interview with Monik. Monik, thank you so much for joining us today. It's so good to be here with you. Hey, Paul, it's my pleasure. Alright, so one of the favorite things that we love about this particular show is that we don't just talk tech and talk strategy. We like to talk about the people side of the business. And one question that I personally love asking is, how did you actually get into the auto industry?

Monik Pamecha: 2:38

Well, surrender pity. I feel like anybody else I've spoken to who's been in the auto business always told me that, hey, the car business chooses. You don't, you don't get to choose. And I think it's been true. I just somehow landed in there, but it's been a fun journey. You know, my background is like, actually engineering, building things, selling things like that had nothing to do with cars for the most part, just software. And then we, we just, we were working on a project with my co founder for a while, and we did a lot of different things, lot of random things, like we built stuff for diet recommendation tools for patients with chronic conditions. Uh, my co founder actually sold nipple covers for a while on Amazon. Uh, lot of learnings, but eventually we started building something that we had a we had one dealer that really liked what we did, and so we were building a voice AI platform for them. And they then went and, you know, spoke to their 20 group, and they told them that, hey, this thing has been kind of working. It was horrible at the first when we made the first version,

Kyle Mountsier: 4:00

you're like, don't tell anyone, please.

Unknown: 4:02

Well, they say anything worth doing is worth doing terribly at first. Yeah,

Monik Pamecha: 4:08

right. And it was so funny, like, if I start talking about the bugs and the issues we had, you laugh. Like we made an error, where somewhere we left a comment saying, act like a cowboy, and we forgot to remove it. And the next thing is, the call goes in, and it's like, how do you partner? Like, would you like to book an oil change? And the person actually goes and books the oil change, but I have the actual call recording. It's really funny. And the general manager then email me and say, hey, the AI is acting like a cowboy. And I'm like, Yeah, because that's what it's

Unknown: 4:42

a cowboy who can close is the only thing you need to worry about, right, apparently.

Monik Pamecha: 4:48

But it was all funny, like that, right? Like, sometimes it take, like, it was like, talking to a walkie talkie, you know, you'd get a response in like, 10 seconds, and people are patient, whatever, right? Like, we just started with that. I. We had one dealer that kind of that that, you know, was okay with all the problems that we had, still liked it. And told the 20 group, and the more folks came into us, and they're like, looks like, you know, what you have is, you know, something that solves our problem. So maybe you should, you know, help us too. And then we said, why not? Let's try it. At that point, we had no idea about, like, why dealerships want this, like, we because we were working with hospitals, with, like, banks, and everybody was moving so slowly. But dealers were like,

Paul J Daly: 5:32

hey, what go? Let's go. Wait a second. We'll test this sucker tomorrow, right? Let's say

Kyle Mountsier: 5:37

that again. Because I think, Well, I think, you know, dealers are a lot of times seen from other industries, and like retail Auto is seen as, like, the slow stream behind. But you're like, No, no, well, and it sounded you were working with a lot of regulated industries, which are already somewhat slow. But of those, at least, it seemed like the dealers were kind of like, ready to throw in a little bit,

Monik Pamecha: 6:02

yeah, like, because a bank gave me a questionnaire, and they said, how can you guarantee that this AI is not going to, like, you know, be informal. And I was like, how can you guarantee if your agents are not being informal at the call center, right? Like, that's a really

Unknown: 6:24

tough question. That's a great, that's a great. Like, how do you guarantee it's like, well, we monitor them. It's like, Yep, so do we?

Monik Pamecha: 6:33

And, I mean, like, how much formality do you need in, like, Home Loans? I mean, I'm sure there's like, you know, obviously the regulations, different countries have different rules, yeah, Howdy.

Paul J Daly: 6:41

Howdy partner might not do it,

Unknown: 6:42

right? Yeah, right. Give

Paul J Daly: 6:43

me my social security number.

Monik Pamecha: 6:45

But obviously, in its infancy, like, you know, the technology is new, there are a lot of risks associated with it has matured a lot over the last year, so these questions don't even come up anymore. But, you know, a year and a half ago, that was like, that was how it was, and dealers were willing to take risks. And personally, I love that so much. Because if you're building something new and you really wanted to break new ground, and like you know, you want to explore new territory, you will fail, of course, right? You'll make mistakes, but you need to have that space for failure. We don't have any of that with other industries because they're, you know, little slow moving, right? For a lot of reasons. That's why we loved it. And when we step into the dealership, like, we walk around and we talk, we spoke to like, every person, right, from the person who's the lot Porter to like the general manager, owners, every person in the chain, and we're like, like, everybody had, like, all of these ideas on like, you know how this should be different, and sometimes it's conflicting with, you know, what other people are saying in the store too, right? Which is pretty natural. That's when we started seeing, like, you know, common thread, like, okay, there is a lot of opportunity, and it's very underserved, despite there being so many, like, tools, I don't think they're really hitting the mark. And, like, solving all the pain points, some of them do a really good job, but like, overall, there's still a lot of scope. So all in all, we're like, We're building something. There's some traction. There's so much more, you know, that we can do, put it all together. We're like, Okay, we're going all in like, forget the banks, forget the hospitals. Like, I don't know how many decades it's going to take to make it something really exceptional over there, but here we're just going to dive in and do our job,

Kyle Mountsier: 8:28

and you're never going back, never go. It's not possible. Just so, you know, you can't get out. You're locked in.

Unknown: 8:37

It chooses you, and then you're in. Yeah,

Kyle Mountsier: 8:40

so we were talking that you're a soccer fan, yeah, before this, here we go. And so I coach, I know, I know, Paul, sorry. Just hang with us for a minute. Paul, hang with us. So unfortunately, Monik, he's also a Real Madrid fan, which is a problem, but we'll get over it for a second. Okay, I'm a city fan. All right, I have no problem. With either. Alright. So, so I'm a soccer coach. I coach kids, and I've been watching a lot of like soccer coaching videos. I'm going somewhere with this, but I was watching the soccer coaching video, and this coach was coaching probably like 12 year old boys and girls, and they kept saying, Sorry, right? Like, it's actually, if you know soccer, if you've been around soccer, there's this very like, it's one of those words, right? Like, unlucky, that people will say. People will be like, Oh, unlucky, you know, Oh, so sorry, miss. Like, if you missed the pass or something like that. And he said, he said, Never, again. Are you allowed to say sorry? Because all that, all that happened when you missed the pass, when you were when you were unsuccessful, is an unsuccessful attempt at greatness. Yeah, right. And so like, keep attempting that. Because, like, if you, if you were attempting that. Pass into the open field, and the person could have run onto it, but you just barely missed it. You don't have to say sorry for messing up because you didn't do anything wrong. Sorry is wrong, right? Unsuccessful attempts at greatness are just unsuccessful. And I think what you were saying there, and I think this is so key, especially when we think about like dealers trying new things, or tech partners trying new things. A lot of times, we just want solutions that are silver bullets, that always work 100% of the time, and they never fail, and we always know that they provide value. But sometimes there's unsuccessful attempts at greatness that if you stop those, if you don't give yourself the opportunity to make those, then you never make the cool thing next is there somewhere that, like point back to something where you're like, we've really tried this, and it was an unsuccessful attempt at greatness, but we know that, like on the other side, there's probably something really cool.

Monik Pamecha: 10:58

So just to, I guess, to to back up that point that you said, right about, like, trying things and things not working out, and same thing with, like soccer, right? Like, I also that people saying sorry, I think that's like a more Europe. Yeah, it's like Europe saying sorry, that's not

Paul J Daly: 11:17

or Canadian. Yeah, little both.

Monik Pamecha: 11:22

But I think, like, when was the last time you know something in your life and according to plan? Like, everything just went according to plan. When was that one time that happened? You know, I don't remember any single time in my life, like, whether it's in business or outside of it, like, so, I mean, that doesn't mean you don't make plans, right? Of course, you'll have plans and you have some goals. So we always, like, with our product, or, like, you know, many things in the past, as well as I've built, like, you know, small pro we, I used to build a lot of like social networking websites, like, in 2007 that was when I was in high school. And we're trying to build all these like products for users. And we would build these amazing features. We'd add like, Oh, if you're in Central Park, you can make these groups based on sports, and people will come in and you'll like, all know when you're gonna play, and all that sounds so great, right? It's what was that app called? It was called active life NYC. So this was, I was in high school. I think I was like, maybe 16 years old or 15 years old, and I found a customer. He was a instructor in Central Park, like, I think, a fitness instructor, and he was like, let's make a social network. Let's get everybody to, like, come here, and then, you know, have these common interests. And then they'll put videos on their photos on here, it'll be like a mini Facebook of like, people who just play sports, right? And on paper, it's like, it's great. You know, of course, you want to have all these communities. You want to know who you play with. You want to come and do all these things. We put all the features in there. Every single thing. Like, I spent, like, a year building it, and I think only four people used it. Wow. And it was, like, rough, because I spent so much time building it, and I thought every single feature, I was like, this would be amazing. This would be blah, blah, blah, and you're trying to do all that, but, you know, people just didn't want it. They just rejected it. And they're like, Screw it. I'll just post the picture on Facebook, for example, right? So I think throughout, like, even with our current product, we've tried, like, if we have five features that work for that, we have like, 20 features that did work. So every single thing, like, I would say, we've, like, done that again and again. We try to think ahead and get there. But I think failure is just a natural part. And if you're even tired, to say, sorry, we're like, you know, whatever, it always happens, right? It's accepted. So Screw it. Let's do the next thing.

Paul J Daly: 13:48

So there's so much, there's so much, I think, there's such a variety of opinions and thoughts on what AI actually is and how it can actually be used inside the dealership to to cut down on repetitive tasks, or to increase, increase, lead quality, close rates, all of these things, you have a unique position in that you get to look inside all of these things, formulate a product around them. What do you see, as we'll call it, the lowest hanging fruit of auto dealers who are thinking like, how do I implement AI into my operations in some way. What do you think is the the lowest hanging fruit that you would encourage dealers to say, hey, dip your toe in the water here.

Monik Pamecha: 14:30

I think if there is any specific thing that you do that does not require creativity, it should be automated. So, for example, right? Like booking appointments for like, specific things, like incomes, like whatever you know, my whatever I need to request for, and then out goes specific static appointment, automated. But if there's like, something that's more requires more creativity, right, where you're constructing, like, some kind of an offer you're trying to, like, appeal to someone that's better? How? Handled by humans. So I think it's like first step is distinction between what should be automated and what should be not. And then whatever has to be automated, you automate that part. So I'll tell you, because I've been listening, I've listened to over 4000 calls, like maybe on 3x 4x feet over the last year, year and a half, and a majority of the calls are actually repetitive, like they have these elements of, you know, like, even within appointment bookings, when you're dealing with specific loan or cars, like, there's always, like, this rule in mind, you know, like, Oh, if this, then that, if this, then don't do that, right? So a lot of it would be even things that we don't think are, you know, repetitive. They actually are, because when you look at like, you know, four or 5000 calls,

Paul J Daly: 15:43

realize how much it actually happens

Monik Pamecha: 15:46

same thing you're just doing, like, if and else, right? So a lot of that can be automated. I think best use of AI is actually doing whatever can be done multiple times. Like, you know, giving it to the AI and only taking the stuff like that's really, really requires you to involve yourself and have that creativity. And also, like, you know, the customer expects that, you know, because, I mean, what personal touch Do you want when you're booking an oil change, right? I mean, of course there is, to some degree you want that. But like, wouldn't you better serve that time? Like, you know, giving, you know, pre qualifying somebody for a used car, you know, things like that, right? So it's understanding what to automate, what not. And once you've decided on what to automate, going all out on that end to end, and then taking the rest and then giving it to someone who's like, you know, trained for that purpose, right? A human, yeah. Do

Kyle Mountsier: 16:36

you get Do you tinker with automation outside of, like, the work thing. Or do you get tired of it in, in building a product?

Paul J Daly: 16:44

He's all real life is full manual, right? Like, even as manual windows?

Monik Pamecha: 16:49

Yeah, exactly. I mean, let's see. I think the Waymo so as some degree of automation outside, you know, where it's it's it's actually really funny, but, yeah, waymos are cool. Like, at least in San Francisco, you can take them now and then, initially it's like, a little scary, but then once you get in it and you're like, okay, like, if it says three minutes, it'll be there in three minutes. And you know, one thing is that if I'm late, I'm always very nervous, because, you know, if I'm like, minute or two minutes late, the Uber drivers. Like, just getting annoyed, but with way more, I'm like, whatever, you know. And also, if you're driving, you can always cut way more off. Like, I mean, you know, it's that aspect too, if you're on the other side. I

Paul J Daly: 17:38

never thought about that, but oh, that's hilarious. People just like, oh, yeah, it's a robot.

Monik Pamecha: 17:43

Just like, Oh, it's fine. Yeah, readjust, yeah. Way more on the other side, Elliot turn left, you know, like, whatever.

Kyle Mountsier: 17:50

Oh, that's hilarious. That's amazing. But,

Monik Pamecha: 17:53

yeah, not, not too much. I mean, um, just with work, like, you have all these email automations and, like, you know, summaries of meetings that you can get after that. So you could do a lot of, like, small things that I've been doing, they're pretty good. It'll just make a to do list after every meeting. So you never have to, like, think of like, oh, I need to do this or that. But, uh, yeah. Like, randomizing what to eat. I mean, some people love, you know, choosing what to eat. For me, it's like, I'm just, I keep getting confused, you know? So I have this extension, I'll just randomize this thing on DoorDash, and we'll just pick, like, all right, no way, item, and then just get it, you know,

Paul J Daly: 18:31

that's amazing. You're like, I'm sick of making decisions. I'm sick of that,

Monik Pamecha: 18:35

and I'll eat it, you know. I'm like, whatever, right? It is, what it is

Kyle Mountsier: 18:40

that's but, but that's a great like, that's, that's actually similar, but it's the same type of thing as, like, Steve Jobs only wears black shirts, right? But to reduce the decision making necessary so that he can make the decisions on the hard things, right? And it's, it's the same thing. It's like deciding what food today put that on the robot like it'll just pick something different for me, I'll do the hard thing, which is listening to 4000 calls to see, oh my goodness, you know, to see if we've made the if then statements correct across that many calls. That's the hard thing. That's what needs the human thing. What are you most excited about in your product as you're like, building toward the next things, I think a lot of people are starting to get familiar with, oh, we can have some level of AI or machine learning, kind of, like, take and capture, especially service calls, because they're high volume, they're repetitive. But what are you excited about as the evolution of, like, voice AI when it comes to consumer interaction.

Monik Pamecha: 19:42

So I think it's really voice, AI is like, just a channel. It could be text, it could be email, it could be any channel, right? It's really the work that gets done behind the receiver. Like, that's complicated. And what has happened over time is that, you know, initially, maybe 20 years ago, you had a. Operator. Then you had a phone tree, right? Which is like, press one, press two, press three, for this, right? This is the next generation, like, next evolution of that, which is like, hey, what do you need help with? Right? And then you can, like, figure out and do things. You know, automatically, a lot of times you'll transfer in cases where you cannot help them. What is happening is that your the things that you have to transport for, will keep shrinking over time. So, you know, let's say the boss you'd be able to handle only 2% of the calls, then it became 10, then it became 15, then it became 40, right? So I think we'll see that number go, you know, like, like, how much you can handle will keep going up because there's a tail end of all these complex things. Like, hey, is my, you know, like, Is my license plate ready? You know, like, number plate ready? Like, can I pick it up? Or is, you know, some question around warranty that only a human would have been able to answer. Or you have some question about minor code that came up and you don't know what to do with it, right? Maybe two years ago, you couldn't respond to that, but now you can, and then you can actually tell them that this is the diagnosis, you know, maybe you should come in and this is how long it would take and how much it would cost. Maybe you can also add this additional service, and hey, I also see that your car is, like, four years old, and maybe you want to check out your trade and valuation, because I just pulled it out from black book and your number, right? Yeah.

Kyle Mountsier: 21:21

And I think it also depends on the amount of data sources available, right? If a data source becomes available that can tell you whether or not the license plate is ready, then the AI can ingest that, right? And more and more data sources are coming online every single day, and that's, that's where I think we get that growing percentage, because it's all just data, and whether it's stored in someone's head, a spreadsheet or in an accessible thing online, like, the more data that can be consumed, it can then be shared, right? Because the natural language is, is, is already there

Monik Pamecha: 21:54

and, and, you know, like, even, like in the past, like, you have all these like data silos, right? They're locked up in, like, this interface and that browser and this login. But, you know, I don't know if you've seen any of those demos where the AI is, like, opening the browser and, like, you know, log an agent, yeah, human things like as an agent. So they will make integrations even easier. So imagine it gets easier to access data, it gets easier to ingest it, and it gets very easy to like, even express it to the customer, like, through voice. Like all these three things happen at the same time. Just imagine the possibilities, right? It's crazy. I could pull anything from anywhere. And also, like these llms and these large language models as you call them, they know more than any anybody can know about physics, about chemistry, about your OEM manuals, about how the engine works. So what are the odds like we can beat? You know, the model at talking about a certain problem, because it can see from all the perspectives possible to mankind, right? So put all of it together. It's going to be a blast. But still, I think the challenge is the tools always exist. But I always like to tell my team that we're plumbers, you know? We're taking all these different things and plumbing it together to make the best customer experience. What that experience is going to be like. No one knows, right? That is the part of the experiment and find out so fun part. Yeah,

Kyle Mountsier: 23:25

well, awesome. Monique, I it was fun chatting with you all the way through, you know, pushing your chips in and soccer and just imagining what can be with the product that you're building, that anybody's building with. AI right now, we can't wait to see you at ASOTU CON and hang out and learn a little bit more. Kudos to all you're doing, and thanks for joining us today on Auto Collabs.

Monik Pamecha: 23:47

Yeah, thanks for having me. It's going to be a blast at ASOTU CON. And you know, we have our racing great set up over there, like come race with us.

Kyle Mountsier: 24:03

You, Annie, I don't care if it's 3x 5x 1x if you're gonna listen to 4000 calls, you're dedicated to building a product that doesn't miss

Paul J Daly: 24:12

I cannot listen to things at 3x the fact that he listened to 4000 what happens in your brain at that point like I feel like you start to absorb another layer of conversational pattern that you probably wouldn't otherwise,

Kyle Mountsier: 24:25

right, like I could probably hear actually a little bit quicker, is voice inflection, because listening fast actually, like you can hear up and down as a pattern across and so that's actually an interesting pattern to think about, because there's so much when it comes to like, conversational AI that has to understand not just what the words that were said or requested, but the intent, or, you know, the the like, what's going on in that person's head when they say those words and and their level of. Frustration or energy or joy. You know, golly, it's a complex thing, but I know that that people like Tom are obviously trying to figure out, like, how do we how do we fix this? I listen

Michael Cirillo: 25:12

to three calls, and I'm instantly enraged. So I don't know,

Unknown: 25:18

I've been there

Michael Cirillo: 25:20

four car, 4000 calls. I mean, like you're telling me that is dedication and also an insane amount of discipline, because if I listen to 4000 calls, I would be rage eating, I'd be I'd be family.

Kyle Mountsier: 25:35

I mean, why did I just finish this much ice cream?

Paul J Daly: 25:38

One of my favorite things from that conversation was the soccer metaphor when his his coach said, you know, it's not an unsuccessful passes and a failure, it's an unsuccessful attempt at greatness. That that reframe was pretty cool, strong, right? Really? Hey, look, well, you know, I'm not going to go listen to 4000 calls, but we don't have to, which is the good news,

Michael Cirillo: 26:01

right? You get to listen to Auto Collabs and get the recap. Even

Paul J Daly: 26:05

better. I don't know if there's a recap of the calls, but regardless, thank you so much to our guests today, and as always, on behalf of Kyle Mountsier, Michael Cirillo and myself, thank you for joining us on Auto Collabs at 1x

Unknown: 26:19

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