← The SaaS Podcast - Growing Profitable AI SaaS & AI Agents
The SaaS Podcast - Growing Profitable AI SaaS & AI Agents
Consultative Selling: How He Closed Instacart Live
00:42:18
Saket Saurabh (Nexla) on closing 15 enterprise deals through consultative selling SaaS, live-coding demos, and pricing against internal build cost
Loading summary
Transcript114 lines
- [00:00]
A
Foreign.
- [00:10]
B
Welcome to another episode of the SaaS podcast. I'm your host Omar Khan and this is a show where I interview proven founders and industry experts who share their stories, strategies and insights to help you build, launch and grow your SaaS business. In this episode, I talk to Saket Sohrab, the co founder of nexla, a data unification platform that helps enterprises connect fragmented data across different systems, formats and data models. In 2009, Saket founded a mobile app company that became one of the first ad serving platforms and was eventually acquired. But looking back, Saket realized the most valuable thing he'd built wasn't the advertising product, it was solving the data problem. So in 2016, he started Nexla to solve a problem he kept seeing. Data's everywhere, but it's messy and hard to work with now. Most founders would have started with SMBs, but Saket did the opposite. He targeted enterprise customers from day one, but he had no customers and no track record. So using his network and warm outreach, he started having conversations. Not to pitch, but to learn. He wanted to understand if other people saw the same problem that he did. About a year after their seed round A Instacart eventually became their first customer. And after signing the first few customers fairly quickly, the team started scaling aggressively. But six months later, they hit a wall. The founders had to make a painful decision. No salaries for themselves, significant downsizing, and only adding team members when new revenue justified it. It was a tough reset, but it worked. By the time they raised their Series A, NEXLA had multiple seven figures in revenue and was already cash flow positive. Today, Nexla serves over 50 enterprise customers, including DoorDash, LinkedIn, Autodesk and Instacart. With nearly 100 employees and $33 million raised. In this episode, you'll learn why Saket targeted enterprise customers from day one instead of starting with SMBs. How Saket found early adopters inside large organizations when he had no customers and no track record How Saket's co founder live coded Affix during an Instacart meeting and what it taught him about creating magical moments in enterprise sales. We we talk about how Saket figured out enterprise pricing and contracts when he didn't even know what a purchase order was and what advice Jensen Huang gave Saket early in his career in the Nvidia cafeteria that he still carries with him today. So I hope you enjoy. I talked to a lot of founders stuck in the same spot. They've got a clear vision. They just need the right team to build or scale It. That's where Gearhart comes in. They're an AI powered product development studio that handles the entire technical side of building your B2B SaaS platform or AI agent. Built by serial entrepreneurs, they understand the unique challenges of startups and can plug into your team to accelerate growth. They've built over 70 successful products, including SmartSuite, which raised $38 million and is used by companies like Capital One. Right now, they're offering our listeners the first 20 hours of development for free. Just book a call@Gearhart IO.
- [03:20]
A
Click.
- [03:20]
B
That's Gearheart IO. Your company's credentials could be on the Dark Web right now and you wouldn't even know until it's too late. Nordsteller detects threats before they escalate. It monitors the dark web for leaked credentials, tracks your attack surface and protects your brand from impersonation. Built by the team behind NordVPN. Don't wait until your data is for sale on the Dark Web. Book a demo and mention Black Friday 20 for 20% off. And at Sasclub IO Nord, that's Sasclub IO. N o r d.
- [03:57]
Sake. Welcome to the show.
- [03:59]
A
Thank you. Thank you for having me today.
- [04:00]
B
My pleasure. Do you have a favorite quote? Something that inspires or motivates you?
- [04:04]
A
One of my seed investors told me that to build a company, it takes a builder, a seller and a profit. And in those three stages. And I think I always go by that and say, well, a big part of the scaling happens when you get that sort of a profit moment, which means that you're able to evangelize what you do and people can follow you. So I think I do go by that.
- [04:29]
B
Love it. So tell us about nexla. What does the product do, who's it for, and what's the main problem you're helping to solve?
- [04:37]
A
Yeah, the main problem that NEXLA is trying to solve is that in the enterprise world today, data, information is scattered across many, many systems. And we are not able to extract full value out of that unless we can connect across that variety. So the problem we solve is connecting across those variety of data systems. Different structures, different formats, different data models. Unifying that and making that available to data at the point of use. And increasingly now the user of that data is becoming the agent. It's not just analytics anymore. And how do we serve that agent that needs data to build that context is the problem that we solve.
- [05:18]
B
Can you mention some of the companies that are customers, some of the logos that you have today?
- [05:22]
A
We serve enterprises across all sizes, but Typically large enterprises like customers like DoorDash, for example, LinkedIn, Autodesk, Instacart, a whole bunch of companies that rely on us to bring that diverse data, normalize it and make it usable for their internal.
- [05:43]
B
Systems and give us a sense of the size of the business. Where are you in terms of revenue, customers, size of team?
- [05:48]
A
We are just under 100 people, company well over 50 customers.
- [05:55]
And being in the enterprise segment, we serve in the six figure plus ACV market as well.
- [06:01]
B
Great. I know you don't disclose revenue, but you are well over the 5 million mark.
- [06:07]
A
Yeah, we have scaled well past that.
- [06:11]
B
Yeah. And I think you've to date you raised like 33 million.
- [06:15]
A
Yes, yes, we have raised about 33 million in capital so far.
- [06:18]
B
Awesome. Okay, so let's go back to 2016 when you founded the company with your three co founders. What were you doing at the time and where did this idea come from?
- [06:30]
A
Oh yeah, great question. So I'll maybe we'll dial back a little bit more. You know, I got into the whole sort of entrepreneurship thing in the 2000s and I was fortunate enough to work at Nvidia building new product ideas within the company. So kind of being an entrepreneur inside a big company. Big company. At that time Nvidia was just 1500 people and got to learn through that process. I was an engineer transitioning into product at the time. And then 2009 I started a company in the mobile app ecosystem and that ended up becoming one of the first ad serving companies. So 2016, I was coming out of that company, had been through an acquisition, the combined company had gone public and I had found myself serving the advertising use case. Whereas I come more from an infrastructure sort of core systems background. So it was a reset for me to then say, wait a minute, I really need to be back in the building core systems and platforms for people. And the key problem that I had solved at the time in the last company was around data in the advertising space and I had built a lot of compute related stuff in Nvidia and data, serving the data side, building the compute for it, scaling that and really making it easy for the data user. So I'm really coming into 2016 thinking everybody needs to be using data in their jobs day to day. But data is complicated, it's hard, it's all over and can we go and deal with that complexity so that the user of data has an easier life? And that was the core mission and value that we started with and that.
- [08:06]
B
Continues till today when we set out in 2016. To start the company.
- [08:14]
What was that pivotal moment? What pushed you to say, I mean, you'd been experiencing some of these challenges in your own work, but what pushed you to say, I'm going to go and solve this?
- [08:27]
A
I think that was also a lesson observing other entrepreneurs where I saw that in the course of building a company along the way, they had built the real valuable sort of powerful thing. And you've heard stories of companies like Slack, for example.
- [08:42]
Were building a game, but actually behind the scenes had built more of a chat application and that became the core business. And I look back at what I had done in the advertising space and I felt like data was really the hard problem that we had solved. And instead of building the data platform for.
- [08:59]
Advertising use case it should become more of a platform now. The pivotal moment for me had been few interactions with my partners at the Blast company, where companies had come to me. I was running the mobile division at the company, and they had come to me saying, can we get access to data? Because you guys give us dashboards and reports, we want access to raw data. So what I was seeing was that more and more companies were becoming comfortable working with raw data, but their data was actually scattered within their ecosystem of customers and partners. And I felt like, okay, that's an important problem to solve because the companies that asked me for data were ultimately not able to do much with it because it was hard to solve that. So I said, okay, why don't we bring that connectivity and actually make it possible for people to work with data? And the initial focus was not the data inside the company, but the data in the ecosystem.
- [09:48]
B
Okay, so you raised a small seed round in 2016, and you mentioned earlier that you have around 50 or so enterprise customers today.
- [10:04]
Most founders would probably have said, okay, I'm going to go out and I'm going to sell this to SMBs first. Hopefully easier to sell, close deals faster.
- [10:15]
You did the opposite. You started with enterprise, you got traction there, and now you're expanding out into SMBs and targeting developers and so on. So you kind of took a very different approach. But I'm curious, why did you decide to target enterprise customers from day one?
- [10:35]
A
Yeah, so I think there are two reasons for that. First of all was the problem that we were solving was that of fragmentation of data. Data in different systems, different formats, different structures. And of course, fragment is higher, the bigger, the more complex the company. The second part of it was also that coming from an engineering product background, I felt that the real value that we can create will be in Enterprises. But if we architect for smaller companies and solve a problem for them, we will not fully understand the depth of the problem and we'll end up not necessarily building the right solution for it. So I felt like for the initial customers it was very important for us to actually go deep into the enterprise, see the complexity and mess of it, which I think many people underestimate how complicated enterprises can be. Many of them still run mainframes by the way. Right. So there's a whole range of technologies and fragmentation that companies deal with. And until you go into that and try to understand that problem and try to design and architect your problem for that, you will not be on the right track. Okay? And now on the flip side, we are able to say, hey, the same platform that serves these large enterprises, complex large scale use cases, we have brought it in a simple enough form for anybody to take advantage of. So that ended up becoming our journey. For right or wrong, various reasons, that's how we ended up targeting.
- [11:59]
B
So walk me through the journey of getting that first customer. How much of the product did you build or start building? How much time are you spending trying to get your foot in the door and have conversations with potential customers?
- [12:14]
Just what was that journey like?
- [12:16]
A
Yeah, the approach there was very, very thesis driven. And we said, okay, there are certain companies that deal with a variety of data and let's go and talk to them. And actually I had a list of target companies that I wanted to talk to and the first one that we ended up signing as a customer was Instacart at the time. And we felt like, okay, let's understand their business. They are doing delivery of groceries, but they're doing it from all these different stores. Clearly they need to have some of what's in the stores, right? I mean, they show me the product on their app and you know, I can go order it. They're clearly getting product listing, they're clearly getting images, nutrition. They also have probably understanding of what is available in a store. How else would they show me what's, you know, what they can go and pick up? Right? They can't just, you know, guess that out. So, so we built similar thesis across a set of companies which we felt sit in a B2B environment and there and serve their business customer. As a result, they have to work with the data of this business. So we built that thesis and started to reach out to people in very, very basic, organic, sort of classic Silicon Valley founder way. Reach out to people, request an introduction, talk to them, understand whether they agree that the Problem is there. And in most cases.
- [13:35]
That'S the learning that we took in to then start to build the product. And we did take some time to build the product. Launched that at TechCrunch Disrupt as one of the finalists. But yeah, there was certainly journey to getting there.
- [13:49]
B
How easy was it to get those meetings?
- [13:53]
You're a founder.
- [13:56]
Don'T have any customers selling to enterprises. It's not what they typically expect.
- [14:01]
A
Right.
- [14:01]
B
They expect to see hundreds of logos and these flashy presentations.
- [14:05]
A
Yeah, yeah, that's absolutely true. I think that's where I would say to some extent, I've been in Silicon Valley since 1999 and I'm a big believer in the ecosystem here where people are willing to talk to anybody. You have an idea, you want to talk to people. Most people say yes, and we'll take the time. And I think we benefited from that, of course, using investors and friends and network and any which way to meet people. I think that in general, even in large companies, there are people who have that early adopter mindset. Think of the person who would stand in line for the new iPhone launch because they want the latest in their hands and they really care about being on the cutting edge. And those people are everywhere. The question is, can we find those people and can we talk to them? Can we engage with them and can we understand their problem well enough to say, look, there is a much better solution. Are you willing to take a bet? And that's all you need as a startup is someone willing to take a bet on you.
- [15:05]
B
How did you find those early adopters? This is something you and I were talking about earlier and you jokingly said, unfortunately there isn't a list published somewhere where you can go and find these early adopters. But how did you figure it out?
- [15:17]
A
Yeah, I think you're right. I mean, I wish there was a list and you could just reach them. Some of the signals that we see are people who would maybe record a case study with another startup or company and like, oh, they are buying something from a startup or you see their LinkedIn posts or their Twitter posts and clearly they are passionate about innovation in one way or the other. So I didn't have much of an automated system to do that at the time. But just looking at our target list of companies, who are the people in here who are likely to be excited about meeting and at least be open to conversation? My first goal talking to someone was not that I'm going to sell you something. I'm really passionate about solving this problem. Do you see this problem as well, and would you be open to talking about it?
- [16:09]
And that's how it would start.
- [16:11]
B
What was a typical conversation like, once you got a meeting? I talk to a lot of founders who, they're almost reluctant to have that kind of meeting because it's like, I don't have anything to show them. But that's actually a good thing at this point, right?
- [16:28]
A
That's actually a good thing. Yeah. I mean, most of our conversations there were like, hey, we believe that this is a problem that's been big. You know, I wonder if you see this problem as well. A lot of the conversation is about listening. I think smart people, given the right question, can actually be very, you know, educational if you just listen to them and don't go to them and saying, hey, I have a solution to this problem that I want to sell to you, because that turns people off sometimes right away. But you are basically saying, look, I'm, I'm a product person, I like solving this problem. It seems like you might have that as well. How do you see it? How does it manifest? How do you have your approach, solving that, what has worked, what hasn't worked? And all of those questions, when you talk to them, you actually learn a lot about what your product should be. Now, we were also very, very agile on the product side. So I remember at some point we ended up in front of the Instacart CTO and we were invited to say, okay, show us what you can do. Here is a new data feed we got from a merchant or partner and some of the pieces were not there. And we had a one hour session. And kid you not, I'm talking to them and telling about how our approach is. And my founder in the meantime, in that session, live coded some fixes to handle some unique thing about the data, which we were not yet able to. But we ended the session showing them something working and getting that, aha, like, okay, you guys did this, like, right on the spot without a prep. And it takes us some weeks or months or whatever to solve the same problem today. Yeah. So you have to create some magical moment. Sometimes a bit of luck also helps. But yeah, nothing. I feel like as a founder, you're constantly being optimistic and pushing the limits.
- [18:13]
B
Yeah. So Instacart became your first customer.
- [18:17]
And the problem that Nexla is solving is pretty complicated because you're trying to take all of these.
- [18:26]
Different data points, the data sources, and give them a better way to handle all this information. That sounds like a really tough thing to build in any Kind of mvp. So when you solved it for Instacart, how did you decide where to focus the product on initially?
- [18:43]
A
Yeah, see the problem definition we had already made. Remember, we had seen a similar problem already in our prior company building that advertising company. So we were getting data from all of these different ecosystem players, all these media companies and ad networks and so on. So we had seen the data variety problem.
- [19:03]
As I said, I've been an engineer, and one of the things I learned at Nvidia was how to think about these complex problems and build elegant solutions to that. And we took some very unique approaches as to how we'll solve that. So we'd already built a good framework towards that, and much of it was based on the. The approach that we are not just going to read data from different systems, we're trying to build a bit of an understanding to it. And if you can understand key things about the data, then we can simplify the process of how it should converge. Because you are going from variety, so different systems, different formats, different data models, they will name things differently and so on. And now you're trying to bring it together so your customer can actually have one clean thing to work with.
- [19:46]
A lot of the stuff that we understood would be common problems. We had built to that so that when we get in front of people, we can go around and make some adjustments and show them something. So that's sort of how.
- [20:00]
We approach the problem.
- [20:02]
B
How did you figure out pricing? When the people at Instacart said to you, how much does this cost?
- [20:07]
A
Are you just like, I'll be honest with you, there was a time when I didn't know what it means to have a purchase order. What do we need in a contract? And I ended up getting a crash course from some of my entrepreneur friends on that. Pricing also was similar. We said, look, it's going to cost you at least these many people to solve this problem and maintain this, and we're going to be a fraction of that cost and therefore it sort of makes sense. And we had some math that whatever it costs the company, what were the total cost would be, we should be like a fifth or tenth of that or something like that. So some sort of approach there. Of course, you're also testing the market. You're sort of afraid to say a number over time. One of the things I learned is that, again, listening, if you ask people how, how much does it matter? What does it cost you to solve it? Today, you'll be surprised how much people will be willing to share and that gives you a much better understanding of how to do it. But we just, you know, we tested and, but we always told people that, look, we're going to be, we're a startup, we're hungry to get your business, we'll do it. But here is what will be, you know, a fair structure and kind of figured our way through that over time. Yeah.
- [21:19]
B
And then did you test like increasing prices with, with every deal that you try to close?
- [21:23]
A
Yeah, over time we did that and we also hit certain limits also, like, hey, where are people comfortable or not comfortable? So there has been an, of course, an evolution through that process. Yeah.
- [21:34]
B
Now when I was looking through, doing some research, I think it was around 2021 that you raised your Series A, which was I think maybe about 12 million. And you're already multiple seven figures in revenue at that point and had about 25 enterprise customers. And you did the first 15 of those customers. You basically closed those deals yourself. It was all founder led sales. But your background is engineering product. You didn't have a sales background. So how did you learn to sell and close these deals?
- [22:11]
A
Yeah, I think that as a founder you almost by definition become a seller, first to investors, then to prospective employees and ultimately to customers as well. Yeah, I did not have any such sort of formal sales training, although having done a startup before, I'd been in situations where you are trying to work with a customer and get something to the finish line.
- [22:38]
The way I approached sales at least was largely, again, more or less consultative. Try to understand what problem people are solving coming from a product person's head, and then be able to articulate like, okay, this is how we can solve your problem. I think that in many ways, working with a bit of a technical buyer on a complex technology piece, sales often sort of becomes like that. You have to really earn the trust of the person. To some extent I did feel that people were willing to trust that, okay, you're not here to just sell to me. You're solving a problem.
- [23:14]
It doesn't scale that well. But I personally am a big advocate of founder selling. I feel like unless a founder goes and actually sells deals on their own, you don't really fully get to connect the dots. Because as a founder, your job is to connect the dots. What is your product, what is the market, what's the need that you believe in? What is customer saying? You know, all of, all of that. You have to connect the dots to really figure out where the company should go. And you're still figuring the direction of the company because you know, a sales conversation could give you a signal that this is not a good market. For example, it's not the right price, and it's hard to sort of delegate. So part of it is first you learn yourself and then you try to teach others.
- [23:56]
That's how it ended up being for me.
- [23:58]
B
Yeah. So you, and there was something you said to me earlier, which I think is really insightful, that you were largely talking to technical people. And although you didn't say this, it sounded like you didn't even see it as sales.
- [24:16]
In large part because you were just talking to them about how to solve these technical issues.
- [24:21]
A
No, I still don't see it as sales. I still see it as, look, we are really focused on solving a hard problem. And, and, you know, my job is to, you know, find, you know, if I talk to somebody who has that problem, I actually really want to make sure that we can solve it well for them. So it's, it's still really that. No, I think it's, you know, it really comes down to.
- [24:46]
Being able to take something off of people's plate, you know, something that is important for them, but something that they probably shouldn't do on their own. That's kind of what enterprise technology selling becomes, because a lot of the conversations and in these cases ends up becoming build versus buy and being able to say that, hey, I have a solution. And look, I'm just focused on solving this one problem, and trust me, I can do it for you, and I can do it better than you'll be able to do it on your own because you have 10 other things to work on. Customers are extremely smart and they know how to solve the problems, but they can't do all of those. So I still don't see it as a selling motion as much as.
- [25:27]
Solving a problem.
- [25:28]
B
Basically, you mentioned the build versus buy, and I think that was a challenge you faced where you'd go in and talk to them about how you could solve this problem. And often the engineering folks would say, well, we can do this ourselves.
- [25:44]
A
Yeah, and fair enough. They've been running their business. They didn't just start it yesterday. Right. So they can absolutely solve the problem on their own. And my thinking largely was like, okay, can I first, Can I have you agree that this is a problem to solve? This type of data fragmentation is a challenge that you need to solve. And once you can on the other side, once your data is in the right place and shape, it helps your business. Okay, at least we agree on that. Okay. You are solving that problem on your own because you had to. I didn't show up until yesterday. Right. So, okay, so if we agree on that, I think then largely it becomes the question of is this person willing to a trust you that you can do a job as well or better than them and really take care of it? Because somebody down in your business depends on that. Second, they don't see you as a threat. That's an evolving thing in general as well. And they truly see you as a partner who can have their back. Because someday on Saturday night, some data flow will stop working and somebody's got to wake up and make sure it's done right and are you going to be that person? And then the final thing I felt was also in that whole build it versus buy conversation was like, what does this person want to really do long term? Because solving this data variety problem starts very simple. I just want to integrate data from one partner or customer. But then when you get to 50 and 100 and 500, it's an endless problem to solve. And that's not necessarily a career growing trajectory for that person. So in many of those cases it becomes understanding the problem, agreeing on that. Sometimes that is not the time when they will make make a decision to switch. And if you stay in touch with them and come back and check in with them, maybe there's a right time when they say, you know what, I'm done with this, this is a good time for you to solve that for me. So I think a lot of sales in my mind doesn't necessarily happen in the time you want. At least you agree on the problem. Timing can be a patience thing sometimes, or being able to come back with the right examples or right features sometimes like, oh, you really wanted to do this thing on data, like clean it up and some entity resolution. Look, we are able to do that a lot easier.
- [27:56]
B
So you said.
- [27:59]
When this would happen and they would say, we can solve this ourselves. It wasn't something you pushed. It was just like, okay, let's just understand that there's a problem, it needs to be solved. This is how you're doing it right now. And then you said, I'd stay in touch with them. What did that look like?
- [28:14]
A
Sometimes it's just sharing updates with those folks on what features you have built. Like they may have said like, oh, my roadmap is to also do this and that with the data and that's my plan. And like, okay, what are we doing in that direction? Sometimes people do have like, okay, you do these five things well, but I don't really like a certain part of your product or solution or approach. It could be many of those. But largely for me, I think one of the things I'm good at is generally texting people and sending them emails and messages and just keeping track of that. It's hard to scale again. But when I was founder selling, you can still make it work.
- [28:53]
B
Was there a customer or prospective customer that you had a meeting and you were like, you walked away thinking they're never going to buy, but you stayed in touch and they eventually did.
- [29:03]
A
Yeah, it has happened multiple times, especially with large companies, because large companies are used to solving problems on their own and they have a lot of resources so they feel like, well, I can do it myself. Yeah, absolutely has happened. The timing of these things can of course vary as to when people sort of really align with you. I think that there are some macro trends that are also happening overall which influence some of those factors and being able to sort of connect the dots with that as well. So, for example, you know, let's say it's much easier to generate code in AI and people are like, oh, I don't, and I can build anything myself. Right. And that can happen as well. But then there are also macro trends happening about how we are advancing our product to be very AI enabled and driven. So I think you have to clearly show that the value you're creating is significantly more than what people can do on their own and that you are continuously on that track of doing that. And one of the things for me is that I am constantly, even when I'm not working, constantly thinking about data and data related challenges. I'm like, oh, we could do this and that. And every time I think about one of those, sometimes it clicks to me like, oh, that person at that company that I talked to mentioned this to me and that's a great point for me to say, oh, let me update them of what we're doing. So I think part of the founder's job is to really connect the dots, as I said, about your product, the market, your vision, but also the customer.
- [30:34]
B
I think when you raise your Series A, you were doing multiple seven figures.
- [30:42]
And you're also cash flow positive, is that right?
- [30:45]
A
Yeah.
- [30:46]
B
So how do you do that? Like you hadn't raised a lot of money to start, you're going out and trying to sell to enterprises. These deals can take a long time, 6, 12 months in many cases.
- [31:00]
What kind of decisions did you make about the business and how you operated that helped you to not only survive during that time, but be so efficient.
- [31:11]
A
Yeah. See, we raised our seed round in 2016, and then we got our first customers in about a year from then. And then we felt like, well, we can just go scale it, right? And you feel like that once you have. And we signed the first few customers in quick succession. So we started to build and scale, but then six months later, it didn't turn out that way. And suddenly we were like, in a difficult situation, had to make some cuts. And then the pact that we did between the founders was that, look, we'll just. We have some customers that we are serving well, okay? So we know our product is valuable. We are all product people. If we don't spend any of the money on ourselves, we don't take any pay, which is a sacrifice to make. And you have to sometimes do that as entrepreneurs. It's not like you start a company and getting paid right away. So we did that and we said, okay, we'll cut that down. And then every time we add a customer and revenue, we'll also add some more people to the team. So we downsized significantly. It was a painful moment. But then we kept on that journey and actually we started to build the product very focused on fast iteration of the product, getting in front of customers, and slowly building the team. That's how we ended up in this whole journey over the next few years, really focusing on the deep enterprise capabilities and coming out the other end with good, steady stream of revenue, good set of customers, and realizing that we had actually grown revenue faster than we had increased our spending.
- [32:44]
B
So we talked about the shift in your target customers. You took the enterprise route. Do the hard thing first. Now you've expanded and are going more towards SMBs, developers and so on. And once you explain that that makes sense, tell me about.
- [33:04]
How the emergence of AI shifted what you were doing.
- [33:10]
You know, in many ways, it's still the wild, wild west.
- [33:13]
A
But.
- [33:16]
B
What were the most significant changes you made to the product that helped you do things that you weren't able to do before?
- [33:24]
A
Yeah.
- [33:26]
AI has been a big shift, and it continues to be. I think one of the things that I mentioned was that to solve that fragmentation problem, we're trying to build a little bit of intelligence in the system to understand the data and then make some sort of decisions based on that. And that helped us solve some really hard problems. I think what it allowed us, the growth of AI did, was that the features that would take us from an AI perspective, machine learning perspective, like maybe months to build, could now be done A whole lot faster because we could take those general purpose models and take advantage of that. The other thing that also happened was we were seeing how software engineering has been changing and you go into cursor or lovable and you just tell what you want to do and you can build that. We realized that we had built enough know how along the way in our system that we could expose something similar for data engineering specific. And data engineering is a software engineering problem, but in a very different way. The type of problems you solve are very different. So we've been able to do that and actually come out with a product called Express, which actually is completely conversational. You tell your intent of what you're trying to do and the system based on all of that sort of prior know how and everything is able to stitch together a solution because it runs on the same platform. Whatever it creates is actually ready to go to production as well. It's not a prototype software.
- [34:45]
B
Right.
- [34:46]
A
So we've been able to bring AI to how AI can be used to make the data work easier. And then on the flip side, there were capabilities that we had built over time and we expanded. So we started to process documents, we introduced the capability to process videos. So we said, well, we should cover the whole. When we talk about the variety of data, it should expand beyond structured data to also documents and stuff. And we've done that and then going more into the place of like, okay, how will we make the data available with the primary user becoming AI? And that's kind of why going towards.
- [35:21]
Earlier stage companies, because much of that solution building AI products are being built by startups and we see ourselves as a place where, look, the startups want to build the best AI solution, but the moment they deploy to an enterprise, they get stuck in the same fragmentation problem because now they have to work with all of that. So do you as a startup go and solve that problem? Because that will eat up a lot of your resources, or us rather being a component and saying, well, so it changes also how we think about who we serve because of AI. Okay, not just how we serve them.
- [35:56]
B
So.
- [35:58]
This Express, you called it, right?
- [36:02]
It seems quite a shift from what you had been doing before. Just to sound clear, was this so people could start to get answers from the data, or was it to help them do a better job at basically turning this fragmented data into something more useful?
- [36:19]
A
Yeah, it's the latter. The thing was that our initial thesis was will everybody will be a user of data, how do we make their life easier? How do we get data clean and nicely to them. And we said, well, we can lower the bar on what that person needs to know and how technical they need to be, because Express can be a conversational way for them. So they can say, I need to work with this data Express. You go figure out what pipelines I need and what data products I need and go help stitch it for me. And I can give instructions. I think it continues on. That same sort of journey is how to get data in the right place to that person. The difference being that now if you are building any application that data can be documents, it can be coming from vector database. You may need real time data to enterprise systems are fragmented. Right. But can you have an agentic way of getting access to that data? So all of those layers are what we then started to solve for.
- [37:11]
B
Yeah, that's great. Okay, we should wrap up. Let's get onto the lightning round. I've got seven quick fire questions for you. Ready?
- [37:20]
A
Yes.
- [37:20]
B
Okay, what's one of the best pieces of business advice you've received?
- [37:24]
A
So I remember getting this in the cafeteria line at Nvidia and Jensen was right behind me and he told me, what are you working on? And his advice to me was, hey, if you're not on the critical path, then get on it, and if you're on the critical path, then get off it. And I think that was really the advice to be constantly in the cycle of pushing hard but not slowing down other things.
- [37:46]
B
What book would you recommend to our audience and why?
- [37:49]
A
The book I would recommend I keep talking about a lot is six types of working genius. It really helps to understand that everybody has two working geniuses, two areas of competency and two areas of frustration. And the better we understand that, I think it better it becomes to work as a team.
- [38:03]
B
What's one attribute or characteristic in your mind of a successful founder?
- [38:07]
A
I think I would say relentless optimism and faith in the art of possible.
- [38:12]
B
What's your favorite personal productivity tool or habit right now?
- [38:17]
A
I would say using ChatGPT in voice mode while I'm driving, getting raw notes out of that, and then using a combination of AI tools to do stuff that are always ideas in my head but would never get to write or create a slide deck around.
- [38:32]
B
What's a new or crazy business idea you'd love to pursue if you had the time.
- [38:36]
A
If I had the time, I'd probably try to build some tool technology platform for future evolution of technology. And I think that when I think about it, I think it'll end up becoming solving the data challenge for AI because I think that's the biggest bottleneck. So I think I'm already doing, I think what I would have done otherwise.
- [38:55]
B
What's an interesting or fun fact about you that most people don't know?
- [39:00]
A
I'm an introvert, so I think most people don't realize that I love adventure and everything from flying a plane to riding a motorcycle to snowboarding to everything. I love doing activities.
- [39:13]
B
And finally, what's one of your most important passions outside of your work?
- [39:17]
A
My life is where work has percolated through every bit of my calendar. So the only thing I do outside of work right now these days is spend time with my kids. And I do love to teach and mentor when possible. So I will make time for that no matter what. If people, if there's somebody I can help.
- [39:35]
B
Awesome. Well, thank you so much for joining me. Sake, it's been a pleasure. If people want to check out Nexla, they can go to nexla.com and if folks want to get in touch with you, what's the best way for them to do that?
- [39:47]
A
Best is to find me on LinkedIn.
- [39:49]
B
Great. We'll include a link to your profile in the show notes. So thank you. I appreciate you making the time to share your story of building NEXSLA and for sharing your lessons, your successes and mistakes along the way. And I wish you and the team the best of success.
- [40:08]
A
Yeah, thank you so much for having me. I hope this was helpful and will be benefit the audience.
- [40:16]
B
I think it will. Thank you. Take care. All the best. Cheers. If you've dealt with Twilio, you know the pain, clunky setup, confusing pricing, support that costs extra. Signalhaus is different. Modern APIs, fast ATP approvals and pricing that actually makes sense. No hidden fees, no runaround when you need help. Whether you're building a SaaS product or a marketing platform that needs messaging, SignalHouse just works. Check out SignalHouse IO. That's SignalHouse IO. Most SaaS companies react to security breaches after they happen. Nord Stellar gives you real time visibility into what attackers see. Leaked credentials, exposed assets, session cookies from malware, brand impersonation attempts built by the team behind NordVPN with access to one of the largest dark web data pools in the industry. Stop reacting to breaches only after the damage is done. Be prepared with Nord Stellar. Visit SasClub IO Nord to book a demo and mention code BLACKFRIDAY20 for 20% off. That's SasClub IO Nord. If you're building an AI agent, a SaaS product or stuck trying to scale. Check out Gearhart. They can act as your fractional CTO and technical team, bringing AI expertise from projects for Meta and Google plus, strong Silicon Valley connections with founders and VCs. And since they're a Ukrainian born company, you get senior engineers with an offshore pricing model with offices in San Francisco and London and a distributed team of 40 experts. They've helped build over 70 successful products. Right now, they're offering our listeners the first 20 hours of development for free. Just book a call at Gearhart IO. That's Gearheart IO.