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Founder Interview

How Smith.ai Crossed $20M ARR with 3,000 Customers and 600 Team Members (Interview with Founder and CEO Aaron Lee)

Interview Date
July 1, 2023
Interviewee
Aaron LeeFounder and CEO
Watch
Watch the full interview on YouTube

Company Metrics at Interview Time

Annual Run Rate (2023)

$20M

Customers (2023)

3,000

Team Size (2023)

600

Gross Margin (2023)

60%

Total Funding Raised

$13M

Historical Snapshot

These numbers were reported by Aaron Lee during his interview with Nathan Latka in July 2023 and represent a historical snapshot, not current figures. See Smith.ai’s current numbers.

Key Takeaways

  • 01Smith.ai crossed $20M annual run rate as of mid-2023, with $1.5M in revenue in June 2023 alone
  • 02The company serves 3,000 customers at an average of roughly $1,000 per month
  • 03Smith.ai has 600 full-time employees, all based in North America
  • 04The company employs 25 engineers out of its 600-person team
  • 05Gross margin is 60%, reflecting the cost of blending human agents with AI
  • 06Smith.ai has raised $13M in total funding, including a $7M convertible note in 2019 and a $6M Series A in 2022
  • 07The company offers three products: inbound voice, web chat and SMS, and outbound SDR as a service
  • 08Aaron Lee bootstrapped the company from 2015 to 2018 without taking a salary
  • 09Smith.ai has more than $10M in cash and more than 24 months of runway as of mid-2023
  • 10Revenue roughly doubled from a year prior, with June 2022 revenue estimated by Aaron at approximately half of June 2023

Company Metrics at Time of Interview

MetricValueSource
Annual Run Rate (2023)$20MFounder interview, July 2023
Monthly Revenue (June 2023)$1.5MFounder interview, July 2023
Customers (2023)3,000Founder interview, July 2023
Team Size (Total) (2023)600Founder interview, July 2023
Engineers (2023)25Founder interview, July 2023
Gross Margin (2023)60%Founder interview, July 2023
Average Revenue per Customer (2023)$1,000 per monthFounder interview, July 2023
Total Funding Raised$13MFounder interview, July 2023
Funding Round (Convertible Note) (2019)$7MFounder interview, July 2023
Funding Round (Series A) (2022)$6MFounder interview, July 2023
Cash in Bank (2023)$10MFounder interview, July 2023
Revenue at $1M Milestone (2018)$1MFounder interview, July 2023
Year Founded2015Founder interview, July 2023
Product Lines (2023)3Founder interview, July 2023
Revenue Growth (Year over Year) (2022)100%Founder interview, July 2023
Convertible Note Cap (Most Recent)$100MFounder interview, July 2023

Growth Breakdown

Revenue

Smith.ai crossed $20M in annual run rate by mid-2023, with $1.5M in revenue recorded in June 2023 alone. Aaron Lee noted that revenue roughly doubled from a year prior, consistent with the company's reported 100% growth in 2022. The company first crossed $1M in annual revenue in 2018.

Customers

Smith.ai was serving 3,000 customers at the time of the interview, with Aaron targeting 5,000 to 6,000 by year end. Growth has come from cross-selling across the three product lines, upselling additional features, and attracting higher-volume businesses.

Team

The company employs more than 600 people, all based in North America, including 25 engineers. The agent workforce operates as universal agents who handle both inbound and outbound work depending on demand throughout the day.

Profitability and Funding

Smith.ai is not yet profitable and is burning cash each month, though Aaron described the burn as not significant relative to the company's scale. The company holds more than $10M in cash, providing more than 24 months of runway. Total funding raised is $13M across a $7M convertible note in 2019 and a $6M Series A in 2022.

Growth Strategy

Cross-Selling Across Three Product Lines

Aaron credited much of the revenue growth to customers moving from one service to another. Clients who start with inbound voice often add web chat and SMS, and increasingly the outbound SDR as a service product launched roughly a year before the interview.

Outbound SDR as a Service

The outbound sales development representative product, launched about a year before the interview, opened a new revenue stream. Customers use it to have Smith.ai make outbound calls on their behalf, effectively outsourcing their sales and marketing outreach.

Upselling and Attracting Higher-Volume Customers

Smith.ai has grown average revenue per customer by adding features that command higher prices and by attracting more mature businesses with higher call and chat volumes, pushing the average monthly spend upward.

AI-Powered Efficiency and Proprietary Training Data

Aaron argued that Smith.ai's moat is its proprietary dataset of real business calls and chats, which public AI models cannot access. The company uses AI for transcription, real-time language translation, and increasingly for front-end voice bots, which reduces the human cost component and improves gross margin over time.

Network Effect and Scale

Aaron described the business model as having a network effect: the more customers use the service, the lower the per-unit cost becomes. This dynamic, combined with AI automation, is the primary path the company sees toward expanding its 60% gross margin.

Best Quotes

We have thousands, like Yeah. Close to Three. 3,000.
Actually more than that. If, let me see, we are doing, I think like a few months ago, we crossed $20,000,000 annual run rate.
I think most of them are actually, like, going from, like, one service to another service, like cross selling. Yep. Another one is, like, we're actually adding more kind of features so people actually pay more to upsell. And also, we're attracting businesses that are more mature, like, meaning they actually have higher call volume, higher chat volume. And on top of that, the outbound services we started about a year ago is where people say, hey, you know, not only do I want you guys to answer the inbound call, I also want you guys to make the outbound calls.
Actually, we're not at that level yet because one of the part that we are blending between AI and the human is the human cost is significant. So I would say our gross margin is 60% plus.
Yes, we did. We did. I think together, like, we raised, like, with us angel investors and the safe note, I think I was close to $13,000,000. Yeah. Okay.
The pain point for the SMB, which US has about 30,000,000 of them, is they don't have the time and money to build up their own team.

What Happened Next

This interview captures Smith.ai at a specific moment in July 2023, when the company had just crossed $20M in annual run rate and was serving 3,000 customers with a 600-person team. Aaron Lee outlined plans to invest further in sales, marketing, and AI talent to expand gross margins and grow the customer base. For current revenue, customer count, funding, and other live metrics, visit the Smith.ai company profile on GetLatka.

View Smith.ai’s current profile and metrics

Full Transcript

Introduction and Company Overview

Nathan Latka

00:00Guys, smith dot ai is doing it did 1,500,000 revenue last month, over 20,000,000 run rate. That's more than doubled from a year ago. They've got 600 folks on the team. And what they're doing is they're helping folks do they, you know, work with customers much better in a more efficient way, whether it's voice, inbound chat, inbound messages, they're using AI to power this. But unlike most AI companies, have real revenue. Again, voice, web chat, outbound SDR

00:22as a service launched back in 2015, 2016 after Aaron sold his first company Red Beacon to Home Depot, then stayed at Home Depot through 2015. Hey folks, my guest today is Aaron Lee. He's the co founder and CEO of smith.ai and former CTO of Depot. His former company Red Beacon won the TechCrunch fifty startup competition in 2009. He's one of the funding founding engineers, at Google Video and holds a PhD in computer science from Princeton University,

00:49now building smith dot ai, which is human centered AI customer engagement. Aaron, you ready to take us to the top?

Aaron's Background at Google Video

Aaron Lee

00:56>> Yep, absolutely. Excited to be Happy to tell you more about smith.ai and some of the excitement in the AI development on customer engagement. We love that.

Nathan Latka

01:06What years were you with Google Video?

Aaron Lee

01:08>> It was in 2004, very early on. I joined Google. And back then Google had web search, image search, but video was a new thing. So, I built a team with my, another engineer and before we know it, it's a team of like a 100 people.

Nathan Latka

01:23And that was 2004 to what year did you leave?

Aaron Lee

01:26>> 2008. Wow. I left 2008. Yeah. When I joined, like Google had 2,000 employees. When I left, it was 20,000, like 10x.

Nathan Latka

01:35Wow. Okay. So so that's did you go right into Red Beacon in 2008?

Aaron Lee

01:40>> Yeah. So I left in 2008 exactly on the month of the financial crisis and started with like the other two ex Googlers And we thought, wow, I mean, that's a good time to build company. And that's how we started Red Beacon.

Nathan Latka

01:57Interesting. So what happened with Red Beacon? You sell it, you shut it down, what you do with it?

Aaron Lee

02:01>> Yeah. So we spent a year half time building the company and back then you have to remember, like there were no funding at all. We said, great, we're going to bootstrap the company. We're going to build the product half time. And by the time we launched in TechCrunch and took the top price in 2009, like, we start like, we actually got the funding within, like, less than a month from RemRock and Mayfield.

Founding and Selling Red Beacon

Nathan Latka

02:25How much did you raise? 7.5. What valuation?

Aaron Lee

02:29>> I actually don't remember. Think it was like, yeah, probably close to, 30. Yeah. Okay.

Nathan Latka

02:35And then what happened after?

Aaron Lee

02:37>> And we just took the money expanding to nationwide and, Rebican was a platform to connect the homeowners with home improvement professionals. And when we start expanding the platform to Nationwide, we got the notice of Home Depot. And turns out Home Depot has been thinking about it for quite a while because they actually have two sides marketplace, the pros, like the contractors and the homeowners.

Nathan Latka

03:03Now, what size did you grow Red Beacon to before you decided to exit in terms of revenue? We're talking like a million, 10,000,000, something in between?

Aaron Lee

03:11>> Yeah. I think it was some somewhere like 10 plus

Nathan Latka

03:13Okay.

Aaron Lee

03:14>> Around the time. But but I think the biggest opportunity is like Home Depot has all the ingredients. They have the distribution channels, 2,000 stores. They have the two sided marketplace, homeowners and the pros. And they're also very excited to get into the like, building that connection between the homeowners. So if you go to any of the Home Depot store today, you will see my work there. I think they rebranded as like pro referral.

Nathan Latka

03:39That's very cool. What did you guys I mean, you're it sounds like you're building something at Red Beacon. It's doing well. You have more than 10,000,000 in revenue. What did you like about the Home Depot offer? Why'd you guys accept?

Aaron Lee

03:48>> I think, like, when we accepted the offer, like, we were looking at how do we expand it to even faster to to even, like, adoption. Right? We could raise money, but on the other hand, when we raised the money, we only got the money. We don't get the network effect. We don't get the distribution channels. Home Depot actually provide all of the above. And we got, like, very significant support from the then CEO of Frank Blake,

04:12>> and he was our biggest sponsor. He saw the opportunity to expand from, like, which is like selling the stuff on the shelf to services. And are

Nathan Latka

04:22you able to share a range of like what multiple you guys sold for?

Aaron Lee

04:26>> I actually don't remember. Yeah. Okay.

Nathan Latka

04:28What was it an amount that you mentioned the valuation on the raise was 30,000,000 ish valuation? Did you sell for more than your valuation so that everyone made money? Or was it more like an

Aaron Lee

04:37>> Like, the investors are very happy. All the employees are very happy. Yeah. Okay. Okay. Just raise one round and Okay. Yeah. That was it.

Nathan Latka

04:46Oh, what's going on there, YouTube? Good to see you guys. Now imagine this. You love watching these interviews with SaaS founders. But imagine if we took all of the valuation data out from over 2,807 interviews I've done manually, Saves you a lot of time. Well, we've done this. We've built it into the beautiful interface inside of Founderpath. Check this out. I'll show you how you can access this in a second, but you log in, you connect

05:10your Stripe account, you see your valuation real time, you can see what it changed over the past eighty eight days and even set goals for valuation this year. Now the secret evaluation is there's many different ways to value a SaaS business. So the reason you're gonna see three or four different valuations inside of your Founderpath dashboard, this is all free by the way, is because depending on who's doing the buying of your SaaS company, you're gonna

05:34get a different valuation. A VC is gonna pay a different valuation. Private equity firm is different. If you're gonna do a minority sale, that's different. And if you sell the whole business, that's a different valuation. You can see all those when I hover over here. Right? So the teal is what a VC would pay. Yellow is what private equity and red is if you sold the whole thing outright. Now what's cool about this is this is

05:56not built off random data. Again, you guys hear these interviews on YouTube. All these datas are built from real time valuation data points founders share with us on the show. So traction, 1,200,000 seed round, 3.7 raise. They sold 22% of their business. Go in here and filter by the event. Maybe you only wanna see companies that have sold the whole business. Well, here are a bunch that have been acquired, the valuation and the multiple. Maybe you're

06:21going out right now and you're raising your seed round. We'll go in here and look at all this recent seed deals that went down, what they raised, what valuation they raised at and what percent that they sold. There's never been a larger dataset of SaaS valuations than what you can get now inside of Founderpath. And we're thrilled to bring it to you. All right, We're gonna go back to the YouTube video here in a second, but

06:43if you wanna check this tool out, if you wanna jump in and sign up, you can check it out for free to get your valuation at this link. This link, founderpath.com/products/valuations. Or if you go to founderpath.com and hover over products, click on get your valuation here, and go ahead and sign up to give it a whirl. Again, all that valuation data live right inside the platform. I hope to see you there. Alright. Let's jump back into

07:10the interview. A lot of times when a company like Home Depot buys a startup like yours, there'll be a portion of the deal that is cash upfront, a portion that is stock options, and a portion that's an earn out. How did you think about your deal?

Aaron Lee

07:21>> Yeah. I think we we we definitely got the heavy size on the on the cash side, and then there's a bunch of, like, earn outs so that we they could retain the talents and the team. And also, one of the biggest reason why we got excited is they actually want us to make it really big. Like, if you look at Home Depot on the services settlement side, now they're multibillion dollar business. So they already have that

07:45>> significant interest and incentive to make it really a big business.

Nathan Latka

07:50I see. So Aaron, is it fair to say more than 90% of the total deal price was upfront cash?

Aaron Lee

07:55>> Yeah, I would say so. Yeah.

Nathan Latka

07:56Okay. Got it. And then okay. So they buy you. How long do you stay at Home Depot?

Aaron Lee

08:02>> I actually stay we got acquired in early twenty twelve and I stayed till 2015, like almost three years. And that was really the time it took to, like, take our idea and our model and our platform and expand it expand it to nationwide. And that was when I just tell you a little bit about the Home Depot terminology. Like, they call this, like, improvement professional, the pros, and they think about it as more like these are

08:31>> the pro community because that what they need is a little bit different than the homeowners. So you need to build a platform to connect these two sides of the marketplace. And that was when we when I was seeing a lot of challenges from the pros that they just don't have the time and the money and the know how on dealing with, like, customers.

Nathan Latka

08:50If it's going so well, why'd you leave?

Aaron Lee

08:53>> It was part of the thing that, like, you need to remember. So I I work in Google from 2,000 people to 20,000 people. When I was at Home Depot, the size of the company was 400,000 people. It was just very, very big. Like, it really comes with, like, the distribution channels, the customer base, but at the same time, it takes a little bit longer to get the new ideas. So one of the reason why I started

09:21>> Smith AI was the fact that the pain point for the SMB, which US has about 30,000,000 of them, is they don't have the time and money to build up their own team. And as a matter of fact, they even approached me and say, hey, Home Depot had huge call centers. Can you guys handle my customer calls and texts and SMS and Facebook messengers? And, of course, Home Depot couldn't do it because the call center was built

09:47>> for the corporate use and not built for their their kind of like the pros. And that was really the the genesis of the idea that came from. It's like, how can we build something that serve this very large underserved community?

Genesis of Smith.ai

Nathan Latka

10:01So smith dot ai was launched in 2015 or 2016?

Aaron Lee

10:04>> It was like we hedged the idea in 2015 with my co founder, Justin Maxwell, and he was at Google. And I said, like, I have this amazing idea. Like, come join me. Like, we both are very passionate about SMB. And I think we launched towards the end of twenty fifteen with like just a few beta customers. The full launch was in 2016.

Nathan Latka

10:26Okay. And did you guys just split equity at the start fiftyfifty or did you get more because it was your idea?

Aaron Lee

10:31>> I think we got more, not because I think we we have the similar idea. It was just like we bootstrapped the company. So in the first few years, we actually didn't take any salaries, like like all the entrepreneurs out there, we put in our

10:45>> own money. From what years?

Nathan Latka

10:462015 to what year did you not take a salary?

Aaron Lee

10:49>> I we it lasts for three years, think to 2000, probably eighteen.

Nathan Latka

10:54Interesting. And can I ask how much of your own money you put in the business at risk?

Aaron Lee

10:58>> Oh, hundreds of thousands of dollars. Yeah. Yeah. It's not, not small amount. It's significant. Yeah.

Smith.ai Business Model and Pricing

Nathan Latka

11:03Yeah. Okay. Okay. So, so let's, before we get the full story of Smith, I want to take a snapshot of where it is today. Can you give us a customer story of someone paying for Smith today?

Aaron Lee

11:12>> Yeah. So if you're running a small meeting business and you have no time to hire people, you have no money to afford a full time, like front desk receptionist, and you also don't have the technological know how on blending between AI and agents, that is when we come to help. So they will sign up on the website. We say, okay, Nathan, how many calls or how many chats are you getting per day or per month? Let's

11:36>> say, oh, maybe you're getting, like, 20 calls per day. That would be, like, 600 calls per month. And then we get you a package and say, every month, you pay a monthly subscription amount. If you go over that, then you pay for the overage. If not, then that is the amount that you need to commit to. It's very I I would call it vanilla SaaS model plus kind of the the usage consumption if you go over

12:02>> the quarter.

Nathan Latka

12:03I see. So so all in, what does the average customer pay you per month to use the technology?

Aaron Lee

12:08>> Yeah. So it's around like, I would say close to a thousand dollars, like, on average. Like, of course, we have like a smaller business that pay a few $100. We have bigger businesses that pay like close to $10,000 per month.

Nathan Latka

12:21So is your largest customer right now, like a $200,000 a year contract?

Aaron Lee

12:25>> I don't remember the largest one, but we have different kind of services. So we actually have three things. We have the voice inbound. We have the web chat, SMS, and Facebook. We also have an outbound SDR as a service. So people tend to play a mix of these services, right, when they start using us because they want us to be the one stop shop to handle inbound, outbound, omnichannel, 20 fourseven.

Gross Margin and Customer Count

Nathan Latka

12:50Yeah. You mentioned services. Is your gross margin above 80% or 75% or no?

Aaron Lee

12:55>> Actually, we're not at that level yet because one of the part that we are blending between AI and the human is the human cost is significant. So I would say our gross margin is 60% plus.

Nathan Latka

13:09That's 60%.

Aaron Lee

13:10>> Yeah.

Nathan Latka

13:11That's not terrible.

Aaron Lee

13:12>> It's it's actually if you look at all the tech enabled business, 60% is actually pretty good.

Nathan Latka

13:18Yeah. That's not bad at all. Got it. Okay. And how many customers are you serving today total?

Aaron Lee

13:23>> We have thousands, like Yeah. Close to

Nathan Latka

13:25Close to what thousand?

13:27>> Three. 3,000.

13:27Wow.

13:28Yeah. Okay. And how many do you think you can get to by the end of the year?

Aaron Lee

13:32>> Oh, I think we can actually get to about, like, if we are lucky, we probably can get a five to six thousands.

Nathan Latka

13:38Interesting. So 3,000 customers at a thousand a month, I mean, you guys are doing like $300 a month in revenue right now.

Aaron Lee

13:44>> Actually more than that. If, let me see, we are doing, I think like a few months ago, we crossed $20,000,000 annual run rate.

Nathan Latka

13:53Oh, you're doing way I'm missing something because you're doing way more.

Aaron Lee

13:57>> Yeah. But like a thousand is actually close to, like, it's a blending between different services. So like we have, let's say the outbound one, people are paying, like, thousands of dollars because they're using us as more like a SDR as a service team. For the chat, it's a lower one because, like, the ASP is actually lower.

Nathan Latka

14:17Yeah. If Sorry. Just to be clear, like, last month in June, you're saying you did more than 1,500,000 in revenue that puts you more than a 20,000,000 run rate?

14:24>> That's right.

14:24Oh, wow. Okay. So if you did 1,500,000 last month, what were you doing exactly a year ago? Do you remember?

Aaron Lee

14:29>> I think it was about half of it.

Nathan Latka

14:30Okay. So what drove all the growth expanding into current accounts, feature upsells, etcetera, or brand new accounts altogether?

Revenue Growth Drivers

Aaron Lee

14:37>> I think most of them are actually, like, going from, like, one service to another service, like cross selling. Yep. Another one is, like, we're actually adding more kind of features so people actually pay more to upsell. And also, we're attracting businesses that are more mature, like, meaning they actually have higher call volume, higher chat volume. And on top of that, the outbound services we started about a year ago is where people say, hey, you know, not

15:08>> only do I want you guys to answer the inbound call, I also want you guys to make the outbound calls. And that is like, I call it the sales and marketing side of things.

Nathan Latka

15:16So how many are those full time employees at smith? How many full time SDRs do you have at smith?

Aaron Lee

15:22>> So the team that we have, we have about total, I would say their headquarter plus agents about more than 600 people.

Nathan Latka

15:30Okay. So the whole company is 600 full time employees today?

Aaron Lee

15:34>> Yeah.

Nathan Latka

15:35Okay. And guess how many of those are SDRs that you will sell at some 60% margin?

Aaron Lee

15:40>> Yeah. So we actually have a mix. So we have a team like we call like kind of the, I call it universal agent where they can handle inbound or outbound depending on the time of the day and the day of the week. Right? Because sometimes you have more outbound than inbound. Sometimes you have more inbound than outbound. Interesting.

Team Size and Geographic Breakdown

Nathan Latka

15:57What's the geographic breakdown of the 600?

Aaron Lee

15:59>> They're all in North America.

Nathan Latka

16:01Oh, wow. Okay. Interesting. How many of them are engineers?

Aaron Lee

16:06>> So we have about 25 engineers.

Nathan Latka

16:09Wild. Okay. Very interesting story. What about how how have you capitalized the business?

Aaron Lee

16:14>> So if you think about our model, right? So we it's a network effect. That means the more people who use the service, the lower the cost that we're gonna be. That's number that's how you capitalize. Number two is with the AI. Right? If you look at, like, AI in the past, I would say before the ChatGPT, We're using AI for, like, transcriptions. We're using AI to do, like, real time language, like Spanish to English. We're using

Capitalization and AI Strategy

Aaron Lee

16:38>> AI to do some of what I call the behind the scenes stuff, back end processing. Now with the ChatGPT, we can actually do kind of, like, more front end. We can have an AI voice bot to talk to you that can collect your name, your email, your phone number. We can actually have a conversation with you. And the outcome of the conversation is we have the entire call flow. We know where your customers are calling you

17:01>> from, the time of the day, the day of the week, where did they call you from, and what they're calling about. So if you look at the call and the chats today, chat GPT, they're using all the public data, public information, but there's so much information that is not public. Like, all the clocks in the chest for the company and for the businesses, they're not. So we have all the advantage.

Nathan Latka

17:24So you have a unique training set. You're arguing that your moat is that you have a unique training set that the other rest of the world doesn't have.

Aaron Lee

17:30>> Exactly. And on top of that, if you look at all the generative AI, they can afford to make mistakes, but for us, we can't. Right? If the business is saying, hey, we charge, like, $50, like, upfront kind of, like, reservation fee, and other business, they charge nothing. We have to tell the exact right answer every time. We cannot make up an answer. I think that is where the AI becomes a little bit challenging because of the

17:57>> accuracy, like AI hallucination, latency. Right? If you look at ChatGPT, sometimes it takes three to four seconds. Yeah. Once to come back.

Nathan Latka

18:06So just to be clear on capitalization, have you raised equity to date? And if so, how much?

Funding History and Convertible Notes

Aaron Lee

18:10>> Yes, we did. We did. I think together, like, we raised, like, with us angel investors and the safe note, I think I was close to $13,000,000. Yeah. Okay.

Nathan Latka

18:22And what was the safe note cap? Did was it capped or uncapped?

Burn Rate and Runway

Aaron Lee

18:25>> I think it was capped. Yeah.

Nathan Latka

18:26Okay. What did you like a 30,000,000 cap, 20,000,000 cap? Do you remember?

Aaron Lee

18:30>> Oh, it's much higher than that. I think it's it's higher than that. It was like close to more like a 100. Yeah.

Nathan Latka

18:36Wow. Okay. That's really rare, but you were able to get that because you're a repetitive entrepreneur, you have a track record, but a 100,000,000 cap on a, on a say, that was a convertible note, right?

Aaron Lee

18:45>> I think we have multiple convertible notes, the the one early in the years were like, I was talking about the most recent one, but the early ones was much lower, But I think there are two things that people really like. One is, like, this is my second time, so people like this is for, like, kind of, like, repeat sounders. Number two, we actually have very significant revenue traction. If you look at all the AI companies today,

19:09>> they have a nice, I call it the UI wrapper on top of AI or ChatGPT, but the revenue is very lacking. Right? It's unproven.

Nathan Latka

19:18Well, bet on Aaron, you bet on yourself though too, right? I mean, if you've raised 7,000,000 on on notes and you've raised 13 today, you guys put in 6,000,000 yourself before the notes. Is that accurate?

Aaron Lee

19:28>> No. No. No. When I talk about the 7,000,000, that was in the first my first company, my first startup. I'm talking about smith dot ai. We raised a total of 13.

Nathan Latka

19:38Yeah. 7,000,000 was a seed round in 2019.

Aaron Lee

19:40>> Correct? Right.

Nathan Latka

19:41At a 100 at a $100,000,000 cap?

Aaron Lee

19:44>> No, that was a much smaller cap. I don't remember the exact cap, but that was that was like way early.

Nathan Latka

19:50I see. I see. So most people on CRM, they're selling 20% of the company. Were you sort of in that same range in terms of the conversion?

Aaron Lee

19:57>> I think so.

Nathan Latka

19:58Yeah. Okay. So that would have like a 20,000,000 cap or something like that. And is what you're saying is you left that note open, you quote, let it roll and you raise an additional 6,000,000 on that over the past three, four years?

Aaron Lee

20:07>> Yeah. We we kind of like open a new note because of the the revenue traction. So was expanding the previous note. Like, I think we have multiple safe notes and most was much closer to a 100.

Nathan Latka

20:19And when did you do that last close? What year?

Aaron Lee

20:22>> I think it was like two years ago.

Nathan Latka

20:24Okay. So twenty twenty twenty one, you did the last close at 100,000,000. Okay. And that's when you were doing about, what you're doing, like 4 or 5,000,000 in revenue at that point of time?

Aaron Lee

20:32>> Right.

Nathan Latka

20:33What year did you pass a million revenue? Do you remember?

Aaron Lee

20:36>> Oh, that that was so long ago. I don't remember that. Yeah. Yeah. But I think in '20 yeah. I think I I don't even remember, but

Next Steps: Sales, Marketing, and AI

Nathan Latka

20:43My research my research says you passed that in 2018.

Aaron Lee

20:47>> 2018? Yeah. That that could be right. That could be right. Because the first year was always difficult, right? 2016, we stopped building the product, we're getting traction, kind of the MVP. And now the product is like fully built, like we're just like leveraging more and more AI.

Nathan Latka

21:04Yep. So are you guys profitable today? Are you burning money each month?

Aaron Lee

21:07>> We're burning money.

Nathan Latka

21:08How much?

Aaron Lee

21:09>> Oh, that I don't remember.

Nathan Latka

21:11Oh, come on. You don't remember that, Aaron. That's the most important thing.

Aaron Lee

21:14>> Well, actually the burn is like not that significant. That's why it wasn't at the top of my mind.

Nathan Latka

21:18Like a 100, under a 100,000 a month?

Aaron Lee

21:21>> Just a few $100,000.

Nathan Latka

21:22Okay. You say just a few 100,000 for a lot of people, that's a lot, but for you, because you're a scale that might not be that much. I mean, so you guys have, it sounds like more than five, ten in the bank, more than twenty four months of runway?

21:32>> Yep.

21:32Okay. Got it. So burning something like $300 a month, more than 10,000,000 in the bank, have runway. What's the next move? Product, hiring, acquisition?

Aaron Lee

21:40>> Yeah. I think right now we're very much focused on sales and marketing because like, people love the product. People say, wow, that works. Right? I mean, it it ask it allows them to expand the business. But then when we look at, like, kind of the the product side, we need more AI talents. We need to build up our that kind of muscle because it is where you can take your gross margin to the next level. Sales

22:02>> marketing is another way that we can drive more growth.

22:06>> Yep.

Nathan Latka

22:07Very cool. We're rooting for you. This is a great story. Let's wrap up with a famous vibe. Number one, favorite book.

Rapid Fire: Books, Tools, and Personal Life

Aaron Lee

22:12>> I would say, I'm just reading about the one, what was the name?

22:20>> Well, Crossing the Chasm is one that like is my favorite classic. Like it's just so like, it just stayed true for decades. Mhmm. That's one of my favorite books.

22:30>> That's a good one.

Nathan Latka

22:31Number two, is there a CEO you're following or studying?

Aaron Lee

22:35>> I am actually very impressed by Microsoft CEO, like Satya Nadella.

Nathan Latka

22:39Number number three, what's

22:42your favorite online tool for building smith?

Aaron Lee

22:45>> Favorite online tool?

22:50>> You mean on the development side

Nathan Latka

22:52or Development side.

Aaron Lee

22:55>> Well, we use GitHub. We use Notion. We use, like, ChatGPT. We use, like,

23:03>> AssemblyAI. Yeah. So just there's really a lot of tools that we use.

Nathan Latka

23:08Number four, Aaron, how many hours of sleep do get every night?

Aaron Lee

23:11>> Oh, actually I would say I do six to eight hours.

Nathan Latka

23:15That's great.

Aaron Lee

23:16>> And situation, married, single kids, I saw a ring on your finger.

23:18>> Yeah. Married, three kids, all girls.

Nathan Latka

23:21Yeah. That's awesome. And how old are you?

Aaron Lee

23:24>> Me, I am 40.

Nathan Latka

23:2640. What's something you wish you knew back when you were 20?

Aaron Lee

23:30>> Oh, I wish I had a mentor that tells me about how to build companies.

Nathan Latka

23:36Guys, smith dot ai is doing it did 1,500,000 revenue last month, over 20,000,000 run rate. That's more than doubled from a year ago. They've got 600 folks on the team. And what they're doing is they're helping folks do they, you know, work with customers much better in a more efficient way, whether it's voice, inbound chat, inbound messages, they're using AI to power this. But unlike most AI companies, have real revenue. Again, voice, web chat, outbound SDR

23:58as a service launched back in call it twenty fifteen, twenty sixteen after Aaron sold his first company Red Beacon to Home Depot, then stayed at Home Depot through 2015. Aaron, thanks so much for taking us to the top.

Aaron Lee

24:09>> Thank you so much, Nathan.

Nathan Latka

24:11One more thing before you go. We have a brand new show every Thursday at 1PM Central. It's called Shark Tank for SaaS. We call it deal or bust. One founder comes on, three hungry buyers, they try and do a deal live and the founder shares back end dashboards, their expenses, their revenue, ARPU, CAC, LTV, you name it, they share it. And the buyers try and make a deal live. It is fun to watch every Thursday 1PM

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24:58fundraise, a big sale, a big profitability statement or something else. I don't want you to miss it. Additionally, if you want to take this conversation deeper and further, we have by far the largest private Slack community for B2B SaaS founders. You want to get in there. We've probably talked about your tool if you're running a company or your firm if you're investing. You can go in there and quickly search and see what people are saying. Sign

25:20up for that at nathanlatka.com/slack. In the meantime, I'm hanging out with you here on YouTube. I'll be in the comments for the next thirty minutes. Feel free to let me know what you thought about this episode. And if you enjoyed it, click the thumbs up. We get a lot of haters that are mad at how aggressive I am on these shows, I do it so that we can all learn. We have to counter those people. We

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