SaaS Scaling Secrets
The SaaS Scaling Secrets podcast reveals the strategies and insights behind scaling B2B SaaS companies to new heights. Dan Balcauski, founder of Product Tranquility, leads conversations with successful SaaS CEOs, exploring their challenges, triumphs, and the secrets that propelled their businesses to the next level.
SaaS Scaling Secrets
The Bumpy Road to Multi-Product Success in SaaS with Ryan Wang, CEO of Assembled
Dan Balcauski talks with Ryan Wang, CEO and co-founder of Assembled, a support operations platform. The discussion covers Assembled's evolution from a workforce management tool to incorporating AI capabilities for customer support, the cultural and strategic challenges of launching new products, and lessons learned from experiences at Stripe. Ryan emphasizes the importance of a clear mission, customer feedback, and managing internal dynamics when expanding product offerings. They also explore the practicalities of introducing new technologies, team coordination, and building a cohesive company narrative.
01:46 What Assembled Does
02:38 The Evolution of Assembled
06:33 Building a New Product
11:37 Cultural and Strategic Lessons
20:30 Comparing Stripe and Assembled: Cultural and Systemic Differences
23:35 Launching New Products and the Role of AI
24:16 Tactical Approaches to Market Entry
26:02 Challenges in Scaling Sales Teams for New Products
29:55 Strategic Decisions on Product Structuring
Guest Links
Yeah it started as a second product and then we accidentally turned into three, four, and five all at once. in 2022, of course chat GT launched and. With our ML backgrounds and just kind of surveying the technology landscape it became really obvious to us, well, what is possible in customer support has totally changed and we should not build AI for support because it's cool or because the technology seems really fun to play around with. But it actually did start in truth with somebody at a hackathon putting together a bot and dropping into Slack. When you talk to customers, magical things happen. my belief is that, you win customers fundamentally by word of mouth. Awareness helps but at the end of the day, there's nothing that beats finding somebody, having them take a bet on you.
Dan Balcauski:Welcome to SaaS Scaling Secrets, the podcast that brings you the inside stories from the leaders of the best scale up. B2B SaaS companies. I'm your host, Dan Balcauski, founder of Product Tranquility. Today I'm excited to welcome Ryan Wang, CEO and co-founder of assembled a support operations platform that helps companies like Stripe, zoom, and Etsy manage their customer support at scale. Before founding assembled, Ryan was employee number 80 at Stripe, where he watched the company scale from 80 to 800 people and helped build the ML systems that powered fraud detection and support automation. Ryan, welcome to the show.
Ryan Wang:Great to be.
Dan Balcauski:Very excited for our conversation today. Ryan, before we dive into your scaling journey, just give us the elevator pitch. What does Assembled do? Who do you serve?
Ryan Wang:Yeah, assembled is the AI platform for customer support. So we help companies with the full gamut of all things customer service. So automation to solve 70, 80% of your customer tickets over chat, over voice, over email augmentation. So co-pilot that makes people 20, 30% more
Dan Balcauski:I.
Ryan Wang:ramp 50% faster. All the way through to operations. So helping companies manage 20,000 plus person contact centers with the forecasting and the staffing planning and the capacity planning between ultimately whether you should talk to an AI agent or whether you should talk to a human agent or whether that should be interspersed, making sure your customers have the right experience. We help companies of all different shapes and sizes, so Ashley Furniture, Stripe, Robin Hood. So, so it's quite the complex set of customer service concerns.
Dan Balcauski:Well quite a broad portfolio and I wanna dive into that set of capabilities directly. So as I understand it, you correct me if I'm wrong, but assembled started as a workforce management. Platform and now you've added these AI agent and copilot capabilities. And so adding that, maybe you consider it a second product or not, but it kind of appears that way, could often be a challenging transition for scaling companies. So I'm really curious about the decision around adding. Additional product because again, this could be a fraught time. Um, what was your thought process in terms of timing? Like how did you think about this is the right time to do it or not? And we go from there.
Ryan Wang:Yeah it started as a second product and then we accidentally turned into three, four, and five all at once. And so really stretched our roadmap. The story goes further back. So I was at Stripe. I was actually working on machine learning for fraud detection. This was around 2014 or so. And had nothing to do with customer service, but Patrick and John Collison, the co-founder of Stripe. They cared about it a lot. They would do support rotations. They would have people over to their apartment in the mission in San Francisco and invite the whole company to do customer support. And then over time, of course, from 80 to 800, that doesn't make sense. It doesn't scale. You bring in outsourcers, you start to have different products that you're servicing across different geographies. You start to need to have different tiers of escalation. And so, it became really obvious right around 20 17, 20 18, that customer support was one of those spaces where you would apply, not ai. But machine learning and it was in service of helping the company scale, great customer support, being high quality, really efficient multi-product, global support. And so when we started, our first product was workforce management. But that actually came out of a question that we asked to the folks at Stripe. There was a guy named Bob Van Winden who ran support and he had come from Google and seen the challenges scaling up Google, and we asked him just What is software you need to buy or you hit your options. And it wasn't just Bob, it was a hundred other support leaders but a lot of them came back with this answer of, well. This thing called workforce management. It's a team that's growing from, several hundred to several thousand and figuring out who should be working on what, getting the right person in the right place at the right time. That was the challenge of 2018 in terms of scaling support and exacerbated in 2020 when we launched. So it actually took us two years to get off the blocks. But in 2020, companies were growing really quickly, and it was all through headcount. And so workforce management became an even bigger problem. And then in 2022, of course chat GT launched and. With our ML backgrounds and just kind of surveying the technology landscape it became really obvious to us, well, what is possible in customer support has totally changed and we should not build AI for support because it's cool or because the technology seems really fun to play around with. But because that answer to that question, how do you scale great customer support. We feel like has changed in this moment. So, it was really easy for us to say, yeah, that's something we wanna do. It's something that fits our mission. It's something that fits our product portfolio in terms of the value we deliver and and then stack these products together.
Dan Balcauski:So, so solving the same core problem and this happenstance of the technology meeting capabilities with your background experience, uh, around, you know, the ability to bring ML to really solve this problem well outside of just adding humans to the mix. So you guys decide to go full bore on this. How did you approach it? Like meaning, how did you think about organizing the team to build this additional set of capabilities? And maybe at that time it was maybe, I dunno if you thought of it originally as a net new product or just a feature set, but kinda lead me through that process.
Ryan Wang:Yeah we did think about it as a new product from the very beginning, I will say it was. 2.5, possibly take three of building a new product that assembled and so company had actually come out of this bottoms up motion at Stripe. We were building ML tools for customer support. But I can tell you. asked us to do it. They actually, they asked us not to do it. I remember having this meeting with the head of product and the head of systems engineering and the head of product said, Hey, you should be working on the dashboard. That's a high priority. There's not enough people working on that. Why are you working on support? And the head of systems engineering said, you're doing machine learning for customer support. Doesn't NLP solve all this, kinda shades of AI doesn't like what's the point? Isn't this just kind of it on and it works? So, so actually the first project within Stripe that was kind of ML support happened. It was almost a startup. Within a startup. It was. myself and one of my co-founders Brian Z trying to solve a problem, going to visit call centers, figuring out what their problems were trying to pull in more engineers and scrap for resources. We borrowed this intern, said, Hey, we have this project going on and it's starting to deliver a lot of impact and we think it could be a really big team eventually. So that was our mindset coming into assembled of, okay, well we had created a product and a lot and that wasn't just customer support. A lot of the great products that Stripe had come bottoms up somebody. There's this thing called the Crazy Ideas Hackpad. looked on the crazy ideas Hackpad, and they were hired to do this, but they said, actually it would be cool to have an API to create a company. That was how Shred Ball came about. So our first. product. We just thought, hey, if we tell people, you know what, here's the story of how assembled even got started and if you see the right opportunity, you don't have to ask, just do it. Create a new product. And it never off the got off the ground. There was a group of people that would meet weekly and they were trying to build a product called Route Automated Routing, and now it's part of our product portfolio. But they would meet every week and it just never caught. And then our second attempt at a product. It turned out to be a killer feature. It was a feature to help BPOs or outsourcers connect into customers like many of our largest customers. So, so if you have multiple BPOs, it's almost like a network of. Rather than individuals, hundreds of individuals at a time. So we wanted to give people visibility in that whole network rather than just the people. And we came super tops down. We wrote a strategy. A woman who is an MBA intern wrote a really impressive document on all the BPOs we should go after in what order, the integrations we would need the size of the market. She's now at OpenAI. So it was really high quality, but just took us so long to get off the blocks there. And then the minute we started building it was like, hmm, the strategy's not quite right. Actually, we gotta start with the integrations, not the BPOs this way. So our new product around AI was kind of the synthesis of those two things was. We know strategically why we're doing this. We want to help deliver great customer support. We know the technology LMS are gonna change everything in terms of what's possible. But it actually did start in truth with somebody at a hackathon putting together a bot and dropping into Slack. And we had this Slack channel called product Questions. People come ask, Hey, how does our in Workday integration work? And in truth, it was, product, 70% of the time you get an answer to your question. but when this bot showed up and it was automatically answering questions, our support team, our sales team, our customer success team, they were, oh my gosh, we're actually getting answers to our questions, and they're right and they're good. So from there it became really obvious, okay, we have something. There's something to build around. There's a use case. And then the top down version of taking that idea and running with it was to say, we're gonna put you in a separate room this time around. We're gonna put you in a different Slack group where you're gonna, gonna have people dedicated to this team and your whole mission, and your whole goal is to create a new product. And we're gonna borrow the playbook. Even of Y Combinator, you have 12 weeks. At the end of 12 weeks, you're gonna do a demo day. And. The fast forward is that it didn't take 12 weeks. It took a little bit longer than 12 weeks, but it operated in that kind of cycle.
Dan Balcauski:So just to, so I could lay out the roadmap for listeners a little bit. So, the AI product not the second product necessarily, but you described it, maybe it's, 2.5 or three, depending upon how you look at it with this interim, products. That was for the BPO. So you have sort of workforce management, uh, this BPO, uh, initiative, and then you have the new AI agent and copilot offering. Now I'm curious you, so you said the, you, so you separate this group off. Y Combinator style to go run for 12 ish weeks. What were you trying to avoid with that situation? I guess what was the lesson learned from that intermediate time where you're like, oh, this will be the better way to go?
Ryan Wang:Yeah. Yeah. The lesson from the first experiment was that it needed to be a dedicated team with a clear goal.
Dan Balcauski:Hmm.
Ryan Wang:it couldn't just be purely bottoms up. And so, for example. We, we didn't tap people. We didn't say, Hey, you're, one of our strongest engineers. Go join this team. Instead. We had an application process. The application was just three short questions. It wasn't super formal. Have you done at assembled so far? why do you wanna join this team? And what do you know about ai? you could kind of, tease apart most of the characteristics that we need in the team from those questions. But the lesson from the second angle of getting too hung up in strategy, not going too tops down was just, it's the classic Mike Tyson quote. Everybody's got a plan until they're punched in the face. We had this plan, we just don't know. The space is evolving, the technology's evolving, and then also in the early days of assembled, it's not like we set out and said. We're going to go solve workforce management. what we said is we're going to go improve customer support. That's the broad bucket in which we play. And then we're gonna go ask a bunch of people who know a lot about customer support, what is the specific problem? So we wanted to put people into that situation where. could just ask that question, run after it, learn really quickly, be unencumbered by a plan that was to set based on things that, that they didn't, we didn't learn from customers. And then have a clear goal at the end of the day.
Dan Balcauski:So, okay, so you put these folks off Tiger Team Isolation dedicated process with a high maybe some sort of filter into that group that, you know, these folks are qualified go-getters, uh, that you want in there. So you know, they go run off. I guess, what was your idea for, okay, like they're gonna go create something. How do you, was the idea like, okay we'll then be just kind of creating like a, another company division. Was it like, what was the thought about like how you're going to reintegrate them, I guess? Tell me what that looked like when, what's that 12 ish week period is done?
Ryan Wang:Yeah. Well this part we really messed up, I would say. And when you read the. HVR McKinsey articles about Horizon 1, 2, 3. They make it sound so clean. You're in the zero to one phase and then you know when you're out and then you put it in this other stage. It just. Maybe we didn't read the articles closely enough, but we definitely messed up on the reintegration part. I think because it took a really long time for it to be clear that it would be successful. In truth, at the end of the 12 week demo day there was some really exciting progress. There was some, adoption charts that were going up, but it wasn't this is in 2023. It wasn't like, oh geez. E everything sorted out and you could see this thing going from zero to a hundred million a r overnight. It was, you kind of had to squint a little bit
Dan Balcauski:Mm.
Ryan Wang:There was early customers where, okay, they're really sticking with what we're doing, but we have to keep iterating here so. The 12 week demo day turned into, you know what, we'll check back in again in three months. And then instead of it just being a demo day for a small group of people, you're gonna go in front of the whole company. It'll be at the all hands. And so, you'll have to do that pitch for everybody. And even then, I think. The mistake was we had kind of set it up as the company is almost like judging you, American Idol style it created a little bit of resentment. come these people who, filled out the application, they got to be on the team and we're over here. Working on the old thing. They get to mess around with AI and it's super fun and they were having a lot of fun in the room. I was like, how come they're having so much fun? This is what the heck, we're over here solving big, multimillion dollar a year customer problems, and they're over there just throwing stuff at the wall and moving fast. The hell, so, so that resentment lingered I think when it became really clear, okay, this is successful. We want more to put more investment for us of the company. We wanna move people from our older product to our newer product. And we get this question all the time. Are we pivoting the company? Are we pivoting the company and how do these products go together and should we jet us in this off? Or the small group said, maybe we should turn this into a separate startup and it's a subsidiary
Dan Balcauski:Mm.
Ryan Wang:And in both directions. And that resentment was really problematic because the story we tell about assembled that I just told you is. Well, our value prop is to be human and ai. It's to own the end-to-end customer experience. And all the products on top of that, like these products are stronger together and they're all about helping our customers deliver better customer support, high quality, super efficient. You can't do that if it's just one piece of this kind of narrow slice. But we really, really had to bring people back into that because of, I think some of this resentment, some of this cultural built up story. And then also because we went into it without a plan.
Dan Balcauski:Mm-hmm.
Ryan Wang:without a plan, but without, but by saying, who cares about the strategy? We'll figure it out after it's successful. But then once it was successful, we really had to build the story back.
Dan Balcauski:I, well, I'm curious. Uh, it's funny because I've actually, uh, my previous life as a product leader, I got put in one of these labs teams as like, you're gonna horri, basically Yeah. These Horizon three group. And so I've lived that. World of resentment. The irony is, is like it's not any better on the other side. Like, like, like they're always having fun over there. They caught you in a moment of like gallows humor probably because like you're over there beating your head against the wall, being like, it's still not working. It's still not working.
Ryan Wang:Yeah.
Dan Balcauski:Our time is running out budget is running out. Like we've got show progress. I'm curious, like as a leader you know what, I guess you know, was that. People, people's feelings I guess are always valid. Do people have feelings? But I'm sure like there was a perception issue. Like are there things that, you found that were successful in diffusing that, or things that guys looking back that you like would have like implemented? Like just think about other people listening if they're gonna like, send off these cyber teams. Right. Like it's interesting'cause you always think about like, okay, what's the market acceptance of this thing? And I could easily see how that sort of cultural resentment. Maybe like it was like, oh, it wasn't even on the checklist of things I needed to worry about, and all of a sudden I'm having to manage that. So like as you think about that, reflect on that time as a leader now, like is there things that you did to help push that forward or, even if it was later than you might have anticipated.
Ryan Wang:Well, I resonate a lot with what you said. It feels like outside looking in, it's so fun. And then inside looking out and this was the resentment going the other way that we saw too. Like, what the heck? This is really hard. It's existential, it's like we don't even have any customers yet. Or, Hey, we have like three customers. We're trying to get to 20. How come the rest of the company won't help us? And I think a lot of the re the literature, there's so much great literature now on how to go multi-product, the mechanics of it, and when to do it and why to do it earlier. There's not much on this cultural piece for me. I think lesson one or the first, most useful thing, and I, it wasn't necessarily that I went in prepared. I talked to this guy, Peter Gasner, who started a company called Veeva,$50 billion Life Science SA Company. And this was about a, this was in 2022. Or so so before we even started this initiative, and he had told me in the early days of Viva, they built a second product and intentionally he said, we pull it as far from our first one as humanly possible. Why? Because the tendency is to go very iterative. So in Viva, in the early days, second product as far away as possible, from the first one he said, we almost broke the company. This is, we almost broke the company and he elude. I didn't understand at the time, but he alluded to the reasons it was, I think because of the cultural pieces. It's these questions. Yeah. Are we pivoting? We thought we were workforce management. No, we, this company like. joined the company and that product was working. No, you joined the company. I thought we were improving customer support. Full stop. So you have to lift people's eye line. You have to show them, Hey, yeah.
Dan Balcauski:Mm-hmm.
Ryan Wang:today's world, in any given moment, there's constraints. is competing against that in terms of resources. Go here or there. But in the long term, all these things are coming back together to be a powerful platform. And you really have to show people that way. And I do think that's part storytelling on the part of the leadership. That was, I would say the second part. And that contributes to the cultural story of why are we doing this? Are we doing this because we need more a RR? Are we doing this because AI is super hot? Are we doing this? Because the other thing's not working. No, we're doing this because these things are better together. But that's a big part of keeping people motivated and them understanding the story and seeing it for themselves. Even though, again, just because of the, how far apart these things are, they might have to squint a little bit and they might not get it for a while.
Dan Balcauski:Well, I mean, uh, is the old, uh, adage, nature of bores a vacuum and humans, if there's no information, we tend to paint in that black spot our worst visions of it. Right. So what you mentioned is what we're doing not working? Or is this like another bet the company thing to get us, you know, you're like, no, no, no. It's all, all in the same plan, but we've gotta experiment. So I could see how that be a challenge. You mentioned before, I wanna. We'll come back to kind of, launching with this new product, but I want to just tie it back because you did talk a little bit about Stripe and how Stripe Atlas came out of this, kind of, sort of bottoms up and I guess what. What do you feel like was, either different in the water of what you saw at Stripe versus what you experienced at assembled? I mean, maybe you were just in a different position as a, you know, if you're, you know, an engineer kind of working on stuff versus now you're the CE leader. Maybe, you know, maybe the Coons had the exact same view of like, dysfunction, right? Of like, oh my God, there's a bunch of people launching stuff and nothing's connected. I don't know, I haven't picked their brain. Um, be happy to talk to'em at some point, but, um, I'm curious, like, kind of looking back like what do you, like, was it a cultural thing? Was it like part of the systems and processes they had for kind of taking those products from incubation into the market? Like what do you what was your sense of like maybe what was different?
Ryan Wang:Yeah, I think the process and systems came later, but I think two big dimensions that worked in concert, I think one was that kind of clarity of mission. That was a really big one. And Stripe, they're not a payments company. It's increasing the GDP of the internet. And when I was an engineer, I thought, that's so stupid. What, what does that mean? Increase the GDP of the internet? No. What? Like, let's build technology, let's improve payments. I don't get it. but now when you zoom though all the way out, it's like, how does Bridge Stablecoin fit in? How does Stripe Atlas fit in? How does having. printing press, you know that. Does books make sense? Well, yeah. It's not a payments company. It's increasing the GP of the internet. All of these bets fit under that big tent. And similar for us, I think we're trying to improve customer support, full stop. But even beyond that, we wanna solve the operational challenges that come after big ideas. For companies like Stripe that become successful. Support is a function. Trust and safety is a function. Operations broadly is stuff that comes after. So we try to fit people into this. It's not a workforce management company, that's one of our products for sure, but we're trying to solve this very big problem.
Dan Balcauski:Hmm.
Ryan Wang:I think the other part that fits that plays well with a big vision is. When, what the best people in the world. They don't wanna just, yeah, I don't want, just wanna make a, 10% better payments company than the thing that came before. Don't wanna just build customer support systems and solve this, legacy problem that's a little bit better. They wanna do big things. And just to put that in perspective, my recruiter at Stripe, Daniella Amedee, she's president of Anthropic my. Onboarding was done by the then CTO of Stripe. Greg Brockman, he's of OpenAI, and so you know, you get this density of people together who are not just super, super smart, super, super humble, all of those things, but just want to take whatever they're doing do the biggest possible version of that. When you combine that with, yeah, we're increasing the GP of the internet. All these products just kind of start to happen, I think. So, so I think my last takeaway for assembled has been to remind people, like at some point with Stripe, it was, it felt like, oh yeah, like, what the heck, what does increase the gp, the internet? We, but at some point then it became, yeah, we're gonna launch more products. This is not just, this is not the end of it, this is not just a second product. It's not just a third product, not just a fourth product. We're gonna do a ton of products. To do that. And that's what we're trying to do with Assemble. It's like, Hey, yeah, we've launched AI agents. That's super cool. It's super powerful. It's a huge market. And there's more coming after that.
Dan Balcauski:So I heard it there. Go hire the amortize and Greg Brockman. If you really
Ryan Wang:right.
Dan Balcauski:want bottom up transformation to successfully work and a have a big vision. Like, increasing the GDP of the internet. Well, hey, so, so I wanna go back to kind of the tactical. So, the team is three, six months in they've got some traction now. You're trying to take this thing to market. I'm, walk me through like how you thought about, okay, like this is going to be a second product. We've got this workforce management thing. This is. It's, you know, enabling customer support. It fits under the broad umbrella. But, I mean, those work well for employees and maybe investors company, customers don't always kind of understand how all pieces fit together, nor do they have the patience for that in a, 30 minute demo pitch. Right. We're improving the world of customer support. So I guess like what happened, how are existing customers reacting when you show up with a new a product AI product? How did you think about, positioning that within, the existing customers that you already had.
Ryan Wang:So the super interesting thing that was unexpected to me was customers understood it immediately. In the early days we'd hear a little bit of, oh wait, I thought you were workforce management, but now you're doing ai. But then immediately the second thing would be. Oh, because you have all the data, because you can route it, whether it's a person or an AI or a BPO agent or somebody you've hired, it's all cost quality on the same curve, and you're trying to deliver the end-to-end customer experience. Like Yeah, that's what we're just gonna rip and put into our deck what
Dan Balcauski:Mm-hmm.
Ryan Wang:exactly. so customers understood it so, so, so early on and I think it just goes back to the truism is. When you talk to customers, magical things happen. The harder part for us was to say customers get it. Some subset of the company gets it, but now we're kind of turning on the go to market engine once again. And we have to, we can't skip steps here because as a, later stage company, you feel like, okay from the early days, you're figuring out from the scaling days, you're making it repeatable. Here's the playbook for how to learn about call centers, how to learn about workforce management, what our product does, who our competitors are, and we to go back to people and say. It's, there's no playbook anymore. Yeah. Take what you know, Gershwin and Thia said and extrapolate that into what David at DoorDash said, and put these things together and mix and match. And the set of people who are really good at doing the repeatability thing are not necessarily the same people who are really good at the mix and match thing.
Dan Balcauski:It kind of almost reverts to like a founder led sales again, right? Where they talk about like early days of the product, like the founder is gonna have to sell for a while.'cause there is not a repeatable way to pitch it until you sort of learn and then you could sort of create the playbooks and hand that off and, you know, build out, you know, the whole sales team. Uh, so when you introduce like a product, you almost have that problem again. I mean, there's obviously a machine already running that you'd like to just drop it into, but you don't have a pitch yet. Quite as, you know, uh, like steps 1, 2, 3, that sort of cookie cutter, that maybe, you know, your new brand new AE off the street can just kind of slot into. Is that,
Ryan Wang:Totally.
Dan Balcauski:yeah.
Ryan Wang:And then the founder-led sale is so important on the second product. But then and you just go all the way back to early days of, what do you tell any company going from zero to one, one to 10, et cetera. Well then you have to build out the sales team. Well, don't go hire a hundred people just because your model, your spreadsheet model says you need, this to, you gotta go one, then two, then four, right? Or, start with two, maybe then four, then eight, and then you have, figure it out and build it up over time. The. The amnesia of success, so to speak, is yeah, look, we've got this sales team that they're really good at selling this other thing. Really understand the space, have all these relationships, let's give all of them this product. No, you gotta start small again and build it back
Dan Balcauski:Is that what you did? Did you, I mean, did you try to roll it out to the whole sales team? What was actually the experience of rolling out the ai agent copilot product? Did you, was that the step.
Ryan Wang:tried to roll it out to the full sales team and it didn't stick the first time. We had some people who were leaning in, some people who were excited. Some people who, still in that kind of mindset of yeah, let me figure it out. This is amazing. This is fun. And some people who are, ooh, this is I need to figure, like, I need some help here. I need some data. Meanwhile I'm doing a pretty good job selling the first product. So we ran into all of those. And so we rolled it all the way back and we got, a little bit lucky in that there was one ae who. Just was having the worst year. Just I think he was all the way through the year, like September or something, or October. And had put n like, no, zero close. It
Dan Balcauski:Ooh.
Ryan Wang:And so we said, Brian do you wanna sell the AI product? And just, you'll be the dedicated person. You'll figure out all this stuff. And I don't know if it was Exci, he's been very successful. So I, we joke around with him, but it was like, I don't know if he had a choice, it wasn't going super well. But he took to it. Filled in a lot of the playbooks. He worked really closely with John, our co-founder. And so they had this really amazing partnership of you could sell a little bit ahead and the more they worked together, the better it got. And then he was able to bring that back into the rest of the team and they go 1, 2, 4, 8 and get it to everybody else because you could see what success looked like. You had the playbooks and you had this person who just felt really confident once again selling the product'cause. He was the only one. So it was kind of like, shooting fish in a barrel.
Dan Balcauski:I love that. So you found maybe an unlikely candidate, but somebody who could lead the charge, pioneer the process iterate, show success, and then return that back to the team to kinda show what the. What that pattern looked like, uh, versus trying to send a hundred people all at once into the jungle and have them all be confused and half the team doesn't know what's going, which way is up. Love that part. So, you know, I because I'm a a huge nerd about this stuff, I was looking at your site, and so you do have, uh, a public pricing page, which is awesome. Um, and then you have your, uh, workforce management capabilities separate from the ai, uh, agent and copilot. I'm curious okay, obviously, you know, we've talked about them in the sense of two products, but didn't have to be that way. Like how did you think about Okay. We're seeing a bunch of different patterns in the marketplace. You know, folks adding, you know, Microsoft with Office 365 has, their kind of co-pilot as an add-on to the main Office 365 subscription. Uh, Gemini has bundled, all of their AI capabilities directly into Google Workspace capabilities. Uh, I think it's pretty smart on their front. And also they don't want to compete head to head against, like, if I spending$20 on Chacha PT or$20 on Gemini, I'll just like, we'll just put it into the higher tier plans of Google Workspace. How did you end up thinking through the structure that exists today? Like what led you to that decision for your market?
Ryan Wang:Yeah, it really did go back to customers once again. So how do they buy in this case? And what we found was we internally and to investors, and even to customers, wanted to tell this amazing platform story. Hey. It's human plus ai. You've got customers coming in and route them to the right ai, route them to the right person. Or if they talk to the AI and they wanna talk to a person, get'em to a person, or you design it, but we give you all the tools to design it. we found that that wasn't the lead. was the answer to the question. Oh, there's a lot of AI agents out there. What's the difference between assembled end? hundred other tools that you've talked to. But it wasn't the lead. And the lead was actually, there's a customer who they're trying to put voice, AI voice in front of their one 800 number. And otherwise, it's off. It's just, it's only certain hours when you have people or it's really expensive to service or you don't have it in certain languages or so, so they were looking for AI voice, or they were looking for an AI chat because they're last gen set of tools. They're not gen ai, they are kind of linear. Like you click the bubble, do you want a refund or do you wanna talk to, click the button. So they wanted to replace that. Or they were still looking for workforce management. Hey, we've got hundreds of people. We're not ready to do ai. Or maybe we already have done AI really quickly, but there's still all these people, and now we're in a world of how do you figure out the resource allocation between people and ai? So they want workforce management. So it went back to how do we set up those multiple front doors for each of these use cases, because that's what people are trying to buy. Then when they ask the question, what's the difference is when we talk about the platform, there is a different asterisk here, which is when we're going to existing customers. When we're going to existing customers, then it's like, yeah, hey, you already use assembled for workforce management for ai, copilot for AI chat. We make it easy for them to. To move the dollars around, frankly hey, we're thinking about kind of total a CV total TCP, contract value. And I've been fascinated by Microsoft's ability to have the enterprise agreement, all you can eat, whatever it is that you want, you
Dan Balcauski:To the chagrin of every company in the world, including Slack, which eventually had to find refuge in Salesforce.
Ryan Wang:exactly. I think that's the gold standard, right? Just. You tell us problems, you pay us, you know enough for us to solve'em and we'll just go solve'em, whatever they are.'cause we do everything under the sun.
Dan Balcauski:I loved what you laid out there. Just reflect back what I heard because what you said there was we want to meet the market where it is and the questions that those prospects are coming to us to solve, not on necessarily how we want to dictate to the market, like our technology platform upfront. Because I see a lot of companies go. The opposite way. And it usually just ends up in a ton of friction and a ton of sadness. And then the other thing you said, which I really like, I agree, the new customer who's looking at your pricing page, very different from the existing customer. Uh, one because that existing customer has a lot more investment in hearing the full story. Then the person who's visiting your page, maybe they spend 30 seconds on the pricing page and maybe they, you know, read a couple of product pages. But, you know, they don't have time or the ability to sort of comprehend the full thing. They're just trying to get their particular questions answered. And so it's a different model. And so I think that's totally, good. That's a perfectly valid approach so that you can have a different pitch for the existing customer because they are already involved in the ecosystem and can kind of grasp that more rich, platform story. I look, there's a ton of things we didn't get to that I would love to ask, but we are running outta time, so I wanna be respectful of yours and the audience. I wanna wrap it out with a couple of rapid fire close out questions. You up for it.
Ryan Wang:That's it.
Dan Balcauski:Awesome. Well. When you think about all the spectacular people you've had a chance to work with, is there anyone who just pops to mind and has had a disproportionate effect on the way that you think about building, running companies?
Ryan Wang:Yeah. The person that comes to mind I has nothing to do with technology, so a woman named Emily Oster. She now is well known for writing a bunch of data-driven books about parenting when I was in high school and she was a second year, I think, assistant professor at the University of Chicago Department of Economics. So. Pretty busy time for a pretty competitive place to be. I emailed her and as well as a bunch of other people at the University of Chicago asking, Hey could you be a mentor on this research project? My, my school had this kooky thing where you could do Wednesdays and you would go do a research project with somebody. it, naturally most of the economists at, the Virgin University of Chicago department economics were pretty busy and did not respond. And Emily did and she said, sure thing. And come by my office and she myself and my project partner just sit outside her office and we must have been 17 or 18 and it, to this day, I still reflect on, wow, this person so busy. So high powered, so much other stuff going on, but took the time outta the day to not just respond to the email, but to mentor us. And so, there's that truism. You send a good email and you find that people are are pretty generous with their time. That was my experience and I try to pay that forward a lot.
Dan Balcauski:Well, props to Emily. Yeah, it's amazing the impact you can have on others when you stop and give some of your time. And there's also that truism that like the busiest people are the. Easiest to reach. It's like the,
Ryan Wang:Right.
Dan Balcauski:they there's either someone will answer email in five minutes or they'll never respond. And like the folks who answered in five minutes also tend to be the people who are like, I never thought you would respond to that email. So props, props to Emily. It made me think of, there's a great book. I've been in the consulting world, a great book how to Talk So Kids Will Listen. It's a parenting book as well. But it's awesome for a consultant who's trying to speak to clients because a lot of the same principles apply. And that's not to demean that's not to mean clients in any way. But oftentimes we don't make our message heard. In a way that can be heard by the other party. So, always a important lesson. Uh, how do you stay sharp as a CEO? What are you reading, listening to learning from right now? What any, uh, key sources that have really changed your world for you recently?
Ryan Wang:Yeah I think two very different directions. I very long ago. When I was at the University of Chicago actually I took this writing class and the writing class was all about science writing. And because we were trying to write like an honors paper or something. And the instructor, it was, I think it was called The Little Red Schoolhouse. It's a well-known class in a book. And he was explaining actually kind of similar to you, how do you write for the audience? And a lot of the times you write this way because that's how you think about it when you really, you have to explain it this way'cause that's how the audience is thinking about it. anyway, he said the Nobel Prize is an amazing example of this. You go pull up the prize announcements for. For physics, for biology, for chemistry, et cetera, et cetera. And you find that there's three versions of every announcement. There's the public press release, there's the technical background, and then there's the speech. Same topic, very different levels.
Dan Balcauski:Hmm.
Ryan Wang:so I find that reading through these both as interesting in an exercise of how do you dumb down concepts, not dumb down, explain in different ways. And just keeps, it's just interesting. It keeps me thinking about like what is happening in the world that's coming not just next year, two years from now, but 10 years from now, 15 years from now, 20 years from now. The total other direction from that is I love the 20 VC podcast. They have this group panel now with R Driscoll and Harry Stabbings and Jason Lemkin, and they just talk a lot about current affairs and specifically with this very deep lens on technology. It's very kind of turpentine tactical, which is what's useful for me in the very, very near term.
Dan Balcauski:Nice. Nice. Well, if I give you a billboard, you can put any advice on there. For other B2B SaaS CEOs trying to scale their companies, what would it say?
Ryan Wang:I. I think I'd say, why are you paying for billboards? Get on the plane. But seriously, I do think my belief is that, you win customers fundamentally by word of mouth. Yeah. Awareness helps goose that for sure. But at the end of the day, there's nothing that beats finding somebody, having them take a bet on you. Doing a really good job for them and them telling everybody else that they know, like, Hey, wow. Assembled their AI agent or their workforce management platform. Here's what they did, and then percolate that out. And then I found that all of the great companies started there is do a really good job. And then your best marketing is your best awareness is the people who you've already worked with.
Dan Balcauski:Do a great job and get on the plane. Love it. This has been fantastic, Ryan. If our listeners want to connect with you, learn more about assembled, how can they do that?
Ryan Wang:We're at Assembled.com. We post a lot of stuff on our LinkedIn, especially around the intersection of AI and customer support. It's best place to check us out.
Dan Balcauski:Awesome. Well, I'll put those links in the show notes for listeners. Everyone that wraps up this episode of Sask Scaling Secret. Thank you to Ryan for sharing his journey and insights. For listeners, you found Ryan's insights valuable. Please leave a review and share this episode with your network. Really helps the podcast grow.
