#276 Ryan Wang: How Assembled is Building the Future of AI-Powered Customer Support

0:00There will be this kind of like hybrid human plus AI support that companies are going to have to think about. Just because you can automate support does not necessarily mean it would be economically viable to do so across the entirety of the business. If you can auto draft replies for people, and then 90 % of the time they're actually just sending the message without really changing it, then that's like the path to automation. What should go to who at what point in time? Or another way to put it, how do we get the right agent at the right time, the right answer? Build the future of multi-agent software with agency.

0:33That's A-G-N-T-C-Y. The agency is an open source collective building the internet of agents. It's a collaborative layer where agents can discover, connect, and work across frameworks. For developers, this means standardized agent discovery tools, seamless protocols for interagent communication, and modular components to compose and scale multi-agent workflows. Join Crew AI, Langchain, Llama Index, Browser Base, Cisco, and dozens more. The agency is dropping code, specs, and services. No strings attached. Build with other engineers who care about high quality multi-agent software visit agency.org and add your support that's a-g-n-t-c-y dot o-r-g i'm ryan wang i'm co-founder co at assembled and for the ai platform for superhuman support so we help hundreds of customers from startups to enterprises with scaling great support and customers like Ashley Furniture, like Brooks Running, like Robinhood, like Stripe.

1:57We started in 2018. We launched in 2020. So I remember March 2020, you know, there was a Hacker News post and like there was the World Health Organization declares COVID a global pandemic. Assembled launches and then World Health Organization again is kind of like the front page. But yeah, we've been at support since before the AI era. And my own background is I was at Stripe. I was a machine learning engineer. I was working on fraud detection. So that was 2014 when it was an 80-person startup. And the trajectory was like 80 to 800 by the time I left like 2017. And the thesis around support was, well, really, it was an observation like at Stripe at 80 people or so like Patrick and John Collison, the co-founders of Stripe, like, and their apartment is just over there, like a couple blocks from our office now in the mission in San Francisco.

3:00They would have people over to their apartment to do support tickets, like the whole company. It was called like a support rotation and obviously that wasn't going to scale and um at 800 people it was uh like multiple products and kind of 24 7 support and like different tiers and different skills it's just a lot of complexity and so um you know the question back then was just like can we apply machine learning to uh to draw the line between you know the experience at scale and when it was patrick and john and the company in the apartment and we kind of found that a lot of companies were kind of going through similar journeys of wanting to scale great support.

3:37Slack was an early customer of ours. They have the same type of story with like Stuart Butterfield and like the whole company. And yeah, so lots of companies go through this journey. My educational background, like how I got to machine learning is I went to the University of Chicago and I went to study economics and actually econometrics was the fun part. So I got a BAMS in statistics, but my MS advisor was this guy, John Lafferty, who had done a lot of kind of like interesting theoretical work. And in particular, some of the work around what essentially became like one of his early students was this guy, David Bly, who did Leighton Dirichlet allocation.

4:21So like topic modeling. So that was kind of how I got interested in machine learning in the first place. That was like 2012. But that's me and, you know, kind of like kind of working backwards into it, into assembled. Yeah, yeah. But 2012, that's, that's early. So, so from Stripe, you left Stripe to start assembled. And did you say 2018? Yeah, yeah, we started 2018. And it took us a little bit of time to get off the blocks. So we didn't launch until 2020. right at the beginning of the pandemic yeah and and so things were slow presumably uh until uh 2022 or 2023 is that right well it's it's it's interesting so um it was very scary and then immediately it was off to the races um and what i mean by that is in And there was like that immediate, like, okay, you know, everything's frozen.

5:27But customer service actually benefited along with, you know, a lot of other functions, because what happened was there were these companies that were going through, like, you know, the Zerp and the COVID era pull forward of demand. And so companies were growing at kind of like pre-AI, you know, this was kind of wild, like unbefore-seen rates of growth. And, you know, one of our early customers was Robinhood, for example. And when they first started using Assembled, we were actually just a tool to replace spreadsheets for the scheduling operation. And so, you know, they had a couple hundred people in customer support.

6:06And the question was just like, what should people be working on? There's no Google calendar, you know, that works for several hundred people. And so they had this spreadsheet of like every single person, the schedule, like across the top, like days of the week and hours of the day, and who would be working on what. And then it would sum together like, okay, based on Robinhood's forecasting, here's how many people they need. And here's how many people that are actually working there. And it was a good time for us because I don't know if you remember the kind of like roughly 2020, 2021, what happened to Robinhood, like Bitcoin, meme stocks, like they went vertical as a business.

6:46And in particular, it was the need to hire a ton of people and customer support to keep up. And so it went from a couple hundred to a couple thousand kind of overnight. And so, you know, Assembled really got started in that world of like companies going through that type of hyper growth, need to grow their support teams to keep up. And then there was another piece of texture where historically customer support is the call center. And that's a pejorative term. Like now we call it contact center. But the contact center was kind of forced to go digital. And like if you go to like call centers or contact centers, there's huge, sprawling places and they're in like rural Oregon or like Cork, Ireland or the Philippines or India.

7:34and all of a sudden they've got to change the way they approach software that people are working distributed and I remember reading this article in the Wall Street Journal about customer service agents in the Philippines all of a sudden they have to work from home and you could hear chickens in the background so it was really like they had to change the way that they were approaching software so those two trends from 2020 to 2022 roughly kind of really accelerated a lot of companies' growth and assembled, like kind of got started at that kind of perfect moment in time. So from scheduling software, essentially, how did you, what is the ladder you climb to the current product?

8:19Yeah, it's, so like the broader category is something called workforce management. And, you know, I think scheduling is doing it only a half service because if you think about the operation of a large contact center. So one of our customers is one of the largest delivery apps in the world. And they have 15 ,000 people, even with AI, they still today have 15 ,000 people in the contact center. And there's a, you know, put my economist hat back on, a supply and demand question. the demand question is how many phone calls how many emails how many chats you're going to get and there's a queuing theory question underneath that or really operations research which is like how many people need to be working if you want to handle all those calls within say 15 seconds or 30 seconds and then there's a supply question which is the scheduling one and then that gets more complex if you know you've got 15 000 people it's not just like a okay literally like a record but there's labor laws all across the world.

9:24There's people in different locations. You need to kind of line up the hours of working. And there's actually an optimization problem. So if people work like in the industry, there's something called the standard five by eight. You work five days, you work eight hours a day or a four by 10, you work four days, 10 hours a day. You can imagine this kind of like Lego blocks lining up to a demand curve. So like in truth, when I say scheduling tool, It's this workforce management problem, which is an operations research problem, which is about quantifying demand and matching it to supply, which is actually all the way back to machine learning.

10:03So when we talk about AI agents today, we started with automating away support, but that wasn't the key issue that people were hitting as they were scaling in 2020 through 2022. but we came back to in 2022 building AI agents on top of the data that we had on top of the kind of like understanding now of like the deep operational complexities of large support teams and you know we have two billion support tickets in our database so it made a lot of sense when you know November 2022 Chachapiti launched to say oh actually like now's a good time for us to take the data that we have the understanding we have and kind of point AI at it and so that's kind of the synthesis of like all the way from customer facing AI agents to this kind of like really like ML kind of support operations layer.

10:47And, and, and so the current, I mean, are you a SaaS platform? Is that how you're, you're making yourself available? And, and yeah, what's the full gamut of are there a series of products or is it soup to nuts for customer support or how does that look yeah when we say we describe ourselves as the ai platform for superhuman support so like there's a sass part which is that kind of like the planning layer and that's like you're the larger your team probably the harder your planning problem is I think there's some nuance there because as more and more companies have this kind of human plus AI agent hybrid workforce, it starts to be not about headcount per se, but almost about the amount of volume flowing through.

11:45And then all the way to the AI agent, that's not SaaS. It is kind of like this usage-based, outcome-based type of pricing as opposed to SaaS for the AI agent product. So the full platform, you know, it is for customer support. And then the piece is like, how do we price different products? Or kind of like, how do we think about the style of the product? It varies based on the use case. And it's a platform. Who are the customers? Are they BPOs that then are servicing, you know, customer companies? or is it the companies themselves? Typically the companies themselves. So if I go back to the Stripe example, they work with many different BPOs or outsourcers and they have this kind of like meta problem of, well, on the one hand, like doing AI automation, like providing an AI agent that'll be the kind of like tier one first layer.

12:53if you chat them or if you talk to them on the phone or if you email them. And then within the bowels of the kind of support operation, it's like, okay, for working with those BPOs, like how are you going to manage them? So, you know, we end up by way of that kind of like relationship working closely with a lot of different BPOs. And, you know, we have, for example, integrations that reach into a BPO's own kind of like planning and staffing system to extract the data of who's working when and what's the demand look like and stitch together a picture for their end clients of what does this look like across your entire BPO network, but less so directly with the BPOs themselves.

13:34One of the problems that you can imagine that they're solving that's changing over time is if you are a large business that has a lot of customers with a large support operation and you were trying to reason about like how many people or really how many resource, like this resource allocation problem before you were thinking of BPOs in this context of like this cost quality curve. So the cost quality curve is like you could hire somebody and train them and they'd be really skilled. And, you know, that's kind of like one part of the cost quality curve. You could outsource it. And there's different flavors of outsourcing.

14:10So like, you know, there's places like the Philippines have some cost structure. there's new places that are near shore like you can near shore or kind of like onshore but within like rural Oregon for example like there's contact centers in the U.S. there's budding contact center in like South Africa one of Nelson Mandela's grandsons is like creating the contact center in South Africa because it's a good kind of like entry-level knowledge work job and so there's kind of like different points on the cost or quality trade curve trade-off of BPOs. And then you put AI agents in frame as well. And so really what we're helping companies do is like pick a point or pick really not a single point, but like different kind of mixtures of strategies to design their support operation.

14:57Yeah. And the agents, are you using third-party agents or are they conversational agents? What kind of agents on the agent end? Yeah, they're conversational agents. So, you know, we are this open platform that, so like Intercom is a customer, for example, like they produce FIN. So, you know, we help them reason about like their human workforce and their AI workforce at the same time. They even have like a head of human support and a head of AI support. But we also build our own agents. And so those are conversational agents. So some of our customers, like Honeylove, for example, is an e-commerce brand.

15:36If you go to their chat bubble, if you email them, Assembled will do the responses and it's deflecting 75 % of the initial contacts. We're also over voice. We just announced a customer of ours who's actually a very fascinating business that I have just been super inspired by. It's a company called Retention Express, and they help membership management. And in particular, they help PE, like private equity roll-ups of car washes with their subscription management. And they use this for AI voice. And so what's been really fascinating in this space is the conversational AI has quickly moved past chat all the way into voice.

16:20And the voice technology space is just changing so, so, so quickly. And I think as consumers, all of us have been there, right? It's like, if I need help, I'd rather just call. You know, I'm willing to wait on hold to call. And the AI agents have gotten good enough to, like, be pretty useful to people. And then also to be able to, like, intake information. So, like, for this particular use case, you know, we're able to resolve, like, membership inquiries. but for like workers' compensation claims, like that's a common voice use case. We'll say, give us the information, tell us about the incident, when it happened, and then kind of like pass that to a person to finish off the rest.

17:03So you don't really have to like spend a lot of time rehashing what happened. And then also the people don't have to spend the time like asking you these real questions. Yeah. But is the platform, I mean, the voice agents, for example, Are you using a third-party voice agent in the stack or is in the platform, is there the place for a voice agent and you offer a default or an other, people can plug in their own or if they're using a third-party, plug in a third party? I've spoken to a lot of people recently about voice agents and they're all, um, not exclusively, but they're targeting customer service because that's the most obvious market.

17:57Yeah. I, I've also seen a lot of this, um, where people are trying to build their own, like we were at this, um, 11 labs launch event recently for, for, um, you know, their V3 voice model. And so I think, yes, like there were a lot of people there interested in building their own agents. So we help with like managing or kind of really like the orchestration layer of how to route volume to humans versus to AI. But we ourselves have built our own AI voice agent. And I think the distinction there is like, you know, a lot of people can, you know, there's this kind of like, if we put like a bunch of knowledge articles and your public help center and, and do rag, you can answer kind of like a first layer of questions and then you can stack like a text to speech thing on top.

18:51And, you know, that'll work pretty well to, to answer tier one support. But, you know, I think the story with assembled is like, that may not be something that you want to do because um like then you get to tier two you get to these kind of like agentic workflows that are multi-step that require you to ask for billing information and then make some decisions and um honey love one of our customers just to go back to you know everybody says refunds are super easy refunds are not easy like their policy is really complex like you pull up the flow diagram and it's like if you're zero to 30 days out from your order, check these particular variables, and it depends on the customer loyalty and your history with them and whether or not they've already given you a discount or something like that.

19:38And if you're 60 to 90 days out, you go on a different path. So these refunds, even just to execute that simple tier two workflow is actually quite complex. And so then to stack voice on top of that is still more complex. And so when we talk to people who are building it themselves, some of the questions we start to to hear are like oh actually you know what um because i was in this boat myself like building ml tools at stripe somebody built the first pass at this and you know like you know we're engineers we get excited about the technology and then you know you you built the first pass and then you move on to the next thing and the question becomes how do you get from 10 automation on your initial pass to that 40 automation with like the tier two workflows And that's where I think a lot of companies are right now.

20:28It's like between, yeah, like it's exciting and fun to build the first part and then to productize all the rest of it and to kind of keep eking out more automation gains. Like that's where you probably add an assembled agent instead. Yeah. And so you guys are not necessarily competing with, I mean, there's another company, Crescendo. I don't know if you've heard of them or Sierra. Yeah. um you're not necessarily competing with them directly right you're building more of the infrastructure for people like them we are i mean we are competing with them like we have a conversational ai agent through chat that competes with sierra right um crescendo a little less directly but in the sense that like ai agents are competing with um any type like bpo like crescendo is basically like a supercharged bpo right like when they were um they were previously something called partner hero and ironically they were a partner of assembles um partner hero and um you know everything is kind of like fighting over what we want to provide to end customers is you have support tickets and you want to make the problem go away great we'll make the problem go away so i think everybody is doing that we are doing AI agents to do that.

21:56Zendesk, Salesforce, Genesys, like every single provider of ticketing systems also has AI agents to automate it away. So yes, we are competing, but then I think uniquely we have this texture where kind of like Amazon, right? Like Amazon, you can use them, you can use RDS for them to run the database or they can say, here's like a bunch of different databases is that you could turn on. We're kind of like that. Like we have it, or you could turn on somebody else's and we could help you manage it. Yeah, I see. So if somebody comes to you, a company, a big multinational, what's the process to integrate you guys into their customer support?

22:44or presumably they have a BPO and they have, you know, maybe a front end chat bot, text chat bot, and they come to you, then what's the process of, of integrating or replacing chunks of their process? Yeah, it depends a lot on the use case. And I'll say that we've seen a lot of fragmentation on where people want to start. So, you know, our key use cases would be like AI chat agent, AI voice agent, AI email agent, AI co-pilot to help people be more productive, and AI workforce management to kind of like manage the entire support operation. And so if you're in any of the like AI chat, email, AI email, AI voice, it's like, give us your knowledge, give us your SOPs, your standard operating procedures that we can encode into workflows um maybe integrate some of your systems like your billing system through a stripe or your order system through a shopify and then just like you can kind of turn it on and start automating regardless of your channel um if you're ai copilot and one of our interesting customers um is is one of the big uh kind of design brands and a consumer design and um you They care a lot about quality.

24:06So they don't want to automate. And then they also have their own internal initiatives around automation that they want to hook it up to. And the business is in this place where they want to do more with the same. They're growing their monthly active users by 30%, and they're not allowed to hire people. But they care so much about quality that they don't want to just drop automation in. So it's actually make people 15 % more productive and crawl, walk, run your way. Like if you can make people, if you can auto draft replies for people and then 90 % of the time, they're actually just sending the message without really changing it.

24:44Then that's like the path to automation. So like, that's a lot of what we see with AI Copilot is you want to start here and you're kind of like collecting all the information and workflows that are first making people more productive then moving to this automation. And then workforce management, I won't talk too much about, but that was kind of like the earlier part the discussion around like, oh, you have a lot of people. And now you're trying to add AI agents in. It's pretty complex. Like, how are the pieces connecting? Well, actually, to me, that's what sounds unique about you guys. But I'm not versed in this.

25:17Is there a whole workforce management world out there that, I mean, You guys are growing into the, the AI, uh, voice agent and from the workforce management, but is there a world of workforce management that you're also competing in? Oh yeah. And it's a delightfully, uh, niche world. I would call it like, if you think about, um, uh, Dungeons and Dragons, like that would be like the workforce management world. And by the way, we had no idea about this when we got started. Like we had left Stripe and we were like, you know, these ML people, ML engineers, and we were going to actually automate away support with ML.

26:06And the way that we got to workforce management was by asking this guy, Bob Van Winden, who used to run operations at Stripe. Ironically, he went to Bridge and then so he's back at Stripe. But we were asking, what is software that you need to buy or you hate your options? And my co-founder came up with that. I said, you should coin that. That should be the product market fit question because baked into that is something that people need to have that they probably want to buy more of. So he told us about workforce management. And there's a workforce management conference that happens every year in Nashville.

26:39It's called the Society of Workforce Planning Professionals. It's so fun. It's a bunch of people who are really interested in the data and the nuts and bolts of contact centers. There's a Reddit for workforce management. People are pretty passionate about their favorite tools. So yes, we're in competition there with companies that most people haven't heard of, but are surprisingly large. There's a company called Nice. If you fly United, the first time I saw Nice out in the wild, not in my day job, like on the United, you know, they do the little ad before they, you know, you take off and it was like Gordon Ramsey.

27:23It's like nice in contact. I was like, what? But that's a, that's a like 20, $30 billion publicly traded company that's in this, this pretty niche space. And it's in that end of the business is just optimizing schedules. is that I mean essentially that's what it is you have a couple of thousand people and you're trying to figure out you know how to have them on call or or in a chair you know in front of a computer so that there's an even flow of service. Is that right? That's exactly right, yeah. I mean, it sounds so trivial, especially because we're AI ML people, right? It must be this super complex set of AI tools.

28:22And again, I'm very optimistic about that. That's the part of our product suite that I spend a ton of time in as well. but like the fundamental questions of workforce management you're like why are we so busy or you know how come we're so you know over under capacity what happens is when you line up supply and demand people find like oh a bulk of our volume comes in on monday morning but we're kind of like maximally staffed on wednesdays right like there's 20 efficiency gains just from like kind of lining these things up. And then Just is really like, it's actually complex to line these things up because people have different schedules.

29:01They're all over the world. They have different preferences. They have different skills. And so it is this kind of like multi-dimensional optimization problem, actually. Build the future of multi-agent software with agency. That's A-G-N-T-C-Y. The agency is an open source collective building the internet of agents. It's a collaborative layer where agents can discover, connect, and work across frameworks. For developers, this means standardized agent discovery tools, seamless protocols for interagent communication, and modular components to compose and scale multi-agent workflows. Join Crew AI, Langchain, Llama Index, Browserbase, Cisco, and dozens more.

29:56The agency is dropping code, specs, and services. No strings attached. Build with other engineers who care about high-quality multi-agent software. Visit agency.org and add your support. That's A-G-N-T-C-Y dot O-R-G. Yeah, yeah, that's interesting. And in your case, it's applying to support staff globally. Yeah. Is there, so where do you guys go from here? Are you leaning into, is the customer support market so huge that there is market share for all these different companies? Or do you end up specializing in medical customer support or automotive customer support or something like that? Or once you've tackled customer support, I mean, it sounds like this workforce management, there are a lot of other applications beyond customer support.

31:11So what direction do you see yourself going? Yeah, I think AI agents and customer support should keep us busy for quite some time. You referenced a couple of fairly large, well-funded companies that have entered this space. And I think that investors don't always get this right. But it is a reflection of a fairly large market. There's hundreds of billions of dollars spent a year on labor and customer support all over the world. And just to give you a sense of it, we're just in the early days and we have hundreds of thousands of people on our platform. and over 50 % of them are outside the US. So it's just a huge labor footprint.

31:56And yeah, putting like a 50%, 60 % automation dent in that is like pretty consequential from an economic perspective. So I think there's plenty of market. And the other thing that's interesting about like the history of customer support is that there are a lot of really big fragmented, It's a big software market as well. A lot of people don't know this. Service cloud is Salesforce's largest product by revenue. And it has been for a very long period of time now. And Salesforce is not even the biggest one here. The nice company I mentioned, Genesis, came out of Bell Labs. You go all the way back to the 2000s, there's a company called Live Person that went public in the year 2000 doing AI chat.

32:48the conversational AI and so on and so forth. So like, it's a big software market, a mature software market. And one that now is kind of clearly poised for disruption. Satya talked about it. Jensen talked about it, you know, from Microsoft and NVIDIA respectively. Like, you know, what are the applications of AI that makes the most sense coding customer service? But I think that there's a lot of space and it's a market that's used to being fragmented and having a lot of players. You know, what I think is interesting is that maybe even on like the tech infrastructure side, I think in customer service especially, like we talked to a lot of customers where, you know, that question like, how's this different from like a, is this just a chat GPT wrapper?