Most startups bolt AI onto old products. Ravenna reimagined the entire workflow.
When we first met Ravenna Founders Kevin Coleman and Taylor Halliday, it was clear they weren’t just chasing the hype cycle. They were pairing AI-native architecture with deep founder-market fit, and rebuilding how internal ops work — from first principles.
Their new company is going after a market dominated by legacy players. But instead of being intimidated by incumbents, they got focused, making some smart moves that more early-stage teams should consider:
- Speak with 30+ customers before writing a line of code
- Define a clear ICP and pain points
- Build natively for Slack — where support actually happens
- Prioritize automation, iteration, and real workflow transformation
- Stayed radically transparent with investors and early customers
In this episode of Founded & Funded, these two sit down with Madrona Managing Director Tim Porter and talk through their journey, what they did differently this second time around as co-founders, and how they’re building a durable, agentic platform for internal support.
If you’re a founder building in AI, SaaS, or ops — this conversation is full of lessons worth hearing.
Listen on Spotify, Apple, and Amazon | Watch on YouTube
This transcript was automatically generated and edited for clarity.
Tim: So I mentioned in the intro, you’ve done a company together before and this is a second one. We’re super excited to have been able to invest in the company, an announcement that just came out here recently. But let’s go back. Tell us about the moment you decided to start Ravenna. What problems did you see that you said, Hey, we got to go solve this for customers?
Kevin: I was at Amazon for four years, and I think the whole time I was there, I was looking around trying to figure out what was going to be the next company that we go and do. It took a while to find it, but about halfway through my tenure there, I realized one day that I was spending a lot of time in an internal piece of software Amazon has that serves as the help desk across a lot of different teams. It was the tool where I would go to request onboarding for new employees, to request new dashboards to get built from our BI team. My teams would use it for other teams to request work from us. I realized I was spending so much time in this tool, it wasn’t a great product experience. The way I always described it to folks is it was like the grease in the enterprise gears, if you will. It was the way that things got done internally.
And so I got obsessed with what is this product category? It’s so foundational to how Amazon as a business operates and I started doing a bunch of research in this space. I found out it’s called enterprise service management, which is the category. ServiceNow is the leader. I finally understood what ServiceNow as a business did and why they’re such a valuable business and how large this market is. I started thinking, what does a next-generation, amazing version of this product look like if a very innovative startup built it that cared about design and user experience and cared about automation as well? So really, what does the next generation ESM platform look like?
Tim: I love that because ESM is a category, it’s a big market. ServiceNow is a leader, but I also think, like a lot of things, Amazon did it in an innovative kind of scrappier way. You actually used it for more things. This was the way you just requested and got things done across different groups, as opposed to “Well, we got to log it into this system of records so somebody has a record of it,” and it’s like, no, this is actually the way it was getting done.
Kevin: Yeah, absolutely. And so I came up with this concept and when a concept gets lodged in your mind, you can’t get rid of it. I went and ran to Taylor, who obviously was my co-founder previously and the guy I wanted to start the next company with, and I said, “Hey, I’ve got this awesome idea. We’re going to build a next-generation enterprise service management platform.”
Taylor: On first blush, it was tickets and queues. I was looking at this like, “Is this Jira? I don’t quite understand what’s going on here.” But it came at a good time. So rewind the clock, ChatGPT hit the scene, Zapier, just like every other company probably on the planet, had a little mini freak out, like, “What do we do with this? What does this mean for us?” Product strategy, what have you. At the time, I was lucky enough to pair up with some of the founders to basically do a listening tour. We headed around to mid-market size companies, talked to C-suite directors, executives, what have you. Obviously, Zapier is known for its automation game; we wanted to try to figure out what would be a great solution here in the world of AI/LLMs to bring a new level of value to them.
We asked an open question. Where would you like to have us poke and prod? We did 20 to 30 of these calls. It became pretty clear, resoundingly — we kept hearing about internal operations, over and over and over.
I had a problem picking through that in my own head, and I even had a blunt conversation with one of the CEOs saying, “I’ve heard this so many times. What’s the deal? What’s going on here? Why do I keep on hearing about internal operations?” I think it was a couple answers. One, I can wrap my head a lot better around internal efficiencies or lack thereof in this company, and what we are hoping to do. There was this desire or gap of a desire kind of thing in terms of “I don’t have a good amount of visibility to what folks are actually doing. I can’t top-down efficiency at my company. They would say, “As much as I say, ‘Hey look, I want you to be more efficient, do these different things,” I don’t know super well what the market or what the engineer is doing.” They all kind of use AI as an opportunity to help maybe bottoms-up some of this efficiency without it being a top-down thing.
I think that was a lot of the interest. And so when Kevin ran to me, it was like, “I have this idea circling around, this internal management tool, and there’s an opportunity perhaps in this larger market that’s old, 30-year plus incumbents are all over the place. That’s what got me interested and sparked a lot of the collaboration in the early days.
Tim: We think a lot about founder market fit when we’re looking at new investments, and I remember our first meeting Matt McIlwain and I had together with you guys and we both left like, “Oh, my gosh. We have to find a way to fund this.” It was this unbelievable founder market fit that you had lived the tool in using it at Amazon. You literally witnessed it across all of these customers at Zapier who are using it to get these automations in place, but it wasn’t a full product. So, awesome to see you both come together with those insights. I’m going to abstract away a bit. We’ll come back to Ravenna and the specifics about enterprise service management, but you guys have done a company before.
Kevin: We have.
Tim: You’ve been through YC, and you decided to do it again. That’s a testament to your working style. But were there things like, “Okay, we’ve got the idea again.” And other things like, “Hey, we’re going to do something different. We’re going to do it the same”? How is it different for you guys going at it a second time around?
Taylor: It’s funny, Kevin and I, you mentioned a past company, I always joke, it depends on how you count a company. Kevin and I have been working together for quite a long time. Whether that be in the early days just finding a coffee shop or bar at night working on the smallest app. Then to San Francisco, we ganged up together and tried to start a CDP of sorts, went through YC with that, and molded that into several different things. But regardless, we were kind of taking stock of that history, if you will.
To your point, what went wrong, and what went right? I think an interesting way of characterizing it, which I feel like a lot of entrepreneurs do this in the early days, and early innings is trying to, “What’s the new, new? What’s the thing that doesn’t exist out there yet?” If you were to take a retrospective look on the stuff that we’ve put a lot of time and effort into, it circles around that, which is frankly speaking some of the fun and exciting when you’re with some buddies and say, “Hey, this doesn’t exist yet. What if we made this?”
We were thinking like, “Hey, look, what if we flipped it on side this time? Instead of doing that approach, let’s try to figure out a market that’s super well-defined and try to focus in on opportunities to actually bring better experience.” Especially in the age of AI, it seemed like the perfect time to target this one in particular.
Kevin: Taylor hit the nail on the head there. We, for the life of us building software together have always been trying to identify something that doesn’t exist and shy away from competition, and this time we’re taking ahead on. So we’re super excited about that. Big markets are exciting. We don’t have to go find a small group of people who need what we want. We know there’s a ton of people out there who need what we’re building. That’s really exciting to us.
Taylor: As part of that analysis of, “What do we want to work on and where do we want to press?” I remember talking to you, Kevin, thinking about taking a step back, “What kind of risks do we want to bring on?” We kind of framed it like that. Going back to my earlier point, I would characterize a lot of the early endeavors as pretty high in market risk. We’re trying to figure out, “Hey, let’s try to not optimize for that this time. Let’s try to optimize for something else.”
To compliment ourselves a bit, I think we’re pretty good at building a lot of products. And doing it pretty fast, too. Also at getting together a lot of good folks to work with. So from, call it a human capital risk, I didn’t see that on the table. Taking a larger market, trying to take the market risk off the table. We tried to optimize more for what we thought of as go-to-market risk.
Kevin: The other thing I’d say that we’re doing better on, I don’t know if we’re doing great at it, but we’re doing better at it this time around, is understanding who our customer is and being super clear about what we’re building and for who. So ICP, ideal customer profile, if you will. Taylor mentioned the last company, the first product that we built was a customer data platform. We effectively at his startup, my startup, we had problems with our customer data. It was sprawling all over the place. Folks who were non-technical were always asking us to integrate various tools so they could get customer data where it needed to go. We would go around to potential customers and say, “Hey, you probably have problems with your customer data. Can we help you?” And they’re like, “Yes, of course we do.”
The problem was the problems were all over the place. There wasn’t a product that we could identify that would cut across a bunch of companies. Part of that was, we were early entrepreneurs and didn’t know what we were doing. This time around, before we even built any software, we spent months talking to customers and understanding the space and understanding what pain they had before we started writing a line of code. We wanted to be super clear about our ICP, what they needed, what their problem was, and then we back into the product from that. So a hundred percent this time around, we’re doing a lot better on that front than we were last time and we think it’s definitely the right way to go.
Tim: Well, this is definitely a super big market, and another thing that came through from the beginning, as we have been engaging and working together, is customer-driven, customer-driven. Sort of the maniacal customer focus that is maybe the core attribute for I think successful startups. So that’s been awesome.
Let’s talk a little bit more about what the product does and bring it more to life. I’ll lead you into that by talking about some of the investment themes that Madrona has that we thought Ravenna embodies. A big part of that is AI and part of the why now for Ravenna. Probably our biggest theme is around how AI can reimagine and disrupt different enterprise apps. You’re using what I would call or many in the industry would call an agentic approach where you can actually create various agents that don’t just surface insights but can automate and finish tasks. This world and this product area is really ripe for that, and you’ve done some interesting things there.
And then new UIs. The user experience, you’ve embraced Slack as a place that work is getting done and made the product be extremely Slack native and fully integrated in people’s existing workflow as well as an ethos around clean, simplistic. Taylor, you and I talked about this the very first time we talked about the product, but maybe give a better description. Okay, great service management, tickets, people have something in mind, but say more about the key features and then maybe tie that back to when you were out talking to these initial prospects, what did you hear about what was missing and what could you deliver in your product to make this experience such a big leap forward?
Taylor: Going back to why we picked this. There’s a well-known UX product pattern that you see basically in this market. We weren’t very impressed by what we saw. In the age of AI/LLMs, the popular thing, I would argue, would be to come at this with an intelligence layer. We definitely consider that, and we made a conscious decision on what we think is maybe where the longer-term value is — but also perhaps the tougher one — which is that we’re not building the intelligence layer for this market. We have a lot of confidence, conviction, if you will, that there’s room for a new rethought platform. What that means in actual practice for those who are familiar with even the space, a help desk is probably the most down-to-earth version.
Tickets and queues, it’s a very similar pattern to how you expect it to be from a customer interface with customer service software, same type of thing except the primary difference is that this software is geared towards basically solving your colleagues’ problems. The canonical example, is the IT help desk. You asked for a password reset, new equipment, what have you, that creates a case, it creates a ticket. That’s the typical way of going about this. We’re not talking purely about the intelligence layer and the agents, which we are super excited about, I think we have a lot of fun stuff there, but also very much of building and rethinking what the larger brick and mortar ticketing platform looks like.
Kevin: Yeah, 100%. So enterprise service management is the category. That’s a very broad term. Most people don’t know what enterprise service management is. The easiest way to think about it is it’s an internal employee support platform, internal customer support platform if you will. So, you have functions across an organization, whether it’s rev ops, HR ops, sometimes called people ops, facilities, legal, etc. They all offer internal services. What I mean by that is they offer services that other colleagues can leverage.
So in legal, a service might be, “Hey, can you review this contract?” In facilities, it might be, “Hey, my desk is broken, can somebody come and fix it?” And so this pattern exists across companies, and what people need is a tool that allows them to intake these requests, assign those requests, resolve the requests, and then get reporting and analytics. Increasingly, with AI and automation, classic workflow automation, they want to automate a lot of this work as well.
What we’re building is a platform that allows any team within a company to offer a best-in-breed service, best-in-breed help desk and provide amazing service to their colleagues and then also automate a lot of their work with our AI. That’s a pretty straightforward way of describing it.
Tim: You recently were part of a launch that Slack did for partner companies. Pretty cool. You’re Slack native but yet a new company, kind of an interesting series of events that maybe led to that. What’s the background on that and what has it been like trying to partner closely with Slack?
Kevin: I’ll say upfront that when you start a company, weird, cool, fun stuff just happens. It’s kind of like Murphy’s Law, right? Anything that can happen will happen. It feels like that is embodied in a startup to a certain extent. So yeah, we were a launch partner for the launch of the AI and assistance app category in the Slack marketplace. You can find Ravenna in the Slack marketplace, which is super cool.
The way it happened is very fun. Matt McIlwain, who is obviously your partner here at Madrona, when we were going through our recent fundraise, he said, “Hey, there’s a local entrepreneur you should go and talk to.” He made the introduction, this local entrepreneur went on a walk with Taylor, heard what we were talking about, what we were building and said, “Hey, a certain high level executive at a large CRM company in the Bay Area,” who happens to be Slack’s parent company, “should learn about this.” We were like, “Of course, anybody who’s an executive of these companies should learn about us.”
They ended up forwarding along our deck. That got forwarded over to the executive team at Slack, and they got in touch with us and said, “Hey, what you guys are doing is super interesting, we should talk.” We had a conversation, and we got a Slack channel open with a couple of those folks, as you do when you’re working with folks at Slack. Then we noticed that this new app category is coming out. So because we had that introduction there, we reached out and said, “Hey, we think Ravenna fits really nicely into this new app category. What’s going on here? How can we get involved?”
It was, fortunately, really good timing. We got connected with the partnership folks over there, and they said, “We’re launching this category in two months. If you guys can get your stuff ready, we’re happy to feature you as a launch partner.” Funny how these things work out.
Tim: You all have been great about using advisers but also using your own networks to get feedback. You never know where it’s going to go.
Kevin: You never know, you never know.
Tim: This is another example of putting yourself out there, and getting the feedback. Sometimes it takes you right through to the CEO’s desk.
Taylor: Kevin mentioned, it’s the funner parts, to be frank with you about it. If you have humility to understand that there’s so much out there to learn — especially going into a category that you’re trying to make some hay in and do a different thing in — It’s valuable to get a lot of perspectives there. The more of that you do, there’s tangible stuff that tactically you might get some Ps and Qs kind of learnings along the way, but there’s also some of the funner random doors to get open, such as that one.
Tim: One thing I think is cool too, and part of it is using Slack, and part of it is how you can pull data in from other places — is that questions get asked, and people didn’t realize, this question’s been answered already. How do you create this instant knowledge base from what’s already in Slack all over the place or maybe from an existing knowledge base that is there, but people don’t go look at it. It’s easier to fire off a Slack like, “Hey, Taylor, can you tell me the answer to X?” And by doing that, you can create an automation that the person, and the task gets finished and you didn’t have to do anything, right? That’s a big unlock here.
Kevin: You’ve mentioned Slack a couple times, and we should revisit that really quickly. Slack is the interface for the end customer of the platform. That’s super critical and was a learning during our listening tour at the beginning of last year. The traditional help desk, there’s basically a customer portal where you go, you fill out some form and then your request goes into the ether and you don’t know what happens to it until somebody pings you back a couple of days later is like, “Hey, we resolved your issue.”
What basically every customer across the board told us is employee support happens in Slack now. So, “If you guys are going to build this platform, everything needs to be Slack native, that’s where our employees work. We don’t want to take them out of there. That’s super key to us. If you go to our website, it’s very clear that we’re deeply integrated with Slack. So, we started building into Slack and then to your point about knowledge, we started talking to customers and said, “Hey, you get a lot of repeat questions. A lot of those questions pertain to probably documents or knowledge bases that you’ve written. If you give us access to those, we can ingest them and use AI to basically automate answers to those questions so you don’t have to answer them over and over again. Just to save you time.”
Some people were like, “That’s amazing, let’s definitely do that.” Other people basically said, “Yeah, it’s not going to work for us.” And so we were like, “Okay, why not?” They were like, “We don’t have good knowledge. We don’t have time to maintain it, it gets out of date really quickly and, frankly, it just doesn’t make the priority list.” And so we asked the next question, which is, “Okay, if you hire somebody, how do they get up to speed? How do they learn how to answer these questions if you’re answering them in Slack?” And they were like, “We literally point them to Slack channels and say, ‘Go read up on how we answer these questions and that’s how you should answer going forward.'”
That was this light bulb moment where there is a treasure trove of corporate information and really knowledge that exists in Slack, or any team chat application, so Teams as well, that is sitting there. And companies don’t derive a ton of value from that. A lot of what we’re trying to build is not only give operators of these help desks tools to turn Slack conversations into knowledge-based articles, but really to build a system that can learn autonomously over time.
You should assume that when you’re using Ravenna, your answers are going to get better over time. The system’s going to get better at resolving your internal employees’ queries over time because we’re listening to that data and evolving the way that we respond and take action based on how your employees are answering their colleagues’ questions.
Tim: One of the things that is super exciting here is that I see this as how work gets done inside businesses, and it’s really broadly applicable. On the other hand, a truism about successful startups is that they stay focused, and there is this IT scenario initially where IT is used to using tickets, people are used to asking IT for things. Those things tend to be maybe more automatable, I don’t know. But how do you balance that? Staying focused on, let’s just go nail IT service management, ITSM, versus we have this broader vision around better automation for how enterprises get work done. How do you get that balance right? What are you learning from customers and where are they drawn to initially as you start talking to them and start working together through an initial set of users and customers?
Taylor: I’m going to tie this back to some of the questions you asked around, what’d you get excited about working on this? Rewind the clocks, Kevin runs over, “I got this great idea. The market’s called ITSM.” I’m like, “What? I haven’t heard of this thing.” “No, it’s a huge market.” “Really? I’ve never heard of this acronym before.” ITSM is the name of the larger market and it’s been traditionally known as, okay. Half that acronym’s IT.
Today if you say, “Look, who’s the ICP? Who do you want us to introduce you to at a company?” We’re going to say, “Look, it’s the IT manager.” And it’s because they know what it is. Again, longstanding industry, they know what to call it. They know that funny acronym. They know the pain points very, very well and they understand how to kind of wade through the software. And so that is typically I’d say the beachhead if you will, for us approaching.
Tim: That’s the initial wedge. That’s a great place to enter.
Taylor: Correct. Where this gets more interesting, in my opinion, though, I remember kind of noodling on this thing. I was looking at Zapier’s help desk channel and I was kind of looking through it and being like, “Huh, this is not the most inspiring, password reset, what have you, kind of stuff. Is this really this massive market that Kevin’s super excited about?” No shade if anyone from Zapier’s listening in. The channel’s great, by the way. But I would say it’s what light-bulbed, looking around the rest of the company, it was the same interaction pattern. The same user pattern that you see in what was traditionally known as the help desk channel — that same pattern is present in HR ops. It’s the same thing that you see in marketing ops. It’s the same thing you’ve seen in engineering ops.
It was interesting, though, because I was being very coy interviewing a lot of folks back then. IT knows what they call it. They know what the class of software is, right? But the folks who were in charge of marketing ops, engineering ops, I couldn’t find many who knew the acronym ITSM, so I stopped doing that pretty early, but they know the pain. I started to realize, I came around, circling around to being like, “Look, if you are in, call it an ops position, marketing, engineering, pick your flavor and/and department. If your job is to provide great service to your colleagues, you are operating a help desk, whether or not you know it. That’s the fact of the matter. So again, to your question about who do you start with, we start with IT, it’s the most well-known commodity in that space.
The excitement for me, maybe it’s broader than IT, maybe there’s more stuff than that. That’s kind of grown to be true so far in the early innings here is that other folks see basically a better class of software being introduced by IT. It’s this interesting thing, it’s an infectious being, like, “Wait, what is that? Where’d that come from?”
And so therefore we are trying to maintain in terms of precedence, IT is the number one persona and that’s the one where we’re going to, I’d say charge ahead on the absolute most in terms the bespoke workflows that they have to do and the ones that we have to help automate better. Nonetheless, though, HR ops seems to be the one that we’ve just seen in organic pull with, it’s kind of second in position, and after that is revenue ops.
Kevin: I’ll give you a very concrete example. This morning I had a demo call with a large email marketing tool in Europe. They got out these four IT guys on the call like, “Hey, we’re looking for a new tool. We need a new help desk tool, we need AI, etc.” We’re like, halfway through, they’re going through all the requirements. They’re like, “Oh, by the way, it’s not just us. It’s facilities, it’s HR,” and I think they said product is the other team. That happens all the time.
We are always talking to IT people, and it always comes up on our calls, “It’s not just for us. Other people who offer internal services need this as well.” So it’s exciting for us because IT is the entry point, but then you’ve got this really nice glide path into the rest of the organization. Again, I don’t know if it’s a secret or whatnot, but it’s one of our core learnings you’re going through this journey — there’s a lot of teams across these companies who need this type of tool. So that’s exciting for us.
Tim: Yeah, it’s an interesting form of land and expand.
Kevin: Yeah, exactly.
Tim: IT has budget, they get it, they need it, but everybody is asking them for something so you can get sort of a viral spread, and there’s no difference in the product functionality to start using it for sales ops as you were using it for IT ops.
We referenced ServiceNow a couple of times, so one of the most valuable application software companies in the world, $175 billion market cap. VCs like to use shorthand to describe companies’ investments, one of our best investments ever, Rover, it was Airbnb for dogs. I’ve shorthanded Ravenna as an AI-native ServiceNow for mid-market and growing companies. ServiceNow is obviously upper enterprise. You like that moniker? Should I keep using that, or do I need to disabuse myself of that type of VC speak?
Taylor: I think that’s a good one. It ties back for me at least to the distinction I made earlier around the platform versus the intelligence layer, kind of like, well what are you guys doing? I always like to joke, for better or for worse, we’re doing both. I say for better or for worse, again because it’s a lot of software, but that’s where the conviction is. ServiceNow is what we view as someone who’s taken a very similar bet a long time ago in terms of, “Look, we want to actually own the data layer. We want to actually be the thing that is close to basically all the customer data and the employee data at a company.” We view that as a more durable, longer-term play rather than just the intelligence layer. And so, I like the moniker.
Kevin: Definitely like the moniker.
Tim: All right, I’ll stick with it. So, it’s been fun in this conversation as you ping-pong back and forth, Taylor talking about go-to-market things, Kevin talking about product things. Taylor, your background is traditional engineering leadership. Kevin, you most recently have been doing go-to-market at Amazon, but an engineer by background. How do you divide it up? How do you divide up the responsibilities inside the company? That’s always an interesting thing that sometimes founders struggle with, is we’re full stack, you guys are both full stack, but we have to have some roles and responsibilities here.
Taylor: For Kevin and I, given how long we worked together, I think it’s probably more blurry than most, but I think that’s also one of the benefits of working with him. I know him so well that I can trust him for a wide range of things. That all said, we do try to basically divide up the product and how we go about this. I’ve tried to focus more on the AI automation side of the fence. Kevin’s very much more on the, call it the broader platform side of the fence, and so that’s roughly speaking from a product angle.
From a go-to-market angle, I’d say it’s messy at this point. We’re a young startup, it’s kind of like hit the network, hit all your networks.
Tim: Most of you’re on customer prospect calls all the time.
Kevin: Of course.
Taylor: I mean — roles and responsibilities only matter so much in terms of if you have people that you think might want to buy this kind of stuff, we got to do that. It’s good to have some delineation between roles, but I think at the earliest stages it’s just messy, and embracing that I think is part of the deal.
Tim: Another way you run the business that was super nice for us in the process of leading up to investing is you’re radically transparent. And ll of the prospect calls or customer calls, all those videos you record, they were all on Slack. You just gave us access to all of them. Like, “Here, go watch them and see what we’re learning and help us along the way.” That was super nice. But that must also permeate through your organization, and maybe it gets to the culture a bit, maybe speak to the culture some and what you’re trying to be intentional about in terms of building culture here in the relatively early days of Ravenna.
Kevin: I think this was, for me at least, a core learning from the first business. We didn’t do a good job of talking about what we were doing or telling people what we were doing. Part of it was, I don’t know, I didn’t think the business that we had was the most exciting thing in the world. So it was a little bit of not wanting to broadcast it as that much. I would hang out with friends and whatnot, and they wouldn’t know what my business was back then, and I would be kind of frustrated internally like, “How do you not know? We don’t have a lot of friends who started businesses, you should know.” But the fact of the matter is, they shouldn’t know. I should have been a lot more vocal about our business.
This time around, I think there’s two things. We want as many people to know about what we’re doing as possible because we think it’s pretty cool. Hopefully, other people will think it’s pretty cool. Hopefully, customers will think it’s pretty cool. The other thing is we want as many sharp minds helping us — in the product, and business, as possible. We think the way to accomplish both of those goals is being radically transparent. It’s radically transparent with our team and our customers. When we talk about roadmap, or when we talk about the stage of the business, what we have and what don’t. It’s all an open book, and we’re very transparent with them on where we’re at and where we’re going.
With investors as well, we shared a ton of stuff with you guys, and it wasn’t an angle to get you guys excited about what we were doing. It was more that we really liked you guys. We thought you were really sharp, and if we share a lot of stuff and you guys see what’s going on, hopefully you’ll get excited about the business. But then also hopefully you’ll, I don’t know, see something that we’re doing and be able to give us feedback on how we can sell better, how we can build better, pattern match across different portfolio companies that you’ve seen and help us. We want everybody to know what we’re doing, and we want as many smart people helping us and being transparent helps us accomplish this.
Tim: Super effective. We should say in other investors that were even investors before us, Khosla, Founders’ Co-op, have been really I’d say best practice in making a great collaborative style where we always are up to speed and can try to add value.
It probably has impacted recruiting too. It’s a hard recruiting market, especially for good technical talent and AI talent. You’ve done an amazing job of building the initial engineering team, including great AI background. Without giving away any hiring secrets, talk a little bit about how you’ve been able to do that. It’s never easy, but you’ve made it look relatively easy in these early days. What’s it been like in this hiring market, especially when you’re competing for AI talent?
Taylor: I don’t have any deeply held secrets.
Tim: At least that you’re going to share.
Taylor: If I did, I wouldn’t give it away anyway on podcast. But, really —we’re super excited about the team we have, and I think equally as proud about the culture that we’ve been much more intentional building this time around. We’ve tried to hold a high bar with the folks that we’re interviewing. I think that was more of a self-serving thing originally. But I like to think that comes through, frankly speaking, for a lot of the folks that we are speaking with.
It’s not just about the mission per se, it’s also about knowing that we basically have built quite a bit of software in our past lives and have a lot of perspective and a lot of conviction. Not just the market we’ve talked a lot about, but also how to go about building this and how we’re thinking about taking a different approach. I think that in itself has helped basically attract a lot of folks that, frankly speaking, we’re honored to be working with at this point as well.
Kevin: Totally. My playbook, I’m happy to share it because it’s pretty simple. I reach out to a lot of people and I tell them that Khosla and Madrona put some money into a company to help go after ServiceNow’s market, and people get excited about it. Yeah, it’s just trying to find good people and trying to get them to have a conversation with you and then explain the vision of what we’re doing and why we think not only the opportunity is really big, but we want to build the next great Northwest software company, if not West Coast software company.
We want to be intentional about building an amazing engineering culture, an amazing product culture, an amazing culture that works backward from customers. Amazon likes to say they’re the most customer-centric company. Hopefully, we’re going to be the most customer-centric company over time. And we’re very much striving to do that right now, but just really build a great place where people want to come work.
Tim: What’s an example of something that maybe you had an assumption coming into this company, now a year later it turned out to be wrong, and you had to quickly work through that. Not necessarily like an 180 degree change in direction, but constantly sort of course correcting.
Taylor: It goes back to perhaps what I would say about picking a large market, and being conscious about that. Nothing in life is for free. You get into that and you quickly realize a couple different things. If you pick a large existing market, sure people know it, you can assign a market cap to it. It probably makes the investor conversation a little more easy in terms of figuring out what the TAM is. But you start actually building here, you quickly realize that a well-known market has a lot of well-known features, a lot of well-known capabilities, a lot of basically well-known expectations from the buyer. Which in some level is good. It kind of clears things up.
The trade-off that what we found is that it translates into a lot of software. So again, for better or for worse fits well to some of our strengths and also some of the recruiting that we’ve done. We’ve been moving extremely fast because we have to. Another quick tenant about Kevin and I, the way we think about doing building companies, the whole stealth thing is orthogonal to us. I’m not going to go so far to bash some of the folks who want to do that type of thing.
One of the things of learnings from our journey is that there’s nothing more true, harsh, and real than the market. Every bit of time that you spend not interfacing with that market with what you’re building is a gap that you were accumulating and accumulating. One thing we always talk about at Ravenna is making contact with the reality as fast as possible.
Tim: I agree. I think the value from asking for feedback, shipping, and getting the feedback from actual shipping so outweighs any risk of, “Gosh, somebody else took my idea, or we should have stayed in stealth longer.” It’s just not even close. You guys have lived that. We keep talking about this big market. We alluded to this, that a way to think about Ravenna is a AI native ServiceNow for mid-market. So ServiceNow just did a big acquisition.
Kevin: Yeah, it did.
Tim: They bought this company called Moveworks, you know, biggest acquisition in the history of ServiceNow. It’s kind of an AI ITSM. How do you think about that move? Is that relevant for Ravenna? How is Moveworks similar or different to the product you’re building in the market you’re going after?
Kevin: In terms of is it relevant? Sure, it’s relevant in the sense that it’s definitely in the market that we’re playing in. We got really excited when we saw it. There’s clearly, we’re not the only smart people in the world who know that there’s a lot of opportunity in this space, but it’s exciting to see the activity and obviously a big acquisition, so it’s cool to see.
Moveworks is a previous generation AI intelligence layer on top of existing help desks. It was brought up a lot by investors when we were going through initial fundraising. Which was like, “Are you guys trying to be Moveworks? Are you to be ServiceNow? How do you guys think about it?” Because there’s AI, but there’s also the platform. Our approach is distinct from them in the sense that Moveworks sits on top of existing platforms like ServiceNow, whereas we’re trying to build the foundational platform plus the intelligence layer on top.
At the end of the day, customers will get similar AI capabilities from Ravenna, current next-gen capabilities, because we’re LLM native. I think they’re built on previous generation NLP technologies.
Tim: Which has a huge impact on accuracy and does it work?
Kevin: We think so. Yeah, exactly. I mean, no shade or anything to the Moveworks folks. They’ve clearly built an awesome business and had an amazing outcome and congratulations to the team because that’s fantastic. That’s what every entrepreneur strives for. We just believe, in the fullness of time, the majority of the value accrues to the platform if you can become the system of record. We honestly felt like this was the time to take a shot at building a new system of record in this space. That’s one of the fundamental differences between us.
Now in terms of near-term impacts on the market, I’m not sure what ServiceNow’s plans are for Moveworks, but there is a large kind of call it mid-market enterprise segment of customers who need this AI capabilities. Whether or not Moveworks continues to play there or ServiceNow kind of brings a more upmarket into large enterprise, which is where they like to play, there’s just a lot of opportunity for us in this space.
Tim: Yeah, that’s a great point because I think the things we talked about, Slack, easy to get up and running, beautiful UI, but another thing is price point.
Kevin: Yeah, very true.
Tim: You get a lot of functionality at the enterprise level, but you’re making this accessible and a price point that’s accessible for faster-growing companies and for them to grow with you.
Kevin: 100%.
Tim: We’ve talked about how AI is an integral part of the product, and you also built AI systems at Zapier, Taylor. One question we think about a lot from an investment standpoint is what’s durable? Is there a moat from the AI itself? What’s your take on that? Do you feel like the technology itself is a place that you can build competitive advantage? You’re building an agent-based system here. What does that mean to you, and is that part of what you think you’ll provide customers with, with durable competitive advantage over time?
Taylor: This goes back to the things that got me excited about this originally. It might be first useful instructive, breakdown, when we say AI and automation here. Like, what’s that mean? Big generalization, big time. 50% of the stuff in terms of automation here falls into the category that we talked about earlier, around like, “Hey, there’s information somewhere. It’s Slack, it’s a KB, it’s in these other interesting places. Can we answer that in a more automated way?” That’s one side of it.
The other side of it is actions. When I say that, for lack of a better example, instead of asking, “Hey, what’s the procedure to reset my password?” It’s more interesting to say, “Hey, can you reset my password,” right? Actions. On the first side, I think we covered decently well. One of the things that Kevin touched upon is creating knowledge. I think that’s a very interesting thing here is whether or not you want to call it us building a KB, we haven’t gone so far to put that stake in the ground in terms of our product feature yet.
Nonetheless, one of the things that gets me excited about the idea, it’s like Ravenna is growing with you. That this knowledge is in all these disparate places and we have the ability to go through and hone in on where people work, and make Ravenna better.
Tim: Awesome. So exciting. So much to go build, so much opportunity. Last question. Any advice for other aspiring founders out there thinking about going to get something started in this market right now with AI?
Kevin: The thing I would encourage everybody to do if you’re thinking about building a product, is go talk to a lot of customers before doing it. It was definitely the biggest mistake we’ve made many times throughout our career is like, “Oh, this seems cool. Let’s go and spend a month, three months, six months.” As people who know how to code as engineers, the bias is just, let’s go build because it’s easy. Building is way easier than going and finding 20 customers who will give you a half hour of their time to validate your idea or whatnot. However, you’re going to save yourself so much time, disproving ideas or you’re going to validate your idea and have a lot more conviction about going off and doing it. The biggest piece of advice I can give to folks who want to start companies is go talk to 20, or 30 companies or customers before you start writing a single line code.
Tim: You think you’ve done enough customer validation? Go do more, double down.
Kevin: You can never have enough. Even now, every customer call we’re on at our stage, I mean, we’re not a super old company, we’re eight months old, but we treat it as a discovery call. We spend most of the time asking questions and trying to learn as much as possible about the pain that they’re trying to solve for, because that influences what we’re going to build next week, next month, et cetera. We spend a little bit of time talking about Ravenna as well, but the learnings are still critical for us, and I think will always be.
Tim: Bring us home, Taylor.
Taylor: I’m always reticent to give advice. It’s because I’ve found that just doing this for a decent amount of time, everyone’s experience is so bespoke to them. I do love advice. I do love hearing other people’s journeys, but that’s the way I kind of think about it.
One of the things from my journey that I try to hold true, and I always get word, even conversations a little bit like this. We’ve talked about ServiceNow so much and the incumbents out there, but at the end of the day, the only thing that matters is the customer. That’s the only thing that matters. I try to very much hold a, call it competitor aware, but customer obsessed point of view.
That’s critical because I’ve seen the playbook the other way around, and I’ve seen basically not a lot of success. Whereas I have been lucky enough to work with folks, even to my surprise, a maniacal, just focus on the customer, despite the fact that we were circling around by crazy incumbents and everything on the wall said we were going to lose, it was that maniacal focus on the customer and the problem that pulled us through at the end of the day. So I’ll try and pull that together where we’re at here too.
Tim: Customers, it’s all about the customers.
Kevin: A hundred percent.
Tim: Thank you both so much. It’s a real privilege and a ton of fun to be working together. Looking forward to the future.
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