In the latest episode of Founded & Funded, Madrona Partner Sabrina Albert sits down with Patrick Thompson and Austin Hay, co-founders of Clarify, the startup that’s pioneering the idea of autonomous CRM.
Their vision? A CRM that actually does the work for sellers of driving outreach, managing pipeline, and surfacing insights without constant manual input.
Patrick and Austin share:
- Their journey from Iteratively, Ramp, and Amplitude to founding Clarify
- Why the CRM market is ripe for disruption with AI
- How Clarify’s unique pricing model flips the script (free CRM, pay only when the AI agent works for you)
- The concept of autonomous GTM teams and the rise of the Go-to-Market Engineer
- Lessons in building culture, hiring talent, and embracing the “beautiful mess” of startups
Listen on Spotify, Apple, and Amazon | Watch on YouTube.
This transcript was automatically generated and edited for clarity.
Well, let’s start with the founding story. You both had other startup experiences before jumping into Clarify. And I’m curious, what made you decide to start Clarify, and why did you decide to tackle such a legacy industry a large one at it?
Austin: We like to joke that it’s a little bit of a romantic story because I was one of Patrick’s first customers when he was building a prior company called Iteratively. I really fell in love with actually Patrick and Ondrej and the way they approached customer-centric problems.
Patrick and I stayed in touch over the years ever since. And a couple summers ago, we got together in New York. And as the story goes, basically had been thinking a lot about the CRM space. I had been thinking about it from the perspective of coming from ramp. I’d spent around two years there watching as we spent millions of dollars to effectively rebuild the same architecture that lots of companies before them had kind of built and failed.
And Patrick was working on Amplitude and kind of experienced the exact same set of problems, really, really smart people, tons of money, talented engineers, but effectively building a huge stack on top of legacy platforms.
And nothing in that stack was incorporating all the modern lessons that we had learned from data hygiene, data collection, taxonomy, and the CDP space. And so when we got together in New York, I remember sitting down for dinner, I was like, or maybe it was lunch. And I was like, “Okay. Well, I’ll share my idea first, and then you tell me what you think.”
And as we shared the idea, we realized that it was actually the exact same one. It was the premise of what would happen if you take all the lessons around a customer data platform and apply that to a piece of legacy software, like a customer relationship management platform. And there’s a lot we could talk about, but I think at its core it was this idea that CDP really invented a much more modern, flexible kind of customer-centric piece of software.
It captured event data, which was a new marketing piece of jargon that now everybody knows and loves. And it was to have the architecture and flexibility to combine the data elements that we’ve become used to from marketing perspective with the sales platform. And I think since then, it’s obviously taken on its own life, and we’ve really leaned into the idea of contextless data. So being able to take almost any type of data source and pull it into the platform and allow you to actually drive a go-to-market motion from there. But when it first started, we actually really brought the lessons from, I think, the CDP space.
Patrick: Yeah, I think the one thing of spending about five years working on CDP is just helping companies put these massive reference architectures together where your CRM was just at the core of it, but you have to bolt on all these other providers, really try to get value out of it. And so when we were talking about the problems and the amount of spend and the amount of tools required to build a modern go-to-market team with dozens of tools, we thought this is a problem that’s worth solving and effectively built a company around it.
Sabrina: I’d say most startups would be very intimidated to go after the $100 billion dollars plus market of CRM. There’s a lot of incumbents in the space, Salesforce, Hubspot, a bunch of new startup players. Why do you think there’s a specific why now behind building the company and going after this huge antiquated industry?
Patrick: In the context of why now we’re at this turning point just as an industry with AI. And so when you think about the different areas that you can actually go address, this was a one that was effectively ripe for disruption. And for us in particular as a team, especially as a co-founding team, there was no other opportunity that we wanted to go tackle that was bigger and bolder than this one.
And we felt like the timing was right. We felt like we had the right team, and we were pretty excited about it. There’s nothing else I’d rather be doing myself. This is exactly what I want to be working on.
Sabrina: I love that. And Austin, maybe you can share a little bit about how you’re seeing some companies that are ripping out their traditional CRM systems, be that Salesforce or others, and building things or stitching together things internally or even just leveraging Clarify to do it.
Austin: I have worked in RevTech for 10 to 15 years now. And I have integrated and manage pretty much every MarTech and RevTech tool under the sun. And what I still remember when we first started talking to people summer or two ago about, “Hey, what’s the number one pain you have with Legacy Solutions?”
It was, one, it just is so painful. It’s a joyless experience. And two, it’s price. So I know we’re going to talk in a little bit about our price and some innovation there. But the number one thing was joy. It was this idea of, “Hey, it’s just hard to use.”
And actually I think there’s some fundamental relationship here between that experience and the law of last clicks, which is to say you want to reduce the number of clicks that somebody has to convert. It’s the same thing that happens for a salesperson every single day inside their CRM.
So if it takes five to 10 seconds for them to get to an action or an outcome. They’re going to be joyless. They’re not going to want to enter the data that they want. And the whole model of CRM is built in this premise that, “Hey, we’ve built a database, and all we need is the human to enter the data.”
And so I think what I’m seeing when I talk to people is this very big willingness to try something new because that assumption of, “Hey, I have to do work in order to get value has just been lingering for so long,” and people are tired of it.
So it’s really interesting in that when we talked about activation energy and what would be the hesitations for people moving from a different solution to Clarify, I thought it’d be like data integrity, and I thought it’d be security, all of the classic things that an SMB and enterprise account will want.
But actually, I think all that is being overcome by the joy and hope for the future. The idea that, “Hey, I actually will have a tool that’s designed purpose-built for me as a founder or sales operator,” and it’s actually really joyful to use. It’s not going to take me 10 seconds to get to an account record.
It’s not going to take me a minute to get to some page. If I have a question about the people I know in New York because I’m visiting, I can get the answer really quickly. These are all the hopes and dreams that people have had with the CRM for the last 15 years, and it’s just remained silent.
So I actually think the thing that is driving a lot of people to Clarify right now is not, “Hey, I’m going to get some specific value.” It’s the hope and the joy that the solution we’ve designed is bringing them. And especially with the fact that we’ve built this in basically a year, it’s really, really valuable. And AI is only getting better every day. So there’s also this anticipation of, “Well, if I can already talk to my meetings and get insights that I can already look at a table and run an insight query,” it’s going to be so much more powerful in six months. I’m here for the ride.
Patrick: I think one of the goals that we have that somebody uses Clarify at a company and then they refuse to join another company because they don’t have Clarify if that’s the type of product that we want build. They love it so much that shapes the way that they think about defining their career path, because I can tell you, having worked organizations where we’re on legacy incumbents, I will never use them again.
Austin: Totally.
Patrick: The tools that you use shape the work that you do.
Austin:
And it’s not even legacy incumbents either. When I was at Ramp, there was a tool that we’re using for productivity. And our CTO, Karim, always used to say, “Let the operators pick the tools, because they’re the ones doing the work.” And all of us wanted this one tool, and it wasn’t even a legacy incumbent. We just had a different tool besides this well-known one in the productivity space.
And it was so bad that we were protesting to our CEO to try to get this productivity tool. So it doesn’t even have to be a legacy provider. There are often alternatives right in market. And we want people to fall in love with Clarify even over our direct competitors and over legacy providers.
Sabrina: Yeah. I love that. Nobody wants to spend time manually adding a new deal opportunity into Salesforce. I think we’ve all been there. And if you could just have it out of magically in the background, be doing those sort of things for you so different people can actually focus on what brings them most joy. I love that kind of concept.
And then just empowering them to be more productive at their jobs and allowing them to do the fun things. One thing that I’ve also heard from customers is that they like how you’re reinventing the business model. With this new wave of AI, you no longer shouldn’t just have to pay for subscription-based, seat-based ways of thinking. How have you guys thought about the business model fundamentally? I know you guys are exploring different ways to price the product. How are we thinking about it?
Patrick: Yeah. So I think generally in the context of AI, I feel like most applications are going to be a race to zero. So we’re not charging for CRUD anymore. We charge for the agent. We only charge when our agent does work for you. It’s effectively a usage-based model. So think of our CRM as completely free. You have free CRM. And what we monetize is the agent that does work on your behalf.
You can think about it as a teammate who’s doing all of those actions for you, and that’s what you’re paying for. This is something I think is radically different than every other competitor that we have in market, which is charging you based off seats. I had the fortune of working in an Amplitude where we had an 800-person company, where we had 400 sales receipts, and we’re paying seven figures for Salesforce at the time.
But half our company didn’t have access to customer data. We think that’s a huge problem. We want everyone inside your organization to have access to customer data. That’s effectively the fuel that drives growth. And effectively, all you’re paying for is our agent.
And if our agent’s great, great, you use it more great. Great. We make more money, but you get more value. And if you don’t like some of the features that our agent does, you can turn them off and save costs.
Sabrina: One concept that you’ve talked a lot about is this idea of autonomous CRM. I think that’s a new shift in some of the other words you’ve used around ambient intelligence and bringing to life what a CRM should be doing for you. Can you share a little bit more about what those two terms mean to you guys?
Patrick: Yeah. I think let’s go talk a little bit about the pain points of the existing CRMs to start with. One is data quality is always a challenge, which has I’d say been that way for decades. The other is that it requires a lot of manual data entry. So we thought about how do we solve some of the challenges that CRMs are plagued by?
How do we want to improve data quality but also help automate as much of the time spent by sellers and founders and reps putting data into the CRM? And that’s really where what we think about autonomous go-to-market. It’s really trying to reduce a lot of the human input and make it so that your tools are really working for you and not against you. And speaking as a founder who set up one of these tools for our last startup, I was spending hours inside of these legacy CRMs, and they weren’t really giving me a ton of value, but I was told that, “Hey, I should be doing this,” and this is the best practice.
So when we really think about the principles of building autonomous go-to-market at Clarify, it’s how do we build things that actually provide you value, take work off your plate so that you can spend more time working with your customers and growing the business less time just doing manual work.
Sabrina: So it’s this shift essentially from autonomy or automation to autonomy and allowing the system to actually go and do all the work in the background on its own, essentially. So you don’t actually have to have the human in the loop to do a lot of these manual things, right?
Patrick: Yeah. And if you think about marketing automation or sales automation, this term automation has existed for decades where we’ve had many, many companies try to go solve the automation side. I think the difference in the context of AI or autonomous go-to-market is the fact that we have agents now that can take action and can problem-solve interaction that you didn’t have previous to this. So a lot of what we’re building at Clarify is how do we incorporate agents or AI at the core of the workflows that we’re doing. But effectively, it is a lot of automation done autonomously.
Sabrina: So how does this work today? If a customer comes to you, how do they get up and running? What are some of the things that the agents are doing in the background already for you that historically maybe you couldn’t have done or a human would’ve been doing in the past?
Austin: Think about the entire journey for the end user. So it’d be a founder, an operator, a seller, sales VP. What are they doing in a day? They prospect. They find people. They go on LinkedIn. They send emails. They have to reach out to those customers. They have to schedule calls. They have the call. Everybody’s recording calls these days.
And then in the manual workflow way, they’re taking that call data. They’re transcribing or creating the emails that they send. And then, afterwards, they have to remember to follow up. They have five, six, seven steps to get to the final deal creation. And at every single step, there’s a touch point that goes back to the CRM usually to inform a CRO or a revenue leader about how is this deal progressing? How is my pipeline doing? How is my business doing? And so when we talk about automation and administrative work, that takes up 20 to 30% of a seller’s life.
People don’t realize that the vast majority of the work and what defines a great seller is doing that work in a critical way. And so not only are we thinking about, “Hey, what’s the next generation of automation?” because you back up five years, people were using tools like Zapier still. They were using out-of-box integrations to move data from one point to another in order to kind of put systems on top of their legacy CRM to do this work.
But we’re really saying, “Hey, actually no, you should just be able to show up with your inbox, get all that stuff out of box naturally from Clarify. When you log in to Clarify, we automatically create deals for you. We automatically send the emails through. We automatically allow you to peruse the meetings that you’ve recorded and ask questions and then maybe draft an email. We automatically remind you from the email that the person you were emailing never responded.
Imagine snoozing your CRM. To me, that is really the power where you don’t have to think about what happens in the sales process. It’s already been thought of for you.
Sabrina: And I think one of the issues with, I guess, if you just go back a few years, is that there are so many tools set off these people. They’re berated with a bunch of different offerings. And one of the things that you just talked about, Austin, is that you can just easily get up and running. It’s kind of like a one-stop shop in a lot of ways.
And so I love that concept of, “Hey, you can just connect your email inbox, and we could do all these things for you.” We have these agents that are running in the background, doing these intelligent tasks on your behalf. Do you think about it a little bit like it’s a co-worker or an employee that you can work with? Is that a concept that resonates with you guys or not so much?
Patrick: It’s an interesting concept. We don’t personify our agents. We don’t give them a name, although we joke about it every so often. It’s like, “Oh yeah, we have a CSM named Devin. Maybe, we should build a Devin support agent.”
And that’s, I think, very common these days. It’s a question of is that the model that we’re going to have two, three years down the line or is this more of a flash in the pan? In the context of humans interacting with agents, yeah, I think that’s going to be around for down until the end of time. I think the hardest part is just how do you personify the work that we do?
And so for us, we’re a lot more trying to design it where it just seamlessly integrates, and you don’t have to think about the AI running versus having to actually interact with the AI. And this is the biggest difference between designing an agent versus designing more ambient AI as we focus a lot of our time on the design side of just making it just work, batteries included, no manual required.
Austin: And I think this actually comes back to, obviously, we have a lot of personas we’re serving, A lot of founders are using Clarify, but there’s also a lot of Rev operators using Clarify, and they don’t want another person to talk to, right? Having another Zapier, having another iPass tool, having another integration to manage is actually a burden.
So I think that’s some of the hypothesis that we’re testing right now, and we’re learning as the market grows, is do people actually want to have the full flexibility of designing an agent or do they just want to show up and be taken care of? And I think we’re watching that evolve right now in the market.
Sabrina: Do you find that your customers are still leveraging Clarify plus another tool of some sort, or do you feel that they’re coming to you and trying to have a truly all-in-one place where they could do a lot of the different functions that a selling organization would do?
Patrick: Yeah. The nice thing right now, when it comes to product prioritization, is that we have a ton of feedback from our customers, which is both good and bad.
They’re asking us to build a lot. I think that’s cementing a lot of the all-in-one strategy that we have to be the one place that people can go both on the pre-sales, sales, and post-sales side. We’re an early-stage startup. So we only have so much things that we can build.
So we do partner with other companies, and we do have customers that use us with other tool, and we have really good open APIs for that as well as things like Zapier integrations to get data in and out of our system. But, yes, our customers are asking us to build a better all-in-one solution for them.
Sabrina: And you guys also recently wrote about this concept of autonomous GTM. It’s a thing that I think you wrote with one of our colleagues, Loren, about, and how teams, again, back to this conversation, are re-architecting the way that they think about going to market with a new set of tools.
Can you define a little bit about how you think about this new world of selling and what an autonomous GTM would mean? And specifically maybe just double-clicking for companies that sell with a more PLG motion and then companies that sell with a more enterprise sales motion, how can an autonomous GTM strategy work for them?
Austin: So if autonomous CRM is helping automate the process of selling, autonomous go-to-market is automating the ways that teams themselves operate and sell. And there’s a lot of market chatter right now around this new role called the go-to-market engineer.
And I actually think that it’s the genesis is something that’s been happening for a couple of years, which is actually that go-to-market teams are becoming more technical in nature because the systems that they’re using are far more technical in nature.
The problems that Patrick and I faced when we decided to start Clarify were not new to the industry. Pat was sitting at Amplitude seeing millions of dollars in spend working as a pseudo-technical resource with teams to try to understand how these systems work. I was working at Ramp as a Rev and MarTech operator managing $10 million in a team of 26 to help our massive sales team be successful.
And I think that, to me, actually is the genesis of autonomous go-to-market is this idea that if you want to be the most successful rev team in the world, you actually have to apply core EPD principals to the art of revenue generation.
So now fast-forward today, you have really complicated systems, but you also have people who have a deep desire to make the most of that. So what I think is happening is that you have many more folks who were traditionally in non-technical roles spanning a huge set of skills in sales.
So just as an example Travis on our team who’s an amazing seller, is working in Clarify, working with workflows, working with APIs. I think part of what’s happened is there’s not only a necessity to be more technical and go-to-market, but there’s tools gifted by AI that allow people to do that.
So what I’m seeing is now there’s a big blend across the roles, and you have less division between a salesperson, a sales ops person, deal desk, and then the sales engineer. And now a lot of those roles, especially at small and medium-sized companies are just being shared.
And what that means is actually I think in the short run, you’re going to have either smaller teams that are more nimble and more resource effective, or you’ll have bigger teams that are just able to produce a lot more volume because they have a vast majority of skills across the team.
And what I think this ultimately leads to is this idea though that you can effectively automate your entire revenue stack and the way in which you generate revenue by using systems tools and people with AI. I still think we’re in the really early stages though.
What’s not talked about on LinkedIn is just how much work and effort goes into setting up these systems and managing them. This gets back to the idea of the autonomous CRM. There’s lots of really cool things you can do with tools now. Even n8n is great, amazing Zapier, Lovable. There’s applications you can build, but the maintenance costs right now is still super high.
So we actually had one of our customers do a post with us last year about the 17 different tools they were using on top of their legacy CRM. They actually ended up winding back a lot of those tools when the person who managed them left because they’re saying, “Okay. Well, we thought that having all these tools would help us, enable us to be more successful in revenue generation.” But actually, what it ended up doing was just creating a lot of maintenance cost, and I think that’s maybe a recurring theme and an unspoken part of the autonomous go-to-market system that we haven’t yet discovered.
Patrick:
The only thing I’d add there is I do think people are trying to do more with less and/or scale quickly. And we talk a lot about even the context of AI the 10-person billion dollar company, right?
And the only way to be able to do that is to be able to build systems that can scale and effectively build your go-to-market motion to scale regardless of if it’s a sales led motion or a PLG motion. Having spent a lot of time working at companies like Atlassian where we did have a huge PLG function and transitioning that over to more of a sales organization over time and then having working at Amplitude that both had a PLG and sales led function. It’s like the more you can look at things like product usage data, the more intense signals you can look at, the more your sellers are working the right accounts.
So when it comes to Clarify, one of the things that we spend a lot of time is just how do we get better intent data into the CRM? How do we be able to action product usage data for building things like PQLs or doing upsell or cross-sell opportunities or even surfacing retention problems or customers that we should be investing more time into?
And that’s effectively just, again, looking at the data and making sure that we have the right automations and the right intelligence on top of that so that the people that we have on our team are more impactful operationally.
Sabrina: Tell us a little bit about your data strategy. Where are you collecting data from? What’s your hypothesis on this idea of intent data? Everybody’s talking about this as it’s kind of like this jewel, but what does that really mean and how do you actually leverage that into Clarify?
Austin: I feel like a lot of this reminds me of the great marketing term in the CDP landscape, which was orchestration. What actually is orchestration? Nobody really knows, but we all talk about it. It’s the same thing here with intent data. I think that a lot of times, you have to back up to what is the actual customer problem.
So if the customer is trying to have insight into the meetings they’re having and the deals that they’re working on, we provide some of the best class data out there. With intent data, in particular, though, I feel like it’s become a little bit of a marketing term to capture data that’s not always clear. You probably won’t get such great data if you’re looking at cookies only.
And then the outcome is what are you trying to do with it? Are you trying to pounce on accounts? Well, if you’re trying to pounce on accounts, you might as well add a third-party SDK to your site to try to deeply identify people and then go after them either with a targeted email message, a website message, or something like that.
So I think a lot of the times, we mix up the marketing term for something new and fancy. And in fact, we should just be looking at the customer outcomes we’re trying to achieve.
Sabrina: And how clean does the customer data have to be? Do you always have to make it structured so you can help them understand what’s behind it and actually utilize that data? Is there a lot of work that you guys have to do for the agents to go be doing the tasks when it comes to cleaning up that data, or is it pretty easily accessible now with how you might leverage?
Patrick: Yeah. So in the context of what we’re building, there’s some level of determinism that you want to build when building how you’re going to market motion. Yes. Agents, AI, and LLMs in particular are really good at building and spotting patterns.
We still believe in structured data. So when you think about the platform that we built for Clarify, it is really focused on relational data, time series data and unstructured data. So we look at things like email bodies and call transcripts to be able to spot patterns.
Our entire system is in venture events. So we look for in particular things like selling intent on an email to decide whether or not we should update a deal or not update a deal. And that’s looking at the overall context of the email and understanding your business at a high level.
But, again, when we go update a deal, we’re updating structured fields on that deal for you. We think that’s extremely important because you want to be able to have an interactivity log of all of the updates on that particular deal, so you can trace it back, you can improve it, versus there’s a lot of, I think, folks who are like, “Hey, I think AI is fairly good.” Just throw it into a noteboo, LM, and make sense of it. That works really good for just general customer intelligence, but it’s not really good for building out automations or capabilities to really scale a business.
Austin: Yeah. And that insight, I think, came from our time working in CDPs, where you absolutely have to have structured data to meet the needs of downstream marketing tools. But it was actually pretty cool. When we started Clarify, when we were talking about the architecture, there was a whole thought process around the idea of what would it look like if you had structured data, but you actually allowed the user to select the interface in which they view that data, which today is not really possible.
But you can imagine a world where even if you collect unstructured data and maybe you structure some of it for marketing purposes or analytics purposes, a person could render the view they want on the fly of the data that’s most meaningful to them. That’s never really been possible before. And I think so much of legacy systems define the mode that you view data and you access data and consume it. And we’re entering this world where, actually, maybe you don’t have to have that at all. And maybe, you can actually let the end user, the salesperson, the VP, the sales manager, define what they see and they’re in control, their own go-to-market process.
Patrick: And you can do this in Clarify because, effectively, Clarify is just an application layer built on top of a warehouse. So you can define or have the AI define whatever fields are being shown up, whatever relational fields effectively build a joint on the fly or do think a computed field as well. And at the end of the day, we’re just rendering SQL that’s querying the data warehouse.
Sabrina: Yeah. I love that, and I love that you’re building a system that surfaces insights and analytics even before you ask a lot of the smart reminders. I’ve used the system before, where it flags. You need to follow up with this person, or you can show signals that it’s been certain amount of days since you’ve let this email pass, and how do you make sure that you’re on top of some of these sort of things? And so I think it’s this idea of surfacing things before you let them slip, which is really, really great.
Patrick: I think at the end of the day, that’s the value that Clarify provides. There are a bunch of crud applications that you can purchase on the market, traditional CRMs. But at the end of the day, when we think about what we’re building, we’re building effectively the platform for go-to-market teams plus the agent that interfaces with it and effectively the agent is really the value here that we’re providing.
Sabrina: One thing that I want to talk about is team and culture. You guys are both founders. Before building Clarify in the past, we are building in one of the most competitive spaces and times of our lives with AI, everybody. There’s a fight for a lot of talent. How have you guys found hiring to be? And what are some of the lessons that you’re learning in terms of just attracting great, great talent to Clarify?
Patrick: One of the things I think we talked a lot about is building the culture to attract great talent.
So I think if you build the right culture, the right people want to come work for your organization. I think we’ve done a really good job, and we codified our values and the culture that we wanted to build early on, in particular, specifically, outcomes to hiring engineers. We think of engineering as part of the product owners. We really want engineers to own more. We want engineers to really be customer-facing.
So there’s a lot of talk right now within AI companies around forward-deployed engineers, but we live and breathe that model. Our engineers are the first line of helping customers resolve issues. That’s helped attract some really great engineers to our team, some that we worked with before and others that are definitely new to the organization. But giving folks a high degree of autonomy and really empowering them to do the best work has made it really easy to hire some awesome talent.
Austin: Yeah, I think two other things come to mind too, one, we chose to reach deep into our networks to hire people that we’d previously worked with and folks that we knew, loved, and trust. So that was an advantage that we had as founders and previous operators just having a large bench to call upon.
But I would say, actually, I give Patrick a lot of this credit is that your job is to be selling everybody on not just the product that they could buy, but the vision for the world. And I think one thing I’ve certainly learned from you, Patrick, is that every conversation is an opportunity not just to sell the person on your product, but to get them to fall in love with the thing that you’re doing and building.
And so one thing that we’ve done early on is we have conversations with people regardless of whether we can sell to them regardless of whether they’re higher or not, we try to help as many people as we can because I think the vision in the long run is that the more you put yourself out there, the more you be consultative in nature, the more you just try to have good karma and help people in whatever they’re trying to do, the more that’s going to come back to you.
And I think one example of that really paying off is we’ve had hires where they didn’t work out and then came back a year later.
We had a hire where we thought maybe we’d sell to them, and they became a customer. We had a hire where there was no chance in heck they were ever going to leave their job. And now, they’re working for us and having a great time. And so I think the lesson, if there is one for founders, is just never miss an opportunity to just lean into a relationship and try to provide value. I know this is hard because everybody’s busy. And so you have to have some level of judiciousness with your time, but you definitely live and breathe this. And I feel like that’s really influenced me and Ondrej and a lot of the team, and just how we think about working in service to others.
Patrick: This also goes into selling. There’s never a “no.” It’s just a “not now.”
Sabrina: I love that. As we close out here, I want to ask a couple of final questions as we think about the bigger picture and the future for Clarify. As we think about 2026, what do we think the go-to-market team of the future looks like? What roles are changing? What roles may disappear or what new ones may emerge?
Patrick: There’s a couple of things here that I’d love to touch on. I think there’s generally convergence back into a unified account manager role where you don’t have the divergence across pre-sales, sales, and post-sales.
I think most of this because you have the tools and you can enable these motions that scale now that you tend to have an account manager that owns the entire relationship, whereas I think previously, there was a lot of different handoffs. But because you want sales and primarily with AI sales to be much more authentic today, you really want somebody to build a relationship with a customer.
And I think that’s what you’re going to have this convergence back into this account manager role. And you’ve kind of seen this with post-sales moving into, I’d say, companies investing less in post-sales, not more in post-sales. And then, additionally, in the pre-sale side, a lot more investment generally in market into automated SDRs, BDRs. And I think that will increase volume over time. But, generally, you still need that handoff to an account manager to actually run the sales process.
Austin: I also think that teams are just going to become more technical in nature. We’re going to see more engineering involvement in revenue tech and revenue teams in general. The line between growth engineer, systems engineer, and rev ops is totally blurred. Now, you just have one person who can actually work across all those different disciplines to just deliver value.
And I think that no matter if we achieve our mission, not of making a fully autonomous CRM, there’s always going to be people who want to use APIs and build their own custom applications on top, especially we go to the enterprise.
And so what I think that means though is that if you have a world where API and technically driven systems are table stakes, then you’re going to have tech teams embedded inside of sales teams that’s going to continue to occur, and it’s only going to get better, especially as things like APIs come out, MCP servers come out. Now, you can get to a world where actually the work of a single engineer could have replaced an entire team of 10 working on integrations between 15 different tools.
Maybe, you just have one to two tools, and then an engineer writing custom code on top of MCP servers to a bunch of different tools handling the kind of custom work that used to be a multi-million dollar division. I think that’s not a crazy thing to see in the next five years.
Sabrina: That’s fascinating. Okay. So for founders listening, what is the most high-leverage thing they can do today to build towards this autonomous go-to-market future?
Patrick: Just make sure that you’re prepping your sellers as well, the people that you’re hiring on the go-to-market side that they’re used to using these tools. My favorite question to ask in any interview, but primarily with AEs and BDRs, SDRs is, “Tell me how you use AI. Are your folks that you’re hiring, are they AI literate? Are you hiring people?” This is a standard question. If I was to hire an engineer right now, I’d want to know how they’re using Claude Code or Cursor or Windsurf.
But we don’t think about asking those same questions on the go-to-market side about how does an AE use Claude or ChatGPT to do account planning and account prep. And you should be drilling this into your team, like, “Hey, we’re going to get left behind if we’re not investing in AI, and we’re not at the forefront of what this is offering us.” The alpha right now for companies is really leaning in on that. And yes, Clarify is a part of that story.
Sabrina: Awesome. Well, everybody heard it here first. You’ve got to use Clarify. Give it a try. I think it will truly change the way that you do selling, and just think about what it means to build the future of a go-to-market team.
So to wrap us up today, just a fun question, what is one lesson from this journey, even just in the past 12 months, that each of you knew maybe going in before starting, Clarify. For founders who are listening, what is something that they can take away with them?
Patrick: The team is everything. Just build a great team that’ll carry half of the way there.
Austin: One thing I was thinking about is that Pat and I have both been through a lot of different startups, and he founded Iteratively. I built a small company. I’ve been working for four different founders in my lifetime. And I thought when we started Clarify that we would make the best company that ever existed because we’d learned all the things that you can possibly learn about startups after working for four or five founders, or seven or eight between all of us.
And the thing that I actually learned is that no matter how hard you try, it’s still going to be a beautiful mess. So my advice to founders would be just embrace the fact that it’s going to be messy. Embrace the fact that you’re going to do things wrong, things are going to go awry.
All the expectations you have about prior building, you can still apply, and there’s going to be new unknown parts of the equation that come up throughout the process. And so just lean into the fact that building startups is messy, and that’s half the fun.
Sabrina: Awesome. Well, thank you so much for joining us today, Pat, Austin. This was a lot of fun for me, and very glad to have you on.
Patrick: Thanks, Sabrina.
Austin: Thanks for having us, Sabrina.