The Data Stack Show - 237: Startups, Sales, and Spreadsheets: How a Real Estate Developer Built an AI Company

Episode Date: April 16, 2025

Highlights from this week’s conversation include:Willz’s Background and Journey (1:25)Discussing Real Estate Data Challenges (2:58)Inspiration for Software Creation (4:05)From Spreadsheet to Software (9:04)Challenges in Ownership Identification (12:24)Company Acquisition (16:00)Pitching Investors with Data Tools (18:46)Lessons Learned from Selling the Company (21:45)The Journey to Ready (26:55)Sales Development Representatives Explained (29:22)Role of Data in Sales (33:30)Real-Time Dashboards (36:54)Human-AI Collaboration (39:53)Human Touch in Data Compilation (44:02)Paradigm Shift in Data Access (46:19)Frustrations with Sales Cycles (48:22)Value of Genuine Conversations (55:23)Optimizing Internal Tools (56:23)Future of Data Interfaces and Parting Thoughts (57:21)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Here on the Data Stack Show, we have been to many of the major data conferences across the industry. But year after year, one of our favorite ones is data council, and it's because of how much value we get when we go. I think this year is going to be the best one yet. It's three days long in-person,

April 22nd to 24th in Oakland, so back in the Bay Area. The theme this year is meeting your AI and data heroes, IRL, and I am personally extremely excited to meet some people that I have admired for a long time and a bunch of people that we've had on the show. I'm really excited to learn what is happening at the cutting edge of AI and data, and also hear

from people building new tools in the standard data space. Hi, I'm Eric Dotz. And I'm Jon Wessel. Welcome to the Data Stack Show. The Data Stack Show is a podcast where we talk about the technical, business, and human challenges involved in data work. Join our casual conversations with innovators and data professionals to learn about new data technologies

and how data teams are run at top companies. ["Data Work"] Before we dig into today's episode, we want to give a huge thanks to our presenting sponsor, RutterSack. They give us the equipment and time to do this show week in, week out, and provide you the valuable content. RutterSack provides customer data infrastructure and is used by the world's most innovative companies to collect, transform, and deliver their event data wherever

it's needed, all in real time. Thanks for joining us. Yeah, man, thanks for having me. All right, give us, you're an entrepreneur, you've done a number of interesting things in building software, and a lot of it relates to data. So just give us the quick flyover of your career. What were you doing before and what are you doing today? Yeah, so I got started as a commercial real estate broker at Lean Associates. Then I went on to do real estate development

and I wanted to start like a tech-enabled brokerage But there wasn't too much tech and real estate that was very good in my opinion So I kind of end up starting my own company map sheet that kind of became the company turned into this SAS company I had no idea what I was doing or getting into but building software is like building a building, right? Yeah, I thought so. But there was a few differences that I wasn't really prepared for. So yeah, I got a crash course in data and APIs and coding

and even though I still can't code, so that was fun. All right, and then after Mapsy, what happened and what are you doing today? Yeah, so we sold out a year ago, then I kind of took some time off to detox from all that. And then about last summer, last fall, started just tinkering around, seeing what's out there. And obviously I've been paying attention to AI a lot.

And I reconnected with a guy who was running the revenue team over at Cherry, and it happened to be a perfect fit. So we decided to put our heads together and it happened to be a perfect fit. So we decided to put our heads together and we started a new company called ready.ai. So, Wills, we are constantly talking about data and AI on this show. I think our last guest was a little bit done with it, but by the end of it, I think we're happy to talk about it more. But I'm so excited. You're two startups in now doing this data AI thing, one for real estate one you're gonna tell us more about so we got to dig into that but what other topics are

you excited about covering well I know y'all are excited about talking about commercial real estate data I'm not as excited I have some scars but I'm happy to share all my lessons from real estate data because it is pretty complicated all right well we can do therapy after we're done recording. If it required. Yeah. I like that. All right.

Let's dig in. Let's do it. Wills. Can we go back to the moment where as a real estate broker and developer, you felt in your gut like, okay, I'm going to start a technology company. What was, do you remember the moment like where you knew, okay, this is going to happen, I'm gonna start a technology company. What was, do you remember the moment like where you knew, okay, this is gonna happen, I'm gonna do this? I don't remember exactly where I was, but I remember it occupying the lion's share of what I thought about during the day and

at night. So I figured that was a reason to jump all in on it. And give us the story. So you were to jump all in on it. And give us the story. So you were building buildings, houses, structures of some sort, buying the land and sort of and doing real estate development. What brought about the idea to create software? Yeah, so I started off, I'm in Greenville, South Carolina, and there's a area called the Westin, the historic Westin district, and this was around like 2014, 15, and I just knew that it was going to explode very soon. We already had the baseball stadium anchoring it, so I basically plotted every single parcel of land in that area on a map and on a spreadsheet. I found out every detail about it, when it last traded, who the owner was, etc. And that became the collateral I used to pitch investors on why they should invest in this property or this development.

The frustrating part was that I just had to manually go in and update it every single time. I couldn't possibly keep that updated. And when you say update, what are some of the things that you're updating manually? What are some of the data points? Yeah. Yeah. So high level, just like if a property sells, then you got to put a comparable sale on that. So the pitch I'm imagining is like, look at these values that keep going up. Is that a lot of the pitch?

Yeah, for sure. And there was a very, I remember when a hotel right across from the baseball stadium sold, it sold for like three times what the price per square foot was out of any other parcel in the area. And I was like, okay, they've now set a new ceiling. And so, yeah, mostly it was like just the comparable sales. But then once something trades, then the taxes change, the ownership changes, et cetera.

Yep. And okay, what I love about this story, John, you're going to probably chuckle when I say this, but so much good software starts as a spreadsheet. Yeah. Where it's like you essentially hit the limits of a spreadsheet and you sort of are like, oh, I guess I'm going to build software. Is that what happened?

Yeah, kind of. But I took it to the next level for a non-coder. I went to Airtable and monday.com and really tried to push the limits of what it could do. And I got something pretty cool built, but at the end of the day, I couldn't pull in real-time data. And that was kind of the whole reason for trying to do it in Airtable. So that's what then led me down the path of Map Sheet. for trying to do it in Airtable. So that's what then led me down the path of Mapsheet. So by real-time data,

the moment a transaction closes or whatever event happens, that we get an update and that's a lot of the value. Is that kind of what you mean by real time or is there some other aspect? Yeah, I mean, sort of this is another one of those problems with real estate data is that the real timeness of it is not always super real time. You're actually getting it from like a third party who's getting it from another party, another party. But a lot of times it would just go straight to the county records and look it up there. But there's 3,000 different counties in the United States and a bunch of different old websites to navigate and things like that. So ultimately what we end up doing is trying to get multiple data sources, including going directly to the county to see who, because sometimes the data would be updated within a week

or a month or never quite daily. So I wouldn't say it's actually real time. Right. But there's an aspect of real time that is like, soonest available, right? There's like streaming or whatever you want to call it that's like to the second like stock trading maybe. Sure. And for other industries, real time is what you're defining of like, the county does take

three weeks to process it but let's say like essentially nobody has the data but like the moment it's on their website then it's in our tool and that's still super valuable to people and in a sense is a real time like to your audience potentially. Yeah and another thing if you're a broker and investor developer you might find out about a trade before it comes online. So then we just made it like an editable spreadsheet as well. So you put in your own information right next to it.

Well, I mean, it is kind of interesting when you think about real time, the stock information, when you say like to the second or I mean, even millisecond stock information, it's actually what you're trying to do is reflect reality because trades are actually happening physically that quickly. With real estate, like a lot of times you're sitting down in person, like actually executing a deal. A lot of times you can like your writing signatures on actual paperwork that then gets scanned and filed and all that sort of stuff.

And so it's like, okay, that takes days to do that and get the information filed and all that. So I mean, that is a reflection of like, what's actually happening in the physical world. Yeah, for sure. And I mean, there's been a lot of startups that have tried to mimic the stock market with real estate data and doing,

tokenizing and things like that. And I'm not sure it's ever actually worked, with real estate data and doing tokenizing and things like that. I'm not sure it's ever actually worked because you have this kind of built-in infrastructure of manual processes and paper and everything that's got to move. You go from the spreadsheet

to Airtable, which I love, and I'm sure there are some statistics around how many things like this Airtable launched because it was... I mean, now there are some statistics around how many things like this Airtable launched, because it was, I mean, now there's lots of tools and AI has sort of completely changed that. But I totally remember that era where you could build, it was one of the first tools where it was like,

wow, I can build essentially software on this and sort of interact with the database in a way that was so hard to do before. And then you take the air table manifestation of that and you decide, okay, we're actually gonna build software. So what did the software do? Were you actually ingesting the data

and sort of plotting it out? So I wanna basically it would be a tool that if you were doing what you did and looking at this particular area of town, the West End, and you wanna understand all the information about it, you built what you would have loved to have as you were plotting out your plan, no pun intended, gathering information, making a market assessment, and then going to investors and saying, hey, let's look at all this data for this particular area.

Was that essentially what the software did? Was that the value proposition? data for this particular area. zoom into any place in the country and see every boundary line and then you could hover your cursor over it and see the ownership entity and sometimes you could see when it last traded just by like hovering over and that was a cool feature. So you could like quickly if you're trying to find you are the owner is but you're not exactly sure where you can just hover around. Then you click on it and it pops up like a little modal or like a side bar of information and you can just press save to map sheet and then it would that property would then be

added to your sheet. From there you could add columns. Let's say you wanted to add the zoning information. You could add a column of zoning. You could also add different manual columns where if it was like a single select or multiple select checkbox or whatever And you could have as many map sheets as you wanted. So the idea was like hey if I'm looking for Hotel properties and I'm looking along this corridor then I could have a map just for that You can move properties between maps different things like that. So it's pretty pretty configurable. Okay, let's talk about PTSD from real estate data. So it was pretty configurable. Okay, let's talk about PTSD from real estate data.

So that sounds great. I mean, it's like, okay, you had this really big challenge collecting all this manual data that was brutal. You build software to solve that problem. I mean, clearly it was valuable because you sold the company, but oftentimes value comes with solving a really difficult pain point and you said you still have some scars. And I'm also always curious, like what did you think going into it? So you did it mainly it was valuable to you, you go through these iterations and you thought like going into it I thought that everyone was going to love it immediately. Okay perfect. Yeah and then like what kind of what actually happened? What barriers did you face?

I feel like you kind of solved and what other barriers did you face? And you're like, oh, this is actually like not really solvable. Yeah, so the biggest problem that needed to be solved was figuring out who the true owner is of a property, especially in commercial though. In commercial, 80% of properties are owned by an LLC and there's a whole process for

trying to figure out who's behind it, but they're owned by LLCs for a reason that people want to be found. So the company that we built on top of Cherry, they had taken a stab at it. There's been a lot of companies that have tried and- The map sheet was built on top of Cherry, they had taken a stab at it. There's been a lot of companies that have tried. Mapsheet was built on top of Cherry. And Cherry, just for its data integration tool, but just give us a quick overview. real estate specifically. So they're tying all the different data points to the address.

And so there's not a universal language or data language within real estate. So you could be in South Carolina and you're doing building SFQT. In California, it's building SF. In Texas, maybe it's building square footage, right? And so just that the naming of the different variables is different across different geographies Yeah, so that was one of the big problems that cherry was solving for Thankfully because I didn't want to have to solve for that. Yeah, but then tying everything back to an entity I mean back to an address when they call it like address resolution that was a challenge in its own But then the ultimate challenge is finding out who's behind the LLC and that's what everybody wants

in its own. But then the ultimate challenge is finding out who's behind the LLC. And that's what everybody wants from an investor, broker, developer. You want that. It's just very hard to do consistently. So I can imagine this cycle of like, you get this out there, then you get the new straight interaction with customers. You're like, man, this is so great. Like, yeah, this is exciting. And then like you get to that question and the answer is like, well, we can sort of do that. Is that accurate? Yeah, I mean, Sherry was more so built for larger, larger cities. Like they started off like New York City, and they would have more coverage in like Atlanta and Charlotte. And so these secondary and tertiary cities was the coverage wasn't quite as good when it came to identifying who's behind the LLC. So I would go pitch it and we'd be like, we can do it for a lot of them. And then the coverage just wasn't quite there. So we started thinking of like roundabout ways to do that process. Basically, I was like, okay, well, what do I do anyways? How can we automate that? Started getting into that when the GPT, Chad GPT moment came out. But by then, I'd already built the whole thing and it was kind of hard to re-engineer from there. So did you just go in,

how did you handle the secondary and tertiary markets? Was it sort of brute forcing it or how did you? Yeah, so we created a button that would let you jump directly to a county link. So like, it was just a little bit of a shortcut because if I were to go into Spartanburg County, that GIS map is really hard to navigate. If I were to go into just any adjacent county

or some place I'm not familiar with, just even finding the map and going through like that was a challenge. So we had a button that was just like any, you could use our map to zoom anywhere in the country click that get the county record So we just take you straight to the records. Okay, so that was kind of a roundabout way that we do You really solve a kind of like navigation problem for them Yeah

so then the next step was like alright, how do we take that data that we're taking them straight to and Kind of ingest that automatically. Yeah, but we didn't quite get there Kind of ingest that automatically. Yeah, but we didn't quite get there. Okay, so you sold the company Who'd buy can you tell us who bought it? Under an NDA on who bought it, but it was another company that was building on top of cherry So it was okay very familiar with each other. They were selling to private equity larger investors and We were selling to more like, larger investors, and we were selling to more like broker shops, individual investors.

So it made sense for them to roll up what we were doing into their product. What's interesting about this is sort of the data aggregation. Would you say it was essentially a data aggregation play, right? Where it's like, okay, well anyone could do the work of going out and manually collating all of this data, but you figured out a way to package it in a way that is like, okay, just here it is for you, right? Like, click the buttons and you have the information that you need. Is that, was that sort of the core value prop and sort of that's, that was like the problem that it solved and why they were interested in rolling that up?

Yeah. I mean, we spent a lot of time on the UI UX and the feel of it and making sure it worked fast and snappy and people really, it was a time saver just in that regard. So the what we call a foundation data, it's nationwide data, it's ownership information, there's some demographics involved. But where the next phase of where I was going with it was, okay, there's other data vendors out there like ClimateCheck, for example, or maybe another one from like Yardi, which is a big accounting data company.

So the idea was that you could add those data connections in and combine it to bring out more insights. But what I realized there was if you don't solve the first problem like perfectly, there's no real point in adding the other data vendors. And especially with the customers we were going after, they weren't combining data sources as much as maybe like an institutional investor would. Right, right, right. That's interesting. Part of the reason I asked about the parallel,

and I don't know why I can't remember the exact name of the company, but there's a tool, maybe it's Westlaw, but it's used by law firms, right? And it's essentially a database. It's actually very similar. There are a lot of parallels where you go in and you look up case law around a certain subject, right?

And it's a similar thing where it's like, well, okay, you could go do that manually yourself, but if you're a clerk and if you're clerking for a judge or you're a lawyer that's preparing for a case or whatever, right, it's like, okay, well, let's just pay Westlaw and we have this really amazing user experience and it's like 100 times faster to access the data. Right. Which is super interesting. Yeah, there's that piece.

And there's also for us, it was it started becoming a tool to pitch investors on. I said at the beginning of the show that I use my man-made map sheet to pitch investors on some deals. And I thought a lot more people were doing that, but come to find out not that many people were, just because it was such an arduous process. And so then we started kind of pitching that as well. We're saying, hey, not only can you look up the information

for your own prospecting and whatnot, but you can then have a map sheet that shows that you know the market better than anybody else. You can display that visually. Yep. Nice. Yep. Okay. I want to talk about Cherry because you met your now current co-founder at Cherry, but one more question on Mapsheet before we move on. You are now building a new company in the age of AI. God, that sounds so cliche. AI tooling, what got me thinking about this is you were saying, you had started to use GPT to solve, to see, okay, could we actually use this to solve some of these challenges, et cetera?

But by that time, you'd already built a bunch of stuff, and then you end up selling the company. But you sort of got a taste of that. And since then, the AI tooling has changed monumentally. And so having gone through that experience when you did, would you approach building the tool differently if you were doing it today based on like the tooling and sort of the data aspects of it with AI specifically? Oh yeah, I would do everything differently.

Yeah, I would just, I would think about it from, I guess, first principles of, okay, now that we have this tool that can go out and get information and it can synthesize data. Do we really need to have this kind of infrastructure layer? Are there problems that we were solving with GraphQL and things like that that maybe they still need to be solved for maybe institutional investors, but maybe if you're more worried about speed and costs.

Maybe you could utilize AI. I haven't planned it all out or anything. There's actually two layers to that question, I think, Eric. One layer is now that there's, like what would you do differently? And I'm sure there's plenty of things in both buckets, right? Yeah. Yeah. I wouldn't start another company in real estate. I think it'd be, yeah, like we can do therapy after the show for sure. But like, I think it'd be really interesting to drill in a little bit on that. Like what was the most painful part of that, like space and selling in that space, as far as like, and I have no idea what the answer is, but it could, like,

tech adoption or just getting in front of people, like, what were some of the key, like, this is just especially difficult. Yeah, I mean, brokers are independent contractors that work for a national brand. contractors that work for a national brand. So if you went to CBRE or lean associates or something like that, you're not going to go sell the whole company on it. Typically you would go to the South Carolina multifamily team, maybe then it's probably more like the upstate team. And it gets really, so like who you're selling into are smaller groups of people. There are institutional players out there

and if you can kind of crack the code to get in with them, then I think that's like the best path for any real estate technology company. But if you're selling kind of bottoms up, it's just, yeah, the price points are pretty low. So we kind of got stuck in the middle where we weren't able to do self-serve because there was some education involved, but we weren't selling enterprise contracts.

So that's another thing I would do differently. I'd probably just go straight for the enterprise. Yeah, that's super helpful because I think a lot of startups end up in that world, especially if you're going consumer or even like, and because in your sense, this is like a very small business because you're working with like a very small group or like a single broker and like you've got the price points that are lower, you've got tip even though it's a low price point, it's still fairly high expectations. Like the expectation doesn't actually like lessen because it's a lower price point because this is what I've found, Yeah, so you have to be fully featured when you're going to market and it has to work perfectly. I want a mobile app.

I want this. I want that. Wow. Okay, so we won't subject you to any more memories about the real estate industry. Okay, you met your now co-founder. Was he working at Cherry, the real estate data integration platform? No, he had left there. He was at a company called Veramex, which was similar to Cherry, but it was kind of

like they did the full BI dashboarding and everything. I think they had customers like JP Morgan and whatnot. But when I reached out to him, he was like, yeah, it's cool. Like all the stuff you're working on, but like my company is selling. So I'm out of a job and I was like, oh, that's actually great. We should team up. Yeah.

Okay. And then like 72 hours later, we're just like mapping out our plan. Wow. Nice. That's all. Wow. Very cool. We're gonna take a quick break from the episode to talk about our sponsor, Rutterstack.

Now I could say a bunch of nice things as if I found a fancy new tool, but John has been implementing RutterStack. customer data can get messy. running production instance of Rutter Stack is that it wasn't a wholesale replacement of your stack. It fit right into your existing tool set. Yeah, and even with technical tools, Eric, things like Kafka or PubSub, but you don't have to have all that complicated customer data infrastructure.

Well, if you need to stream clean customer data to your entire stack, including your data infrastructure tools, head over to rudderstack.com to learn more. So what company are you building now? Yeah. So the- Or was there a gap? Or did you have ideas that didn't turn into anything?

Oh yeah, there was a gap there. It was basically the whole summer of last year. I basically took the spring off, came back in the summer. I was really just like, I guess you could say not building in public, but just like throwing ideas out there on LinkedIn to see if people liked any of them

and kind of going from there. At first I was gonna get into like the customer success space with AI and maybe some talent abroad emerging, those two, that was kind of the initial idea. Then Clay raised a bajillion dollars and I was like, oh, I used to use Clay, like let's check that out. And there was a whole like ecosystem around that. So I started getting plugged in there.

So there was a few ideas. It started off kind of like a Clay agency. And then now we've kind of narrowed in a little bit more on what we wanna do going forward. And okay, what's the name of the company? I should have asked with the name. Start up there. It's ready with two Y's, die AI.

So ready. Okay. And actually, I actually would, I think probably a lot of our listeners have heard about clay or have like sort of interacted with it because there's a major data piece there. But what is clay for those for those that don't know? Yeah. Mapsheet was actually trying to do some of the things that Clay did. We just went off in the wrong sector and they did an amazing job.

So what they've done is they've aggregated a bunch of different data sources and made it easy for non-developers to kind of bring in multiple data sources into a single spreadsheet view. So they can do a lot of different things. They're focused on go-to-market, so companies and prospects. A lot of people use them for outbound research, researching their ICP, Crafting messages and enriching data enrichment things like that. We're mostly using it for for account research to kind of compile really deep account reports for a ease or account executives Okay, let's dig into this a little bit. So

Account executive let's just break down some terms Okay, because we're not afraid to get basic on the show and we don't wanna assume that our listeners know all the acronyms. What's an AE account executive? What is an account executive? Those are the sellers. So they're the people who are actually selling usually mid-market enterprise software. They're building relationships.

They're getting on calls. They're not doing a lot of the research work and cold calling they will call but they're much higher level. Sometimes they're a solutions architect as well. They can wear many hats but they're definitely the highest level on from a sales perspective. And so they generally are commission based so they have a base salary. They're trying to get commission-based, so they have a base salary, they're trying to get people to buy software and they meet their quarterly quota and they get a commission check and that's great. Okay, so that's a salesperson.

You mentioned executive assistant or I guess also this is sometimes called SDR, which are oftentimes, which would be sales development representative, which are often assigned to an account executive. Yeah, it's not always like one to one, but below the AE would be an SDR, so sales development representative. Sometimes they're called BDRs, so business development representative. Sometimes they're called BDRs, business development representative. A lot of what they do is research and they will do some cold calling. It just kind of depends on the company, but they are not often customer facing. They're not shaking hands. They're not getting on the Zoom calls and actually trying to sell and demo the product.

They're kind of behind the scenes. Yep. And what, okay, if I'm an account executive, what's my day-to-day sort of interaction with a Sales Development Representative, SDR? How are we interacting? What value are they providing me? Where does the responsibility split? Yeah, I think the ideal world is that you're an AE

and you wake up in the morning and your SDR has you coffee right there and has a whole lineup of all your meetings saying they've done a ton of research on everybody in the organization who the decision makers are and everything about that company and they kind of have a nice report. I don't think that's actually the reality. That's kind of what the relationship should look like.

Okay, I have a question stepping back a little bit about the about the a role of the seller. Okay. Because I think this is all like, and I'm very guilty of sort of poking fun at salespeople and they're there are means Not as much as marketing. Yeah, not as much as marketing people Well, if Marty would just do their job the sales part with the easy exactly right? It's all the lead you just get on calls that came in from inbound. Yes leads are crap But okay in all seriousness, what makes a salesperson's job really hard? Well, I haven't been an account executive before, so I wouldn't want to speak for them, but...

Well, you sold, you had to be a seller for your own company, and so like that visceral experience of having to hawk a product. I was based, I did founder led sales based. I've tried hiring a few salespeople, but I was not quite ready to let it go. So yeah, I mean, a lot of it is, it depends if you have like inbound leads coming in, if you're having to do outbound, like really, if you're an AE, like you don't want to have to worry about that a lot of times especially for earlier startups if they're founder led sales or maybe they have one head of biz dev like they're kind of doing all the busy work

of researching accounts scoring leads trying to figure out who they should be talking to all that kind of stuff is not high enough level for them they need to be like actually taking the calls, actually doing discovery, like figuring out what the pain points are. So I guess the problem is if you don't have a highly functioning sales and go-to-market organization,

then you bring your AEs who you're paying a lot of money in to do work that should either be used, the AI can do part of it and then maybe a kind of a lower level salary person in combination should be able to do that. So let's talk about data in the context of the AE's job and then I'd love to dig into to the way that you're framing this at ready.ai. What role does data play in the AEs job? Well, yeah, let's just start there.

I have a number of other questions and I know John does too, right? But I mean, part of it, like you said, is they're trying to understand what is the customer pain point and is there a match between the problem that our product solves and the pain that you're experiencing at a fundamental level if there's a match then you'll exchange your money for our software right that's the transaction that they're trying to facilitate how what role does data play in that customer relationship process etc yeah I think that it depends if you have like a freemium model where you're letting

someone come into your platform and you're trying to capture some product insights. There could be a whole slew of things that you're trying to understand about their involvement. But if we're talking about maybe it's a first meeting, whether they came in inbound or cold called someone, they agreed to meet with you. In that case, it's more so just being confident going into that first meeting that you know what you should know, I think. It's you don't wanna be like going into a meeting and just, oh, they just acquired a company

like two weeks ago, you didn't even know about it, you look like an idiot. So things like that. I would say- Or the CEO just got ousted. Yeah, so you don't wanna, you know, look like a fool going into a meeting. I think that's kind of an ae's worst nightmare So I don't know like how data driven it is at the hey

This is a brand new lead because you don't have too much data to go off of it doesn't look like publicly available Publicly available. Yeah, but there is another unique challenge. Some larger companies, if you have 20 products and it's not like maybe you don't have sellers in one pod that are selling one product, but you're trying to sell multiple products. When you have say a few hundred sales reps and tens of products, the executive team, they can come up with what they think the messaging should be, the value props, the pain points, how you should sell this. But then in reality, when you have all these sales reps out there

kind of doing their own thing, and they're not being prepared by the SDRs, that becomes a whole challenge in itself. So I've got kind of a fundamental question, I think. And this is like LinkedIn headline land for me, because I'm not very deep on the subject, but I've seen like over the course of like, it could be in like one week, inbound is dead. And then like some posts about that, like outbound is dead and some posts about that.

So I'm curious, like high level your thoughts on that because there's an aspect of like with all the AI stuff, how inbound is harder and trickier now when it comes to like filling a pipeline based on content because there's all this like AI content out there people are confused and there's just like this big pouring of content out there which I think dilutes like inbound and then on the outbound side you've got a problem of like Outbound got ramped up because people are using AI and outbound a lot of which is pretty bad as far as use cases So how is that impacting like what you guys are doing? Yeah, when we first started we were Kind of like a clay agency so to speak

So we're kind of like a demand gin and we only worked with a couple customers on this but we quickly realized like it's it's kind of a noisy space because everyone sees what AI can do and you can have all these personalized messages and you can use signals and things like that. So there are a lot of either agencies or just go-to-market teams using tools to do that. I guess a lot of times you'll get LinkedIn requests with just like the most obviously AI DM and then you'll get all these emails that are just like, yeah, you clearly have another me turn this off How does yeah, where's the answer? So I mean, there's a lot of cool things you can do with clay And there's some people in that in that community who pull off some pretty pretty interesting stuff

Like doing really deep research. I saw one that was, they were doing like a pilot, I guess, for SEMrush or S-E-M-rush. They would like use SEMrush's data. They would then pull it in. They wrote a script to basically be able to create real-time dashboards of their internet speed score, their different keywords, so they could generate these real actual dashboards of real data at scale really fast and use that as collateral to then say here's not just another boring outbound. That's like the extent, that's something we can do. Yeah, and I very rarely see people do that, but I do think that's the future. Because if you can give me something in your outreach that's at least a little bit valuable

to me, and I get outreach on a lot of the stuff like all the time, and like less than 1% actually has any like give as part of it. Like I did get one, I'd say one in a hundred, I'll get like a personalized video or something. give as part of it. If you can do that at scale, I could definitely see that being available for people. Another one of our clients has an AI tool basically where they ingest all the publicly available information from county council meetings and board meetings and things like that. And then they kind of synthesize it and bring it up into opportunities. That's cool.

So what we noticed there is, well, if you can just come up with a handful of opportunities and you just send those Yours, hey, here's an opportunity might not be aware of the city of Greenville is gonna be doing a new park over here You should reach out to this person like yeah, that's brilliant. Yeah, it's not selling you're just I think that is the future of it. So one thing that is Really interesting is that there's all this work, preparation work, to facilitate this human-to-human interaction, which I think is really interesting. And so in the old world, and I think, I mean, okay, let's rewind. I think data and technology has been part of it as long as

technology has been around, right? Let's figure out how to get the data that we need and prepare to your point. You wanna go into the meeting, the sales meeting, the sales conversation, like as prepared as possible, right? And technology's always been an assist there, but historically it's been a very manual process, right? And so let's say an SDR is doing just a ton of prep work, right? That is, I would say, exhibit A for what AI is good at. Let's take a bunch of data, provide context, and then produce a summary that helps you

understand. As one of our guests said, AI is really good at reading fast. Yes. Yeah. Which is such a funny way to say it, but it's actually super helpful. It's so true. Yeah, yeah. It also has a great memory. Until it doesn't super helpful. Yeah, it also has a great memory.

Until it doesn't. Yes, until it has a great memory, but then it forgets. Yes. So is that right? Is that like, is that kind of what's happening? Right? You see, I mean, there's still this human to human interaction, but all of the work that goes into it as sort of being replaced or will be replaced.

I say, oh, that's a big statement. I guess that's the question for you, right? Is like, is the AE actually just gonna have an agent that is an SDR where they wake up and maybe outside of the coffee, they get everything that you had mentioned? Yeah, I mean, I think there's a lot of VCs out there and some founders out there who believe

AI SDRs are the future where it's just purely an AI agent There's a lot of VCs out there and some founders out there who believe AISDRs are the future where it's just purely an AI agent doing all the work. We don't believe that. We believe that AI will be able to do a lot of the work. And there's always going to need to be either the last mile, being able to organize the information in a way that makes sense or it's customizable. But then also just being able to hop on a call with a real person who has noticed trends as they're putting together some of the information. Because yeah, if people can, you know, if we can hire people to use these tools,

like that's just going to be fundamentally better. They can use a lot of the same tools that an AI is using, but when you combine the two, we think that there's just an added value there. We don't think that it has to be a college educated American making 120K a year that is doing that job. That's why we find really great talent in places like South Africa, who they have five to 10 years experience. They're very personable, very articulate, and they're really hard workers. great talent in places like South Africa who they have five to ten years experience.

They're very personable, very articulate, and they're really hard workers. And you don't have to pay them equity and bonuses and all this stuff. We think marrying those two together is actually a really good combination. Super interesting. Super interesting. So what is that day-to-day experience look like for the account executive in this new world? Yeah, so in the new, I guess, ready world. Ready, that's what I should have said, not the age of AI, which sounds so cliche. The

ready.ai. The age of ready. Yeah, you just you wake up and you have your meetings for the day and hopefully the day before you've you have all the reports that you need ready to go you should be able to review them the night before and then hop on a call with a friendly voice that we would employ and they would be able to kind of walk you through what they've done, where there's maybe gaps in the information, where some things that they should look out for, if there's competitors that the person that they're about to meet with, maybe there's

a known competitor that they're using, we incorporate battle cards and things like that. So yeah, you're just fully prepped for the day, you never have to worry about finding information and you have someone who's kind of an executive assistant for you that is actually trained on all the best practices with sales research and all that. And when you say there's an executive assistant you mean AI? No, I mean a real person. Okay. Yeah. Yeah. This like leveraging AI for components of the process. Yeah. So we leverage AI heavily in being able to help them compile these, what we call ready carts. Got it.

Got it. Okay. Yeah. Yes. Okay. Got it. Got it. Yeah.

Yeah. So they do like the last mile kind of compilation of it and then just have that human touch there to be able to kind of identify different things that a human would be able to identify. Yep. human types there to be able to kind of identify different things that a human would be able to identify. Yep. What are a couple examples of those things where, okay, you have the human in the loop, they're the executive assistant. What is something that is so valuable to the account executive that you just haven't been

able to replicate with AI and you're like, we have to have a human here to do this? Yeah. haven't been able to replicate with AI and you're like, we have to have a human here to do this. Yeah, so we're doing a lot of interesting things with Clay and a couple other APIs that, it's just the first thing they do is they understand all the different products that the company sells. So they, for each product line, they have different personas, they have pain points, value props, all that.

And then we create AI agents with that information. And so then we run that across the different prospects. And so we can do a lot of that, but then we want to actually put in a format that is not a spreadsheet, right? So we could send it out to like a Google Doc or whatnot, but what we're doing is kind of the manual part is taking it from Clay and putting it into a really nice Notion doc and then having like a database, multiple databases with the

different products, the different playbooks, the different ready cards, the different battle cards and having those relationships with each other within Notion. And then also being able to have just like a simple toggle to be able to hide some information so you're not looking at like a super long page. So just that last mile. Yeah. One thing that's interesting and John, I'm interested in your opinion on this because you and I are notorious. We text all the time about weird stuff that we're trying with AI. But one thing that I think is really paradigm shifting about

this and I'll sort of explain like a previous world so let's say you're prepping for an executive meeting right okay so let's just say it's your quarterly executive meeting and typically how this goes is there's some subset of people I mean the actually this is funny thinking back on past experiences right that there's an executive meeting coming up or a board meeting, whatever it is. Like when you as reporting to your boss, like your boss starts to have a bunch of really specific requests for data and data sets and all that. Okay, like, oh, can you share the spreadsheet, like the source data, whatever, right? And so you sort of, there are all these people who go in and aggregate a bunch of data and are probably putting some

light insight on top of it and scrapping it. And that materializes maybe into a summary spreadsheet or document or something like that. This materializes into a deck, right? And so the fidelity is sort of... The granular fidelity is decreasing as this gets distilled down into the major points, which is really all the executive team needs to know necessarily, right? But then you have this situation in the executive meeting where someone starts asking specific questions about something, right?

And so then you start to zoom back out where it's like, okay, well, let's go back and look in the document. And then if people are doing a good job, there's links to the source spreadsheet, and then if people are doing a really good job, that links to all of the source datasets, and so you can scroll down and get there. That's actually, what happens really often is, oh, that's a good question, we need to go back,

give us a couple of days, and we'll go back and answer that. Wills, what's fascinating to me about what you're describing is all the data's there, and you can actually just ask that question and essentially get an immediate answer because all of the data is just right there. Not only is it being presented to you but there's no like fidelity loss. The back, like the corpus of data is still there and like you can just ask questions, right? That's a pretty serious like shift. Yeah, I, I think that's like where we want to start moving towards, at least. I think that as we build out more of these reports and we're building out more of these playbooks

and products and all this, like, how do you then create like a repository of data that is evergreen that you can reach back and ask questions because especially across a large organization there could be people who are meeting with customers and in the ideal world, yeah, everyone's putting in the CRM and everything but that's probably not the truth. So like if someone like in California is meeting with a customer like it would be nice to be able to have some, California is meeting with a customer. It would be nice to be able to have some, be able to see across the organization, not just CRM notes, but deep research on every prospect,

even if they're not a customer. So I feel like this is my space to complain a little bit about this cycle, because I've been in previous roles on the end of hundreds of these conversations. So I was a CTO in a previous life for five years and did tons and tons of software evals and in prior roles. So I think it's been hundreds, I think thousands is an exaggeration,

but maybe close to a thousand. And there's such a typical cycle, right? At least like in my experience, of like original qualification call for like 15 minutes was a complete waste of my time that I learned nothing. The other person, like I repeat things I already put in a form somewhere, right? So like that often happens.

And then I go to a second call, there's like also usually a waste of time where they like demo the product they've already read about on their website and like receive content on. And then a third call where they, where maybe I have technical questions and they'll bring in like a more technical person, they won't know the answers. They'll go back to their

team to ask those questions. And then the fourth call. So like, that's not like every time, but that's a lot of the time. So I fully see the potential here. And that like, really like, and you can't, obviously you can't like know everything up front but if you can like eliminate like some of those like painful steps and just put as much in the hands of the AE on like day one how powerful that is and is like a second component to that when you have a really complex cell like you're saying with like, we've got dozens, maybe of internal products that are branded this way and that way and the other,

that even a new AE might not know half the products for a while. So see that, and then, because I have to say something good too about the sale cycle. The one, there's one that I remember from probably four years ago, still remember the sales cycle. The one, there's one that I remember from probably four years ago, still remember the sales cycle.

Get on the call, initially, like we skipped the like, like the like, I don't know, qualification call essentially that just dropped right on the call. They like, we had some exchange around like their content. I felt like they had like a decent understanding of what I was trying to do. And they followed up with an awesome email. Like it was so good. Like I still remember it. It was detailed email of like We talked about this like here's this link to this article that like explores it more

We talked about this here's this other link and then some more content at the bottom That was like you also might be interested in these two things. It was just an email. It was a full text email with like three or four links in it. And I remember it four years later because it had never happened to me before. Wow. That is... It is fairly simple. That's not like rocket science. It's like complaining. I feel like that was a very balanced view.

Yeah. And that was free all the AI note takers, right? Oh, yeah. Yeah. And that was a lot balanced view. Yeah, that was free. All the AI note takers, right? Oh yeah. Yeah, that was a lot of it too. Is the person that actually taken the time and the call to like, or I had such common questions that they could copy and paste.

I mean, either was fine with me, but I actually like, it's one of the only like super valuable followup emails that I remember like referencing back to it a couple times when I was like thinking through the solution I was looking for. Yeah. Yeah Okay, I have a quick I have a quick story This isn't complaining but this is kind of funny since we're telling stories. Yeah story this actually happened

This happened yesterday actually. Okay. This is a story about an SDR so I I usually try to run in the morning, but things are kind of crazy with the kids. So I ran, I went home a little bit early, and I was like, okay, I'm gonna run before the kids get home. I start out on my run, I'm listening to some podcast, and Siri tells me I'm getting a call,

so I look at the call. It's a California area code, and so I'm working with some vendors and some other people and blah blah blah California. It's like okay yeah like this I'm going to answer the call because whatever this might be important. It was an SDR okay it was an SDR I think. The cold call or somebody you were working with? No actually from a company that we use amazing company amazing company I'm not going to name

the name but. They're that amazing we can't name them. but they're that amazing we can't name them they're that amazing then we can't name them actually I think this is totally fine I'm messing with you I'll tell you who it is it was it's Vercell oh yeah and we have been it was actually I was like okay this is a legitimate call because the account activity has changed dramatically in the past couple of weeks. And we've had some like, I think we've had certain overages or whatever.

Okay, anyways. So I answer and this person's like, okay, I'm so and so from Vercel. Okay, great. And I actually thought, okay, this is pretty well timed. Like I was like, you don't have all the context, but I was like, if I were you looking at this, I would probably call too, just to say, hey, whatever.

Anyways, he kind of does the SCR thing of like, hey, I was just wondering based on your account activity, like, it looks like maybe you guys would be a good candidate for the enterprise plan. Okay, pretty standard. Pretty standard, you're fishing inside sales, SEO stuff or whatever. Yeah. And so, and by the reason, by the way, the reason I mentioned the name is it was such a great call, like I actually legitimately appreciated it. And so whoever's doing that of herself, like great job.

We had this great conversation. I was like, Hey, I was like, I want to save you. I want to save you a lot of time right now. And like, I was like, okay, what do you mean? And I was like, that's not my budget. I was like, I know based on you looking at me being an owner of the account and recent account activity, I was like, I know that probably got flagged for you,

which makes total sense. But I was like, let me help you navigate this. I don't own the budget, there's another guy, he's the VP. He owns the budget, I'm a stakeholder because I'm a user, but it's not ultimately my call, like you need to go directly to him and ask, and that's actually where Alliance Share,

of like what we pay you comes from, because they're using it to deploy all this like other stuff or whatever. Anyways, it was basically like an organizational map, like here's what you need to know about how to navigate like our organization and that I'm not a decision maker. And so I was like, look, basically, like there's not a lot for us to talk about. And the guy was so great. He was just like, he's so much. He's like, I really appreciate.

Yeah. I was like, look, we're in software too. And you guys are a great product. I legitimately think you can help us figure some stuff out. But it was just really funny because he was like, after that, he was like, he asked me some product questions. How are you guys using? I was like he asked me some product questions. How are you guys using it? I was like wow this is great like this is a very outstanding for an HDR. And then he took that call translated it into I mean I'm guessing like however they do that translated it into what intelligence for the AE and we got a great email from the AE that's what made me think about this story. It was a great email it was AE. That's what made me think about this story. Yeah. It was a great email. It was like high context. The guy clearly remembered very specific things

that I told him about how we use the product in the call. That's very cool. I was like, this is great actually. Yeah. I mean, that's what people are uniquely good at. It's like identifying those trends and then having like a genuine conversation that made you feel good when you walked away.

Yeah. Right. You know what I mean? Totally. Totally. That's awesome. Yeah. So it was great. It was great.

SDR thumbs up. SDR for the win. Yeah. And I'd be interested to know what, like, what tools they're using. Yeah. Probably like Nooks. It was one that a lot of people use for calling. And then like they did a really good job of like transcribing and a bunch of other stuff.

Yeah. Totally. Okay. We're getting close to the end here. good job of like transcribing and yeah a pretty big scope. Wow. Yeah. So we're really trying to reel that one in. Their problem is that they have 400 reps across a bunch of different product lines, kind of what I described earlier.

And so we're trying to put our best foot forward there. And we're really trying to leverage all the different tools out there that exist without building our own. We think that there's probably going to be some places that we can optimize through essentially building internal tools, right? Because no one wants another like login portal, if no one wants another app to go into. But if we can meet the customer where they are

with their requests, so what are those triggers for whether it's like a calendar invite or you just wanna send us a Slack message, say, hey, I just got this meeting, can you help me out? I'm just seeing how fast we can turn around really quality ready cards or account reports. So that's kind of where our whole focus is. And then, yeah, John, maybe I'll ping you at low

and see if you can help me map out how to make this a little bit more efficient, but that's kind of what's next. Always. I love it. I mean, I think the Concept of not having a concept like a portal to log into I think it's fascinating I think we're gonna see way more of that. Yeah, right

Well, one of my absolute favorite vendors I've ever worked with was that way essentially it was like we they did What really like magic with the data? This was before the gen.AI that did all this like really neat like data science, like forecasting, modeling stuff. And they're like, we have no interface. Literally like give us these data points. We will send the file back and load that into whatever tool you use. It was brilliant.

And I think there's gonna be more of that. I agree. I agree. Well, Wills, keep us posted on progress. Would love to have you back on the show to hear how things go after you land that big publicly traded fintech company. Yeah, hopefully. Let's keep our fingers crossed. I appreciate you guys having me on. It was fun. The Data Stack Show is brought to you by Rutter Stack, the warehouse native customer data platform. Rutter Stack is purpose-built to help data teams turn customer data into competitive advantage.

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