AI, Data & Finding the Right Buyers in Real Estate | Drew Fabrikant of Scout | The Brainiac Blueprint Podcast

In this episode of The Brainiac Blueprint Podcast, we’re joined by Drew Fabrikant, Founder & CEO of Scout, to explore how AI and data intelligence are changing the way real estate professionals find, engage, and build relationships with the right people.

Drew shares how Scout evolved from lead routing into one of the largest consumer data platforms in the U.S., why timing and intent matter more than volume, and how AI acts as an “orchestration layer” that strengthens - not replaces - human relationships. We also dive into hyper-personalization, outbound best practices, and what the future of AI-powered real estate intelligence looks like.

Full transcript below.

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⏱ In this episode, we discuss: 

00:00 | Intro

00:30 | What Scout does & the problem it solves

03:30 | From proptech to scalable outbound strategy

05:48 | “I think AI is…”

07:30 | Why data + timing beat volume

11:35 | How Scout helps agents find likely buyers & sellers

13:35 | Real-world outbound success stories

15:09 | Platform walkthrough & campaign orchestration

21:42 | Defining ICPs without overthinking

23:49 | Email volume, deliverability & best practices

30:23 | AI in real estate: fears vs reality

32:22 | AI’s role in strengthening relationships

36:45 | AI tools Drew recommends

39:10 | Scout’s AI-first philosophy

42:51 | The future of AI, data & real estate intelligence

48:09 | Rapid-fire questions

🔗 Drew Fabrikant

LinkedIn → https://www.linkedin.com/in/drewfabrikant/

🔗 Scout

Website → https://trustscout.com

LinkedIn → https://www.linkedin.com/company/trustscout/

📲 Connect with Left Brain AI

Website: https://www.leftbrainenterprises.biz/

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Episode Full Transcript:

Kyle: All right. Welcome back to another episode of the Brainiac Blueprint, where we discuss the intersection of AI and how it impacts business and the world around us with our esteemed guests.

I'm your host, Kyle Lambert, founder of Left Brain AI and Action Hero Marketing.

In today's episode, we discuss how to find the right contacts and data for your real estate business. And as we're going to talk about, even other real estate or non-real estate businesses now, you guys are expanding.

With that being said, today's Brainiac is Drew Fabrikant. How are you, Drew?

Drew: Hey, Kyle. I'm excited to have been called a Brainiac. That's a new one for me.

Kyle: Hey, man. You got a wealth of knowledge over there, so I'm excited to tap into it.

Drew: I'll take it. Action hero, Brainiac.

Kyle: That's right. That's right.

Well, Drew, you are the founder and CEO of Scout. People can check you out at TrustScout.com. You guys do a lot of really cool stuff in terms of data and finding the right people to grow different businesses.

If you don't mind, just tell everybody a little bit about you and Scout.

Drew: Yeah, absolutely. So I have been working in the prop tech space and tech in general for a little over a decade now. And we started as an algorithmic lead routing platform. We were going out and kind of working through the SEM space. And things got really expensive very quickly.

It just became very hard, I think, to pull through conversions in a meaningful way and sustain ROI. As we kept going on our journey, we were working really a lot in the home services and residential real estate space.

There's a book out there, and I'm sure you've read it or have heard of it. It's called Predictable Revenue by Aaron Ross.

Kyle: Absolutely.

Drew: Many people's sales bible. For those of you who don't know, Aaron Ross was the guy who scaled Salesforce using this strategy. He kind of put together this blueprint for outbound sales.

I was looking at a couple of industries and I said, "Wow, residential real estate is absolutely perfect for this one." And nobody was doing it. Nobody was executing outbound in a meaningful way, and predictability around revenue was such a big pain point in this industry.

So we said, "Okay, challenge accepted. Let's see if we can go do it."

I actually had a friend who really kind of put our feet to the fire there. He was looking for a home in Brooklyn and couldn't find anything, kept on getting outbid.

He said, "Hey, I need you to go get everybody's contact information who lives in this neighborhood and this building, and just reach out to them."

I was like, "I'm lazy. I can't do that." But what I could do was hire a data team and get a data team to start pulling data and pulling the right data and then executing meaningful outreach.

About 45 days later, we were in contract for $2 million. And then it just became scalable and repeatable. We realized, okay, this doesn't just work in theory. This is something that, if executed well, can grow and scale.

With the presence of AI, it's even gotten better. I mean, you can say there's more noise. But what Scout is today: it's about a 256 million consumer database, which is the largest consumer database in the US.

We have about a billion phone numbers, a billion emails. We have about 550 different demographic data points, and we actually monitor signal for certain types of events across 35,000 websites.

The majority of that is around mortgage data. Are people looking for a mortgage? Are they looking to buy or sell a home? And we build propensity models on top of that.

So, like you said, we work a lot in residential real estate, home services, but we also work with politicians and mortgage platforms and all sorts of different customers.

Kyle: That's awesome. I remember, so we originally connected at the New York Real Estate Expo, and you kind of showed some of the power that the Scout platform has.

At the time I was having a conversation with someone who's looking to target high-net-worth people, and you're like, "Oh, follow me. Check this out." And just went and showed all the different information that you had.

As a marketer, I was just like, ping, ping, ping, seeing all the light bulbs go off.

You mentioned the integration of AI right on your homepage—find and engage your ideal customers 10 times faster with AI. Having the data points, then having AI to superpower, it really is driving some success for you guys.

Drew: Yeah, I mean, this is a marketer's dream, right? It's your unfair advantage. It's having a heat-seeking missile directly to your ideal customer.

That's kind of what we say. It's like: find your ICP, know the right person, the right thing to say, and reach out at the right time.

If you can do all of those things right—and, you know, a couple of other things at the same time, so this is not as easy as it sounds—you're halfway up the mountain.

Kyle: Very cool. Very cool.

Well, as you know, I have all of our Brainiacs finish the prompt, "I think AI is." So if you don't mind, kind of set the stage for us. "I think AI is..."

Drew: So when we talked about this before, I think my gut reaction was "AI is scary." But I didn't want to go there.

I was thinking about this last night. I'm up at like 3:45 a.m., and I'm finishing a deck that we're about to send out to a major, major enterprise.

This deck is wild. We're running predictive modeling analysis and doing crazy AUC ROC analysis and kind of benchmarking how good our predictive technology is.

I'm realizing the deck is built by AI. The models are built by AI. The data is cleaned using AI.

And I'm like, what is AI? I started thinking, it's like your magical book of incantations.

I say that a little bit in jest, but also it is kind of spot on. There's like a magic language where if you get it right, it works. And if you get it wrong, it doesn't work. Really bad things happen when you get it wrong.

But it's one of those things where if you know the right language, if you know the right prompts, if you know the right inputs, you can kind of make anything happen. And it really does become magic on the other side when you get to see some of these things that you didn't even know you were capable of.

So AI is a magical book of incantations.

Kyle: Very cool. I'm thinking about going to Google VEO right now and having it make an image of you with a cauldron or something like that, like a black robe.

Very cool. Very cool.

So let's jump in a little bit more here. Touch on the top part of Scout here. You're the founder, obviously you're filling this need for additional data and touch points. But correct me if I'm wrong, you have a little bit of a legal and finance background prior to this, right?

What kind of, you know, lessons or adjustments did you have to make? Obviously, totally different brainwaves, if you will.

Drew: Oh, yeah. So going back to the beginning of my career, I actually worked in sports first. I was working at the NFL Players Association as a salary cap and agent administration intern in law school. That kind of launched my career into law, but also into sports.

Then I went to the largest global law firm in the world in terms of revenue, a firm called Skadden Arps. I was doing some M&A work—that's what they're known for—financial institutions. So helping to buy and sell banks during a lot of the mortgage craze.

For me, law was always a way to think. It really teaches you how to think in a different way and question a lot of things and not make assumptions, but also see things in shades of gray that a lot of people don't really think about.

I went from law. I did maybe three and a half, four years at Skadden. Then I went down to Wall Street and started a multifamily office with a partner down there. We built that and scaled it.

But my heart was always in scale and growth and kind of like building predictability around things and just being an architect in general.

So, you know, law and finance and then I caught the real estate bug. Did real estate development in Brooklyn, which I thought was a ton of fun. Actually used the Scout platform to go buy a ton of air rights. We bought like 17,000 square feet in air rights.

Kyle: That's cool.

Drew: To build on top.

Kyle: I've been hearing a lot about air rights recently. It's very, very interesting. A whole new world, if you will.

Drew: Yeah, it's— I don't think we should go off on that tangent.

Kyle: Yeah, yeah.

Drew: But it's a way to kind of like add a tremendous amount of value in a very creative way if you can get in touch with the right people.

When you talk about getting in touch with the right people, I know that I'm going to keep bringing it back here because this is what marketing and sales is about. If you can target the right people, if you can find exactly who you need to reach out to—air rights has to be like a contiguous set of neighbors. It has to touch the block.

If you go across the street, like it no longer applies. So you have to be meticulous and hyper, hyper focused in your marketing to know exactly who to reach out to.

We were able to use the system to pinpoint the exact people to reach out to. Ended up having a bunch of conversations with some new neighbors and bought some air rights and built a stunning building in Gowanus Carroll Gardens.

So, real estate later and then I think the whole entire world got eaten by tech long ago and I just kind of rode that wave.

Kyle: Might as well, right? You got to adjust, otherwise you're going to get left behind. That makes perfect sense.

Before we jump into a demo of the platform, I'm curious if you could just walk us through: if a real estate agent or a team or an organization were to partner with you, what does that look like from day one? What can they expect?

Drew: That's a good question. Our platform is fairly self-service and we service everybody from the smallest to the largest.

You can be an individual real estate agent who's trying to boost your business. I got a text message yesterday from a guy—he's probably like the poster child of success on our platform. He signed up and he wanted to reach out to local sellers in his area and his market.

He ended up with around $7 million in new listing presentations in a 30-day window when he turned on Scout campaign. He was all excited. He called me up one day and I was like, "Guy's name is Gary." I was like, "Gary, stop, stop, stop. We have to wait to get this on video." He couldn't help the words coming out of his mouth.

It turns out we were able to find somebody who was looking to sell their place using the data. We put together a lot of personalized emails. We send them out on his behalf. So he's sleeping. He wakes up the next morning and he's got something in his inbox saying, "Hey Gary, you know, thanks so much. I'm actually interviewing real estate agents right now and I'd love to talk."

He goes on the listing presentation, does a pitch, and turns out this woman is a teacher. She said, "You know, I never respond to cold emails, but yours was so well written and so well timed that I had to give you a shot."

Kyle: That's awesome.

Drew: So he's like a perfect case study right there. Perfect case study. He ends up winning the listing against a bunch of other agents. We had nothing to do with that, but he's so excited that he just landed his first deal through email.

He calls his wife and he's like, "You'll never believe it." And she goes, "You'll never believe it. So-and-so just dropped by my store and said, they got your email and they want to interview you for a listing." He landed two in like a 24-hour span.

Kyle: Oh my God. That's beautiful right there.

Drew: Bring it back to yesterday. He just turned the campaign back on. He landed another $5 million in listing presentations in three days.

Kyle: Wow. So what does it look like? You kind of bring your end goal. What's your end goal? What do you need? What do you need to accomplish? Start with the end in mind and bring it back from there.

Build your ideal customer profile. Determine what your goal is in your marketing campaign and how that attaches to your ideal customer profile and then let it go. Everything hooks up to email.

What Scout is doing in the background is putting together this ICP: hyper-targeted, geo-fenced, whatever you want. Attaching that to marketing campaigns and then letting them go on your behalf and hitting the inbox with like a 98% primary deliverability rate. So you're not getting stuck in spam. We built in all the best practices around email, so it kind of works for you.

Kyle: It's cool to hear those success stories, especially since as an entrepreneur and a business owner myself, I know how annoying, but how powerful, cold email can be and having to personalize and get it out there and just how deliverability is such a pain. So it's great to hear that you guys have this built into it and there are some wins to be had, of course. I'm sure there's many other success stories there.

So, great job seeing it all kind of come together here. I think this is a perfect time. Let's jump into a little demo. Just show everybody what the platform looks like. We've talked about ICPs, so we can see how all the different data points people can use to slice and dice.

Drew: Absolutely. So you can see my screen?

Kyle: Yes.

Drew: What most people do when they open up the platform is they'll come to a page like this with a bunch of campaigns, a campaign library. We've got everything for every different industry.

If you're in lobbying and politics or you're in real estate or mortgage or moving and storage, it doesn't matter. You'll see things that are relevant for your industry.

Where most people go to from here is a map. They're always trying to find new customers. By the way, this works for an existing list of leads too. If you have a CRM with, say, 10,000, 20,000, 30,000 people in it and you need more information on those individuals, you just throw your CSV right into Scout. We can enrich all that data for you, tell you about them.

You've got predefined segments and here's your high-net-worth or ultra-high-net-worth segment. That's the one that we connected on. You've also got things like seniors, likely sellers, likely buyers, investors, high equity, people who are renting with the financial means to own—anything like this.

A lot of our power is in our predictive analytics because of that behavioral signal. We can do something like go to likely sellers. Give me a zip code or I'll just make one up.

Kyle: Yeah, go ahead. Just make one up.

Drew: We'll do 11747. We'll go to Long Island. They got a lot of snow. I'm sure they don't want to be doing any door knocking or anything like that.

You've got 712 likely sellers in this zip code, 11747. Now what you can do is start to really drill down. If you want to get super specific: a certain net worth or income, if they're actively looking for a job, if they're actively looking for a mortgage, a new home, a refi, if they have an assumable mortgage, children in the household, LTV, if it's an owner-occupied, we make sure that everybody has emails—so that's kind of our only preset. You can obviously turn that off. Phone numbers.

Really anything like this. We'll just, you know, for a second, go to ages between 45 and 70. See if that changes anything. Recalculate. Now see 314.

So you can take those 314, add them to contacts. What this is doing right now, once you create a list or add it to a list, it's actually saving the entire segment. Everything that you just filtered for, all of that criteria—you don't have to come back here and continue to do it.

The segment knows what you're looking for and continues to add people every time the data updates or we get signals or flags or updated data. It puts them into this segment for you, which is really nice when you're thinking about orchestration layers and how busy most people are, especially SMBs or residential real estate agents.

Once you pull the segment, I'm just going to show you one that I've pulled previously like Southlake, Texas. You get things like email, first name, last name, address, city, state, zip, phones, marital status, age, children, income, property type, square feet, bed, bath, year built. You can see all the data that we have. Looks like this person's even got a dog.

One of the cool things is segment tags. We'll actually take all of this data and build tags around those ICPs so you can really easily filter them. Whether you're uploading your own data or we're just getting our data, you have a really easy way to find, I think, the best way to approach that individual.

This is hooked up to like 3,500 APIs. So if the data is not in here, if we don't have it in one of those 550 data points, it's very easy to get it from another source.

Kyle: Very cool. So you guys have the ability to kind of reach out and have other, you know, connections added in or whatever it may be?

Drew: Yeah, absolutely. It's a very, very flexible platform.

Once you have this, here are all the campaign templates. All this is personalized, so it's all built out for you. Some of it needs a little bit of customization. Your language is always the best language or your voice.

You can set a follow-up email seven days. Anyone who didn't reply, keep going for the next one, the next one. We've got a bunch of AI built in here to either put in your own prompt, make it shorter, make it longer, change the tone, improve the language—kind of like our magic incantation button, I guess.

Behind the scenes, we also have things that are changing language, creating different versions of things, so you're not constantly sending out the exact same thing. Obviously, you can turn that on or off. Then everything gets batched, throttled, randomized, validated—so all the best practices around email that's landing you in the primary inbox.

Kyle: Very cool. My brain just went through and had a whole bunch of different questions that I wanted to fire at you here.

First, it's obviously super powerful that you have so many different data points and so many different connections. But thinking about it from, let's say, a newer marketer or a person who's trying to attempt some of this outreach, I feel like it might be a little bit overwhelming.

What advice would you have for a starter in trying to decide what their ICP is and what targeting to use?

Drew: We typically have a tendency to overthink these things. Then we end up in a place where it's like paralysis by analysis. That's the worst place you want to be.

As a business owner, you're always striving for perfection. Oftentimes that's what's stopping you from any type of growth. As a marketer, you gotta constantly iterate and try things.

One of the beautiful things about a platform like Scout is that we have these ICPs built in for you. It's not so much: how long have they been living there? Is that even a determining factor? How much income do they have? What are their interests and hobbies? How old are they? How old are their kids? There are thousands of things that can go into creating this ICP.

This is really like ready, aim, fire. You literally just have to know where to point it. Are you looking in a zip code, a city, a state, or a geo? Every industry is going to be a little bit different, but we make it really easy to find the exact type of person that you're looking for.

Kyle: I am going to ask you a question that I personally get annoyed when I receive it, but I think a lot of people do ask this. Is there a recommendation that you have in terms of list size as well as the success of a campaign? Do you say, "I only need to make sure you have at least 100 emails out there before you judge it," or anything like that?

I like to focus on the ratio efficiency metrics and stuff like that versus volume, but a lot of people do ask that. So I wanted to throw it out there.

Drew: When it comes to email and when it comes to what we do, this is a really important question to ask. The truth is it varies by industry, but I'll give you two examples.

We have one customer. He sends about 30,000 outbound per week. Thirty thousand individuals. For him, that's the right size—120,000 people every single month. That's essentially the universe of the people that he can reach out to based off of the trigger point. Right person, right time, right message. It's 120,000 people per month. That is his ICP. So he fully saturates it. We enable him to do that.

With somebody like a residential real estate agent, they can't handle more than 350 per month.

Kyle: Great point.

Drew: So why 350? This is a number that we've kind of calculated for this industry as being the right number to start off with as an individual. If you don't have systems and processes in place to handle this, you will get very overwhelmed very quickly.

You take a couple of numbers into account. One, SMTP versus IMAP. Are you sending from Google, Microsoft, or SendGrid?

If you are sending from Google or Microsoft, you do not want to send more than 70 emails per day. If you do, you're going to get flagged. The ESP is going to say, "Who's this person? What are they sending? Why are they sending it?" And you'll end up in spam jail really quickly.

What does that translate to? Like 1,700 outbound on a monthly basis if you're sending Monday through Friday, not on the weekends, respecting best practices. Could you theoretically ramp up to 1,700 as an individual using one IMAP-based email sender? Absolutely.

Once you get above that, you're in transactional email territory. You gotta use something like an SMTP. Then you've probably got infrastructure, you've probably got a team. You know how much your BDs, your business development reps or ISAs or SDRs can handle.

It's that constant balance. We have another customer who had two salespeople. He needed to feed them. We were lining up like 35 new meetings a week for that team based off of sending out, I think it was like 3,000 to 5,000 per day.

So there's a sweet spot. It's part technical, it's part process, it's part industry specific.

Kyle: Fair enough. I think about it from an "I'm an ads guy." It's like, "Well, how much should we be spending?" We gotta do some research and let's focus on click-through rate, not impressions.

Drew: Number one, you get diminishing returns. If you don't have a system or process in place to convert those leads, your CAC is through the roof, even if your CPA is really good. You can have a great CPL. If you don't have the right mousetrap, if you can't convert, you're just throwing money away. So don't hit scale immediately. There is such a thing as scale too fast.

Kyle: So when you were walking through the demo there, you had mentioned, "Oh, look, this person has a dog." I'm curious if you've seen anybody win in their campaigns from an interesting segmentation like that. Maybe you look for dog owners and say, "Hey, we have this beautiful yard," or something like that. Have you seen anything like that off the top of your head that maybe isn't your normal campaign?

Drew: For sure. We have interesting clients. We have a client that builds catios. For wealthy people who have cats, they build patios.

Kyle: That's awesome.

Drew: So they're looking for dogs or cats. In the residential real estate industry, they jump on some really obscure statistics. They're always throwing events and hosting dog walks and coffee or something like that.

There are some really interesting platforms out there right now. In terms of marketing, especially email marketing, what they will do is ingest a ton of data. They'll say, "Okay, how old are they? How many kids do they have? Do they have a dog or a cat? What are their interests and hobbies? Do they like hiking, skiing, biking, kayaking? Are they into sports?"

They'll take all this information and feed it into an AI model. The AI model will spin out a recommended campaign within the structure of what they're reaching out about. The language will be different. The images will be different.

So if you're targeting somebody young, maybe it's got a young family. If you're targeting somebody a little bit older, it's somebody who's maybe retirement age. I think you're starting to see people experiment with these platforms and data points. It's going to be a wild world. Things are getting hyper, hyper personalized.

Kyle: My marketer hat went on and I just immediately was like, "Oh yeah, specific language to the dog owner, specific language to the senior." There's so much that you can do to slice and dice it and make it even more personalized and of course powerful and effective. Very cool to see it all come together.

I do want to shift slightly here to talk about AI and where it's fitting into the platform and even just real estate in general. You mentioned early on that it can be scary. I'm curious if there's a biggest misconception that real estate agents might be seeing or thinking about when AI comes up that you might want to dispel.

Drew: There's a lot of misconceptions. I think in residential real estate in particular, it's a very, very antiquated industry. The people who excel don't necessarily have that skillset or knowledge. They're typically older. They've typically been doing it a certain way. They don't want to change it.

They really feel like AI is this thing that's supposed to replace them. The saying goes in the industry: AI will not replace you, but somebody with AI will replace you.

We've been hearing that for a long time. What does it actually mean? In the beginning, everybody was using AI as a listing rewrite tool. They would just write their listings using AI.

I think what people are starting to realize now is, whoa, this can be an orchestration layer. This can be a targeting layer. This can be a content generation layer. This can be a code layer. This can be something that helps you respond to emails, keep your day organized.

There are a lot of innovative companies. I think most of them come outside of real estate and then go into real estate. They see a big shiny TAM and think, "Yeah, we can go disrupt this or make it better."

But you really have to meet people where they live. So much of real estate is relationship based. The biggest fear is that AI will replace the relationship.

What I would love to dispel for this industry or any industry is AI is not here to disrupt the relationship. It's to strengthen the relationship.

There's a law, it's called Dunbar's Law. A Cambridge or Oxford professor came up with this theory that you can only have 157 close friends, like close contacts, people you know. After that, your brain starts to turn to mush and you can't remember things about people.

With AI, with the right data, with the right incantations, you can increase that number exponentially. So AI is not here to replace relationships. It's here to enhance them.

Kyle: I couldn't agree more. My whole slogan is human empowerment. It's going to make you be able to focus on the relationship stuff, the stuff that you're passionate about, the stuff that you're good at, and take away some of the minutia, the time-sucking activities, the things that take you away from revenue driving actions.

Drew: Here, let's turn a question back on you. How many things did you outsource to AI this week that you absolutely dreaded doing? How many frogs did AI eat for you?

Kyle: It is becoming more and more by the day. I have spent all of Q4, I've broken down all of my internal operations and I'm automating each one or one per week. Some of it's content creation. Some of it is follow-ups. Some of it is our prospecting campaigns. All of it is just day by day.

So to answer your question, it's like 95% of my tasks we're getting to, so that I can do this kind of stuff. I love having these conversations. I love the podcast. I love connecting with different people and building packages and marketing strategies and things like that.

So now I don't have to worry about maybe a little bit of content research or following up with a form fill or something like that.

Drew: There you go. Dunbar's Law in effect. We can now connect on a deeper level because we're able to outsource the—can I curse on this podcast?

Kyle: You absolutely can. Yes.

Drew: Let's outsource the bullshit.

Kyle: That's right. That's right.

Drew: I actually am in the process of trying to automate some personal stuff too. A fun analytical experiment I'm trying to go through is understanding the best players by value for my dynasty hockey league.

Kyle: There we go.

Drew: Yes, exactly.

Kyle: We're getting into the personal realm and fantasy and everything like that. It's very fun stuff.

Drew: I think in my sports betting days, when I was a power Bodog user, I would be having a field day.

Kyle: I was thinking about this literally earlier this week. I wonder how Vegas is using it. They were good even before AI. Now that they have this kind of stuff, how is Vegas using it? It's gotta be cool.

Drew: It's crazy. You see all these sports lines and they're just getting tighter and tighter. I would actually love to see somebody do some sort of regression analysis or backward-looking to see how these lines have gotten either tighter or looser with AI models.

Kyle: I agree. That'd be a fun experiment.

All right. We can go down the sports AI rabbit hole for a while here. I'm curious from your perspective, again, we talked about some new people, how they might use Scout and build a campaign. Let's just talk about AI in general. Do you have one tool that you would recommend to someone? If you're just trying to dabble in AI, what would you recommend?

Drew: It's such a boring question for me to answer because I think ChatGPT is just the easiest.

I personally bounce between ChatGPT, Gemini, and Claude. And it's like battle bots. I make them go at each other. I'll throw, "Here's what my data team told me. What do you think of this?"

Once you've gotten to a place where you're comfortable interacting with AI and you kind of know how it responds or the right way to frame questions, then you start to build it into processes. Then you start your orchestration layers. Then you start your content generation. Then you start your meaningful outreach or automating anything in a platform like n8n.

If you're getting started, just go to ChatGPT and mess around and figure out what you can do. If you're a power user and you're starting to change your platform or your service or you need real automation, n8n and Make.com are great.

If you're like, "Hey, I've got to build an app," Willy Wonka's Chocolate Factory: if you can dream it, you can do it. Replit, Lovable and platforms like that. We've built entire apps and platforms that we are in production with customers have been on board and fully self-servicing entirely on Streamlit.

I've got about a thousand more. I would say GenSpark is an amazing one if you're looking for decks like PowerPoint presentations. Also Gamma. Gamma is wonderful for the same, just an alternative.

Kyle: You can definitely go down the rabbit hole. I think to your point, if you just kind of go to Gemini or ChatGPT and even ask, "What AI is there for this?" I think there's even a website, "There's an AI for this."

I'm curious, internally, have you set a philosophy at Scout for how to use AI or when to use it, when not to use it?

Drew: 100%, we're AI first. AI-first everything. If you cannot find the answer using AI, flag it internally and raise it. If you are not using AI, consider yourself banished.

It is absolutely mandatory and it is part of your day and part of your skill set to learn how to utilize AI. If you are not, you are not as efficient as you can be. If you're not using AI and relying on it—I think at this point like 75% of our new code base is coming through AI—you can find the door.

Kyle: Fair enough. I love that. I mean, you got to use the tools. To your point, you're going to get replaced if you're not using AI.

Is there, from your perspective, such a thing as over-reliance on AI? Is there a checks and balances that you think needs to be in there as well?

Drew: Of course. I want to say everything in moderation, but that's not true with this tool. This is the most powerful tool that we've ever seen in our lifetime, and you should be using it as such.

At the same time, I won't say over-reliance. I will say redundancies. If your mission-critical processes are built on the back of a single model and that model goes out—which is something that we saw with.

If your mission-critical processes are built on the back of a single model and that model goes out—which is something that we saw with ChatGPT or OpenAI—we were some of the first people using OpenAI before ChatGPT was even a thing.

When ChatGPT came online, a lot of those API calls would just time out. We had to have redundancies in there. Then you came into the era of DeepSeek. DeepSeek was the hot kid on the block for like a minute. We couldn't use it because it was totally unreliable.

You need to balance that. If the question is going to brain rot—does your brain turn to mush if you use it too much or you're over relying on it—there's some studies done on it, and I think they're probably valid.

But if you're using it as a tool and you know it's a tool, it's only as good as how inquisitive you are around it. You can up-level your game, or you can turn your brain off. Don't turn your brain off.

Kyle: I totally agree. I oversimplify it and equate it back to the day of the calculator. In math class, we don't necessarily need to be remembering our times tables anymore because you can just use the calculator.

So is your brain turning into mush because you don't know seven times seven off the top of your head? I guess you can make that argument, but you have this tool even in our phone that's there 24/7 to be used. So is it that big of a detriment?

Drew: Exactly. It's there to make us smarter. It's there to up-level our game, take those rote, repetitive tasks that actually turn your brain into mush because you're not thinking, you're not being challenged, you're not growing—and attack harder problems. AI gives you the leverage to attack those harder problems.

Kyle: Let's final kind of question here, two-part question.

Let's say Scout is at V1 right now. I'm sure you've had multiple iterations. We'll just call it right now V1, like V2. What does V2 look like with AI continuing to expand? And similar question, what does V2 of just real estate intelligence or real estate and AI kind of look like from your perspective in the next couple of years?

Drew: I was up on a panel talking about this one a couple of months ago and I caught a lot of flack for my response. I'll go to a concept of human-in-the-loop versus human-out-of-the-loop.

Human-in-the-loop is: you need to be sitting in the middle, you need to be checks and balances. That's where we are right now. No matter how good AI is, you still need a human in the loop for the majority of your complex tasks. You cannot turn off your brain.

Fast forward, I don't know, six months, a year, we're going to have out-of-the-loop in a lot of things. There's a lot of talk about AGI. I don't know how close we are to AGI. Two years ago, Musk said we were like two years away. So that puts us here now.

Based on your definition of it, maybe there are people that are experimenting with it. We've certainly passed the Turing test. I guess bringing it back to the original question of: where does Scout 2.0 go and where does the industry go as a whole?

We are moving really into a tremendously data-driven world. The data is getting updated on a more frequent basis, and it's being tied to different data sets. Where we're truly focusing is the intersection of all of that data and all of those data sets, and then how to actually make it usable—how to distill it from a universe down to an ICP, and why you need that data.

Scout 2.0 is really focused on the back end, on the data models. I would say where the residential industry goes as a whole is much more interactive and personalized.

I think thinking about real estate in a way that people have not thought about before. It's always been about location, location, location. Maybe the fundamental location, location, location doesn't change. But the factors that go into it—which were dollar per square foot or square foot, bedrooms, bathrooms, exposure—HouseCanary was like the first data player on the scene and they could tell you what your view was going to be and what's obstructed and how close you are to water.

Those things were kind of V2 of real estate data. We're now moving into V3. How many of my friends live in the area? What restaurants do I love to eat at and are there similar restaurants? How far am I to work or what does my commute look like? What do my taxes look like?

It's going to take into account all of your lifestyle factors. Forget about the way that we interact with a purchase or sale of a home in terms of AR or VR. I think you're going to see a lot of really, really interesting data points: dog, cat, right? These are the things that we think about when we think about our home.

Kyle: Commute to work, things like that.

Drew: Commute to work. When's the last time that I thought about how big my home was, the square footage of my home? I love the layout and I love the utility of it. Also: do I have amenities in the area that can make up for something that I had to compromise on?

I think this is the next iteration of real estate search, and it's 100% going to be plugged into all social channels like your Facebook, your Twitter, your Instagram, your LinkedIn. I think we'll be feeding a lot of data from those social platforms into real estate searches.

Kyle: Very interesting. That's a great answer. I think that's very cool to be thinking about. You make a great point. When you're shopping, you're like, "Oh yeah, this is X square feet." But once you make that decision, you don't think about that ever again. It's like, can I walk to this restaurant? I'm excited to see what that looks like.

All right, Drew, we have reached the rapid fire section. I got five quick questions here for you. You ready?

Drew: Yeah.

Kyle: You have a genie in a bottle. You rub it, it comes out. It gives you one wish of an automated process just implemented right now into your business or your life. What does that look like?

Drew: I'm taking in my life. Find me the best vacation at the cheapest rate and just book the tickets for me and send me there.

Kyle: That is a perfect example. I love that.

Question two: do you have, for lack of a better word, a mantra or anything that you always come back to and that you continue to use when you need it, especially in hard times or decision-making processes?

Drew: Focus on what matters.

Kyle: Love that. Awesome. Got to prioritize.

Question three: do you have a dream person or organization you would love to partner with?

Drew: I failed the rapid fire.

Kyle: There's no failing here. You're good.

Drew: I think Anthropic is just so ahead of the curve right now. I would love to find a way to work with Anthropic in a deeper way.

Kyle: Very cool. Question four: do you have a podcast that you regularly listen to?

Drew: All-In.

Kyle: Who's that by?

Drew: Jason Calacanis. He hosts David Sacks, Friedberg, and Chamath. It's VC focused. Jason Calacanis was actually our first investor. They cover everything from AI, finance, politics. Great podcast. If you guys aren't checking it out...

Kyle: I'll have to give it a listen. That's not one that's in my regular rotation.

All right, and then number five, what would your death row meal be?

Drew: New York strip, Kobe, probably American wagyu, beautiful medium rare. And like those thousand-layer potatoes.

Kyle: There you go.

Drew: And we'll probably finish off with some crème brûlée.

Kyle: All right, nice. You just got me real hungry.

Awesome. Awesome. Well, Drew, we're at the open forum section here. Is there anything that maybe we didn't discuss that you're really passionate about and just want to get out there to the audience?

Drew: I think we covered a lot. I'm obviously very passionate about data and how we use it and just making systems work for you.

If there's a hard problem out there that somebody's struggling with—whether it's in the marketing space or not—there are so many applications and uses of data that power entire infrastructures and organizations. If you have one of those really hard problems and you know that data can solve that for you or at least contribute to solving that or improving it, I would love to talk. But make sure it's a really hard problem.

Kyle: There you go. You like a challenge.

Drew: Yeah. So many of these things can already be solved by typing something into ChatGPT. So I would say, go there first. If you don't get a satisfactory answer, come to me. Let's make it work.

Kyle: Love it. Very cool. Very cool.

Well, Drew, I appreciate you joining. Anybody can check you out, Drew Fabrikant on LinkedIn. They can also find you or Scout at TrustScout.com.

Drew, thank you again for joining the Brainiac Blueprint. If you don't mind, just look at the camera and say, "Stay brilliant, Brainiacs."

Drew: Stay brilliant, Brainiacs.

Kyle: Awesome. Thank you so much, Drew.

Drew: All right. Thanks, Kyle.