Meeting Title: Brainforge x Breezy: Data Discussion Date: Dec 2 Meeting participants: Bareket Sigal, James Lee, Rico Rejoso
Transcript:
Them: Within the infrastructure of our very, very young and new company. And knowing that every company that’s being built these days probably needs to challenge whatever playbook it used. In terms of setting up and tools and dataflow. Jamesy and I are looking from some support and help with everything that has to do with the way the data is structured. At Breezy and we have two perspective in or two areas in which we need help.
Me: Great.
Them: One is more straightforward. We’re starting a company from scratch, and we all have been in two companies that fucked up the initial setting and then spent years fixing it. We would love to make sure that we are designing our data tech stack and dataflow from getgo in the right way. And Jimzy in a second will tell you more about what we currently have and what we’re looking for. But we are looking for like we heard it called, the fractional lead data scientist or just a consultant or someone that will help us set up our data infrastructure right through the get go, serving both product and marketing needs. This is very important and there at the moment. So that’s one big more of a come I’m seeing these things before.
Me: Yes.
Them: Secondly, and this again will. James will get more into the details. One of our claims to fames as a company is the accuracy of data that we provide real estate agents as they pull data from the app. Mainly comms data, but not only. Again, we’ll talk more about that. And as we started talking to Clint, who introduced us, we were thinking maybe we should paint a bit of a broader picture of what our needs are in kind of data and talk a bit about how we see data accuracy regarding real estate as one of our claims to fame.
Me: Yeah.
Them: Maybe ask for help on this one. Dude, these are two separate things.
Me: Yeah.
Them: We don’t even need to connect them in the way we work together. But if we are talking about our fourth set of data needs, we have the regular. Just get us off the ground running. And also how can we continue using real estate related data as our main claim to fame and as competitive advantage. So this is who we are.
Me: Sure.
Them: I would love. We would love to hear more about your business and then get into the details of what we actually need.
Me: Yeah. Sure. Definitely. And, you know I appreciate it. Yeah. Clint N. I have been friends for a few years now. We’re starting to use this product for a few clients, and it’s finally showing some life, so we’re really, really excited. And yeah, so my background, I’m a data engineer. I worked as a data engineer for a number of years before starting Brainforge. You know, kind of like everywhere through data, both at the PI level, at the analytics engineering level, the data engineering level, lead data teams and previously led product that I did a startup left that company. I was living in New York for about five years. Moved here to Austin, where I’m living now, about three years ago. Started this company about two and a half years ago. So Brainforge is a data nai consultancy. We’re completely bootstrapped business. Most of our team is engineers. We’re about. Maybe 16 people now this week. So we have quite a lot of people, which is not necessarily a good thing, but in the services business,
Them: Well, impressive.
Me: This is a headcount business. But like you guys are thinking about running a consultancy and running an agency in a lot different ways. Not only in the level of delivery for our clients, but also in our pace.
Them: I know.
Me: And so the way we run our business. So, as I mentioned, done a lot in data. Actually, my first gig in data was how we work. So a lot of familiarity with the commercial real estate and sort of the real estate business as a whole. Of course, I also have a lot of friends that work in the residential real estate business. Very, very familiar with all the work that goes to support residential real estate. Of course, I’m just assuming that’s sort of the focus here, but generally familiar with that industry. And so for us, right now, we have a slew of clients that we come in and we sort of run fractional data. Teams for this can be both at the level of like, as you mentioned, sort of just like consulting on what is a data strategy, what are the tools, the people, the process that’s needed and what how much that can get fulfilled by Brainforge, how much I can get filled by other agencies, how much of that is people you need to hire. And so basically helping with strategy. What we don’t do is we’re not really the folks for, like, we’re not a dev shop. We’re not sort of. We don’t do a lot of staff augmentation. In the sense of, like, hey, just go. Sped up my snowflake and then head out. Most of our stuff we try to do is partnering with the organization to understand what are the outcomes you’re driving for. And then how can we use data and AI, you know, as our, like, picks and shovels to sort of make that happen. So our crew, you know, is a. Is a mix of data engineers, data architects, AI engineers. And then me and my business partner, sort of. Sort of lead strategists. So we typically operate a little bit on a pod model. So if we were to work together, you get like us and maybe one or two people, depending on sort of the scope. But, yeah, I mean, of course it’s. It’s super dependent on your needs and, you know, where you guys are as a business and where you need support. But what I like to express is that we’re sort of like full stack data. We don’t just do one piece. We. We can support on the more strategic side as well as, you know, go all the way to data engineering and data science. But, yeah, we’re totally, completely open to any questions, but we’d love to sort of get a sense of where the product is and definitely get a little bit more idea about both these areas. And again, like, we’re. I like to leave with, like, where we have superpowers and where we can be. The most effective for you all. I will be very clear with where we can’t help. I’m not, like, a salesperson. So we try to find work where we can knock it out of the park. And if there is other work that we can do, I’m sure I have a couple phone calls from some. Some people or tools that. That can be of help. So. So, yeah, that’s a little bit about us.
Them: Awesome. That sounds great. That sounds great. Do you. Do you think might be worth us talking a bit around what our product is and what, maybe giving you a bit of color into what we’re looking for? I think Sigal did a good tee up.
Me: Please.
Them: Ultimately speaking. We’re Breezy is founded by our founder, James Harris, who is a real estate agent himself. A very successful one. He’s in charge of the most successful real estate agent team in the US by revenue. Originally, he came from London. He’s based in LA for the last 20 years, I think 15 to 20 years. Built his team out there and I’ve never met someone who’s been as obsessed. With real estate as he has. And the real estate prop tech space for real estate agents is a fascinating vertical because maybe most companies start this as part of their elevator pitch. But I really mean it, and I’m really serious when I say this. That vertical for real estate agents has been grossly underserved. As a target audience, the tech and the tools that they use to be productive in their work. The bar is super low. And so when James created Breezy, the number one, almost like North Star and KPI that we’re looking to solve is to save agents time in everything that they’re doing on an exponential level, not just an incremental level. And so we don’t do lead gen or focus on the top of funnel parts because they’re just like, you’ve got social media there, you’ve got the metas of the world, you’ve got a whole set of companies who try and do that suite and automation. James is adamant that his success in this life and what got him to be number one is actually not that. It’s actually how you manage. Your people build reputation. Level up as an agent. And get very sophisticated in the way you manage and organize people and organize yourself. And so what we focused on at Breezy is essentially a platform, or we like to call it an operating system, that allows agents to massively save time, to be more productive and more fulfilled, more engaged and more efficient in everything they do. And the big elements of what agents do on a day to day basis is manage people. Know the market. And spot opportunities. And really understand the subtext, the detail and the context of their conversations with people in order to get ahead of everyone else. And to do so in the way that makes them stand out from the crowd. And so when we look at what.
Me: Yeah.
Them: The great agents versus the good agents versus the not so great agents. Because it is a 1099 Commission based world. Like your brand and your reputation, the way you manage yourself. Makes you earn a lot more money and be successful in your business, essentially. And that’s the. Let’s call it. That’s the philosophy and the ingredients that James has cracked throughout his whole career. And so that’s the mission and the goal of what we’re trying to do. And the way we do it through the product is when we talk about value properties. Agents have to do things called comps every day. When they comp property, you find a target property of someone’s home address or you’re looking to buy NN an agent has to get a very accurate read based on market data. As to what that price is. And it can’t just be plucked from Zillow or Redfin or whatever it is. There needs to be rigor behind it, needs to be proof behind it. There needs to be branding marketing behind it. So comps is something that we help agents literally do in seconds. It takes them hours or days, sometimes to do, depending on the property. And we do that through mobile right now, which seems to be one of the biggest kind of like, selling points of our product.
Me: Yeah. And what part of this are. What part of the market is this in? Like, is this. Are you guys in a certain price range or a certain type of. I assume this is all residential, or is that even part of the equation?
Them: Or residential. And all price ranges. So we actually go from luxury properties to like, mid market to, like, you know, more of your kind of cookie cutters as well. I mean, you’re. Cookie cutters generally tend to be a lot easier to comp, so an agent might be only spending an hour doing that.
Me: Okay? Yeah.
Them: They’ll be rough around the edges and in that. But if the app gives them kind of like, an ability to do that very quickly, it gives them greater confidence and speed. And also because it’s branded with their logo, their face, their brand, that does it in seconds.
Me: Yeah.
Them: It’s like canva or portion of canva. Meeting a part of Zillow. They had a baby. Create a report, and that’s you as an agent. And instead of you having to get a marketing person or just canva yourself,
Me: Yeah. Yeah Simply your logo in the corner yeah, yeah, yeah.
Them: Easy. It’s just meant to be easy, basically.
Me: Great.
Them: And another thing we do, which is proprietary to us, is something called underbuilt radar. So again, type in an address. And then we give you the build potential of that property.
Me: Great.
Them: Based on how big you can extend, build larger, build bigger and increase the potential. That property, a lot of people, if they want to know, hey, I wonder how much I don’t know, 10,000 foot lot. I’ve got a 3,000 foot property on it. How much bigger can it go? 5,000, 6,000, 7,000? What is the value of that? And a lot of people don’t know that. They can guess. But it’s really hard to research, and it takes weeks to research. We give you that information in seconds by just searching the address.
Me: Great. Great. Yeah. Okay?
Them: And so that’s a unique part. Sorry, product that we’re doing right now. We have a real estate agent. Focused and real estate agent prompted. Focused AI notetaker. Which doesn’t matter whether I’m an agent doing inspections or I’m in a listing meeting or I’m trying to get new business from a client. The notes from that summarize based on a real estate agent perspective. So that that goes into lastly what we call a pipeline. A pipeline is essentially like a task management based platform that is specifically designed for real estate. Think Monday.com notion again was in the vertical real estate.
Me: Yeah.
Them: And that’s Breezy, is we’re not a CRM. We don’t like to be called a CRM.
Me: Yeah.
Them: Yet. I think we cannibalize and eat into elements that agents wish their CRM did.
Me: Okay?
Them: And so that’s what we’re trying to do. We’ve sort of burned the boat inside of, like, saying we don’t want to be a traditional CRM. We want to go down the route of, like, helping you task, manage your clients, your prospects, your listings. Your escrows under contracts to completion. Do that in a way that an assistant.
Me: Makes sense. Just, like, the operating system for this agent. So there’s probably some elements of, like, Monday air table. Probably also moments of, like, a clay. Like. Yeah.
Them: 100%. You’re spot on accept. I would say it’s those products, but probably simplified down to 100 degree. Yeah, yeah. Because we. We found the type of person we. And I would be careful using this phrase, but I think there’s a lot of truth in it.
Me: Just. Yeah. Okay?
Them: A lot of the really successful agents or the ones that make this their career. Are pretty adhd. In the way that they, they, they are, like, doing all these activities, and so they’re not going to spend hours doing one thing.
Me: Yeah. Yeah.
Them: So actually, I think Sigal is extremely kind to me, and I would probably say it’s not my. This didn’t come from me. This came from James because he’s the one who said, I know what agents want. But obviously eight. James is our founder. He doesn’t build products like Sigal and I do. We’ve been. We. We. We have a. We have a core value we call Asian obsessed. And so I’ve approached this business, and I think the only thing that has got us to this stage of success is I. Have not put my own opinion into this. I’ve, I’ve only put the, that person, that wide Persona of agents, kind of like, footprint into this. And so I think, and, and I, I echo everything Sigal says, like, just because we found success, like, I, I, you know, I’ve exited twice before in companies. This is my fourth time doing a startup.
Me: Yeah.
Them: I’m. I’m humbled enough to know that I. I’ve got a lot of experience in doing stuff, but the world changes so fast that if you’re trying to repeat the stuff that I. I did it once with a coaching startup and it, you know, I did a lot of great things, but. Then a lot of things just didn’t work out. And you learn from that and you, like, you know, obviously. And I had perfect analytics and perfect stuff in that startup. Ironically, the company before that, I had a bunch of. And that’s the one I exited from. So it’s like, again, I think we, we we’re, we’re very, like, out there looking for people who can actually help us get those things sorted out. And the last thing I’ll add is we have a really talented engineering team across the US And China in particular. The China team is pretty, pretty spectacular in the speed of which they do stuff, so. We do have some in house element. We do have. Let’s call it someone.
Me: Yeah. Great.
Them: BI business analytics, QA analyst person plus an engineering lead.
Me: Cool.
Them: Who would be very happy doing a lot of the heavy lifting.
Me: Perfect.
Them: But at the end of the day, I. I think what Sigal says is really true. Like, the data for us is not just what’s in the product. It’s the whole life cycle journey between when an agent looks at an ad. On meta. To like how we track them in customer I O. How then we see what their behaviors are in. I don’t know, stats. They mix panel. I don’t know, whatever. Bi tool, you know, we blades, whatever. You know, brace. Sorry. Braze. Braze. SQL Whatever it is. You know, we. We. We want to really step back and get some really good insights on what our journey looks like there. So that’s. So that’s. Lastly, that’s the. Let’s call it the standard. What is the BI Product analytics behavioral part.
Me: Yeah.
Them: Similar data journey stuff. And then the second part I’d say, I talked about that. Build potential. Piece. What we do is we have been using AI and using architects, essentially. To glean through building code across the United States. If you just imagine what builder coding is. It’s like legal documents that scatter around the Internet.
Me: Yeah.
Them: Basically. And so what we’ve been doing is we’ve been passing that through, putting it through, like.
Me: And has it just been just in la or where have you guys been focusing? Because I know that is, you know, that’s a huge factor, right, in terms of, like, that data, has it just been in la? Are you focused all over the US right now?
Them: We’re in 500 cities right now.
Me: Okay?
Them: So we will be in 2600 cities in April. We would love to. Ask someone to say, how might we get there faster? And how might we get even faster?
Me: Sure.
Them: Through more intelligent usage of AI and, and, and and processes there. Ultimately speaking. It’s URL. To document. Let’s call it passing through.
Me: OCR or some type of extraction. Yeah.
Them: To extraction. Extraction is not always necessarily. I don’t know, I, I, like, we’re looking for someone to maybe do a bit of an audit and say, like, hey, I. I don’t think AI is particularly great at extraction.
Me: Yeah. Yeah. You need to consider layering if you on and yeah. Yeah, I hear you. That’s why. Yeah, yeah.
Them: All we’ve learned, it’s not great. Yeah, yeah. But the stage before, after that, once you have a clean 100 page PDF document, how do you best, you know, pass that through into a structured data schema or a JSON or, you know, whatever it is to then put into your backend to? I feel like as a team, we’re probably and we have some talented engineers on our side as well, but I feel like that is a work piece. We’re probably. We. We could level up. I don’t know.
Me: Yeah. Sure.
Them: That’s an open question we have. And then the last thing I would say is that we. We have an obsession over data accuracy. Now I think based on our initial beta group testing are actually people have not highlighted many issues with data accuracy. However, we are found that James has an obsessive level of wanting to be at the best at property data in the market for real estate agents. And so we are we we just signed a deal with a third party data processor who is, let’s call it the Bugatti of data processing for MLS listings across the United States. And so that’s a bulk data implementation, not an API. And so databricks. Snowflake, set that up. Become your own API. Get that done. So we have, let’s call it, like, a lot of juicy. Let’s call it data lead projects right now. And so we could do it ourselves.
Me: Yeah.
Them: And we could. We’re a startup, so we might have to be leaner or it might take longer, or we. We want to put on the table options of, hey, if we were to give this to someone else, How might someone else. Yeah, what can that look like? How can that be accelerated? That. I’d say it’s more of a project.
Me: Ok? Ay. Yeah.
Them: In terms of an implementation, so I’ll pause there. That’s the lay of the land of what we’re looking at. Breezy. I think it’s really exciting. We have a lot of, like, you know, momentum behind our sales right now.
Me: It’s a really cool product, especially the what can you build product that’s awesome. That’s really, really cool. Yeah, I feel like it’s a lot of. Yeah, it’s a lot of unstructured data into agents.
Them: Yeah.
Me: To basically figure something out. So how.
Them: That’s a company that. It’s actually a company called Underbuilt that we acquired so that it’s a unique value. You. You’re a spot on. That’s a really unique value proposition.
Me: Is. Okay?
Them: And the reason why James is so obsessed about data is because agents are like they were always moved to the next shiny tool unless we can really gain their trust. And if we can gain their trust in this little things. Of like, hey, this is actually spot on, and it gave me an advantage with a client. This is where can we can actually lock them in with Breezy for a long time.
Me: Yeah. So on the, you know, on the first piece, I feel, you know, a lot of the work that we do, you know, for folks, for startups, is, you know, setting up product analytics, setting up data warehouses, basically understanding customer journey. Helping with setting up, like amplitude or mixpanel, like tagging within your product. So giving. It’s kind of like the works there. Additionally, you mentioned we do always love to have other folks on your team that can help us, so it’s great that you have some people that are already doing some data work. If there are other people that want to learn alongside of us, we work really great. With existing teams, but I totally hear you on, like, there may not be a strategy. Driver person right now. So that is totally something that we would support on not only understanding, like, what do we need to procure, but, like, what is an implementation strategy of all these? And like, what are the outputs, whether it’s dashboards, whether it is stuff you need to reverse ETL back into customer I O or whatever. And so what reports. So that’s sort of like the reporting side. Happy to give a lot more context on. On what we can do there, but I think both of y’all seem pretty understanding, like, kind of what that world is. I think where we’re differentiated is just our pace. Like this for us isn’t like a six or nine month thing. Like, we sort of move fast as like you guys want to move. And we, when we walk into a client, we often help procure five to ten pieces of infrastructure. We have relationships with kind of like all these folks. And we’re driving towards like that first key insight as fast as possible, whether it’s an understanding of your sign up funnel, understanding of like, how to do pricing, understanding of like key feature usage. Like, combined feature, like, you know, whatever that is. So. And for us, it’s actually less about like, okay, we’re going to answer this one question and then move on to the next question. How do we help you build a culture of experimentation and people fishing for questions and then being able to do that in, like, a reasonable amount of time? With not just like, hey, I think it’s this is the answer. Like, oh, actually, no. And I can get you that answer in, like, a week. You know, so that’s like, sort of everything on the reporting analytics side, is that, like, roughly, like, sort of like what the ask is on on that first side of the house?
Them: Yeah. Yeah. 100%. Yeah. NT could you Utah what what is your usual. Sort of like, how do your people work with typically in house or startup, you know, folks like, what is the working engagement look like?
Me: Yeah. Yeah. So we. Our whole business, even our internal teams, we all. We run on one week sprints. So at minimum, that’s how we work. We run, we’ll run at least internal daily standups. For our folks that are on your project. Additionally, if there are folks that, that you have that you’re like, hey, you should. You should. You’re on the data team now. We’ll loop those people in and run those standards with those folks. We at least will on a weekly basis. We’ll have, like, at least one meeting on the calendar that we all meet and we do demos and we sort of do like a weekly thing and then we sort of will do like more broader, like monthly project review. So that’s more like our agile stuff in terms of how we, how we work. You know, for us, it’s we’re trying to hit both short term and long term infrastructure decisions, like, at the same time.
Them: Yep.
Me: So how can we still drive to get you insights, you know, or still answer some probably pressing questions that you need that are blocking product decisions? Maybe. How can we do that while still making sure you have really good infrastructure set up for product analytics, for data warehouse, for etl? For bi. So that’s kind of how we work in terms of working. We have companies where there’s, like, two people that are just, like, lonely analysts that kind of join our stuff and sort of see how we run a data team and we run the weekly pacing. There’s also times where, hey, we, we’re doing everything in Slack, and we’re sort of. We’re working with some startups that can’t meet every day, and everything’s through looms and slacks. So it is sort of molding my background. I worked in startups my whole career, increasingly smaller ones. So very, like, moldable, I would say. At minimum, on our side, we’re talking about you guys every day and meeting every day. We do linear and project plans.
Them: Awesome.
Me: And everything.
Them: Awesome. Yeah. And then, like. What would you expect? Our team. And I won’t just make that an open question. I’ll get more specific. Like. I’m assuming our team would be responsible for streaming events from our platform. You would be in collaboration, the team in naming, defining the data dictionary, getting the pages, the buttons, the call to action, the, the, the. The specific events, the things that. We have some PRDs written up, like what we like to do. I’m assuming you would help us refine and streamline some of our PRDs. And get that into, like, whatever, you know, whether it’s a mixed panel or a bi sort of thing that allows us to see that. And you would just incrementally deliver alongside our team on, on those sort of outcomes as you go.
Me: Exactly. Yeah, that’s correct. So we would be basically a client of your back end team in terms of data. So we would need to land product. Data would be one source. Of course, you may have other, you know, a bunch of other sources. That’s what helps us guide like an ETL tool decision and the warehouse decision, of course. Also, we will be considered price. We consider how fast we need to get stuff set up. But, yeah, we would work basically in conjunction with that team. They would give us all the, you know, information about, like, how does those schemas operate and, like, what’s all the core data? So there’d be some discovery there. And then, yeah, in terms of implementing, like, product analytics, like, we do a lot of mixed panel amplitude work, post hog work. This would be setting up like those initial Navy conventions, working with your front end team to set up those events. Like taxonomies. I mean, we, you know, we’re documenting, like, everything along the way, so at any point, if, like, if, if you all move on from us, Like, there’s no, like, you kind of are set up to run at that point. So we, we, we do operate kind of like a full team, I think, where we like to work with folks. If there are folks internally that are, are on it, like sort of doing data stuff and maybe they don’t have the. Guidance, or they’re sort of just like, they’re able to do it and they need to be part of a unit. Like, we would totally welcome them to join. And we’ll, we’ll. We have. We’ll basically work to take tickets and take priorities. From you. So this so it’s both like that, like head of data. Like, what are, what is the strategy of like our procurement? Like, our cavity solutions talk to each other the broader architecture as well as like, okay, implement, implement, drive towards thing. And then usually the way we work and we hope to work as, as your data stack matures is we like to move into more one, we like to move into, like, whatever the trickiest thing is, but also moving into more, like, opportunistic data bangs. What you mentioned about, like, hey, we have this huge MLS listing export. Like, what can we do there? That’s like a great example. Of like, okay, we just need. We need someone just strategize around, like, how we can leverage whether it’s in the product.
Them: Yeah. Yep.
Me: Whether it’s in something else, whether it’s something to inform product decisions. And also we also start to move into, like, more like, more like true analytics, like understanding customer segmentation, customer cohorting, understanding product usage and how that impacts. You know, multi feature usage. How to do a B testing. That’s where we love to live. And that’s like, the true, like, holy grail for, like, you know, building experimentation, culture. And so that’s like, typically, as we mature your data stack, that’s a really great place to utilize us. But we still will set up DBT set up Snowflake, manage data models like orchestration, you know, all that. But I would say, like, that stuff that we typically will, will hand off, you know, to internal engineers to manage. And a lot of it, as soon as we add like, observability, tends to run pretty smoothly.
Them: Yeah. Great.
Me: And so a good place to use this. And at that point, we have a really, really great understanding of, like, the data in your organization. And so a great place that we typically move is the more like found revenue opportunities where can we can leverage analytics to sort of, you know, find more nuggets.
Them: That makes sense. So it sounds like you’re. I think what you described is, is. Is extremely high level adaptability in terms of, like, whatever the right circumstances for us and evolving with us as we go. I mean, our team is fairly good at, like, tagging events and naming them and stuff like that, but I think like in terms of making sure that what, what, what we wrote in terms of our needs from leadership between Sigala and making sure that there are no missed opportunities.
Me: Yeah. Yeah.
Them: In terms of how that gets downstream to like, like, like a mix panel amplitude or that kind of stuff. I think that’s generally an error. I think we would really benefit from, like, we would do a very good job of doing, let’s call it the first pre MVP round of like, doing stuff. And then you coming in. Say, hey, you could if based on the spec it looks like we can or not just a spec based on the product vision.
Me: Yeah.
Them: This is actually what you could do better. And then our team is, you know, our engineering needs. Said that where he probably lacks a bit of experience or error that he wouldn’t want to do is they’re kind of like that reverse ETL process back to, like, a customer I o and joining up that whole journey. So, I mean, that’s a. That’s a really good area for us.
Me: Yeah. Yeah. We do a lot of work also on, like. Yeah. On attribution and on, like, tagging and sending all that information back to a Facebook or back to, you know, whatever, for reverse etl. So, like, everything around Mart basically, like, marketing tech and, like, real tech. Yeah.
Them: Yep. Yep. Okay. Cool. Cool. No, that. That. That’s. That’s. That gives me a really good idea. And so, Sigal, do you have any more questions? Not necessary other than. How soon? And so you mentioned weekly sprints, so we would love to get an off the ground running quite quickly. So do you have capacity to onboard a new client at the moment? And what do you need from us in order to come up with maybe a proposal or. Or at least a structure that can work for us?
Me: Sure. Yeah.
Them: Or we can start just using.
Me: Yeah. So on our side one is like, you know, I would like to just throw all this into, like, some type of, like, Gantt Chart and just show you guys sort of, like, what timing is. On certain deliverables. We talked kind of, like, about a bunch today. So I want to give you guys something visual to see to be like, we need that. We don’t need that. That needs to move up because of X. Right. So. So I’ll do that. I do have it. One thing that would be also helpful. If there are any other timelines or, or milestones that you’re looking to hit that are critical for, you know, the analytics team to be aware of, that would be really helpful. I would love to, you know, sort of get that and, like, sort of put this together into a scope.
Them: Yeah.
Me: In terms of timing, we are kind of slammed this month. But, I mean, you let me know, I’ll show you what the scope is and what we can get done. And you kind of. You kind of tell me what you think. We do have capacity. But this is also where, again, like, we, we tend to move pretty fast, you know, and so there are also a lot of things in data that aren’t that. There’s a lot here that is unique to Breezy. There’s also a lot that isn’t, which is like just choosing the best in class data warehouse, those things like to we can crank out in a week and get a lot of those done. And so there’s ways for us to parallel process a lot of this. So why don’t I I’m going to go ahead and sort of put a little bit of a scope together. It would be great to sort of review that. You know, Async, if you guys. We can also start a joint Slack channel, if that’s easy. And so we can. We can chat there. I’ll loop in some of my team and then maybe we. We can plan to hop on. We can kind of go back and. Forth and hop on again. I think Jimzy would be helpful to talk to also have that the the bi lead or the engineering lead.
Them: Right.
Me: Either in that channel or on that next meeting. That way we. We can say I, and then, yeah, we just can confirm that scope. I can start to put a proposal, you know, around that.
Them: Sounds good.
Me: And then, yeah.
Them: Maybe so go.
Me: N go.
Them: Maybe just two pieces of information. So as Let me just give you two major milestones, which are just.
Me: Sure.
Them: To have in the back of your mind. So one is January 20th. When we will go out with the pr. Announcement, and we’ll start onboarding people to our waitlist.
Me: Yeah, good question. Tell me about the users right now. It’s all sort of like self, like team onboarded. Like is there, is there like a, a self onboarding motion right now?
Them: So.
Me: Or. Yeah.
Them: So we have. So this is all we call them, like they’re a bit friendly beta testers. We’re using James network and influence in the market and people are. Once they hear that James is involved, there’s kind of like waiting in line, just standing by and waiting for. To get. To get it, so. We onboarded 100 users so far and yesterday and today we’re onboarding another hundred. So Jimzy N I can hop on a call and talk to them. They’re all free users. There is no onboarding or monetization built at the moment. We are hiring a PM that will help us do that starting publicly from January.
Me: That’s great.
Them: And then the beginning of next year, January, February would be announcing, getting the rumor out there and getting more and more excitement while we develop this onboarding and monetization. And building. A waitlist.
Me: So james 20th is. Is jane 20th? Like more just like a PR blitz around, like, announcing that this platform exists, but you’re collecting wait list at that point as well.
Them: Yeah. Yes.
Me: Okay, but there. But they’re. But they’re. You’ll still. Will you be onboarding folks from January 20th? Like, immediately after or okay.
Them: We will. But we don’t have. We. We don’t have a fully fleshed out onboarding demonetization. And are up just yet, so as soon as we have it. Somewhere like, let’s say, March, we will start onboarding paying users, which gets us to end of Q1. This is what we want to start onboarding paying users.
Me: Yeah.
Them: And open people up from the waitlist into our just paying experience.
Me: Okay?
Them: And so March, April, this is when we’re actively going to get data that will flow across the entire journey from Acquisition to onboarding to. So this is the time where we need to be ready with everything. But again, the waitlist and people signing up for the waitlist. There’s a lot of information there. The information that we gather from the three or 400 people that will use the app until.
Me: Sure.
Them: We start onboarding paid resource. That’s another part. So that’s Q1 is going to be very hectic on this part. And. Fin is being more power conversation. Yawning at the back. I’ll add another shorter term goal for us like.
Me: Yeah.
Them: As we’re getting into December, probably usage will drop because of people are thinking about holidays and stuff like that. But like, I would really love to get some of our initial, like, dashboards on what D0 to D7 to D30 retention looks like. And then I would really like to understand where the curve starts. Flattening. At what point?
Me: Yeah.
Them: Are we? And then, you know, I’d like to know what our Dauber Mao or DBW would be like, at least on a short term perspective. And then what those funnels of where people are going. Like, you know, we’ve. You’ve heard our products. Yeah. You got comps, you’ve got underbuilt, you’ve got pipeline and you’ve got a note taking. It’s kind of like, what is the behavioral sort of fun.
Me: Yeah. Is the. Do you already have mixed panel or do you already have, like, what is the stack right now? Okay.
Them: It really standard. Stuff. You know, these platforms are so funny. Like, they want your business, so they’re giving you, like, a billion events.
Me: I know. I know.
Them: Like so, like our team, bless their hearts. They’re like, well, if you’ve got all these events and they’re free,
Me: Keep switching here to whatever it’s free.
Them: Yeah. No. So, like, our engineering needs. Like, I had a great.
Me: It’s fair. I’m not hating. It’s just. I just want to know what’s there right now. We do a lot of work with all these characters, by the way, and we don’t. We don’t take money from any of them.
Them: Yeah, you know, he, he, he.
Me: And so it’s really going to just be dependent on, like, what you guys need. But, yeah, we work with all these guys.
Them: Yeah, we. We set up because we’ve got literally a billion events free with mixed panel. We’ve set that up and we’ve also got stats. Dig. So stat SIG has also got, like, I don’t know, an insane amount of events. And I think the same is with customer I O right now. So I think in terms of our. And we’re not wedded to it, I think. And. And we just. We just natively streamed events in because, like, What? What? I think they’ve got a great mentality. Like, let’s just start streaming and let’s just see what’s there.
Me: Okay? Great. Y. Es. That’s good.
Them: And if it needs to change, it needs to change. If it needs to level up, it needs to level up. And then if we need to do more like that. But like, they, they, that’s their kind of philosophy right now, which I think is served us quite well. And, and like I said, we, we wrote some specs. Of which we want to see. And they’re basically writing their own sort of like, scopes and specs and, you know, more technical specs out of our PRDs. And so I think there’s probably a short term. We. We’re probably incorrectly using Data Doc to do some bi stuff.
Me: Right. Right. Right. Right. Okay?
Them: I’m not in love with what we’ve got, but it’s something we’ve got there.
Me: Something to see at least users and yeah, okay.
Them: Yeah. So leveling that up. As a short term, because right now I, I, I think probably our founders not sophisticated to ask, but, like, right now it’s kind of like we really should be asking ourselves. How often are people actually using it versus what they’re telling us they’re actually using? So, yeah. And, you know, one. One more thing. I’m sorry. I’m just running out of time, and I don’t want to miss. Miss this part. The other, more unique to Breezy parts, requires more information. That DMZ cannot share without an NDA.
Me: Yeah. Yeah. Yes. But even again, like. No, no, no problem. Okay, okay.
Them: So maybe if it’s okay with you, James can just shift in NDA and. And we can set up another meeting.
Me: Please.
Them: As soon as you can. Just so Jimzie can dive deeper into the underbuild part. Just so you can feed your analytical brand with what we need.
Me: Okay? Sure.
Them: And. And maybe when we talk about next steps, we can Even though these things are not tied to each other, it would be great to work with the same.
Me: I hear you. Is that it all? Sort of like someone with a data brain should think about all these things.
Them: Exactly.
Me: Yeah, yeah.
Them: So just for you to be able to give us a full like thinking warmness, this type of like you and James and. You don’t need me on this conversation. Diving deeper into the under built parts. And for that, we need NDA.
Me: Okay, okay. So that’s perfect. So if we can. So today, if we can get, if we can get a slack going and yeah, if you guys want to send an NDA over, happy to sign. And then we’ll start cranking out sort of some. We’ll put together a little bit of a proposal that we can all poke at, and then. Yeah, James, I can hop on probably tomorrow or at least later this week. You know, I. I could tell you guys want to. Okay, so let’s do tomorrow. That’ll give me a little bit of time.
Them: More is good. Yeah. Want to do same time.
Me: Let’s do the same thing. Yeah.
Them: Great. Great. Do you want to send an invite over?
Me: Awesome. I’ll send it.
Them: Great, great.
Me: Let’s do the same time. It’ll give me some time to soak into this, and then, yeah, we can sign the NDA today.
Them: Great.
Me: And I could get any. Anything you have, Jimzy, in terms of like, if I can get like, looms of the product or something.
Them: I’ll say no. But.
Me: I can poke at stuff today with. With my team, and then, yeah, it should be a good meeting. Tomorrow.
Them: If you get the NDA signed, I’ll just give you access to the product.
Me: Okay? Okay? Okay? Perfect, guys.
Them: I can also suggest it’s probably really, really, really last minute, but we. In two hours, we have an onboarding section with agents.
Me: Yeah. Add me. I’ll join. I would love to listen in. I mean, I’ll just be a flying a lot.
Them: Al.
Me: Okay? No, this is a great product, by the way. I met some other firms that are doing, but you guys are probably the furthest down the line. And, like, really, really cool. And it’s, it’s an awesome to see the pace, by the way. Like, we work with our wide variety of clients. But we’re all, we’re usually working. Some people are like, slow down. So I’m, I’m really happy to sort of hear how fast you guys want to move. And it’s a huge industry, and the underbuilt part and the MLS data so far in particular, are really, really, you. Know. Awesome. So excited to kind of dive deeper.
Them: Z D N D is signed. Let me know and I’ll. I’ll add Utam to the meeting today. It will just save him hours of onboarding just to hear James see everything like. We’ll just make things so much later. Yeah, I think it’s a smart move. Yep. 100 once the NBA is signed. All integral to the inflate. All right, I’ll send that to you now.
Me: Okay. Okay. All right. Thank you both.
Them: Lieutenant. Awesome.
Me: Yeah, I appreciate it.
Them: Thank you. Great to meet you.
Me: Yeah, great to meet you, too. Talk to you soon.
Them: Chat too.
Me: Bye.