Meeting Title: Brainforge x Breezy: Data Discussion Date: 2025-12-02 Meeting participants: Sigal’s AI Notetaker, James’s AI Notetaker, Uttam Kumaran, sigalbareket, Jimsy
WEBVTT
1 00:00:40.360 ⇒ 00:00:41.520 sigalbareket: Hello?
2 00:00:41.520 ⇒ 00:00:43.400 Uttam Kumaran: Hi! Good morning.
3 00:00:43.400 ⇒ 00:00:47.000 sigalbareket: Who’s your friend on the couch behind him?
4 00:00:47.000 ⇒ 00:00:49.170 Uttam Kumaran: This is Finn.
5 00:00:49.820 ⇒ 00:00:50.890 sigalbareket: Hey, Feng?
6 00:00:51.410 ⇒ 00:00:52.720 Uttam Kumaran: Finn, F-I-N-N.
7 00:00:52.720 ⇒ 00:00:55.280 sigalbareket: Finn, he knows that we’re talking about him, because he’s.
8 00:00:55.280 ⇒ 00:00:59.090 Uttam Kumaran: Yes, he does, yeah, I know. He’s a smart guy, that’s my boss.
9 00:00:59.440 ⇒ 00:01:04.370 sigalbareket: Oh, yeah. Usually, I have two of them running around here, so,
10 00:01:04.580 ⇒ 00:01:07.429 sigalbareket: So it’s super nice… how do I pronounce your name?
11 00:01:07.660 ⇒ 00:01:08.390 Uttam Kumaran: Utam.
12 00:01:08.520 ⇒ 00:01:11.399 sigalbareket: Utan, what is the origin of this name?
13 00:01:11.400 ⇒ 00:01:12.580 Uttam Kumaran: It’s South Indian.
14 00:01:12.850 ⇒ 00:01:15.830 sigalbareket: Amazing, I really like it. This is Jamzy.
15 00:01:15.830 ⇒ 00:01:16.869 Jimsy: Hey, hey, hey.
16 00:01:16.870 ⇒ 00:01:17.610 Uttam Kumaran: Yay!
17 00:01:17.880 ⇒ 00:01:18.399 Uttam Kumaran: Nice to meet you.
18 00:01:18.400 ⇒ 00:01:20.679 Jimsy: Is that a corgi in the background?
19 00:01:20.680 ⇒ 00:01:22.749 Uttam Kumaran: No, this is a much bigger dog.
20 00:01:22.750 ⇒ 00:01:25.949 Jimsy: Oh, okay. I saw, I saw a… sorry, your body…
21 00:01:26.440 ⇒ 00:01:29.000 Jimsy: A large part of it, and it looked like a… like a…
22 00:01:29.000 ⇒ 00:01:36.749 Uttam Kumaran: I think his head, he’s a little bit far, too. I think his head may be as big as a corgi. He’s a German Shepherd, Yellow Lab, great.
23 00:01:36.750 ⇒ 00:01:37.430 Jimsy: Oh, right.
24 00:01:37.740 ⇒ 00:01:38.830 sigalbareket: That’s such a big…
25 00:01:39.040 ⇒ 00:01:40.670 Jimsy: Such a big difference from a cool game.
26 00:01:40.670 ⇒ 00:01:48.930 Uttam Kumaran: Yeah, no, he just… he sits and just makes sure that I keep my words per minute up, you know, that’s what his job is, so…
27 00:01:48.930 ⇒ 00:01:51.690 Jimsy: Awesome, awesome.
28 00:01:51.690 ⇒ 00:02:12.900 sigalbareket: Jim Z is our CPO. I’m running marketing at Breezy, and Breezy is a tool that’s trying to reimagine the set of tools… it’s a service, not a tool, a service that is trying to reimagine the set of tools that real estate agents are using to run their day-to-day and their business.
29 00:02:13.160 ⇒ 00:02:19.459 sigalbareket: We started this company a few, like, earlier this year.
30 00:02:20.810 ⇒ 00:02:21.260 Uttam Kumaran: Congrats.
31 00:02:21.260 ⇒ 00:02:27.629 sigalbareket: Just recently started putting the products in hands of agents to test it, and so far.
32 00:02:27.770 ⇒ 00:02:43.090 sigalbareket: I can say, because I didn’t build the product, DMZ did. I’m really impressed by how accurate DMZ was in predicting the day-to-day needs of an agent and to address them. Of course, we have a lot of work to do.
33 00:02:43.260 ⇒ 00:02:52.580 sigalbareket: But so far, in this, like, very initial beta phases that we all are, we are quite happy with where we are at, and there is, of course, a lot of work ahead of us.
34 00:02:54.330 ⇒ 00:03:09.119 sigalbareket: As we start building the infrastructure of our very, very young and new company, and knowing that every company that is being built these days probably needs to challenge whatever playbook it used
35 00:03:09.650 ⇒ 00:03:13.990 sigalbareket: in terms of setting up and tools and data flow.
36 00:03:14.100 ⇒ 00:03:23.500 sigalbareket: Jim Z and I are looking for some support and help with everything that has to do with the way the data is structured.
37 00:03:23.630 ⇒ 00:03:30.869 sigalbareket: At, at Breezy, and we have two perspectives in which we… or two areas in which we need help.
38 00:03:31.320 ⇒ 00:03:34.630 sigalbareket: One is more straightforward.
39 00:03:34.900 ⇒ 00:03:42.670 sigalbareket: We’re starting a company from scratch, and we’ve all been into companies that fucked up.
40 00:03:42.830 ⇒ 00:03:54.730 sigalbareket: the initial setting and then spend years fixing it, we would love to make sure that we are designing our data tag stack and data flow from get-go in the right way. Sure.
41 00:03:55.000 ⇒ 00:04:05.789 sigalbareket: And Jim Zi 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,
42 00:04:05.960 ⇒ 00:04:11.860 sigalbareket: Lead data scientist, or just a consultant, or someone that will help us set up our,
43 00:04:11.860 ⇒ 00:04:12.440 Uttam Kumaran: Totally.
44 00:04:12.440 ⇒ 00:04:24.539 sigalbareket: data infrastructure, right from the get-go, serving both product and marketing needs, this is very important, and they’re very much at the moment. So that’s one big, more of a…
45 00:04:24.740 ⇒ 00:04:27.120 sigalbareket: come, I’m sure they’ve seen these things before.
46 00:04:27.470 ⇒ 00:04:36.880 sigalbareket: Yes. Secondly, and this, again, where GMZ is… will get more into the details, one of our claims to fame as a company
47 00:04:37.000 ⇒ 00:04:42.529 sigalbareket: Is the accuracy of data that we provide, real estate agents
48 00:04:42.830 ⇒ 00:04:50.609 sigalbareket: as they pull data from the app, mainly comms data, but not only, again, we’ll talk more about that.
49 00:04:50.870 ⇒ 00:04:56.969 sigalbareket: And as we started talking to Clint, who introduced us, And we were thinking…
50 00:04:57.290 ⇒ 00:05:13.929 sigalbareket: Maybe we should paint a bit of a broader picture of what our needs are in terms of data, and talk a bit about how we see data accuracy on, regarding real estate as one of our claims to fame.
51 00:05:13.930 ⇒ 00:05:14.290 Uttam Kumaran: Yeah.
52 00:05:14.290 ⇒ 00:05:26.439 sigalbareket: maybe ask for help on this one. These are two separate things. We don’t even need to connect them in the way we work together, but if we are talking about our full set of data needs.
53 00:05:26.660 ⇒ 00:05:40.030 sigalbareket: 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?
54 00:05:40.360 ⇒ 00:05:45.110 sigalbareket: Sure. So this is who we are. I would love… we would love to hear more about…
55 00:05:45.110 ⇒ 00:05:45.730 Uttam Kumaran: Yeah.
56 00:05:45.730 ⇒ 00:05:49.969 sigalbareket: Run your business, and then… and then get into the details of what we actually need.
57 00:05:50.320 ⇒ 00:05:54.990 Uttam Kumaran: Yeah, sure, definitely, and, you know, I appreciate it. Yeah, Clint and I have been friends for… for…
58 00:05:55.040 ⇒ 00:06:12.130 Uttam Kumaran: a few years now. We’re starting to use this product for a few clients, and it’s, it’s finally showing some… 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.
59 00:06:12.130 ⇒ 00:06:15.769 Uttam Kumaran: You know, kind of, like, everywhere through data, both
60 00:06:15.820 ⇒ 00:06:22.760 Uttam Kumaran: at the BI level, at the analytics engineering level, at the data engineering level, led data teams, and led… previously led product at a data startup.
61 00:06:22.870 ⇒ 00:06:42.639 Uttam Kumaran: Left that company. I was living in New York for about, 5 years, moved here to Austin, where I’m living now, about 3 years ago, and started this company about, two and a half years ago. So Brainforge is a data and AI consultancy. We’re a completely bootstrap business. Most of our team is engineers. We’re about
62 00:06:43.230 ⇒ 00:06:46.389 Uttam Kumaran: Maybe 16 people now, this week, so we…
63 00:06:46.390 ⇒ 00:06:46.970 sigalbareket: Stop!
64 00:06:46.970 ⇒ 00:06:52.580 Uttam Kumaran: We have quite a lot of people, which is not necessarily a good thing, but in a services business.
65 00:06:52.580 ⇒ 00:06:53.230 sigalbareket: I know.
66 00:06:53.230 ⇒ 00:07:08.989 Uttam Kumaran: This is a headcount business, so, but we, like you guys, are thinking about running a consultancy and running an agency in a lot of different ways, not only in the level of, delivery for our clients, but also in our pace.
67 00:07:08.990 ⇒ 00:07:27.639 Uttam Kumaran: And in sort of the way we run our business. So, as I mentioned, like, I’ve done a lot in data. Actually, my, you know, my first gig in data was at WeWork, so I have 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.
68 00:07:27.640 ⇒ 00:07:31.250 Uttam Kumaran: Very, very familiar with, you know,
69 00:07:31.270 ⇒ 00:07:42.249 Uttam Kumaran: all the work that goes… that goes to support, residential real estate. Of course, I’m just assuming that’s sort of the focus here, but, generally familiar with… with that industry.
70 00:07:42.820 ⇒ 00:08:02.150 Uttam Kumaran: 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 whatever…
71 00:08:02.250 ⇒ 00:08:15.890 Uttam Kumaran: how much of that can get fulfilled by Brainforge, how much of that can get fulfilled 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.
72 00:08:15.940 ⇒ 00:08:35.759 Uttam Kumaran: we’re not, like, sort of… we don’t do a lot of, like, staff augmentation in the sense of, like, hey, just go sped up my snowflake and then, like, head out. Most of our stuff we try to do is partnering with the organization to understand, like, 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.
73 00:08:35.760 ⇒ 00:08:53.690 Uttam Kumaran: So our crew, you know, is a mix of data engineers, data architects, AI engineers, and then me and my business partner are sort of lead strategists. So we typically operate a little bit on a pod model, so if we were to work together, you’d get, like, us and maybe one or two people, depending on sort of the scope.
74 00:08:53.770 ⇒ 00:09:13.010 Uttam Kumaran: But yeah, I mean, of course, 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’d like to express is that we’re sort of, like, full-stack data. We don’t just do one piece, we can support on the more strategic side, as well as, you know, go all the way to
75 00:09:13.050 ⇒ 00:09:16.050 Uttam Kumaran: data engineering and data science.
76 00:09:16.350 ⇒ 00:09:27.030 Uttam Kumaran: But yeah, I mean, we’re totally completely open to any questions, but would 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
77 00:09:27.180 ⇒ 00:09:30.090 Uttam Kumaran: Again, like, we… we’re…
78 00:09:30.360 ⇒ 00:09:44.580 Uttam Kumaran: I like to lead with, like, where we have superpowers and where we can be the most effective for y’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.
79 00:09:44.580 ⇒ 00:09:55.180 Uttam Kumaran: And if there is other work that we can’t do, I’m sure I… I’m a couple phone calls from some people or tools that… that can be of help. So, so yeah, that’s a little bit about us.
80 00:09:58.490 ⇒ 00:10:05.289 Jimsy: Awesome. That sounds great. That sounds great. Do you… do you think it might be worth us talking a bit around what our product is and what we’re…
81 00:10:05.290 ⇒ 00:10:05.810 Uttam Kumaran: Please.
82 00:10:05.810 ⇒ 00:10:07.549 Jimsy: Maybe giving you a bit of…
83 00:10:07.880 ⇒ 00:10:14.679 Jimsy: color into what we’re looking for. I think Seagal did a good tee-up. Like, ultimately speaking, we’re,
84 00:10:14.870 ⇒ 00:10:19.409 Jimsy: Breezy is founded by, our founder, James Harris, who,
85 00:10:19.510 ⇒ 00:10:22.359 Jimsy: Is a real estate agent himself,
86 00:10:22.360 ⇒ 00:10:42.040 Jimsy: 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 he is… I’ve never met someone who’s been as obsessed with real estate as he has.
87 00:10:42.040 ⇒ 00:10:51.200 Jimsy: And, the real estate prop tech space for real estate agents is a fascinating vertical, because,
88 00:10:51.640 ⇒ 00:11:03.679 Jimsy: I mean, 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. Like, that vertical for real estate agents has been grossly underserved.
89 00:11:03.840 ⇒ 00:11:11.730 Jimsy: as a target audience, the tech and the tools that they use to be productive in their work,
90 00:11:11.880 ⇒ 00:11:19.210 Jimsy: is… I mean, the bar is super low, and so… when James created Breezy, The number one…
91 00:11:19.480 ⇒ 00:11:26.149 Jimsy: almost like Northstar and KPI that we’re looking to solve is to save agents time in everything that they’re doing on a…
92 00:11:26.450 ⇒ 00:11:32.550 Jimsy: on a, you know, exponential level, not just an incremental level. And so,
93 00:11:33.030 ⇒ 00:11:52.990 Jimsy: 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, like, a whole set of, like, 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
94 00:11:53.050 ⇒ 00:12:06.620 Jimsy: your, you know, people, build a reputation, level up as an agent, and get very, very sophisticated in the way you manage and organize people and organize yourself. And so, what we focused on at Breezy is essentially,
95 00:12:06.670 ⇒ 00:12:19.949 Jimsy: 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…
96 00:12:20.060 ⇒ 00:12:27.699 Jimsy: The big elements of what agents do on a day-to-day basis is manage people, know the market.
97 00:12:28.550 ⇒ 00:12:31.269 Jimsy: And… spot opportunities.
98 00:12:31.630 ⇒ 00:12:32.680 Jimsy: And…
99 00:12:32.930 ⇒ 00:12:47.919 Jimsy: 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 a way that makes them stand out from the crowd. And so, when we look at what those…
100 00:12:48.470 ⇒ 00:13:00.450 Jimsy: 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…
101 00:13:00.760 ⇒ 00:13:01.990 Jimsy: Manage yourself.
102 00:13:02.490 ⇒ 00:13:19.549 Jimsy: 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 we,
103 00:13:19.610 ⇒ 00:13:28.350 Jimsy: when we talk about valiant properties, agents have to do things called comps every day. When they comp a property, you find the target property of someone’s,
104 00:13:28.450 ⇒ 00:13:36.580 Jimsy: Home address, or you’re looking to buy, and then an agent has to get a very accurate read based on market data.
105 00:13:36.620 ⇒ 00:13:48.810 Jimsy: As to what that… 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, there needs to be proof behind it, there needs to be branding behind it, marketing behind it.
106 00:13:48.810 ⇒ 00:14:01.249 Jimsy: 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.
107 00:14:01.250 ⇒ 00:14:11.429 Uttam Kumaran: And what part of the… sorry, 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 it’s… is it all residential, or is that even part of the equation?
108 00:14:11.750 ⇒ 00:14:30.400 Jimsy: All 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, your cookie cutters generally tend to be a lot easier to comp, so an agent might be only spending an hour doing that.
109 00:14:30.400 ⇒ 00:14:43.159 Jimsy: 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.
110 00:14:43.160 ⇒ 00:14:43.570 Uttam Kumaran: the ground.
111 00:14:43.670 ⇒ 00:14:51.609 Jimsy: it does it in seconds. Like, it’s like Canva, or a portion of Canva, meeting a part of Zillow. It had a baby.
112 00:14:51.610 ⇒ 00:14:52.290 Uttam Kumaran: Yeah.
113 00:14:52.290 ⇒ 00:14:59.000 Jimsy: You create a, you know, a report, and that’s you as an agent, and you, instead of you having to get a marketing person or.
114 00:14:59.000 ⇒ 00:15:01.760 Uttam Kumaran: Yeah, to put your logo in the corner. Yeah, yeah, yeah.
115 00:15:01.760 ⇒ 00:15:11.140 Jimsy: It’s just meant to be easy, basically. So, and another thing we do, which is proprietary to us, is something called underbuilt Radar. So, again.
116 00:15:11.300 ⇒ 00:15:12.730 Jimsy: Type in an address.
117 00:15:12.840 ⇒ 00:15:29.100 Jimsy: And then, we give you the build potential of that property, based on how big you can extend, build larger, build bigger, and increase the potential of that property. A lot of people, if they want to know, hey, I wonder how much, you know, I’m on a, I don’t know, 10,000 foot lot.
118 00:15:29.240 ⇒ 00:15:41.930 Jimsy: 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.
119 00:15:42.010 ⇒ 00:15:58.499 Jimsy: We give you that information in seconds by just searching the address. And so, that’s a unique part of our property, sorry, product that we’re doing right now. We have a real estate agent-focused and real estate agent-prompted-focused AI note-taker.
120 00:15:58.500 ⇒ 00:16:04.000 Jimsy: Which, it doesn’t matter whether I’m an agent doing inspections, or I’m in a listing meeting, or I’m trying to get new…
121 00:16:04.280 ⇒ 00:16:06.929 Jimsy: Business from a client.
122 00:16:07.050 ⇒ 00:16:24.529 Jimsy: 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.
123 00:16:24.770 ⇒ 00:16:25.700 Jimsy: again.
124 00:16:25.880 ⇒ 00:16:37.980 Jimsy: was in the vertical of real estate. Yeah. And that’s what Breezy is. We’re not a CRM, we don’t like to be called a CRM, yet I think we cannibalize and eat into elements that agents wish their CRM did.
125 00:16:38.050 ⇒ 00:16:49.479 Jimsy: And so that’s what we’re trying to do. We’ve sort of burned the boat in sort 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.
126 00:16:50.300 ⇒ 00:16:56.990 Jimsy: your, escrows under contracts to completion, do that in a way that, and essentially…
127 00:16:56.990 ⇒ 00:17:04.480 Uttam Kumaran: It’s, like, the operating system for this agent. So there’s probably some elements of, like, Monday Airtable, probably some elements of, like, a Clay, like…
128 00:17:05.640 ⇒ 00:17:13.040 Jimsy: 100%. You’re spot on, except I would say it’s those products, but probably simplified down to…
129 00:17:13.040 ⇒ 00:17:14.089 Uttam Kumaran: Just the, yeah.
130 00:17:14.099 ⇒ 00:17:32.029 Jimsy: degree, yeah, because we found the type of person we… and I’ll be careful using this phrase, but I think there’s a lot of truth in it. A lot of the really successful agents, or the ones that make this their career, are pretty ADHD in the way that they are, like.
131 00:17:32.219 ⇒ 00:17:37.269 Jimsy: Doing all these activities, and so they’re not gonna spend hours doing one thing.
132 00:17:37.339 ⇒ 00:17:38.439 Uttam Kumaran: So actually.
133 00:17:38.499 ⇒ 00:17:45.129 Jimsy: I think Seagal was extremely kind to me, and I would probably say it’s not my…
134 00:17:45.609 ⇒ 00:17:56.669 Jimsy: This didn’t come from me. This came from James, because he’s the one who said, I know what agents want, but obviously, James is our founder, he doesn’t build products like Sigal and I do.
135 00:17:56.669 ⇒ 00:18:07.059 Jimsy: we’ve been… 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
136 00:18:07.059 ⇒ 00:18:17.489 Jimsy: I have not put my own opinion into this. I’ve… I’ve only put the… that persona… that wide persona of agents, kind of, like, footprint into this. And so, I think…
137 00:18:17.829 ⇒ 00:18:27.419 Jimsy: And I echo everything Sigal says, like, just because we found success, like, I, you know, I’ve exited twice before in companies, this is my fourth time doing a startup.
138 00:18:27.509 ⇒ 00:18:38.219 Jimsy: I’m humbled enough to know that 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 did it once, with a coaching startup, and…
139 00:18:38.219 ⇒ 00:18:57.599 Jimsy: 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 had a bunch of shit, and that’s the one I actually got. So, it’s like, again, I think we’re very, like.
140 00:18:57.989 ⇒ 00:19:03.889 Jimsy: out there looking for people who can actually help us get those things sorted out. And a last thing I’ll add is,
141 00:19:04.239 ⇒ 00:19:18.389 Jimsy: 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 who’s a slash…
142 00:19:18.809 ⇒ 00:19:29.159 Jimsy: BI, business analytics slash QA analyst person, plus an engineering lead, who would be very happy doing a lot of the heavy lifting.
143 00:19:29.219 ⇒ 00:19:31.169 Uttam Kumaran: But at the end of the day, I…
144 00:19:31.169 ⇒ 00:19:33.779 Jimsy: I think what Sagal says is really true, like.
145 00:19:33.979 ⇒ 00:19:39.759 Jimsy: The data for us is not just what’s in the product, it’s the whole lifecycle journey between when an agent looks at an ad.
146 00:19:39.779 ⇒ 00:19:57.539 Jimsy: 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, StatStick, Mixpanel, I don’t know, whatever BI tool, you know, we… Blaze, whatever, you know, Braze, sorry, Brace SQL, whatever it is, you know, we, we, we wanna…
147 00:19:57.539 ⇒ 00:20:09.069 Jimsy: 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,
148 00:20:09.070 ⇒ 00:20:09.410 Uttam Kumaran: Great.
149 00:20:09.410 ⇒ 00:20:13.959 Jimsy: some data journey stuff. And then the second part, I’d say,
150 00:20:14.750 ⇒ 00:20:20.400 Jimsy: I talked about that build potential, piece. What we do is we have been…
151 00:20:20.600 ⇒ 00:20:27.000 Jimsy: Using AI and using architects, essentially, to glean through building code across the United States.
152 00:20:27.000 ⇒ 00:20:27.640 Uttam Kumaran: Pretty.
153 00:20:27.640 ⇒ 00:20:30.349 Jimsy: If you just imagine what builder code is, it’s like…
154 00:20:30.630 ⇒ 00:20:32.419 Jimsy: Legal documents that are scattered around the.
155 00:20:32.420 ⇒ 00:20:33.050 Uttam Kumaran: Yeah.
156 00:20:33.270 ⇒ 00:20:37.799 Jimsy: Basically, and so what we’ve been doing is we’ve been passing that through, putting it through…
157 00:20:37.800 ⇒ 00:20:48.609 Uttam Kumaran: And has this 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, or are you focused all over the U.S. right now?
158 00:20:48.610 ⇒ 00:20:56.500 Jimsy: We’re in 500 cities right now. Okay. So, we will be in 2,600 cities in April.
159 00:20:56.710 ⇒ 00:20:59.729 Jimsy: We would love to…
160 00:21:00.510 ⇒ 00:21:10.829 Jimsy: ask someone to say, how might we get there faster, and how might we get even faster through more intelligent usage of AI and processes there.
161 00:21:10.940 ⇒ 00:21:16.859 Jimsy: Ultimately speaking, it’s… URL to document.
162 00:21:17.530 ⇒ 00:21:18.050 Uttam Kumaran: Great.
163 00:21:18.330 ⇒ 00:21:22.129 Jimsy: Let’s call it passing… OCR, some type of…
164 00:21:22.130 ⇒ 00:21:23.239 Uttam Kumaran: Extraction, yeah.
165 00:21:23.240 ⇒ 00:21:34.739 Jimsy: extraction… extraction is not always necessarily… I don’t know, I… like, we’re looking for someone to maybe do a bit of an audit and say, like, hey… I don’t think AI is particularly great at extraction.
166 00:21:34.740 ⇒ 00:21:40.540 Uttam Kumaran: Yeah, you need to consider layering a few on, and yeah. Yeah, I hear you, that’s what I…
167 00:21:40.540 ⇒ 00:21:41.790 Jimsy: Right. Yeah, yeah.
168 00:21:41.790 ⇒ 00:21:42.140 Uttam Kumaran: Yeah.
169 00:21:42.140 ⇒ 00:21:49.109 Jimsy: But the stage before… after that, once you have a clean 100-page PDF document, how do you best…
170 00:21:49.110 ⇒ 00:22:03.889 Jimsy: 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,
171 00:22:04.550 ⇒ 00:22:13.339 Jimsy: that as a workpiece, we’re probably… we could level up, I don’t know. That’s an open question we have. And then the last thing I would say is that we…
172 00:22:13.560 ⇒ 00:22:28.749 Jimsy: we have an obsession over data accuracy. Now, I think, based on our initial beta group testing, actually, people have not highlighted many issues with data accuracy. However, we… our founder, James, has
173 00:22:29.130 ⇒ 00:22:35.770 Jimsy: An obsessive level of wanting to be at the best at property data.
174 00:22:35.770 ⇒ 00:22:52.720 Jimsy: In the market for real estate agents. And so, we are… 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.
175 00:22:52.810 ⇒ 00:22:56.750 Jimsy: And so, Databricks, Snowflake, set that up.
176 00:22:56.870 ⇒ 00:23:02.019 Jimsy: become your own API, get that done. So we have, let’s call it…
177 00:23:02.230 ⇒ 00:23:18.970 Jimsy: like, a lot of juicy, let’s call it data-led projects right now, and so we could do it ourselves, and we could… we’re a startup, so we might have to be leaner, it might take longer, or we want to put on the table options of, hey, if we were to give this to someone else.
178 00:23:19.010 ⇒ 00:23:35.020 Jimsy: How might someone else… Yeah, what can that look like? How can that be accelerated? That, I’d say, is more of a project, in terms of an implementation. So, I’ll pause there. That’s the lay of the land of what we’re looking at at Breezy. I think it’s really exciting. We have a lot of, like, you know, momentum behind our sales, right?
179 00:23:35.020 ⇒ 00:23:45.130 Uttam Kumaran: No, 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…
180 00:23:45.250 ⇒ 00:23:49.169 Uttam Kumaran: Data into agents to basically figure something out, so…
181 00:23:49.320 ⇒ 00:23:53.610 sigalbareket: And that’s a company that… it’s actually a company called Underbuilt that we acquired.
182 00:23:53.610 ⇒ 00:23:54.710 Uttam Kumaran: Oh, okay.
183 00:23:54.710 ⇒ 00:24:07.320 sigalbareket: It’s a unique value… you’re spot on, like, that’s a really unique value proposition. And the reason why James is so obsessed about data is because agents are… like, they will always move to the next
184 00:24:07.430 ⇒ 00:24:19.099 sigalbareket: shiny tool, unless we can really gain their trust. And if we can gain their trust in these little things, of like, hey, this is actually spot on, and it gave me an advantage with a client.
185 00:24:19.210 ⇒ 00:24:23.489 sigalbareket: This is where we can actually lock them in with Brizzy for a long time.
186 00:24:23.910 ⇒ 00:24:24.460 Uttam Kumaran: Yeah.
187 00:24:25.500 ⇒ 00:24:43.100 Uttam Kumaran: 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 MixedPanel, like.
188 00:24:43.310 ⇒ 00:25:00.859 Uttam Kumaran: 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.
189 00:25:00.860 ⇒ 00:25:19.030 Uttam Kumaran: 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…
190 00:25:19.060 ⇒ 00:25:27.939 Uttam Kumaran: 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.
191 00:25:28.010 ⇒ 00:25:35.840 Uttam Kumaran: 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…
192 00:25:35.950 ⇒ 00:25:51.060 Uttam Kumaran: understand of, 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 6- or 9-month thing. Like, we sort of move fast as, like, you guys want to move.
193 00:25:51.180 ⇒ 00:26:04.180 Uttam Kumaran: And we… when we walk into a client, we often help procure 5 to 10 pieces of infrastructure. We have relationships with, kind of, all these folks, and we’re driving towards, like, that first key insight as fast as possible.
194 00:26:04.180 ⇒ 00:26:11.340 Uttam Kumaran: Whether it’s an understanding of, like, your sign-up funnel, understanding of, like, how to do pricing, understanding of, like, key feature usage, like.
195 00:26:11.340 ⇒ 00:26:29.830 Uttam Kumaran: combined feature, like, you know, whatever that is. So… and for us, it’s actually less about, like, okay, we’re gonna answer this one question, 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…
196 00:26:29.970 ⇒ 00:26:34.370 Uttam Kumaran: this is the answer, like, I actually know, and I can get you that answer in, like, a week.
197 00:26:34.530 ⇒ 00:26:37.459 Uttam Kumaran: You know, so that’s, like, sort of everything on the…
198 00:26:37.640 ⇒ 00:26:39.899 Uttam Kumaran: reporting analytics side. Is that, like…
199 00:26:40.270 ⇒ 00:26:44.480 Uttam Kumaran: Roughly, like, sort of, like, what the ask is on that.
200 00:26:44.730 ⇒ 00:26:46.119 Uttam Kumaran: For the side of the house.
201 00:26:46.280 ⇒ 00:26:52.300 Jimsy: Yeah, 100%. Yeah, and you… could you, Utom, what… what is your usual… Sort of, like,
202 00:26:52.620 ⇒ 00:26:59.479 Jimsy: How do your people work with, typically, in-house or startup, you know, folks? Like, what does the working engagement look like?
203 00:26:59.480 ⇒ 00:27:09.359 Uttam Kumaran: Yeah, yeah. So, we… our whole business, even our internal teams, we run on one-week sprints. So, at minimum, that’s how we work.
204 00:27:09.360 ⇒ 00:27:17.590 Uttam Kumaran: We run… we’ll run at least internal daily stand-ups for our folks that are on your project. Additionally, if there are folks that
205 00:27:17.590 ⇒ 00:27:36.779 Uttam Kumaran: that you have, that you’re like, hey, you’re on the data team now, we’ll loop those people in and run those stand-ups with those folks. We at least will, on a weekly basis, we’ll have, like, at least one meeting on the calendar that we, like, all meet, and we do demos, and we sort of do, like, a weekly thing, and then we sort of will do, like, a more broader, like, monthly project review. So that’s more of, like.
206 00:27:36.780 ⇒ 00:27:51.550 Uttam Kumaran: our agile stuff. In terms of how we’re… 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. So how can we still drive to get you insights?
207 00:27:51.550 ⇒ 00:28:06.880 Uttam Kumaran: you know, or still answer to 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?
208 00:28:07.160 ⇒ 00:28:26.490 Uttam Kumaran: 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’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.
209 00:28:26.490 ⇒ 00:28:33.590 Uttam Kumaran: So, it is sort of molding. My background, I’ve worked in startups, my whole career, increasingly smaller ones, so…
210 00:28:33.590 ⇒ 00:28:42.619 Uttam Kumaran: very, like, moldable. I would say, at minimum, on our side, we’re talking about you guys every day and meeting every day, and we do linear and project plans and everything, yeah.
211 00:28:42.770 ⇒ 00:28:44.680 Jimsy: Awesome, yeah, and then, like,
212 00:28:45.690 ⇒ 00:28:56.909 Jimsy: 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.
213 00:28:56.980 ⇒ 00:29:03.200 Uttam Kumaran: You would be in collaboration, the team, in naming, defining the data dictionary, getting the pages, the buttons…
214 00:29:03.200 ⇒ 00:29:14.430 Jimsy: Exactly. The specific events, the things that… we have some PRDs written up of, like, what we like to do. I’m assuming you would help us refine and…
215 00:29:14.510 ⇒ 00:29:30.180 Jimsy: 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 those sort of outcomes as you go?
216 00:29:30.590 ⇒ 00:29:49.990 Uttam Kumaran: Yeah, that’s correct. So, we would be basically a client of your backend 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 a warehouse decision. Of course, also, we consider price, we’d consider how fast we need to get stuff set up.
217 00:29:50.040 ⇒ 00:29:53.230 Uttam Kumaran: But yeah, we would work basically in conjunction with that team.
218 00:29:53.300 ⇒ 00:30:06.839 Uttam Kumaran: 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.
219 00:30:06.840 ⇒ 00:30:13.750 Uttam Kumaran: This would be setting up, like, those initial naming conventions, working with your front-end team to set up those events, like, taxonomies.
220 00:30:13.750 ⇒ 00:30:26.779 Uttam Kumaran: And then we, you know, we’re documenting, like, everything along the way, so at any point, if, like, if y’all move on from us, like, there’s no… like, you kind of are set up to run at that point, so…
221 00:30:26.780 ⇒ 00:30:34.000 Uttam Kumaran: 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…
222 00:30:34.320 ⇒ 00:30:44.449 Uttam Kumaran: are on the, like, sort of doing data stuff, but maybe they don’t have the guidance, or they’re sort of, 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
223 00:30:44.450 ⇒ 00:31:00.370 Uttam Kumaran: we’ll… we’ll… we have… we’ll basically work to take tickets and take priorities from you. Yeah. So it’s both, like, that, like, head of data, like, okay, what are… what is the strategy of, like, our procurement, like, our… of how do these solutions talk to each other, the broader architecture, as well as, like.
224 00:31:00.370 ⇒ 00:31:12.589 Uttam Kumaran: okay, implement, implement, drive towards thing, and then usually the way we work, and we hope to work, as your data stack matures, is we like to move into more… one, we like to move into, like, whatever the trickiest
225 00:31:12.590 ⇒ 00:31:23.850 Uttam Kumaran: thing is, but also moving into more, like, opportunistic data things. 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.
226 00:31:23.860 ⇒ 00:31:32.899 Uttam Kumaran: Okay, we just need… we need someone to just strategize around, like, how we can leverage this, whether it’s in the product, whether it’s in something else, whether it’s something to inform product decisions.
227 00:31:33.000 ⇒ 00:31:36.449 Uttam Kumaran: And also, we also start to move into, like, more…
228 00:31:36.890 ⇒ 00:31:50.550 Uttam Kumaran: 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…
229 00:31:50.570 ⇒ 00:31:57.860 Uttam Kumaran: like, holy grail for, like, you know, building experimentation culture. And so that’s, like, typically as we mature your data stack.
230 00:31:58.170 ⇒ 00:32:01.060 Uttam Kumaran: That’s a really great place to utilize us, but…
231 00:32:01.250 ⇒ 00:32:08.020 Uttam Kumaran: we still will set up dbt, set up Snowflake, manage data models, like, orchestration, you know, all that, but…
232 00:32:08.020 ⇒ 00:32:09.379 Jimsy: Right. I would say.
233 00:32:09.430 ⇒ 00:32:12.609 Uttam Kumaran: Like, that’s stuff that we typically will hand off.
234 00:32:12.620 ⇒ 00:32:29.750 Uttam Kumaran: You know, to internal engineers to manage, and a lot of it, as soon as we add, like, observability, tends to run pretty smoothly, 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.
235 00:32:30.030 ⇒ 00:32:34.420 Uttam Kumaran: Found revenue opportunities where we can leverage analytics to sort of.
236 00:32:34.590 ⇒ 00:32:36.429 Jimsy: Yep. You know, find more nuggets.
237 00:32:36.870 ⇒ 00:32:55.229 Jimsy: Yep, yep, that makes sense. So it sounds like you’re… I think what you described is an 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,
238 00:32:55.410 ⇒ 00:33:08.379 Jimsy: in terms of making sure that what we wrote, in terms of our needs from leadership between Sega Online, making sure that there are no missed opportunities, in terms of how that gets downstream to…
239 00:33:09.420 ⇒ 00:33:28.620 Jimsy: like a mixed panel amplitude, all that kind of stuff, I think that’s generally an area 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 saying, hey, you could, if, based on the spec, it looks like we can… or not just the spec, based on the product vision.
240 00:33:28.620 ⇒ 00:33:39.970 Jimsy: this is actually what you could do better. And then our team has, you know, our engineering needs, said that where he probably lacks a bit of experience or area that he wouldn’t want to do is they’re kind of like that reverse ETL process back to, like, a customer I.O.
241 00:33:39.970 ⇒ 00:33:55.940 Uttam Kumaran: joining up that whole journey, so I think that’s a really good area. So 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.
242 00:33:56.250 ⇒ 00:34:00.959 Uttam Kumaran: So, like, everything around mar- basically, like, marketing tech and, like, growth tech, yeah.
243 00:34:01.590 ⇒ 00:34:09.389 Jimsy: Yep. Yep. Okay, cool, cool. No, that gives me a really good idea, and so, Sigal, do you have any more questions?
244 00:34:15.179 ⇒ 00:34:30.819 sigalbareket: Not necessarily, other than, how soon, and so you mentioned weekly sprints, so, we would love to, get off the ground running quite quickly, so do you have capacity to onboard,
245 00:34:30.929 ⇒ 00:34:45.019 sigalbareket: A new client at the moment, and what do you need from us in order to come up with maybe a proposal or at least a structure that can work for us?
246 00:34:45.469 ⇒ 00:34:46.059 Uttam Kumaran: Yeah.
247 00:34:46.060 ⇒ 00:34:48.150 sigalbareket: That we can start just using.
248 00:34:48.889 ⇒ 00:34:55.259 Uttam Kumaran: Yeah, so on our side, one is, like, you know, I would like to just throw all this into, like, some type of…
249 00:34:55.509 ⇒ 00:35:09.619 Uttam Kumaran: like, Gantt chart, and just show you guys, sort of, like, what timing is on certain deliverables. We’ve talked kind of, like, about a bunch today, so I want to give you guys something visual to see to be like, okay, we need that, we don’t need that, that needs to move up because of X.
250 00:35:09.619 ⇒ 00:35:20.619 Uttam Kumaran: Right, so I’ll do that. I do have… one thing that would be also helpful, if there are any other timelines or milestones that you’re looking to hit that are critical for
251 00:35:20.629 ⇒ 00:35:33.789 Uttam Kumaran: 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. In terms of timing, we are kind of slammed this month.
252 00:35:33.889 ⇒ 00:35:34.729 Uttam Kumaran: But…
253 00:35:35.159 ⇒ 00:35:50.659 Uttam Kumaran: I mean, you let me know. I’ll… I’ll show you what the scope is and what we can get done, and you kinda… 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.
254 00:35:50.689 ⇒ 00:36:08.379 Uttam Kumaran: You know, and so there are also a lot of things in data that are… 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, 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.
255 00:36:08.379 ⇒ 00:36:27.749 Uttam Kumaran: So why don’t I… I’m gonna 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 plan to go back and forth and hop on again if…
256 00:36:27.749 ⇒ 00:36:31.619 Uttam Kumaran: I think Jim Z, it would be helpful to talk to… to also have that,
257 00:36:31.619 ⇒ 00:36:44.059 Uttam Kumaran: the BI lead or the engineering lead, either in that channel or on that next meeting, that way we can say hi. And then, yeah, if we just can confirm that scope, I can start to put a proposal, you know, around that.
258 00:36:44.060 ⇒ 00:36:44.830 Jimsy: Sounds good.
259 00:36:45.260 ⇒ 00:36:46.440 sigalbareket: And then…
260 00:36:46.440 ⇒ 00:36:46.910 Uttam Kumaran: Yeah.
261 00:36:46.910 ⇒ 00:36:48.040 sigalbareket: Sure, go.
262 00:36:48.040 ⇒ 00:36:48.800 Uttam Kumaran: There we go, yard.
263 00:36:49.350 ⇒ 00:37:02.320 sigalbareket: Maybe just two pieces of information. So, as… let me just give you two major milestones, which are, just to have in the back of your mind. So, one is January 20th.
264 00:37:02.610 ⇒ 00:37:14.159 sigalbareket: when we will, go out with the PR, announcement, and we’ll start onboarding people to our waitlist.
265 00:37:14.610 ⇒ 00:37:15.150 sigalbareket: See?
266 00:37:15.150 ⇒ 00:37:19.380 Uttam Kumaran: And also, maybe a good question, tell me about the users right now. It’s all sort of,
267 00:37:19.530 ⇒ 00:37:26.409 Uttam Kumaran: like, self, like, team onboarded? Like, is there… is there, like, a… a self-onboarding motion right now, or…
268 00:37:26.410 ⇒ 00:37:42.839 sigalbareket: So we have, so this is all, we call them, like, they’re 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.
269 00:37:42.840 ⇒ 00:37:53.770 sigalbareket: waiting in line, just standing in line waiting for… to get… to get in. That’s right. We onboarded 100 users so far, and yesterday and today, we’re onboarding another 100.
270 00:37:53.790 ⇒ 00:38:06.619 sigalbareket: So Jim Z and 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 probably from January.
271 00:38:06.860 ⇒ 00:38:24.230 sigalbareket: And then, the beginning of next year, January or February, would be, announcing, getting the rumor out there, and getting more and more excitement while we develop this onboarding and monetization.
272 00:38:24.280 ⇒ 00:38:28.129 sigalbareket: And building up, a waitlist.
273 00:38:28.560 ⇒ 00:38:30.100 sigalbareket: So.
274 00:38:30.100 ⇒ 00:38:39.620 Uttam Kumaran: Jan 20th is… is Jan 20th, like, more of just, like, a PR blitz around, like, announcing that this platform exists? But you’re collecting waitlists at that point as well?
275 00:38:39.780 ⇒ 00:38:41.209 sigalbareket: Yep. Yes. Okay.
276 00:38:41.210 ⇒ 00:38:45.859 Uttam Kumaran: But you’ll still… will you be onboarding folks from January 20th? Like…
277 00:38:45.860 ⇒ 00:38:46.790 sigalbareket: We will.
278 00:38:46.790 ⇒ 00:38:47.890 Uttam Kumaran: after, or… okay.
279 00:38:47.890 ⇒ 00:38:58.530 sigalbareket: We will, but we don’t have… we don’t have a fully fleshed-out onboarding amortization and our app just yet, so as soon as we have it.
280 00:38:59.150 ⇒ 00:39:01.199 sigalbareket: somewhere at, let’s say, March.
281 00:39:01.350 ⇒ 00:39:10.640 sigalbareket: We will start onboarding paying users, which gets us to end of Q1, this is where we want to start onboarding paying users.
282 00:39:10.760 ⇒ 00:39:17.359 sigalbareket: And, and open people up from the waitlist into our, just paying experience.
283 00:39:17.400 ⇒ 00:39:21.439 Uttam Kumaran: Okay. And so March, April, this is when we’re actually gonna get.
284 00:39:21.560 ⇒ 00:39:29.020 sigalbareket: Data that will flow across the entire journey, from acquisition to onboarding to,
285 00:39:29.060 ⇒ 00:39:37.279 sigalbareket: So this is the time when 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.
286 00:39:37.280 ⇒ 00:39:52.110 sigalbareket: The information that we gather from the 300 or 400 people that will use the app until, until we start onboarding paid users, that’s another part. So that’s… Q1 is going to be very hectic on this part, and .
287 00:39:52.110 ⇒ 00:39:52.630 Uttam Kumaran: Okay.
288 00:39:53.170 ⇒ 00:39:55.309 sigalbareket: Finn has been very bored by our conversation.
289 00:39:56.380 ⇒ 00:39:58.150 sigalbareket: Yoaning at the back.
290 00:39:58.300 ⇒ 00:40:01.500 Jimsy: I’ll add another shorter-term goal for us, like.
291 00:40:01.500 ⇒ 00:40:02.330 Uttam Kumaran: Yeah, please.
292 00:40:03.010 ⇒ 00:40:10.219 Jimsy: as we’re getting into December, probably usage will drop because of people are thinking about holidays and stuff like that, but, like,
293 00:40:10.400 ⇒ 00:40:15.130 Jimsy: I would really love to get some of our initial, like, dashboards on…
294 00:40:15.510 ⇒ 00:40:15.870 Uttam Kumaran: Yeah.
295 00:40:15.870 ⇒ 00:40:27.469 Jimsy: D0 to D7 to D30 retention looks like, and then, I would really like to understand where the curve starts flattening, at what point,
296 00:40:27.690 ⇒ 00:40:37.209 Jimsy: Are we… and then, you know, I’d like to know what our Dow of a Mao or DAO of a wow would be, like, at least on a short-term perspective, and then what those funnels of where people are going
297 00:40:37.340 ⇒ 00:40:47.769 Jimsy: like, you know, we’ve… you’ve heard our products, yeah? You’ve got comps, you’ve got underbuilt, you’ve got pipeline, and you’ve got AI note-taking. It’s kind of like, what is the behavioral source?
298 00:40:47.910 ⇒ 00:40:48.469 Jimsy: It really…
299 00:40:48.470 ⇒ 00:40:52.840 Uttam Kumaran: Do you already have Mixpanel, or do you already have, like, what is the stack right now? Okay.
300 00:40:52.840 ⇒ 00:41:04.370 Jimsy: You know these platforms are so funny, like, they want your business, so they’re giving you, like, a billion events. I know. So, like, our team, bless their hearts, they’re like, well, if you’ve got.
301 00:41:04.370 ⇒ 00:41:06.700 Uttam Kumaran: Keep switching it to whatever’s free.
302 00:41:06.700 ⇒ 00:41:07.480 Jimsy: So, like, I…
303 00:41:07.480 ⇒ 00:41:18.540 Uttam Kumaran: 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,
304 00:41:18.960 ⇒ 00:41:23.829 Uttam Kumaran: And so it’s really gonna just be dependent on, like, what you guys need, but yeah, we work with all these guys.
305 00:41:23.900 ⇒ 00:41:42.830 Jimsy: we set up… because we’ve got literally a billion events free with Mixpanel, we’ve set that up, and we’ve also got StatsDig. So, StatsDig 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 we just… we just natively…
306 00:41:42.880 ⇒ 00:41:49.340 Jimsy: streamed events in, because, like, what… what I think… they’ve got a great mentality. It’s like, let’s just start streaming it in, let’s just see what’s there. Yes.
307 00:41:49.420 ⇒ 00:41:50.010 Uttam Kumaran: That’s good.
308 00:41:50.010 ⇒ 00:42:11.199 Jimsy: 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… but, like, that’s their kind of philosophy right now, which I think has served us quite well. And like I said, we’ve 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,
309 00:42:11.460 ⇒ 00:42:24.789 Jimsy: more technical specs out of our PRDs, and so I think there’s probably a short-term… we’re probably incorrectly using Datadog to do some BI stuff. I’m not in love with what we’ve got, but it’s something we’ve got there.
310 00:42:24.790 ⇒ 00:42:26.520 Uttam Kumaran: To see, at least users, and yeah.
311 00:42:26.940 ⇒ 00:42:31.860 Jimsy: Yeah, so leveling that up as a short term, because right now, I…
312 00:42:32.150 ⇒ 00:42:44.549 Jimsy: I think probably our founder’s 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.
313 00:42:44.550 ⇒ 00:42:45.090 Uttam Kumaran: Yeah.
314 00:42:45.090 ⇒ 00:42:45.460 Jimsy: So…
315 00:42:45.460 ⇒ 00:42:46.020 Uttam Kumaran: Yes.
316 00:42:46.230 ⇒ 00:42:46.680 Uttam Kumaran: Yeah.
317 00:42:46.680 ⇒ 00:42:50.639 sigalbareket: But even again, like… One more thing, I’m sorry, I’m just running out of time, and I don’t wanna…
318 00:42:50.640 ⇒ 00:42:51.400 Uttam Kumaran: No, no, no, no problem.
319 00:42:51.400 ⇒ 00:42:51.890 Jimsy: Boom.
320 00:42:51.890 ⇒ 00:42:56.820 sigalbareket: Miss, miss this part. The other more…
321 00:42:57.080 ⇒ 00:43:05.999 sigalbareket: unique to Breezy part, requires more information that GMC cannot share without an NDA.
322 00:43:06.000 ⇒ 00:43:06.560 Uttam Kumaran: Okay.
323 00:43:06.790 ⇒ 00:43:16.989 sigalbareket: So, maybe if it’s okay with you, James, you can just shoot an NDA. Please. And we can set up another meeting as soon as you can.
324 00:43:17.170 ⇒ 00:43:34.379 sigalbareket: just so Jimsy can dive deeper into the underbuild part, just so you can feed your analytical brain with what we need. Sure. 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.
325 00:43:34.380 ⇒ 00:43:39.739 Uttam Kumaran: I hear you, is that it all sort of, like, someone with a data brain should think about all these things.
326 00:43:39.740 ⇒ 00:43:47.480 sigalbareket: Exactly. So, just for you to be able to give us a full, like, Thinking.
327 00:43:47.480 ⇒ 00:43:47.800 Uttam Kumaran: Totally.
328 00:43:47.800 ⇒ 00:43:55.599 sigalbareket: this type of, like, you and Jim Z, and you don’t need me on this conversation, diving deeper into the, underbuilt part.
329 00:43:55.890 ⇒ 00:43:58.020 sigalbareket: And for that, we need an NDA.
330 00:43:58.240 ⇒ 00:43:59.190 Uttam Kumaran: Okay, okay.
331 00:44:00.050 ⇒ 00:44:13.619 Uttam Kumaran: So that’s perfect. So if we can… so if 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…
332 00:44:13.650 ⇒ 00:44:23.589 Uttam Kumaran: Yeah, Jim Z, I can hop on, probably tomorrow, or at least later this week, you know, I can tell you guys wanna… Okay, so let’s do tomorrow, that’ll give me a little…
333 00:44:23.590 ⇒ 00:44:24.569 Jimsy: At the same time?
334 00:44:25.420 ⇒ 00:44:27.900 Uttam Kumaran: Let’s do same time, yeah.
335 00:44:28.330 ⇒ 00:44:29.930 Jimsy: Great, great. Do you want to send an inbox?
336 00:44:29.930 ⇒ 00:44:30.570 Uttam Kumaran: Senate.
337 00:44:30.570 ⇒ 00:44:31.600 Jimsy: I’ll send it.
338 00:44:31.600 ⇒ 00:44:36.149 Uttam Kumaran: Let’s do the same time, it’ll give me some time to soak into this, and then, yeah, if we can sign the NDA today.
339 00:44:36.520 ⇒ 00:44:46.999 Uttam Kumaran: Anything you have, Jimsy, in terms of, like, if I can get, like, looms of the product or something, I can poke at stuff today with my team, and then, yeah, there should be a good meeting tomorrow.
340 00:44:47.320 ⇒ 00:44:51.110 Jimsy: If you get the NDA signed, I’ll just give you access to the product.
341 00:44:51.110 ⇒ 00:44:51.640 Uttam Kumaran: Okay.
342 00:44:51.950 ⇒ 00:44:52.460 Jimsy: Nope.
343 00:44:54.500 ⇒ 00:44:54.930 Uttam Kumaran: Okay.
344 00:44:54.930 ⇒ 00:44:58.360 sigalbareket: Perfect, guys. I also would suggest,
345 00:44:58.590 ⇒ 00:45:06.529 sigalbareket: It’s probably really, really, really last minute, but we, in 2 hours, we have an onboarding session with agents.
346 00:45:07.420 ⇒ 00:45:09.559 Uttam Kumaran: Yeah, add me. I’ll, I’ll join.
347 00:45:10.790 ⇒ 00:45:13.830 Uttam Kumaran: I would love to listen in. I mean, I’ll just be a fly on the wall.
348 00:45:14.900 ⇒ 00:45:16.780 sigalbareket: I’ll add you in?
349 00:45:19.400 ⇒ 00:45:32.780 Uttam Kumaran: No, this is a great product, by the way. I’ve 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 awesome to see the pace, by the way. Like, we work with a wide variety of clients.
350 00:45:34.240 ⇒ 00:45:51.029 Uttam Kumaran: But we’re all… we’re usually working… some people are like, slow down, and so 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 set part in particular are really, really, you know, awesome. So, excited to kind of dive deeper.
351 00:45:51.750 ⇒ 00:46:02.679 sigalbareket: Once the NDA is signed, let me know, and I’ll add you to the meeting today. It will just save him hours of onboarding, just to hear James, see everything, like, it will just make things so much quicker.
352 00:46:02.680 ⇒ 00:46:05.039 Jimsy: Yeah, I think it’s a smart move. Yep, 100%.
353 00:46:05.040 ⇒ 00:46:09.020 sigalbareket: Once the NBA is assigned, I’ll identify.
354 00:46:09.160 ⇒ 00:46:10.260 sigalbareket: to the inflate.
355 00:46:10.600 ⇒ 00:46:12.500 Jimsy: Alright, I’ll send that to you now, Item.
356 00:46:12.710 ⇒ 00:46:15.120 Uttam Kumaran: Okay, okay. Alright. Thank you both.
357 00:46:15.500 ⇒ 00:46:17.479 Jimsy: Thank you, great to meet you.
358 00:46:17.480 ⇒ 00:46:18.690 Uttam Kumaran: Great to meet you, too. Talk to you soon, bye.