Meeting Title: Brainforge <> Rill <> Omri Date: 2024-06-07 Meeting participants: Sidd Panigrahi, Uttam Kumaran
WEBVTT
1 00:00:31.710 ⇒ 00:00:32.310 Omri (oh-mree): You, but.
2 00:00:39.650 ⇒ 00:00:40.380 Uttam Kumaran: Hey Albert.
3 00:00:40.680 ⇒ 00:00:44.080 Omri (oh-mree): How’s it going? Can you see? Oh, it’s on camera.
4 00:00:45.500 ⇒ 00:00:46.160 Omri (oh-mree): weird.
5 00:00:46.160 ⇒ 00:00:47.319 Uttam Kumaran: Just grabbing.
6 00:00:48.140 ⇒ 00:00:48.850 Uttam Kumaran: Sure
7 00:00:52.240 ⇒ 00:00:53.060 Uttam Kumaran: take off.
8 00:01:03.630 ⇒ 00:01:06.299 Omri (oh-mree): How is your Friday? Well, when you’re back.
9 00:01:07.751 ⇒ 00:01:09.460 Uttam Kumaran: Friday’s good. How about you?
10 00:01:10.978 ⇒ 00:01:13.250 Omri (oh-mree): Good! I might have to run
11 00:01:13.910 ⇒ 00:01:19.359 Omri (oh-mree): to my door for like 30 seconds. In the middle of this. My mom is in town.
12 00:01:20.340 ⇒ 00:01:22.710 Omri (oh-mree): She’s coming by my office
13 00:01:24.330 ⇒ 00:01:28.119 Omri (oh-mree): to wait for my son’s arrival or end of his class.
14 00:01:29.759 ⇒ 00:01:30.220 Uttam Kumaran: Nice.
15 00:01:30.700 ⇒ 00:01:31.250 Omri (oh-mree): Yes.
16 00:01:31.510 ⇒ 00:01:33.740 Omri (oh-mree): school’s like block away from me here.
17 00:01:37.300 ⇒ 00:01:40.469 Omri (oh-mree): just hung out with my old stompy grounds
18 00:01:40.930 ⇒ 00:01:44.290 Omri (oh-mree): in West sauce in like off West 6th or all West 5.th
19 00:01:45.200 ⇒ 00:01:46.240 Uttam Kumaran: Oh, nice!
20 00:01:47.200 ⇒ 00:01:48.909 Sidd Panigrahi: Jonathan, hey? I’m Rick.
21 00:01:49.170 ⇒ 00:01:50.780 Omri (oh-mree): Hey, Sid, how’s it going.
22 00:01:51.950 ⇒ 00:01:58.700 Sidd Panigrahi: Good man. Nice looking at your setup. I better get back to my desk. At least I have my dual monitors. Look, this is like a proper professional setup. And I’m just.
23 00:01:58.700 ⇒ 00:01:59.150 Omri (oh-mree): I.
24 00:01:59.150 ⇒ 00:01:59.829 Sidd Panigrahi: My, you know.
25 00:01:59.830 ⇒ 00:02:00.170 Omri (oh-mree): My.
26 00:02:00.170 ⇒ 00:02:00.500 Sidd Panigrahi: Yes.
27 00:02:00.500 ⇒ 00:02:01.010 Omri (oh-mree): Really.
28 00:02:01.010 ⇒ 00:02:01.760 Sidd Panigrahi: Yeah.
29 00:02:02.510 ⇒ 00:02:07.113 Omri (oh-mree): Well, hopefully, it’s not just the the the microphone. Everyone’s really impressed by the microphone.
30 00:02:07.420 ⇒ 00:02:08.160 Sidd Panigrahi: It is. Yeah, yeah.
31 00:02:08.160 ⇒ 00:02:09.649 Uttam Kumaran: So the ride I was. I was.
32 00:02:09.650 ⇒ 00:02:10.530 Sidd Panigrahi: Yeah.
33 00:02:10.539 ⇒ 00:02:16.726 Uttam Kumaran: And they got a new rug, and the rug looks like everything the actual living like, yeah.
34 00:02:17.070 ⇒ 00:02:20.324 Omri (oh-mree): Between us. It costs like 30 bucks, but it looks.
35 00:02:21.910 ⇒ 00:02:26.250 Omri (oh-mree): I worked at startups for 12 years, and just was
36 00:02:26.700 ⇒ 00:02:29.900 Omri (oh-mree): just so much wasted just everything, including money.
37 00:02:29.900 ⇒ 00:02:30.640 Sidd Panigrahi: Yeah.
38 00:02:30.640 ⇒ 00:02:58.714 Omri (oh-mree): I was actually just. I was just saying I was that we rented this like in the deaths of pandemic, when no one was coming to the office at all. We rented like this like 3 3 story building in a really nice part of like downtown Austin. No one came. The company has since totally lost everything on it, totally su totally sublet. I think they burned 20 K. In cash just because of that horrible decision. And so I have very specific rules around here, and they’re not paying more than $30 for a rug.
39 00:02:59.040 ⇒ 00:03:02.450 Omri (oh-mree): But you know what apparently it looks good enough to pass.
40 00:03:03.480 ⇒ 00:03:04.330 Sidd Panigrahi: Okay. Yeah.
41 00:03:04.330 ⇒ 00:03:07.669 Omri (oh-mree): My wife always makes fun of this light switch, and he fixes light switch.
42 00:03:09.245 ⇒ 00:03:10.130 Sidd Panigrahi: Yeah, yeah. It’s.
43 00:03:10.130 ⇒ 00:03:11.030 Omri (oh-mree): Brown. It has like a.
44 00:03:11.030 ⇒ 00:03:20.360 Sidd Panigrahi: Yeah, it’s funny. At one time I visited one of my one of my, one of the guys I work with very closely in. He’s in new. He’s in Brooklyn.
45 00:03:20.380 ⇒ 00:03:37.689 Sidd Panigrahi: and his office always look like, you know, on video like, okay. Oh, wow, okay, you have nice setup. And then I went to see him. He was actually in the small area behind the staircase is literally like a tunnel. How do you like you sit here all day? So claustrophobic about on video, it feels like so.
46 00:03:37.690 ⇒ 00:03:39.140 Omri (oh-mree): Excited. Yeah.
47 00:03:39.140 ⇒ 00:03:39.669 Sidd Panigrahi: That’s just.
48 00:03:39.670 ⇒ 00:03:41.919 Omri (oh-mree): Fish, fish, nose, camera, lens.
49 00:03:42.242 ⇒ 00:03:48.549 Omri (oh-mree): I do love my office, but I feel like I need more. I need something back here on my list of things to do.
50 00:03:49.170 ⇒ 00:03:56.780 Sidd Panigrahi: Yeah, yeah, I think it’s the the one problem I haven’t solved is to manage daylight without screen glare, like, I want daylight. But then the screen gets.
51 00:03:56.850 ⇒ 00:04:00.400 Omri (oh-mree): Oh, I know it’s actually the sun right now is perfect. So it’s like.
52 00:04:00.900 ⇒ 00:04:04.384 Sidd Panigrahi: Yeah. And where are you? Remind me again. I know Tom is in Austin. Where are you? Are you in Austin?
53 00:04:04.550 ⇒ 00:04:07.740 Omri (oh-mree): Awesome. I think we’re pretty darn close to. He was here. He was here.
54 00:04:07.740 ⇒ 00:04:11.281 Sidd Panigrahi: Yeah, you said he was there yesterday. So of course, yeah, yeah, okay. Got it. Got it.
55 00:04:12.440 ⇒ 00:04:13.810 Omri (oh-mree): Where? Where are you?
56 00:04:14.550 ⇒ 00:04:15.869 Sidd Panigrahi: I’m in San Diego.
57 00:04:16.010 ⇒ 00:04:19.139 Omri (oh-mree): San Diego. Good! The better is Sam. In California.
58 00:04:19.693 ⇒ 00:04:21.906 Sidd Panigrahi: Yes, the better sign.
59 00:04:22.460 ⇒ 00:04:26.779 Omri (oh-mree): Way better. I have a cousin, I have a cousin who lives in La Hoya, and.
60 00:04:26.780 ⇒ 00:04:28.390 Sidd Panigrahi: Oh, wow, yeah, yeah.
61 00:04:28.650 ⇒ 00:04:30.400 Sidd Panigrahi: amazing. Yeah.
62 00:04:30.400 ⇒ 00:04:33.059 Omri (oh-mree): So so sweet. Dad’s got a great setup.
63 00:04:34.180 ⇒ 00:04:34.525 Sidd Panigrahi: Yeah.
64 00:04:35.510 ⇒ 00:04:36.410 Omri (oh-mree): Awesome, awesome.
65 00:04:36.410 ⇒ 00:04:37.040 Sidd Panigrahi: Awesome.
66 00:04:37.040 ⇒ 00:04:38.040 Omri (oh-mree): Good to meet you.
67 00:04:38.520 ⇒ 00:04:45.849 Uttam Kumaran: Yeah. So I just wanted to bring everyone kind of together. I said, I kind of kind of showed Omari
68 00:04:46.140 ⇒ 00:04:49.119 Uttam Kumaran: real, maybe like a month or month and a half ago.
69 00:04:49.140 ⇒ 00:05:14.469 Uttam Kumaran: and was basically like, you know, try it out and let me know what you think. And I mentioned him that like we’re implementing a brain forge for clients. He has like a client he’s working on. Now that I mentioned, which is kind of like in like the b 2 b Ecom we kind of focus on in manufacturing where, you know it’s kind of like a tongue opportunity. I think that Om, reason, which is on the design side, but also now sort of on the product development side.
70 00:05:14.923 ⇒ 00:05:28.670 Uttam Kumaran: Of course, there’s potentially some opportunity for reporting but basically, I’m like, Hey, the real products, great like, we’re gonna be implementing it wherever we can. So this, there’s some opportunity. But also, I think we’re trying to.
71 00:05:28.800 ⇒ 00:05:38.409 Uttam Kumaran: We’re also trying to think strategically about that industry as well and see like, where there’s opportunity to do one or many things. So yeah, I just wanted to kind of bring everyone together to chat.
72 00:05:39.120 ⇒ 00:05:40.600 Omri (oh-mree): Yeah, for sure.
73 00:05:40.880 ⇒ 00:05:45.349 Omri (oh-mree): I can kind of give you a background on, like where I come from. I think it’s kind of kind of helpful. So.
74 00:05:45.350 ⇒ 00:05:46.100 Sidd Panigrahi: Yeah.
75 00:05:46.100 ⇒ 00:06:10.186 Omri (oh-mree): I ran, so I ran like head of. I was the head of product at a handful of starts here in Austin, Cj. To B 3 times and it was fine, whatever it was. I almost did it again recently, but I was like, No, I still wanna do that. And so now I sort of do 2 things, one I’m a fractional head of product and do some like product advisory. Which is fine. Sorry. It’s not where my passion lies.
76 00:06:10.530 ⇒ 00:06:27.260 Omri (oh-mree): but the space where my passion does lie is sort of like in what what we’re. You know. The so the context of the using real I essentially am becoming sort of what I call product studio, where I build products for companies that don’t have the wherewithal to do it for themselves.
77 00:06:28.110 ⇒ 00:06:33.699 Omri (oh-mree): those look like startups, but they also look like more forward thinking
78 00:06:33.830 ⇒ 00:06:39.690 Omri (oh-mree): slightly. Bigger companies like to give you example about the one, the the one we just referenced like
79 00:06:40.280 ⇒ 00:06:44.639 Omri (oh-mree): they do about a billion and a half 2 billion top line. They’re huge.
80 00:06:45.002 ⇒ 00:06:49.467 Omri (oh-mree): They have their their own customer. Rp, for various reasons that probably would not worth getting into.
81 00:06:50.420 ⇒ 00:06:51.284 Omri (oh-mree): But
82 00:06:52.210 ⇒ 00:07:09.920 Omri (oh-mree): there’s like this 80 20 thing that feels like it’s a pattern in this space. We’re like 80% of your orders. You want them to be transactional. You don’t want to care about them. So you want to get them off your sales teams plate, and that looks more like an e-commerce motion that I spent years. And so I was building out on the D to see and B to C side.
83 00:07:10.347 ⇒ 00:07:22.242 Omri (oh-mree): But the 20% of the site you want sales team, you know. You want your inside salespeople outside salespeople. You want them running the deals. But like for those 80%, it could be done transactionally. And so that’s sort of like a thesis. Them building around
84 00:07:22.830 ⇒ 00:07:25.760 Omri (oh-mree): so far. There’s a lot of people that have customerps
85 00:07:26.169 ⇒ 00:07:32.629 Omri (oh-mree): actually just hung out yesterday. I don’t think I told you I hung out with big commerce yesterday.
86 00:07:33.890 ⇒ 00:07:34.090 Uttam Kumaran: Nice.
87 00:07:34.290 ⇒ 00:07:51.289 Omri (oh-mree): This is interesting. Yeah, they’re they have a whole B twob focus now, but they go about it almost diametrically opposed. The way that I want to go about it where it’s like much more custom, and like building from the ground up, whereas they’re like just building from the top down, just dropping on them, dropping it down from the skies and seeing what what sticks?
88 00:07:51.710 ⇒ 00:08:02.889 Omri (oh-mree): But so I’m doing this, and we’re kind of like going. I’m effectively going down the stack, and I think the next layer of the stack that I’ll hit is something like reporting. So you know they’ll do when we launch this new
89 00:08:03.130 ⇒ 00:08:07.139 Omri (oh-mree): B Twob. Ecom platform, though probably on day, one
90 00:08:07.210 ⇒ 00:08:12.070 Omri (oh-mree): like after 30 days, they’ll be on like an annual rate of like
91 00:08:13.230 ⇒ 00:08:21.539 Omri (oh-mree): 150 million in Rev. And we’ll have no idea where it comes from. The best they can do that from the customer. Rp perspective is like
92 00:08:21.660 ⇒ 00:08:24.157 Omri (oh-mree): an order table. They have an order table.
93 00:08:24.470 ⇒ 00:08:25.160 Sidd Panigrahi: Yeah.
94 00:08:25.160 ⇒ 00:08:36.777 Omri (oh-mree): And so sorry we’re talking. I was shown real dumb, showed it to me. And I was like, I’m gonna like, roll some roll real and see what happens. I was a dev years ago, so I can like, get around a system like that.
95 00:08:37.600 ⇒ 00:08:40.250 Omri (oh-mree): And I set them up a dashboard, and they were like.
96 00:08:41.419 ⇒ 00:08:42.409 Omri (oh-mree): I mean.
97 00:08:43.039 ⇒ 00:08:45.570 Omri (oh-mree): but they’re pretty non sophisticated. So the average thing I.
98 00:08:45.570 ⇒ 00:08:46.090 Sidd Panigrahi: It’s like.
99 00:08:46.090 ⇒ 00:08:49.945 Omri (oh-mree): It’s like manna from heaven. But still they’re like, Oh, my God, this is amazing. We need this
100 00:08:50.805 ⇒ 00:09:06.079 Omri (oh-mree): and so I think that there’s going to be, and some ideas like. And I’ve already done this. Now, once of essentially taking what I built them. Selling is the person across the street. I feel like as I go down the stack. There’s something here, both in that direction. But then also, like
101 00:09:08.270 ⇒ 00:09:17.110 Omri (oh-mree): you know Utah myself, or brain forage and myself would have this ability to kind of like productize and data, or something of the sort and build something around it.
102 00:09:17.662 ⇒ 00:09:25.227 Omri (oh-mree): And I was telling I was telling we’re we don’t know we’re talking about like my new obsession with what I call or what is being called like
103 00:09:25.980 ⇒ 00:09:37.929 Omri (oh-mree): ecosystem led growth of like partnering with the companies like to do. You know, you all sell the shovel? We’ll go figure out how to help people dig so there might be more there, just from like a working together partnering. And
104 00:09:37.970 ⇒ 00:09:39.249 Omri (oh-mree): yeah, I’d be interested.
105 00:09:39.260 ⇒ 00:09:47.679 Omri (oh-mree): I’m not. This is not my my place to bet on a horse, but it’s clear that this is a horse that’s being bet on this conversation. So like I’d
106 00:09:47.980 ⇒ 00:09:49.240 Omri (oh-mree): love to learn more.
107 00:09:49.750 ⇒ 00:09:51.640 Sidd Panigrahi: Yeah, absolutely. I think. So.
108 00:09:51.680 ⇒ 00:09:55.021 Sidd Panigrahi: We started. So let me give you that’s super helpful, Omari. So
109 00:09:55.640 ⇒ 00:10:08.670 Sidd Panigrahi: we so let let me explain. Maybe a little bit about background in history of role the real used to be. This company called Meta Markets website is still of metamarkest.com metamarket. Dot com served
110 00:10:08.700 ⇒ 00:10:15.510 Sidd Panigrahi: large scale analytics to advertising companies ad tech companies for millions of dollars.
111 00:10:15.690 ⇒ 00:10:18.889 Sidd Panigrahi: It was acquired by Snap for millions of dollars.
112 00:10:18.890 ⇒ 00:10:19.730 Omri (oh-mree): Snap.
113 00:10:20.350 ⇒ 00:10:21.000 Sidd Panigrahi: Snapchat.
114 00:10:21.000 ⇒ 00:10:22.150 Omri (oh-mree): Chat. Okay. Snap. Okay.
115 00:10:22.150 ⇒ 00:10:23.660 Sidd Panigrahi: Athletics. Yeah.
116 00:10:24.453 ⇒ 00:10:29.050 Sidd Panigrahi: Mike ran meta markets within snap. It is still used as snap.
117 00:10:29.340 ⇒ 00:10:31.540 Sidd Panigrahi: And then, for whatever reason, snaps
118 00:10:31.700 ⇒ 00:10:37.189 Sidd Panigrahi: top trader investor pressure. Blah! Blah! Blah! Snap started divesting from all these acquisitions they made.
119 00:10:37.710 ⇒ 00:10:47.239 Sidd Panigrahi: and so Mike saw an opportunity. So he spun meta markets back out and bought all the assets like, oh, I’m gonna own the cush. I’m gonna restart this every all the customers loved
120 00:10:47.260 ⇒ 00:10:54.109 Sidd Panigrahi: the platform, but they backed out when it the acquisition happened because they’re like, I’m not giving my data to Snapchat. Forget about this right? Of course.
121 00:10:54.110 ⇒ 00:10:54.880 Omri (oh-mree): Yeah, okay.
122 00:10:54.880 ⇒ 00:10:55.650 Sidd Panigrahi: Exactly
123 00:10:55.790 ⇒ 00:10:59.210 Sidd Panigrahi: so. Then the company was spun back out in 2020.
124 00:10:59.390 ⇒ 00:11:01.160 Sidd Panigrahi: It’s known as real. Now
125 00:11:01.320 ⇒ 00:11:03.740 Sidd Panigrahi: we rebuild the front end and back end
126 00:11:04.460 ⇒ 00:11:06.840 Sidd Panigrahi: to basically still have the same philosophy of
127 00:11:07.100 ⇒ 00:11:21.719 Sidd Panigrahi: past slice and dice metrics. Most the reporting tools, the whole philosophy is, you’re spending most of the time designing dashboard. So I’m gonna put a filter on the top right? What filters will I make available rule takes this approach where you just decide.
128 00:11:22.030 ⇒ 00:11:30.260 Sidd Panigrahi: or you focus on. What are the metrics you want to monitor? And then we auto generate these dashboards. Where says, Yeah, here you go across time. You can compare. You can look at all these metrics.
129 00:11:30.370 ⇒ 00:11:36.110 Sidd Panigrahi: we ordinary, these dashboards. Now at Meta Marcus. The common complaint was.
130 00:11:36.610 ⇒ 00:11:41.669 Sidd Panigrahi: it was only designed for terabyte skill data, and it was expensive, like, if someone had
131 00:11:42.340 ⇒ 00:11:51.700 Sidd Panigrahi: 50 gigs of data, there was no price point that made sense for them to use Meta Marcus. There was just no price, because the minimum cost of setting up it was basically bill to only handle huge workloads.
132 00:11:51.940 ⇒ 00:11:52.410 Omri (oh-mree): Here.
133 00:11:52.410 ⇒ 00:11:59.020 Sidd Panigrahi: Like the minimum cluster we could set up was like $2,000 a month, and then it didn’t make any sense. If you had only 50 gigs of data, 10 gigs of data.
134 00:11:59.490 ⇒ 00:12:07.159 Sidd Panigrahi: So part of the big rebuild was to the world is changing. Machines are becoming powerful where everyone doesn’t need a big data center sitting somewhere.
135 00:12:07.290 ⇒ 00:12:19.709 Sidd Panigrahi: There’s a lot of use cases for small to medium data sets, tens of gigs, hundreds of gigs. But that’s where we’ve redesigned roll to even support just smaller price points. And of course our hope is, and we still have big big
136 00:12:20.430 ⇒ 00:12:22.010 Sidd Panigrahi: Qc. Customers.
137 00:12:22.140 ⇒ 00:12:34.200 Sidd Panigrahi: But we allow customers to get in early, and then we can grow with them like if their business grows from 10 gigs to 10 TB. Great, but that doesn’t like the answer can’t be. Call me when you have 10 TB. Don’t call me today like, that’s a lot of lost opportunity.
138 00:12:34.260 ⇒ 00:12:35.779 Sidd Panigrahi: So that’s what we build.
139 00:12:36.480 ⇒ 00:12:39.340 Sidd Panigrahi: So we saw primarily in 2 motions. Right? One is
140 00:12:39.840 ⇒ 00:12:45.709 Sidd Panigrahi: to touch upon is sometimes as companies starting to just like, I have all this data. But I have no tools.
141 00:12:45.740 ⇒ 00:12:57.849 Sidd Panigrahi: I could go get a license to Snowflake. Then I could go ahead a data engineering team. Then I could implement Dbt, then I could go get a bi tool like this all takes time and money. Or I have data
142 00:12:58.150 ⇒ 00:13:02.059 Sidd Panigrahi: sitting as Csv files, parking files on a table, I could use no
143 00:13:02.080 ⇒ 00:13:07.459 Sidd Panigrahi: and start sort of getting. That’s so. That’s a primary target is we have a lot of startups who just like.
144 00:13:07.630 ⇒ 00:13:11.209 Sidd Panigrahi: I don’t need to go buy 4 tools. I can just buy real, of course.
145 00:13:11.310 ⇒ 00:13:19.819 Sidd Panigrahi: as if they mature and they grow. They might need specialized tools like they might need a warehouse. They might need a dpt, but they don’t need that on day, one or even year one.
146 00:13:19.960 ⇒ 00:13:29.090 Sidd Panigrahi: they’re like this is, I still need a fast way to access all my data. And that can’t be. Everyone has their own version of a Google spreadshe, Google sheet like, that’s that’s like, not tenable.
147 00:13:29.130 ⇒ 00:13:36.050 Sidd Panigrahi: So that’s where we solve to, and the big companies also we solve the big companies we sell against is who I did all the whole setup.
148 00:13:36.300 ⇒ 00:13:40.619 Sidd Panigrahi: Now, my Cfo said, Oh, my snowflake. Bill went off the roof. What are you guys doing.
149 00:13:40.620 ⇒ 00:13:41.120 Omri (oh-mree): Yeah.
150 00:13:41.120 ⇒ 00:13:43.540 Sidd Panigrahi: So now they’re like backing off some data where
151 00:13:43.670 ⇒ 00:13:48.029 Sidd Panigrahi: all these warehouses are not built for slice and dice, and we obviously are.
152 00:13:48.700 ⇒ 00:13:58.839 Sidd Panigrahi: We’ve gone. We use these all appendages which are much more favorable to analytical processing. If you just want to see count of orders, average order, size, boom, boom, boom super fast, super cheap.
153 00:13:59.355 ⇒ 00:14:01.070 Sidd Panigrahi: but not meant for role level. So.
154 00:14:01.070 ⇒ 00:14:26.450 Omri (oh-mree): Oh, yeah, my last company, we had this like, really great data engineer that built out this wonderful data. Architecture is beautiful. The snowflake will was through the roof, but it was gorgeous. It’s like great. What are you doing with this data like then? What like? Just for the sake of having the data like, Can you get something out of it? And then we backed all that out, ended up using some pretty rugged tools actually real, would have probably been really good candidate for, like where to go next, like what are actually trying to operationalize it, and not just like
155 00:14:26.930 ⇒ 00:14:28.579 Omri (oh-mree): build for the sake of building.
156 00:14:29.050 ⇒ 00:14:31.450 Sidd Panigrahi: Yeah, now we’ve gotten
157 00:14:31.730 ⇒ 00:14:33.960 Sidd Panigrahi: tons of feedback that
158 00:14:34.030 ⇒ 00:14:39.899 Sidd Panigrahi: I think it’s more to remove sales objection we have built. So our sort of you’ve seen. The default dashboard is basically slice and dice.
159 00:14:40.250 ⇒ 00:14:40.600 Omri (oh-mree): Yeah.
160 00:14:40.600 ⇒ 00:14:42.709 Sidd Panigrahi: Metrics on the left, dimensions on the right.
161 00:14:43.100 ⇒ 00:14:45.150 Sidd Panigrahi: because we have gotten
162 00:14:45.770 ⇒ 00:14:46.920 Sidd Panigrahi: so much
163 00:14:47.745 ⇒ 00:14:51.600 Sidd Panigrahi: I don’t want to say push back, but ask, there’s more a sales objection
164 00:14:51.670 ⇒ 00:14:55.639 Sidd Panigrahi: we have built. I don’t even know if I’ve shown you this is that means we have built.
165 00:14:55.790 ⇒ 00:15:02.869 Sidd Panigrahi: He’s gonna release this in the next, I think month is we have. So this is a default dashboard, right? You see a slice and dice
166 00:15:03.030 ⇒ 00:15:04.609 Sidd Panigrahi: we are building
167 00:15:05.454 ⇒ 00:15:13.990 Sidd Panigrahi: these more traditional dashboard capabilities where you can design these summary dashboards and say, fine. You want all these donor chart, a sanky chart, a pie chart. During.
168 00:15:13.990 ⇒ 00:15:14.470 Omri (oh-mree): Oh, yeah.
169 00:15:14.470 ⇒ 00:15:15.500 Sidd Panigrahi: And do this.
170 00:15:15.910 ⇒ 00:15:21.470 Sidd Panigrahi: so this that we can have. I still think this is limited use like great. I saw this, what am I gonna do
171 00:15:21.540 ⇒ 00:15:23.039 Sidd Panigrahi: I have to look underneath?
172 00:15:23.080 ⇒ 00:15:32.380 Sidd Panigrahi: So I have to look at the data. And that’s why I prefer this view. But we get it. It’s like everyone says, Hey, I know the car drives great. But I want these 3 colors. I’m like fine dude. Here’s 3 colors
173 00:15:32.440 ⇒ 00:15:35.300 Sidd Panigrahi: end of the day used, you know. So we’re gonna have these
174 00:15:35.330 ⇒ 00:15:50.020 Sidd Panigrahi: surface areas where at least we remove the sales objection like, I don’t think we are tomorrow getting in the market and saying, we are better than look at our sigma at visualizations. But I get where people are like, hey? At least in some cases, maybe for a client call or Qbr. I want to be able to do stuff.
175 00:15:50.020 ⇒ 00:15:57.079 Omri (oh-mree): No 100% that happened that happened to me with that company? They asked. They were like, We want to see a table or like a a bar graph of something I was like
176 00:15:57.240 ⇒ 00:16:01.000 Omri (oh-mree): I was like, I don’t think it does that, but now I can tell them it does.
177 00:16:01.220 ⇒ 00:16:14.050 Uttam Kumaran: Well, that’s so. That’s the difference. I mean, I have with clients, too, that they’re like they want to see this. But then, at the same time they’re like, can you just tell us, like what the answer is? I don’t know. It is a sales objection. It’s more for you guys to maintain. But.
178 00:16:14.580 ⇒ 00:16:40.800 Sidd Panigrahi: Exactly, or or usually what happens is, can you give me a top right? Export a Csv option? I’m like, dude. You gonna export the data. Just look at it as as tables. You know, you’re gonna export the data, look at it as tables. And now you can actually analyze everything and see what is going on. You don’t need to export it somewhere else. And now, working with Excel like just work on, you know, Bill, you wanna build a pivot table here, you can, you know, build, or do all do all this within the tool, and it’s super fast.
179 00:16:40.800 ⇒ 00:16:44.989 Sidd Panigrahi: But I get so so we are building this different surface area
180 00:16:45.220 ⇒ 00:16:56.940 Sidd Panigrahi: honestly for sales objections that hey? Fine! You want visualizations. Here you go. You can build any chart. We are doing it in a code full way. So you can actually build any chart you want. There’s no restrictions that we support only 5 chart types.
181 00:16:56.940 ⇒ 00:16:57.690 Omri (oh-mree): Oh, cool!
182 00:16:58.740 ⇒ 00:17:03.865 Sidd Panigrahi: yeah, we use this library. This is getting into body we use for your better. We use this library. They have.
183 00:17:04.119 ⇒ 00:17:04.549 Uttam Kumaran: Yeah, yeah.
184 00:17:04.550 ⇒ 00:17:05.469 Sidd Panigrahi: Child you want.
185 00:17:05.470 ⇒ 00:17:07.203 Omri (oh-mree): Oh, I’ve seen this. Yeah.
186 00:17:08.010 ⇒ 00:17:10.338 Sidd Panigrahi: So anyway. So that’s what we are doing.
187 00:17:11.190 ⇒ 00:17:15.029 Sidd Panigrahi: and I think, yeah. So that’s as I said, I mean, we, we get a lot of success on
188 00:17:15.950 ⇒ 00:17:29.009 Sidd Panigrahi: for lack of a better, for a probably a horrible analogy, like real dashboards, are almost almost like a drug. Once you get used to this fast performance dashboards. You don’t want to see anything else, because you don’t want to like. See a spinner load up bar chart
189 00:17:29.110 ⇒ 00:17:32.129 Sidd Panigrahi: for 30 seconds. They’re like, no, I just want to be able to see
190 00:17:32.200 ⇒ 00:17:42.810 Sidd Panigrahi: how things are going. And I want to be able to analyze and then go talk to the right person. If I see like Hey, revenue drop by a channel, I see it right away. I don’t need to like. Wait for a month end of month. Report.
191 00:17:43.220 ⇒ 00:17:43.800 Omri (oh-mree): Yeah.
192 00:17:46.230 ⇒ 00:17:49.060 Sidd Panigrahi: So that’s sort of that’s sort of our our focus is.
193 00:17:49.800 ⇒ 00:17:59.200 Sidd Panigrahi: I think we are. I think they’re seeing. We bet on these Lake house architectures where people just have data in S. 3 in Gcs on under table. They don’t. Everyone doesn’t
194 00:17:59.310 ⇒ 00:18:05.500 Sidd Panigrahi: have it in some snowflake or big curry, or people who do have it. They’re also backing out because it’s expensive to operate in those environments.
195 00:18:06.040 ⇒ 00:18:12.770 Sidd Panigrahi: Now we go see, you know, Lake House architectures are becoming a thing, you know, like data breaks paid a billion dollars for tabular.
196 00:18:13.298 ⇒ 00:18:18.549 Sidd Panigrahi: So it’s so I think you know, we think our bets will pay off in terms of, you know, just operating on.
197 00:18:18.700 ⇒ 00:18:24.390 Sidd Panigrahi: People have data. And people want a password to analyze it without needing 3 headcounts and 4 systems to do it.
198 00:18:24.510 ⇒ 00:18:25.290 Sidd Panigrahi: Yeah.
199 00:18:27.860 ⇒ 00:18:28.490 Omri (oh-mree): Yeah.
200 00:18:28.680 ⇒ 00:18:32.319 Sidd Panigrahi: So what what else can I answer? Can I share in terms of
201 00:18:32.330 ⇒ 00:18:39.430 Sidd Panigrahi: maybe the immediate thing you’re working on? What will be helpful or just forward looking. Do you want to? You know, from a structure standpoint, what? What is it that you’ll.
202 00:18:39.430 ⇒ 00:18:44.359 Omri (oh-mree): Yeah, no, I mean, I think it. I think that so as the same conversation had with
203 00:18:46.160 ⇒ 00:18:48.910 Omri (oh-mree): big commerce yesterday. So I think that I think that
204 00:18:49.220 ⇒ 00:18:51.419 Omri (oh-mree): for us sort of like we have.
205 00:18:51.610 ⇒ 00:18:59.939 Omri (oh-mree): I think that we’re actually just texting yesterday. I think, like we’ll iterate towards something that looks like something, and I’m not 100% sure we’ll be able to draw a picture. Draw
206 00:19:00.210 ⇒ 00:19:02.751 Omri (oh-mree): taboo around it right now.
207 00:19:03.470 ⇒ 00:19:04.620 Omri (oh-mree): but
208 00:19:05.120 ⇒ 00:19:18.749 Omri (oh-mree): what? So the conversation is having with big Homers yesterday is like, is there something or some area like thinking about this as an ecosystem? Right? You have an offering, and like what sort of thing that looks
209 00:19:18.930 ⇒ 00:19:24.499 Omri (oh-mree): like if you had a full product team and full data team at your disposal
210 00:19:24.590 ⇒ 00:19:33.879 Omri (oh-mree): to fuel real in some direction, based on the conversations that you’re having based on the clients that you have now other clients you’re going after. Is there some sort of corollary
211 00:19:34.300 ⇒ 00:19:36.490 Omri (oh-mree): complimentary something that
212 00:19:36.740 ⇒ 00:19:39.050 Omri (oh-mree): we should be thinking I should be thinking about.
213 00:19:39.050 ⇒ 00:19:41.109 Sidd Panigrahi: Yeah, yeah, absolutely. So I think
214 00:19:41.190 ⇒ 00:19:45.410 Sidd Panigrahi: today, like, so one thing, I think it’s just a reality with enterprise software is any
215 00:19:45.450 ⇒ 00:19:48.340 Sidd Panigrahi: large deal as a big
216 00:19:48.610 ⇒ 00:19:56.769 Sidd Panigrahi: component of pro serve involved like, no matter, I can think like, oh, they just gonna use the software and pay license fees. And it’s gonna be big 6, figure 7, figure that just does not happen
217 00:19:57.210 ⇒ 00:20:05.999 Sidd Panigrahi: today. We take on. In fact, right now, we gonna do between us, like, I’m gonna do a deal with one of my buddies. He’s at Disco. They’re a large shopify e-commerce company.
218 00:20:06.240 ⇒ 00:20:11.318 Sidd Panigrahi: Literally. His call to me is like dude. We spend a million bucks a year, and I have no clue. What the fuck we are doing. Help me.
219 00:20:11.500 ⇒ 00:20:13.529 Omri (oh-mree): They pay that to you, they pay that to you.
220 00:20:13.900 ⇒ 00:20:17.229 Sidd Panigrahi: Amazon. Right then. Amazon Bill is a million dollars.
221 00:20:17.920 ⇒ 00:20:36.580 Sidd Panigrahi: They pay millions to event analytics, companies that makes panels, snow, plow, and everything. Everyone has done everything so almost like someone new comes in or like, I need to prove my value. I’m gonna bring a new system on. And here we go. And then it’s like, Wait, why is the same data going everywhere? What are we actually getting out of it? So he. So things like this happen.
222 00:20:37.060 ⇒ 00:20:45.149 Sidd Panigrahi: And selfishly now, because we are still early enough. If we want to learn, so we will go. Take them on. But in a near future state we’re like dude.
223 00:20:45.180 ⇒ 00:20:51.669 Sidd Panigrahi: we have a solution we are building. I’m not gonna put 3 engineers on this for 6 months and fix your company.
224 00:20:52.010 ⇒ 00:20:53.689 Sidd Panigrahi: But I have
225 00:20:53.720 ⇒ 00:20:56.480 Sidd Panigrahi: teams I worked with who are happy to take this on.
226 00:20:56.830 ⇒ 00:21:00.510 Sidd Panigrahi: They know how to implement stuff. They’ll make a recommendation. So I’ll be able to like.
227 00:21:00.660 ⇒ 00:21:02.370 Sidd Panigrahi: you know, kick business. I’m like, Hey.
228 00:21:02.610 ⇒ 00:21:13.439 Sidd Panigrahi: yeah, we to meet brain Force talk to them. They’ve done this. If nothing, they’ll at least 1st do a maybe a 2 month discovery come back with the recommendations. Then you have choices to make. What do you want to do?
229 00:21:13.460 ⇒ 00:21:15.969 Sidd Panigrahi: There’s a lot of companies that have gone through like
230 00:21:16.130 ⇒ 00:21:24.189 Sidd Panigrahi: different CTO came in different product. They all bought their own system, and then they left. Now you have these hanging systems that are they just paying
231 00:21:24.220 ⇒ 00:21:30.490 Sidd Panigrahi: knows bleed money to them, and they’re not actually using them. Even. So that is a discussion that we happen oftentimes
232 00:21:30.580 ⇒ 00:21:41.680 Sidd Panigrahi: like today. Like we understand. As I said, large deals. We’ll have to do some pro serve. But there is a large pro serve component where we don’t want like we want to build product. We we are not cause pro serve.
233 00:21:41.690 ⇒ 00:21:51.179 Sidd Panigrahi: Margins are all them. You know, the software company. They look on the paper. It’s just more difficult also, and the other deal with like if someone leaves. Now, there’s a whole like we are not.
234 00:21:51.250 ⇒ 00:21:52.890 Sidd Panigrahi: Sales were there. Oh, and every deal.
235 00:21:52.890 ⇒ 00:21:58.239 Uttam Kumaran: Well, that’s the long tail that’s like the long tail for you guys. But again, it’s like a completely different focus. And.
236 00:21:58.240 ⇒ 00:21:59.310 Sidd Panigrahi: Exactly, exactly.
237 00:21:59.310 ⇒ 00:22:02.979 Uttam Kumaran: Totally not again to win the deal, for sure. But
238 00:22:03.390 ⇒ 00:22:04.920 Uttam Kumaran: then it’s more like trained there.
239 00:22:04.920 ⇒ 00:22:22.309 Sidd Panigrahi: You know, we we exactly like like with them. We sign, you know, the deal we did these guys like they would never have built the dashboard using drill unless you built it for them, and we also wouldn’t have built it for them. You’re like no man. I don’t have the time and cycles, because we also have to pick and choose. But there is a big
240 00:22:22.340 ⇒ 00:22:38.960 Sidd Panigrahi: need, and we can scope out what that I mean, not scope out like we can refine what that means. And that’s why I’ve also asked legal companies like, Hey, what are the right? What are the right things I need to hear where I can pick up the phone or slack and like, Hey, we gotta have a call with these guys like, what are things we should, I should hear, because we have these calls all the time
241 00:22:39.030 ⇒ 00:22:41.620 Sidd Panigrahi: like one of the things I think I mentioned last time, right
242 00:22:41.950 ⇒ 00:22:46.670 Sidd Panigrahi: in advertising a lot of these companies have integrations with like 6 different systems.
243 00:22:46.670 ⇒ 00:22:47.240 Uttam Kumaran: Yeah.
244 00:22:47.240 ⇒ 00:22:56.980 Sidd Panigrahi: And they ask us, will you integrate for us? I’m like, no. Once you integrate and bring the data, we’ll help you visualize and analyze the data, but we are not going to build the integrations for you. But hey.
245 00:22:57.490 ⇒ 00:22:58.350 Sidd Panigrahi: someone else might.
246 00:22:58.350 ⇒ 00:22:58.750 Uttam Kumaran: The company.
247 00:22:58.750 ⇒ 00:23:02.540 Sidd Panigrahi: Because exactly you don’t. You don’t want to spend the you don’t have the
248 00:23:02.660 ⇒ 00:23:05.299 Sidd Panigrahi: no, you don’t want to have the head count, because you’re not a
249 00:23:05.640 ⇒ 00:23:11.260 Sidd Panigrahi: technology company for lack of a better term. And you, you know you, you still want to be able to have the data at your fingertips.
250 00:23:11.260 ⇒ 00:23:35.879 Uttam Kumaran: Sure, that’s completely different business model. And then overall like for me, I see the for me. It’s like the Comp. The competitive edge for me is like, how fast can I get the value for folks right? Nobody cares on the business side until we have the dashboard. We still have a meeting around making a decision. So until so, even if I can get data in a warehouse, get the warehouse set up, get the model, set up every step of the way I’m trying to speed up.
251 00:23:35.980 ⇒ 00:23:50.539 Uttam Kumaran: I’m trying to speed up each component. And there’s certain things we can do, and there’s certain things we can’t do thankfully. You guys exist and are speeding up part of the pie. That does take quite a while and is actually quite expensive, even though I would say they, it’s like so last mile.
252 00:23:50.710 ⇒ 00:24:09.329 Uttam Kumaran: right? And, in fact, like all the work that goes into Dbt and everything really enables that. And then commonly the bi tool just mucks it up even more. And so in this sense that I want, I like, I’m really hoping that people adopt it to, because it actually makes our success rate higher. It speeds up the fact that we could do that. And then again.
253 00:24:09.774 ⇒ 00:24:22.120 Uttam Kumaran: the nice thing about like meeting someone like Omari, and who’s involved in this new kind of industry is like, Oh, they’re going from like 0 to this. They can even skip looker and stuff which is like it’s gonna be even better right?
254 00:24:22.120 ⇒ 00:24:41.039 Sidd Panigrahi: Yeah, I mean, end of the day. We know this right people value outcomes at some level. They don’t give a shit. How difficult it was to make the sauce. I don’t. I don’t need to need to see the sauce making. Show me the outcome. And so, and from your perspective you want to get to the outcome in an efficient manner because you don’t want to be like, oh, I’m gonna grind 3 people for 6 months.
255 00:24:41.040 ⇒ 00:24:50.560 Uttam Kumaran: No, we’re also like I if I could get the outcome in 2 months, it’s gonna buy us another 6 months. I’m not actually worried about there being a year of work anywhere. We go.
256 00:24:50.560 ⇒ 00:24:51.060 Sidd Panigrahi: But I.
257 00:24:51.060 ⇒ 00:25:01.960 Uttam Kumaran: There’s so much data optimization to do everywhere we go. It’s actually, is it? Gonna take me 3 months to get to something reasonable. And then it, I’m basically gonna be like every week, like
258 00:25:02.070 ⇒ 00:25:02.949 Uttam Kumaran: apologizing.
259 00:25:03.830 ⇒ 00:25:04.230 Sidd Panigrahi: Exactly.
260 00:25:04.230 ⇒ 00:25:05.399 Uttam Kumaran: Cool. I’m happy to do that. Yeah.
261 00:25:05.400 ⇒ 00:25:12.650 Sidd Panigrahi: You all have some outcomes, and then, of course, optimize and get things better. But people people gravitate towards optim like, Show me the you know, it’s like.
262 00:25:12.810 ⇒ 00:25:21.839 Sidd Panigrahi: Oh, I don’t care yet to work all weekend, because my snowflake tables were messy and like especially is they’re like they just don’t even not even they don’t care. They’re unaware. They just don’t.
263 00:25:22.079 ⇒ 00:25:36.440 Uttam Kumaran: Unaware. And so it’s like it’s. And the thing is, it’s like, if you’re going from someone who didn’t have it to that to explain to them what the challenges. Again, if you go from someone who was like, it’s been bad to us, perfect, for from nothing to us, then they’re like they didn’t even know what bad is.
264 00:25:36.440 ⇒ 00:25:56.939 Uttam Kumaran: So there, I have to almost explain. Like trust. I have to be like, trust me. But again it it’s that’s that’s a challenge I have. But again, we, we have these like step change functions in our industry, like duck, dB, like your technology. And it’s up to us to take advantage of that, and then the customer wins right and again like it.
265 00:25:56.940 ⇒ 00:25:57.680 Sidd Panigrahi: Exactly.
266 00:25:57.680 ⇒ 00:26:14.699 Uttam Kumaran: I think, like partnering with someone like Omari. And even people on the design side is just trying to have an ecosystem. People that are getting in and whatever their wedges, and then being like, if they’re trying to expand their offering like, how do we tie in data to once they create the e-commerce solution. We tie in the data component, right? So.
267 00:26:14.700 ⇒ 00:26:15.220 Omri (oh-mree): Yeah.
268 00:26:15.220 ⇒ 00:26:16.020 Sidd Panigrahi: Exactly.
269 00:26:16.020 ⇒ 00:26:18.550 Omri (oh-mree): I think that I think that if I had to kind of cast
270 00:26:18.640 ⇒ 00:26:22.049 Omri (oh-mree): vision cast forward where this ends up for me, it’s
271 00:26:22.240 ⇒ 00:26:23.450 Omri (oh-mree): they’re in.
272 00:26:23.800 ⇒ 00:26:35.037 Omri (oh-mree): You know, big conversation this yesterday. This is something they contend with it, but they don’t have an offering around it. But just so happens this thing I could do well is that they don’t know how to work with the customer. Right? They don’t know how to work with the custom systems.
273 00:26:35.510 ⇒ 00:27:01.769 Omri (oh-mree): I can build anything around a custom system, because this is effectively what I’ve done kind of my entire career. And part of that is, you know, a shop that generates orders into the Erp. But then part of that is going to be visualization like this, and to the point that you made right? I mean, like I can’t show you is actually don’t have access there. Erp. They just broke it because their engineering team is not very effective. Different topic. But
274 00:27:02.110 ⇒ 00:27:14.240 Omri (oh-mree): alright show you was just bunch of tables, and so like they’ve never seen Looker. 1st of all, like, you know, they wouldn’t. They don’t even know what looker is. They don’t know what it is. They don’t know what these things could be. They’ve never heard of tableau, they don’t know what they could be missing out on.
275 00:27:14.350 ⇒ 00:27:17.059 Omri (oh-mree): But then I so I show them something like real
276 00:27:17.748 ⇒ 00:27:19.841 Omri (oh-mree): and they’re just in shock
277 00:27:20.750 ⇒ 00:27:40.086 Omri (oh-mree): and they’re and the thing is that they’re strong operators, right? Like they would of anyone in this world like startups, spin up all sorts of dashboards, stare at it, and do nothing like these people with data operate right? Like they make decisions and moving directions. And take that calls based on the data they just need to have be shown that
278 00:27:40.700 ⇒ 00:27:41.660 Sidd Panigrahi: Access yeah.
279 00:27:42.088 ⇒ 00:27:45.520 Omri (oh-mree): And I think that there is something there.
280 00:27:45.909 ⇒ 00:27:50.259 Omri (oh-mree): Of just having it be one of the tools of like, how do you extend
281 00:27:50.540 ⇒ 00:27:54.839 Omri (oh-mree): your custom world. And I’m a really big proponent of the fact that, like.
282 00:27:55.040 ⇒ 00:28:02.819 Omri (oh-mree): yeah, there’s a whole. And there’s still obviously really big move around like no code and all that. But I think that in if I had to guess what this looks like in the future, is that
283 00:28:03.680 ⇒ 00:28:17.039 Omri (oh-mree): right now? It, like everyone has, like either an it in house or an Msp. Like this managed services for you. I feel like eventually, software development or something like that will be like, you don’t have to worry about new code anymore, because AI and various technologies will make code.
284 00:28:17.040 ⇒ 00:28:17.440 Sidd Panigrahi: Correct.
285 00:28:17.440 ⇒ 00:28:46.170 Omri (oh-mree): Much lower barrier. So no code made sense when there’s no way that you could ever like climbs the the very steep learning curve of code but so longer the case, anyone can kind of spin up code. And so I think that more and more companies are going to want to do things that are custom like, you know, you saw salesforce lose 20% of its market cap like in a day, because people are like we don’t need sales for anymore. Because, like, you know, we get a halfway talented developer around our customer. Rp, we can do anything. Salesforce didn’t build it for ourselves.
286 00:28:46.740 ⇒ 00:28:50.169 Omri (oh-mree): And so I think that’s kind of the way we’re moving. And I would love to have like.
287 00:28:50.170 ⇒ 00:28:50.770 Sidd Panigrahi: Yeah.
288 00:28:50.770 ⇒ 00:28:53.359 Omri (oh-mree): A tool such as yours is like one of the things the weakest.
289 00:28:53.360 ⇒ 00:29:01.440 Sidd Panigrahi: Absolutely. And I mean the other thing we already do is we do already this, I mean, I mean, there’s all these buzz words, another buzz like we get asked this all the time.
290 00:29:01.460 ⇒ 00:29:07.779 Sidd Panigrahi: Or do you do? Nlp, I’m like, okay, sit down and tell me, what questions are you gonna ask a data system
291 00:29:08.260 ⇒ 00:29:35.400 Sidd Panigrahi: like you cannot in your mind, you think, oh, tell me what the problems I need to solve. And it’s gonna give you problems, a solution like that is, just think about when you Google Google search, do you prefer to type your searches or you prefer a voice back and forth? You type, because that’s how you operate now in your mind. You think like, oh, I should be able to ask a natural language question. Why did my orders drop yesterday, and it’s gonna give you an answer and a fix like it is like, sure I love you. I hope you’ll get there. But that is like just a vanity metric.
292 00:29:35.400 ⇒ 00:29:47.330 Omri (oh-mree): I had a I had a conversation about this that my actually my last head of data, my last company is super super capable. And we talked about. It’s like, what if you had that? Okay, what if you had that tool? And you’re like, what’s my Ltv.
293 00:29:48.450 ⇒ 00:29:51.400 Omri (oh-mree): Ltv. Is defined like 17 different ways, right.
294 00:29:51.400 ⇒ 00:29:52.130 Sidd Panigrahi: Exactly.
295 00:29:52.130 ⇒ 00:29:58.529 Omri (oh-mree): It’s defined to like, what’s my churn? What’s like? Even like things like, you know, like we used to always talk about Cac right?
296 00:29:58.610 ⇒ 00:30:03.769 Omri (oh-mree): And Ltv to Cac. But Ltv. To cac me measured like a thousand different ways and like
297 00:30:03.810 ⇒ 00:30:11.920 Omri (oh-mree): it’s often actually Ltv cat is often measured incorrectly and like you bring in different things like you don’t include some aspect of operational costs or something. And like
298 00:30:12.870 ⇒ 00:30:20.099 Omri (oh-mree): it’s largely to be garbage and garbage out. Yeah, it’ll probably give you an answer like I could like I could roll something for you that you can put your data in front of you.
299 00:30:20.100 ⇒ 00:30:21.110 Sidd Panigrahi: Selection.
300 00:30:21.110 ⇒ 00:30:27.139 Omri (oh-mree): I mean, I’d strongly recommend that anything it tells you double check like a thousand times. But yeah, we can build you that front end.
301 00:30:27.140 ⇒ 00:30:37.870 Sidd Panigrahi: So, yeah, I mean, what we bad thought is at least in the near term. If we give a fast enough experience, people will be able to click around and see what they want to see versus have to come up with a question, and somehow magically
302 00:30:37.890 ⇒ 00:30:55.690 Sidd Panigrahi: give the right, because, as a the other common. La. Last example is people want to see forecasting. I’m like, look, I can build a simple, linear forecasting dotted line. But that is so bullshit, because that is so many factors going to forecasting. It’s not a simple. I can show you a dotted line, but like that might not mean anything.
303 00:30:55.690 ⇒ 00:31:02.239 Omri (oh-mree): No, it’s videos. Actually, I will say the one thing that you all had I really liked. There was like the AI building a dashboard.
304 00:31:02.528 ⇒ 00:31:11.699 Omri (oh-mree): And it built me something, and it wasn’t exactly what I wanted, but it got me like 80% of the way there. I saw the patterns like copy and pasted. I saw some of the like, the the functions that went.
305 00:31:11.700 ⇒ 00:31:13.459 Sidd Panigrahi: And then you can edit stuff. They can edit.
306 00:31:13.460 ⇒ 00:31:19.949 Omri (oh-mree): I went to the functions, I changed it up, and it made it what I wanted to. In the absence of that, if I would have spent at least another hour or so trying to figure it out, but it took me way less time.
307 00:31:19.950 ⇒ 00:31:20.500 Sidd Panigrahi: Cool.
308 00:31:20.500 ⇒ 00:31:26.290 Omri (oh-mree): So that was nice. But that was like you feeding me, not like you doing all the work for me. That was you feeding me how to actually use the system.
309 00:31:26.850 ⇒ 00:31:27.690 Sidd Panigrahi: But exactly.
310 00:31:27.690 ⇒ 00:31:29.830 Omri (oh-mree): Sent. Feature was great that saved you a lot of time.
311 00:31:30.060 ⇒ 00:31:39.830 Sidd Panigrahi: Yeah, I think what? So a couple of things. So we’re gonna improve on that, because that I think uses candidly at 3.5 we gonna update to the 4 0 Gp model that do better. The other thing we are gonna do using AI is
312 00:31:39.840 ⇒ 00:31:57.239 Sidd Panigrahi: you’ll be able to just tell the dashboard like, Hey, generate a dial bar chart visualizations or Sankey chart for me for this rather than typing any code, so that we’ll be able to do that is more instructive, and you can control what people want versus. Tell me what my Ltv. Was. I’m like dude. So I.
313 00:31:57.530 ⇒ 00:32:04.570 Omri (oh-mree): What what I’ve come to. I’ve come. I’ve had that few referralations. What AI will mean in the future. But for the time being AI means.
314 00:32:04.690 ⇒ 00:32:07.060 Omri (oh-mree): don’t trust AI to do something you can’t do yourself.
315 00:32:07.480 ⇒ 00:32:19.999 Omri (oh-mree): So like if you’re gonna build a a group of if you want AI to help you build a table like great, have it. Take a 1st crack at it, then you fix it because you know how to do it. But if you tell me what’s my Ltv. To cack if you don’t know the answer.
316 00:32:20.270 ⇒ 00:32:21.150 Omri (oh-mree): and I show you.
317 00:32:21.150 ⇒ 00:32:21.740 Sidd Panigrahi: Yeah, when I see.
318 00:32:21.740 ⇒ 00:32:28.169 Omri (oh-mree): I know you might work, you better be able to confirm it one way or the other, other in the absence of that. Then what is this here like I can’t do for you.
319 00:32:28.170 ⇒ 00:32:32.740 Uttam Kumaran: It’s mainly like I could spend a few hours and do this, or let’s just try to get in a few.
320 00:32:32.740 ⇒ 00:32:33.233 Sidd Panigrahi: Yeah, it’s.
321 00:32:33.480 ⇒ 00:32:34.319 Omri (oh-mree): Yeah, I mean, it’s like.
322 00:32:34.320 ⇒ 00:32:34.920 Sidd Panigrahi: Exactly.
323 00:32:34.920 ⇒ 00:32:44.659 Omri (oh-mree): You could be like, Hey, build me an Ltv calculation based on this data. And then from henceforth that becomes your definition of Ltv, and you track it going forward. Great like
324 00:32:45.170 ⇒ 00:32:46.510 Omri (oh-mree): I I
325 00:32:47.160 ⇒ 00:32:51.120 Omri (oh-mree): I don’t know. It’s just operational metrics, and that’s and that’s like, why I like these like
326 00:32:51.390 ⇒ 00:33:03.679 Omri (oh-mree): these like what I’ll call like blue collar industries. Because these people work hard and like any operational leverage they can have, they know how to use. But then you go to these startups. They have, like the greatest data systems.
327 00:33:03.680 ⇒ 00:33:04.320 Uttam Kumaran: I agree.
328 00:33:04.320 ⇒ 00:33:11.079 Omri (oh-mree): They miss data, and they don’t know how to use it. They just know how to stare it and show like. Put in a board and show it to their venture. Backers like that’s what they can do.
329 00:33:12.430 ⇒ 00:33:19.430 Omri (oh-mree): It actually helps them in no way operationally, if anything, it like analysis for us to stare charts all day, and don’t actually make any good calls.
330 00:33:19.940 ⇒ 00:33:21.500 Omri (oh-mree): but exact aggression.
331 00:33:22.150 ⇒ 00:33:23.040 Omri (oh-mree): I’m sort of.
332 00:33:23.040 ⇒ 00:33:23.640 Sidd Panigrahi: Yeah.
333 00:33:23.640 ⇒ 00:33:24.150 Omri (oh-mree): Cool, on.
334 00:33:24.150 ⇒ 00:33:26.270 Sidd Panigrahi: Yeah, so awesome. Yeah, no. I mean, look, I think
335 00:33:26.550 ⇒ 00:33:42.220 Sidd Panigrahi: as I mean, we are happy to if you come across this, and we’ve been open to like if you need to build integrations, for if you say this is the most commonly used Drp or the tables are in terms of, I think there’s a lot of like. And also today, candidly, from a focus standpoint we primarily sell into
336 00:33:42.860 ⇒ 00:33:52.369 Sidd Panigrahi: at tech and e-commerce. Only we are not selling across industry. We don’t even look at other surface. It is because I mean the risk we can drop into. I always say building a bi tool.
337 00:33:52.580 ⇒ 00:33:54.319 Sidd Panigrahi: It’s like opening a new restaurant.
338 00:33:54.980 ⇒ 00:34:01.560 Sidd Panigrahi: Can you open a new restaurant? Of course. Does every restaurant succeed? No, it feels. But if you just try to say, Oh, I have a restaurant. Everyone needs a restaurant.
339 00:34:01.560 ⇒ 00:34:01.910 Omri (oh-mree): Yeah.
340 00:34:01.910 ⇒ 00:34:06.299 Sidd Panigrahi: There’s so many restaurants like, there’s so many Vi tools. So we are very focused on
341 00:34:06.310 ⇒ 00:34:08.629 Sidd Panigrahi: a, we are focused on what we solve.
342 00:34:08.870 ⇒ 00:34:23.530 Sidd Panigrahi: which is operational metrics. We are not a visualization tool. Operational metrics. And B is, we’ve seen surface like e-commerce. And Atteg is like time has to be critical like data is moving fast. You need to be critical. If you said my data refreshes once a month, once a week.
343 00:34:23.600 ⇒ 00:34:28.919 Sidd Panigrahi: you don’t need role. It’s not as time time is. Time is a big component in our tool.
344 00:34:29.060 ⇒ 00:34:30.169 Sidd Panigrahi: So if you need to see.
345 00:34:30.179 ⇒ 00:34:31.139 Uttam Kumaran: I think this is like.
346 00:34:31.664 ⇒ 00:34:32.189 Sidd Panigrahi: Matter.
347 00:34:32.420 ⇒ 00:34:49.779 Uttam Kumaran: Yeah. But for me, this is the second example. Where in a legacy industry like manufacturing both from Omri and a client we’re working on they both saw roll over like, Oh, that’s it like that. That’d be amazing, right? And so, although you’re not going after, I think, like we’re gonna go like I, I wanna totally right. So.
348 00:34:49.780 ⇒ 00:34:50.439 Sidd Panigrahi: And percent. But.
349 00:34:50.449 ⇒ 00:35:05.184 Omri (oh-mree): Well, I think I think part of like the trend that I’m seeing is the 80 20 thing I talked about about like, you know, you want to take 80% like there’s no reason for you to service 80% of your orders from business to business perspective, or even if it’s like business directly to the buyer.
350 00:35:05.749 ⇒ 00:35:19.939 Omri (oh-mree): with 20%. You want to deal with them. You want to keep your, you know. You don’t pay our salespeople to get the big deals, not to buy a thousand dollar order here and there and there and there, and there, because it’s most your orders anyways. I bet you get this question all the time. Do you have any integrations with quick books online.
351 00:35:22.390 ⇒ 00:35:25.249 Sidd Panigrahi: Ponies. I think we use quick weeks for some of our invoicing.
352 00:35:25.630 ⇒ 00:35:27.990 Omri (oh-mree): You all. I’m sure you all very well, at least of.
353 00:35:27.990 ⇒ 00:35:28.959 Sidd Panigrahi: Yeah, it is.
354 00:35:29.504 ⇒ 00:35:39.335 Sidd Panigrahi: Yeah, yeah, no, we do use it. I can find out now, I don’t think we do. But we can build. I mean again, integrations are easy enough for us to build as long as the system we are integrating to has decent Apis like, if they.
355 00:35:39.540 ⇒ 00:35:40.700 Omri (oh-mree): Very good. There’s nothing.
356 00:35:40.700 ⇒ 00:35:42.350 Sidd Panigrahi: Like books will be okay. We will be okay.
357 00:35:42.350 ⇒ 00:35:50.660 Omri (oh-mree): Yeah, super decent Apis. But I don’t know, like trying to work on something actually for myself, and try to see if it has applicability more because I use swift books online for my own business.
358 00:35:51.335 ⇒ 00:35:51.750 Omri (oh-mree): Yeah.
359 00:35:51.750 ⇒ 00:35:57.799 Sidd Panigrahi: Right now, the easiest might be hacky. Where might be you just spit out some Esp in a Google drive, or whatever, and just connect to those.
360 00:35:57.800 ⇒ 00:35:58.440 Omri (oh-mree): Yeah.
361 00:35:58.440 ⇒ 00:35:58.980 Sidd Panigrahi: Else.
362 00:35:59.560 ⇒ 00:36:04.620 Omri (oh-mree): Yeah, their their day. Their data is all over the place. It’s crazy like there’s no.
363 00:36:05.150 ⇒ 00:36:10.200 Omri (oh-mree): I don’t know. Their chart of accounts makes the the whole prefix online. It’s a mess, but it’s still like
364 00:36:10.280 ⇒ 00:36:13.050 Omri (oh-mree): 70% of small business. Use it. So.
365 00:36:13.050 ⇒ 00:36:14.820 Sidd Panigrahi: Yeah, exactly. Exactly.
366 00:36:14.820 ⇒ 00:36:15.930 Omri (oh-mree): Including myself.
367 00:36:17.060 ⇒ 00:36:17.940 Omri (oh-mree): Cool.
368 00:36:18.720 ⇒ 00:36:19.989 Omri (oh-mree): Well, this is great. Well.
369 00:36:22.000 ⇒ 00:36:23.380 Omri (oh-mree): we’ll keep talking.
370 00:36:23.910 ⇒ 00:36:24.620 Omri (oh-mree): Yeah.
371 00:36:24.620 ⇒ 00:36:37.899 Sidd Panigrahi: Let me know how it goes right, how we can help. And I mean I’ve been frozen out with the thumb and slack as well, so anything you need just let you know, happy to always help, obviously need to help you bring home something. If there’s couple of things we need to do, open to doing whatever we can.
372 00:36:37.900 ⇒ 00:36:55.301 Omri (oh-mree): Yeah. And if you ever see anything that like in your world that’s like, Oh, like that could be something we like, something adjacent that looks like a product that we wouldn’t want to build, but would like be on the backs of us. That’s like that’s essentially, I think we’ll end up doing for or with big commerce, is building something on top of their platform that they would never bother building
373 00:36:55.842 ⇒ 00:37:02.950 Omri (oh-mree): actually meeting with their Vp of product next week. If you ever see anything like that where it’s like you’d need a product team to build like a ui and like
374 00:37:03.040 ⇒ 00:37:07.720 Omri (oh-mree): hook it in and build real on top of it. I think it’s something that 2 of us could come together.
375 00:37:07.720 ⇒ 00:37:08.040 Sidd Panigrahi: Okay.
376 00:37:08.300 ⇒ 00:37:09.600 Omri (oh-mree): And figure something out around.
377 00:37:09.600 ⇒ 00:37:14.729 Sidd Panigrahi: Yeah, I mean, we get, we get this. I mean, I get this all the time. So that’s why I’m excited also to have this.
378 00:37:14.770 ⇒ 00:37:21.510 Sidd Panigrahi: No, it’s like I can actually give an answer versus yep. Nope, sorry. Go find. Let me know who you find. Kind of scenario. No.
379 00:37:21.510 ⇒ 00:37:22.110 Omri (oh-mree): Yeah, yeah.
380 00:37:22.730 ⇒ 00:37:23.810 Omri (oh-mree): Cool, so.
381 00:37:23.810 ⇒ 00:37:25.910 Sidd Panigrahi: Awesome cool. It was nice meeting you a ton.
382 00:37:25.910 ⇒ 00:37:26.570 Omri (oh-mree): Yeah. Good. To meet you.
383 00:37:26.570 ⇒ 00:37:27.040 Sidd Panigrahi: Other, one.
384 00:37:27.040 ⇒ 00:37:28.490 Uttam Kumaran: Yeah, yeah, of course.
385 00:37:29.670 ⇒ 00:37:30.820 Uttam Kumaran: talk soon. Guys. Thanks. Audio.
386 00:37:31.102 ⇒ 00:37:32.800 Sidd Panigrahi: Gosh! Thank you, guys. Bye, bye.