Meeting Title: Brainforge <> Contextual: Bi-Weekly Catchup Date: 2025-11-19 Meeting participants: Uttam Kumaran, Hannah Wang, Rajiv Shah, Holly Condos, Mike Klaczynski, John Marini
WEBVTT
1 00:00:27.710 ⇒ 00:00:28.570 Uttam Kumaran: Hey!
2 00:00:28.930 ⇒ 00:00:30.040 Rajiv Shah: Hey, how you doing?
3 00:00:30.280 ⇒ 00:00:31.430 Uttam Kumaran: Hey, Kit, how are you?
4 00:00:31.430 ⇒ 00:00:32.100 Rajiv Shah: Good.
5 00:00:55.190 ⇒ 00:01:00.339 Uttam Kumaran: Yeah, I know this week, Casey on her team is doing, like, a little bit of a spike around,
6 00:01:00.810 ⇒ 00:01:06.469 Uttam Kumaran: text to SQL, so… I know he asked some questions earlier on Monday. Yeah.
7 00:01:09.279 ⇒ 00:01:10.949 Rajiv Shah: Yeah, no, that’s cool, yeah.
8 00:01:15.990 ⇒ 00:01:17.450 Holly Condos: Raj, how are you?
9 00:01:18.130 ⇒ 00:01:19.309 Rajiv Shah: I’m doing well.
10 00:01:19.590 ⇒ 00:01:20.310 Rajiv Shah: So…
11 00:01:20.380 ⇒ 00:01:22.110 Holly Condos: Good. Where are you based?
12 00:01:22.260 ⇒ 00:01:23.529 Rajiv Shah: I’m out of Chicago.
13 00:01:23.760 ⇒ 00:01:24.600 Holly Condos: Oh, nice.
14 00:01:24.600 ⇒ 00:01:25.380 Rajiv Shah: So, yeah.
15 00:01:25.570 ⇒ 00:01:27.620 Holly Condos: I love Chicago, my son lives there.
16 00:01:27.620 ⇒ 00:01:29.070 Rajiv Shah: Oh, nice, yes.
17 00:01:30.470 ⇒ 00:01:31.210 Holly Condos: Let’s.
18 00:01:31.610 ⇒ 00:01:40.270 Rajiv Shah: It has snowed, it’s not… it’s not snowing today, but it’s, it’s definitely, like, like, in fall, kind of winterish weather now, so…
19 00:01:40.270 ⇒ 00:01:41.950 Holly Condos: Well, it is time.
20 00:01:41.950 ⇒ 00:01:44.580 Rajiv Shah: So, yeah, we had a good summer, so…
21 00:01:45.490 ⇒ 00:01:46.110 Mike Klaczynski: Hi, folks!
22 00:01:46.110 ⇒ 00:01:46.910 Holly Condos: Hi, Mike!
23 00:01:47.110 ⇒ 00:01:47.860 Uttam Kumaran: Hey, Mike.
24 00:01:48.440 ⇒ 00:01:49.519 Uttam Kumaran: How’s everything?
25 00:01:50.720 ⇒ 00:01:52.460 Mike Klaczynski: Good, I was,
26 00:01:52.580 ⇒ 00:01:57.830 Mike Klaczynski: Just trying to wrap up our AWS Marketplace listing, they… they don’t make it easy. And there’s John!
27 00:01:58.440 ⇒ 00:01:59.269 Mike Klaczynski: I.
28 00:01:59.270 ⇒ 00:01:59.880 Uttam Kumaran: John.
29 00:01:59.880 ⇒ 00:02:03.740 Mike Klaczynski: I love their shirt, the way it, contextually?
30 00:02:03.740 ⇒ 00:02:05.980 John Marini: utilizes it. Got it.
31 00:02:05.980 ⇒ 00:02:07.210 Holly Condos: That’s cool!
32 00:02:07.210 ⇒ 00:02:09.000 John Marini: I had a green screen.
33 00:02:09.009 ⇒ 00:02:10.219 Holly Condos: How did you do that?
34 00:02:11.490 ⇒ 00:02:12.890 Uttam Kumaran: On your shirt, Corinne?
35 00:02:12.890 ⇒ 00:02:15.149 John Marini: No, it just seems a… it’s…
36 00:02:16.220 ⇒ 00:02:19.319 John Marini: I was doing a webinar before I got here with a green screen.
37 00:02:19.940 ⇒ 00:02:22.620 John Marini: I don’t know, it’s the white chair.
38 00:02:22.620 ⇒ 00:02:24.210 Rajiv Shah: I take mine down still.
39 00:02:24.210 ⇒ 00:02:26.850 John Marini: I know. Raj and I just finished our webinar, so…
40 00:02:26.850 ⇒ 00:02:27.710 Uttam Kumaran: Oh, great.
41 00:02:29.610 ⇒ 00:02:30.320 Mike Klaczynski: So…
42 00:02:30.320 ⇒ 00:02:34.009 Holly Condos: Yeah, you’re trying to do an AWS Marketplace listing?
43 00:02:34.480 ⇒ 00:02:38.289 Mike Klaczynski: Yeah, we’re just wrapping it up, but they have a lot of requirements, so John, I just…
44 00:02:38.290 ⇒ 00:02:44.960 Holly Condos: And, yeah, I’m trying to get one through, and I’m stuck on the S3 bucket for the logo.
45 00:02:45.660 ⇒ 00:02:53.550 Mike Klaczynski: Yeah, we crossed that bridge a month ago. We’re further along, but, yeah, it’s… it’s fun. Yeah.
46 00:02:56.000 ⇒ 00:03:04.990 Mike Klaczynski: But John, anyway, since I have you here, I tagged you, I put together a draft, because they’re quite prescriptive in what they want, so it should be super simple to set up on our website, just a landing page.
47 00:03:05.450 ⇒ 00:03:07.079 John Marini: Okay, I will look.
48 00:03:08.380 ⇒ 00:03:09.080 Mike Klaczynski: Awesome.
49 00:03:09.080 ⇒ 00:03:23.349 Uttam Kumaran: Well, I know… I know today we, we prepared a couple things. So, one is we… we went ahead and set up a, like, lead, sort of, lead list, sort of Google Sheet. I’ll share that, here in the chat.
50 00:03:23.540 ⇒ 00:03:31.650 Uttam Kumaran: But we have, like, 2 customers that we’re starting to evaluate and put… they put the contextual product in front of right now.
51 00:03:31.680 ⇒ 00:03:47.759 Uttam Kumaran: Abc Home, is a customer, I think we may have mentioned a while ago, but they’re a large home services company here. We’re doing a few things on the AI side with them. We’re starting another discovery process for some future opportunities, and so…
52 00:03:47.760 ⇒ 00:03:52.340 Uttam Kumaran: I mean, I would love, as an outcome of, like, even some of the work that we do.
53 00:03:52.360 ⇒ 00:04:02.370 Uttam Kumaran: for co-marketing to be able to put that in front of them, and then Eden Health is another customer of ours. We run their whole data team, so they were an online pharmacy for…
54 00:04:02.370 ⇒ 00:04:07.190 Holly Condos: several different products, but Ozempic, Manjaro, like, a lot of GLP-1.
55 00:04:07.300 ⇒ 00:04:13.539 Uttam Kumaran: And they’re interested in… I mean, it’s… they… they own a pharmacy, they… they…
56 00:04:13.940 ⇒ 00:04:18.930 Uttam Kumaran: They have a lot of internal use cases, so again, like, trying to use some of the active
57 00:04:19.050 ⇒ 00:04:26.059 Uttam Kumaran: clients that we have to test some of our messaging, and so both of those are ones that have squarely asked us for
58 00:04:26.170 ⇒ 00:04:38.730 Uttam Kumaran: this sort of work. And additionally, we have… almost all of our clients on the data side are asking us for chat with data capabilities. And so that’s what Casey on our team is doing, a little bit of a spike.
59 00:04:38.790 ⇒ 00:04:46.480 Uttam Kumaran: on around, like, how we can facilitate that. So that I know he was asking some questions this week on Texas SQL, so…
60 00:04:47.810 ⇒ 00:04:50.590 Mike Klaczynski: So there’s a couple things we could do right off the bat. One is…
61 00:04:50.820 ⇒ 00:04:53.500 Mike Klaczynski: For folks that are more serious, we could do a workshop.
62 00:04:53.850 ⇒ 00:05:11.239 Mike Klaczynski: So, folks like Raj and a couple of the other SEs know how to do a workshop. They can prepare, grab some data that the client has, and spend an hour running them through. So, like, we spin up a tenant and literally walk them through the whole process of ingesting the data and building the agents themselves. We could run that.
63 00:05:11.240 ⇒ 00:05:25.679 Mike Klaczynski: with you guys. Yeah. The other one is, like, we could record some videos, like, quick couple-minute, you know, 2-3 minute videos, so they could see and understand what it looks like. And then the other one would be a solution offering page. So something you could hand to them that says.
64 00:05:25.870 ⇒ 00:05:32.219 Mike Klaczynski: For your particular industry, here’s some of the things we’ve built, and some of the capabilities we could build for you.
65 00:05:32.410 ⇒ 00:05:39.929 Mike Klaczynski: Like, chat with your data, connect to your Snowflake data, your Databricks data, build a product explorer, a support chatbot.
66 00:05:39.930 ⇒ 00:05:40.550 Uttam Kumaran: Yeah.
67 00:05:40.550 ⇒ 00:05:47.910 Mike Klaczynski: like, we could just brainstorm what those would be, but it sounds like you’re having the conversations, it’s just, I’d love to figure out what the next step is to really get them…
68 00:05:48.340 ⇒ 00:05:50.930 Mike Klaczynski: Interested enough to then have that next conversation.
69 00:05:51.300 ⇒ 00:06:01.209 Uttam Kumaran: Yeah, so for… we have several customers that are e-comm, and so we’ll be putting in front of them, like, a chat with data use case, so that is a clear use case for us to wrap something up where
70 00:06:01.320 ⇒ 00:06:13.230 Uttam Kumaran: for… in our situation, we would probably… we’d either work on a sample e-commerce database and have a demo of Contextual on top of it, where you’re asking questions. In addition to that demo, it’s not only asking over database.
71 00:06:13.250 ⇒ 00:06:21.949 Uttam Kumaran: but of course, the rest of the contextual platform asking over documents and, like, a larger retrieval process. So, I think maybe that’s something I’ll ask Casey
72 00:06:22.020 ⇒ 00:06:25.440 Uttam Kumaran: As part of this spike to try to just drive towards something that
73 00:06:25.630 ⇒ 00:06:29.530 Uttam Kumaran: We can just have as a reusable demo.
74 00:06:29.590 ⇒ 00:06:39.810 Uttam Kumaran: And even if that ends up, like, we do an integration into, like, an own BigQuery or into our own Slack or something, we can think about something there. That way.
75 00:06:39.810 ⇒ 00:06:49.620 Uttam Kumaran: the use of the spike actually just helps us deliver something there. We also are… again, we’re starting to work with some larger customers
76 00:06:49.720 ⇒ 00:07:02.059 Uttam Kumaran: And we’re starting to sort of get into more of, like, okay, what are the operational issues caused by, like, having… like, not having all your documents in one place? And it’s really a lot of what we’re talking about in
77 00:07:02.090 ⇒ 00:07:19.150 Uttam Kumaran: for this legal… in this legal document, which is things around policies, things around, like, employee handbooks, internal documents. To give you a good example, we just, started working with CTA. They are the company that puts on CES.
78 00:07:19.200 ⇒ 00:07:31.540 Uttam Kumaran: And they are… it’s, like, insane. They have, like, tons of SharePoints, tons of CRMs, tons of documents everywhere, and…
79 00:07:32.260 ⇒ 00:07:36.660 Uttam Kumaran: Or, like, right now, they have, like, we did… we’re basically kind of going through
80 00:07:37.030 ⇒ 00:07:55.780 Uttam Kumaran: diligence in terms of, like, as a data team, once we got in there, and they just almost have, like, 50 different sources of data that we’ll have to figure out and ingest. I think 15 or 20 of which are, like, document-related stores. And so, kind of, like, I’m just trying to think about a use case while we’re in there.
81 00:07:55.810 ⇒ 00:08:05.049 Uttam Kumaran: poking around, about, like, how they can leverage Contextual to either speed up our discovery process, or,
82 00:08:06.410 ⇒ 00:08:26.299 Uttam Kumaran: give them something for… and they’re doing… they’re going through some digital transformation, basically, so I want to support those leaders internally with… with a use case for them, hey, you have all this stuff in a SharePoint, or somewhere, and you just need to make it… you need to be able to chat over it to find things. So I’m starting to also think of something with them. We just… we just started working with them.
83 00:08:27.900 ⇒ 00:08:47.829 Mike Klaczynski: How can we help? Like, we’d love to get on a call with you guys, or… with you guys and the client, and then as long as we have the documents, we’re happy to sit down, and Raj and some of the other SEs are happy to build out, an example for you. And then again, if you guys want to be the face of the client, that’s fine. If you want us to also be part of it, we’re happy to be.
84 00:08:47.830 ⇒ 00:08:48.450 Uttam Kumaran: Yeah.
85 00:08:48.450 ⇒ 00:08:50.099 Mike Klaczynski: But, like.
86 00:08:50.100 ⇒ 00:08:57.050 Uttam Kumaran: So maybe we can even just brainstorm, Raj, while I have you. We just started with them last, like, last week, so, but.
87 00:08:57.210 ⇒ 00:09:00.400 Mike Klaczynski: And Utah, can you, can you, real quick, what’s the name of them?
88 00:09:00.750 ⇒ 00:09:05.719 Uttam Kumaran: It’s CTA, so CTA.Tech, it’s the Consumer Trade Association.
89 00:09:07.550 ⇒ 00:09:08.160 Mike Klaczynski: Cool.
90 00:09:08.950 ⇒ 00:09:16.770 Uttam Kumaran: And then… I guess, Roger, like, these guys have a lot of data in SharePoint and in, like, OneDrives.
91 00:09:17.060 ⇒ 00:09:21.100 Uttam Kumaran: I guess I want to go to them and basically talk
92 00:09:21.210 ⇒ 00:09:26.850 Uttam Kumaran: Chief, in the process of, like, identifying all this data, some of it is definitely not structured, but, like.
93 00:09:27.130 ⇒ 00:09:40.040 Uttam Kumaran: if maybe we can brainstorm for just a second on, like, what do you think is… is something that I can discuss with her, or put in front of her, given, like, that set of data. Sorry, that’s, like, so broad, but, like…
94 00:09:40.180 ⇒ 00:09:42.949 Uttam Kumaran: Maybe we can just start talking, I can fill in some gaps.
95 00:09:43.560 ⇒ 00:09:54.340 Rajiv Shah: Yeah, no, I think it is broad. I mean, like, we have the connectors to bring that stuff in, but I think the question is, like, what’s the value that you’re gonna get? Like, you know, how are they doing it now? Are people.
96 00:09:54.340 ⇒ 00:09:58.510 Uttam Kumaran: So you do have the Microsoft-specific connectors to those? Okay.
97 00:09:58.510 ⇒ 00:10:03.379 Rajiv Shah: Yeah, we have a SharePoint connector, and I think there’s some documentation on our webpage about that.
98 00:10:03.380 ⇒ 00:10:04.840 Uttam Kumaran: Great, okay, so… Okay.
99 00:10:06.420 ⇒ 00:10:14.880 Rajiv Shah: you know, there’s some pieces, of course, Microsoft’s got a complicated ecosystem, but yes, I mean, in general, that SharePoint with the entitlements, we can bring over like that.
100 00:10:15.450 ⇒ 00:10:28.789 Uttam Kumaran: So, one thing I’ll maybe… maybe I’ll even ask her is, one, just if they have a solution already. Second is, like, she’s going through, like, this discovery phase, so maybe one way I can easily pitch this is, like, there’s all these documents in there.
101 00:10:28.800 ⇒ 00:10:40.429 Uttam Kumaran: you just need a quickly way to find relevant things. Yeah. You know, and so maybe there’s an opportunity for us to spin something up. So I think that’s a good place to start, and as I asked her that, I’ll sort of know more.
102 00:10:40.500 ⇒ 00:10:45.480 Uttam Kumaran: I mean, you know, the… The difficulty in their business is they…
103 00:10:45.650 ⇒ 00:11:03.779 Uttam Kumaran: They run, like, several really large events, and so there’s a lot around memberships and things like that, but a lot of that is structured data that we’re helping them with. I will ask a little bit about, like, are there policies or other unstructured data or documents in that sort of flow that… that matter that we can put a demo around?
104 00:11:04.060 ⇒ 00:11:04.730 Rajiv Shah: Yeah.
105 00:11:06.410 ⇒ 00:11:17.200 Mike Klaczynski: I shared a link to this new customer case study, which I’m really excited about. John and Jay did an awesome job on our marketing team for it. It’s… ClaimWise is a legal tech company, but…
106 00:11:17.340 ⇒ 00:11:18.390 Mike Klaczynski: I think that…
107 00:11:18.550 ⇒ 00:11:26.039 Mike Klaczynski: it’s obviously very valuable for the legal campaign we want to launch, but I think it could be something useful to share with CTA tech as well.
108 00:11:26.460 ⇒ 00:11:29.550 Uttam Kumaran: Okay, so let me, yeah, let me just put this in my notes today.
109 00:11:30.870 ⇒ 00:11:31.440 Holly Condos: And thanks.
110 00:11:31.440 ⇒ 00:11:33.479 Uttam Kumaran: Okay, awesome. Yeah.
111 00:11:34.360 ⇒ 00:11:35.500 Holly Condos: If you’re dealing…
112 00:11:36.710 ⇒ 00:11:37.829 Uttam Kumaran: Okay, perfect.
113 00:11:38.240 ⇒ 00:11:52.280 Uttam Kumaran: So yeah, and then, the… for Eden, the tequilia, like, we want to open… we want to start talking about chatting with our data, but we’re… we’re getting more into their pharmacy operations, and so one thing I’ll… I’ll also
114 00:11:52.540 ⇒ 00:11:54.650 Uttam Kumaran: maybe I’ll ask Casey to… to…
115 00:11:54.770 ⇒ 00:12:00.579 Uttam Kumaran: Explore more is, like, do they have any… Like, document intelligence-related use cases.
116 00:12:03.130 ⇒ 00:12:06.800 Mike Klaczynski: Yeah, and in their case, you know, they’re gonna have a lot of contracts.
117 00:12:07.110 ⇒ 00:12:08.020 Uttam Kumaran: Yeah, yeah.
118 00:12:08.850 ⇒ 00:12:23.029 Mike Klaczynski: And then I’m sure then they have to troubleshoot, right? Like, people reach out and say, hey, I was at CES, I didn’t get my internet drop link, like, hey, what was it in the contract, and being able to find all that. So there’s so many different ways we could help them, based on the documents that they have.
119 00:12:23.030 ⇒ 00:12:37.370 Rajiv Shah: Yeah, and one other thing is where the… what are they doing for extraction, right? If they have a lot of structured document, like, what is their process for, like, getting that stuff filled? Is there room for, like, harnessing, right, some of our extraction technologies?
120 00:12:37.370 ⇒ 00:12:44.040 Uttam Kumaran: for extraction into, like, another system, and… oh, and from input, from the user input into structured, okay.
121 00:12:46.030 ⇒ 00:12:54.950 Mike Klaczynski: Yeah, because they could harvest all their contracts that they sign, and then fill out a spreadsheet, and just have everything in there, if they don’t already have it in a database or a spreadsheet.
122 00:12:55.310 ⇒ 00:12:56.000 Uttam Kumaran: Yeah.
123 00:12:56.210 ⇒ 00:12:58.929 Uttam Kumaran: So what are they doing for extraction?
124 00:12:59.580 ⇒ 00:13:05.869 Rajiv Shah: Yeah, like, where’s all that structured data coming from? Are there humans that we can figure out, that we can reduce their work?
125 00:13:06.760 ⇒ 00:13:13.970 Uttam Kumaran: Yeah, like, a good example is, it takes, like, 30 minutes to, like, sign up for CES.
126 00:13:14.290 ⇒ 00:13:15.220 Uttam Kumaran: to go.
127 00:13:15.260 ⇒ 00:13:22.849 Holly Condos: Right? And, like, and… but see, there’s just a lot at play in this company that we’re just, like, figuring out, like, who owns what.
128 00:13:22.890 ⇒ 00:13:28.479 Uttam Kumaran: but they’re bringing on new talent in order to, like, modernize a lot of stuff, so I think, like.
129 00:13:28.620 ⇒ 00:13:43.920 Uttam Kumaran: there is some opportunity. They’ve already… you know, we’re already talking about exploring Texas SQL use cases once we’ve, like, right-sized a lot of their data, and, like, they’re… we’re establishing Snowflake for them, we’re doing a lot of the data platform work for them, so…
130 00:13:44.400 ⇒ 00:13:50.000 Uttam Kumaran: and they’re very open to seeing demos of things, so I feel pretty confident if I can…
131 00:13:50.240 ⇒ 00:13:56.140 Uttam Kumaran: have one more conversation with her, and kind of think through what’s an opportunity. Like, they would be open to talking, for sure.
132 00:13:56.440 ⇒ 00:13:59.200 Mike Klaczynski: Let’s do it. Sounds perfect, perfect use case.
133 00:14:02.520 ⇒ 00:14:08.589 Uttam Kumaran: Yeah, out of our current list, like, I feel like that’s… those are, like, kind of the three that really…
134 00:14:10.830 ⇒ 00:14:17.950 Uttam Kumaran: stand out, but I think, Hannah, even if you go to the sales meetings on Mondays, one thing to ask.
135 00:14:18.220 ⇒ 00:14:21.629 Uttam Kumaran: And that meeting is to see if anyone on that call
136 00:14:21.850 ⇒ 00:14:28.439 Uttam Kumaran: like, has any other… and I can ask in a delivery meeting for use cases that kind of would fit this criteria.
137 00:14:28.660 ⇒ 00:14:34.230 Uttam Kumaran: So, worth asking Robert on the whole team on Monday.
138 00:14:35.770 ⇒ 00:14:42.340 Uttam Kumaran: Okay, great. And then the second thing I wanted to sort of talk through was about,
139 00:14:42.390 ⇒ 00:15:01.230 Uttam Kumaran: insurance, so yeah, we… we scheduled… we have a kind of email thread going on with, sort of a friend of the company who also is a friend of mine, and also does our insurance, Ian, and so he’s, like, really deep in the weeds. He’s a commercial insurance broker, in particular, does a lot in construction.
140 00:15:01.350 ⇒ 00:15:09.520 Uttam Kumaran: And actually, like, more, like, a legacy insurance, where, like, the… some of the companies it works for are not that,
141 00:15:09.590 ⇒ 00:15:13.850 Uttam Kumaran: There’s just a lot of documents involved, and they’re not, like, super digitized.
142 00:15:13.890 ⇒ 00:15:33.109 Uttam Kumaran: The reason why I… originally, I talked to him, this is almost, like, two years ago, is that in… and even in their insurance process, they do a lot around structured extraction and inputs into forms when they go submit, even in the… in kind of two places. One, in not only in submitting claims, but also,
143 00:15:33.110 ⇒ 00:15:51.670 Uttam Kumaran: when brokers go shop your insurance policy around and try to get you a quote, they have to fill out all of these supplemental forms and things like that, all of which are bespoke to each insurance provider. So State Farm will have something that’s different than farmers and things like that. So.
144 00:15:52.150 ⇒ 00:15:58.050 Uttam Kumaran: I was talking to him about this use case a few years ago, but I think one thing that I want to talk to him about is not only
145 00:15:58.480 ⇒ 00:16:14.019 Uttam Kumaran: is… how does… like, I wanted to learn a little bit about the buyer at an insurance firm, sort of who… who, like, where… what is the real pain point? And then I think there’s a clear opportunity for a demo, that we can go to market with in insurance.
146 00:16:14.100 ⇒ 00:16:21.380 Uttam Kumaran: This specific, like, supplemental form inputting problem is something that’s notorious around
147 00:16:21.440 ⇒ 00:16:38.929 Uttam Kumaran: insurance brokers, and they have tons of staff that all they do is… is this. I get… to give you an example, he had a client that’s on a farm, and you have to go, like, count, like, the… the grain silos, like, how many do you have? You have to go, like, that’s like… and I was like, okay, you should easily be able to…
148 00:16:39.170 ⇒ 00:16:43.760 Uttam Kumaran: Record that in, like, a transcript, and then have that get structured, you know, extracted into.
149 00:16:43.760 ⇒ 00:16:44.959 Rajiv Shah: reuse it.
150 00:16:44.960 ⇒ 00:17:03.580 Uttam Kumaran: Yeah, a number of forms, and even if the State Farm form is different than the other one, and so they actually have people on staff that is all they do every day. And what does it affect? Deal cycles take longer, there’s… there’s errors, they can’t get enough quotes, and so I think there’s a really great use case in insurance, and also none of that is super sensitive. Like, it’s.
151 00:17:03.580 ⇒ 00:17:04.010 Rajiv Shah: Fuck yeah.
152 00:17:04.010 ⇒ 00:17:06.470 Mike Klaczynski: It’s all data they already have, so…
153 00:17:07.359 ⇒ 00:17:09.269 Mike Klaczynski: If I understand this use case.
154 00:17:09.579 ⇒ 00:17:26.490 Mike Klaczynski: It’s like, they go and collect all this from the client, and then they have to go fill it out on, like, 10 different providers’ sites. So, like, State Farm Farmers. How… we could… could we do a tool that would use, like, a cursor or something, and, like, literally understands that and fills out all the fields? Because the fields are going to be the same.
155 00:17:26.490 ⇒ 00:17:39.060 Rajiv Shah: auto-suggest, basically, kind of thing like that, that would, based on all the historical information it has, it could look and almost, like, fill out the fields, and somebody could just tab right through it, change anything that’s different like that.
156 00:17:39.770 ⇒ 00:17:49.780 Uttam Kumaran: Yeah, and I’ll send you guys… I have a bunch of these example forms, because I went through this exercise when we were just starting the company about building tool around this, so I can send you examples of these forms.
157 00:17:50.140 ⇒ 00:17:53.180 Uttam Kumaran: you know, and so…
158 00:17:53.340 ⇒ 00:18:02.140 Uttam Kumaran: I think there’s… that is, like, I think a clear wrap of a demo with… with a subject matter expert internally. Like, in Ian’s case.
159 00:18:02.140 ⇒ 00:18:18.580 Uttam Kumaran: he’s… he’s in a… he’s an account executive, I don’t know what his title is, but he’s selling a lot of the business, and his deal cycles get jammed by this. But the thing I don’t know is, like, who’s the… who’s the buyer? Like, I don’t know anything about the IT buying cycle within that industry, so that’s, like, what I want to ask him.
160 00:18:19.870 ⇒ 00:18:23.590 Mike Klaczynski: But that would be a direct cost savings. Like you said, they have these massive teams that literally.
161 00:18:23.590 ⇒ 00:18:25.090 Uttam Kumaran: 100%.
162 00:18:25.090 ⇒ 00:18:33.439 Mike Klaczynski: Yes. So, having the AI do that would be massive. Okay. And what did you say the name of that company was? Can we put that in your sheet?
163 00:18:33.710 ⇒ 00:18:37.319 Uttam Kumaran: Yeah, so his company is T-Sib.
164 00:18:37.950 ⇒ 00:18:39.180 Mike Klaczynski: I’ll leave it on me.
165 00:18:39.600 ⇒ 00:18:47.119 Uttam Kumaran: And, and I’ll get the links, but I want to even show you guys, like, an example of, like, one of these documents, like,
166 00:18:51.720 ⇒ 00:18:54.050 Uttam Kumaran: Yeah, like, here… this is a…
167 00:18:54.680 ⇒ 00:19:00.799 Uttam Kumaran: This is an example of a doc, and this is how, like, kind of,
168 00:19:02.050 ⇒ 00:19:19.640 Uttam Kumaran: like, specific things can get. For example, when… when you’re, this is for some construction company, they’re… this is, like, if you need to hire a contractor or something, you have to fill out supplemental applications. And this is, like, there’s things for farms, there’s things for, like, all types of stuff, and so…
169 00:19:19.910 ⇒ 00:19:22.529 Uttam Kumaran: He has to literally go fill this in.
170 00:19:22.530 ⇒ 00:19:23.350 Mike Klaczynski: Like.
171 00:19:23.780 ⇒ 00:19:32.850 Uttam Kumaran: And what this… what this is, is basically he has to call and just have… and not only gets to get all these forms from all these vendors, go and call. Instead, I’m like.
172 00:19:33.050 ⇒ 00:19:43.419 Uttam Kumaran: Dude, you should just upload all these things, have ChatGPT write you the interview, and then once you get the transcript, you should have a tool right back to the documents.
173 00:19:43.890 ⇒ 00:19:51.439 Uttam Kumaran: And also, not always… it’s always… it’s actually kind of a game, because they don’t necessarily need to fill out every single part of this.
174 00:19:51.440 ⇒ 00:20:04.339 Uttam Kumaran: Because Ian has a relationship with each of the different insurance providers, and so you can kind of get 80%, 90% there, and then he can sort of get an understanding of a quote to kind of go back, and so they broker these deals.
175 00:20:04.350 ⇒ 00:20:14.939 Uttam Kumaran: But it is all about the speed at which you can go back and forth, and the amount of quotes you can get, because… and they’re… they’re paid on the… the… basically…
176 00:20:15.130 ⇒ 00:20:19.420 Uttam Kumaran: the policy getting, you know, extended, every year, so…
177 00:20:19.420 ⇒ 00:20:26.479 Mike Klaczynski: Underwritten, yeah, yeah. And how many of these forms are there? Like, are there gonna be, like, 20 of them, 50 of them?
178 00:20:26.670 ⇒ 00:20:31.530 Uttam Kumaran: It depends on, like, this is, like, this is just for, like, one…
179 00:20:31.670 ⇒ 00:20:35.930 Uttam Kumaran: This is just for one insurance company, for one supplemental application.
180 00:20:35.930 ⇒ 00:20:37.780 Mike Klaczynski: Okay. Like, there are a lot.
181 00:20:37.780 ⇒ 00:20:42.579 Uttam Kumaran: I mean, there is a taxonomy around it, like, there’s not… there’s not infinite amount of
182 00:20:42.760 ⇒ 00:20:45.620 Uttam Kumaran: Types of insurance you can get, but…
183 00:20:46.190 ⇒ 00:20:48.470 Uttam Kumaran: There is quite a lot.
184 00:20:48.470 ⇒ 00:20:50.249 Mike Klaczynski: The reason I ask that is…
185 00:20:51.080 ⇒ 00:20:55.989 Mike Klaczynski: Like, purely, he’s got the tribal knowledge and the context, because he knows how to fill these out.
186 00:20:55.990 ⇒ 00:20:56.450 Uttam Kumaran: Yeah.
187 00:20:56.450 ⇒ 00:21:16.199 Mike Klaczynski: he could teach the system, and we could input into the system on how he does it, because, like you said, there’s some fields he can completely ignore, some fields that are important, and once we teach the system to do that, then it can fill out these forms on his behalf. That’s why I was also asking how many. Because, I mean, if he goes to the same 10 or 20 different insurance companies, and there’s the same revolving handful.
188 00:21:16.200 ⇒ 00:21:31.930 Uttam Kumaran: No, so that’s exactly right. So you’re… in that question, yes. So there is a handful of companies that he works for. They have a repository of all the supplemental applications they typically need. What is manual is, like, again, he’s talking to someone, he’s talking to people that run big construction companies.
189 00:21:31.930 ⇒ 00:21:40.129 Uttam Kumaran: And he has to kind of go one by one, but not only that, like, then the process starts of, like, inputting into this form to get the quote, ultimately.
190 00:21:40.200 ⇒ 00:21:41.490 Mike Klaczynski: Yup.
191 00:21:41.490 ⇒ 00:21:45.480 Uttam Kumaran: You know, like, this is a great example of, like, how crazy this…
192 00:21:45.590 ⇒ 00:21:48.260 Uttam Kumaran: For… you have to literally put, like, how many cows do you have?
193 00:21:48.260 ⇒ 00:21:49.110 Mike Klaczynski: I’m a horse.
194 00:21:49.110 ⇒ 00:22:04.670 Uttam Kumaran: you have. So he goes on site to the… and I’ll have him kind of explain… he goes on site and does these things, just in order to get… and you can’t, you can’t not do this. Of course, if you… if you get this wrong, or it’s, like, egregious, you won’t be able to claim.
195 00:22:04.670 ⇒ 00:22:10.900 Uttam Kumaran: Right? So, it’s important, but it is an extremely manual process.
196 00:22:10.900 ⇒ 00:22:11.460 Mike Klaczynski: Yeah.
197 00:22:11.460 ⇒ 00:22:12.770 Uttam Kumaran: And so…
198 00:22:13.370 ⇒ 00:22:29.590 Uttam Kumaran: I’m just glad, like, this is something that me and him have talked about for a number of years, so I’m glad… I think there’s… at least… ideally, there’s something at TSIB, and if not at TSIB, I think he’ll be the helpful guy to help us put together a demo that really directly hits at this.
199 00:22:30.300 ⇒ 00:22:36.049 Mike Klaczynski: So, we’ll start with this, and then, I mean, we add robots and drones, and then he just sends one of those out there.
200 00:22:36.510 ⇒ 00:22:36.940 Uttam Kumaran: It’ll go.
201 00:22:36.940 ⇒ 00:22:38.819 Mike Klaczynski: I liked all of a sudden…
202 00:22:38.820 ⇒ 00:22:49.180 Uttam Kumaran: I told him, you know, this was 2 years ago, because this is right when I started the company, I was like, you should go there, and you… you said, like, why aren’t you able to just, like, talk, record everything you say, like…
203 00:22:49.240 ⇒ 00:22:59.560 Uttam Kumaran: And then just have it go uploaded here. Instead, he’s like, no, it’s incre… and he has to almost go… if he misses a couple things, he has to call, and of course, these guys aren’t, like, on the phones all the time, so it’s like…
204 00:22:59.600 ⇒ 00:23:10.160 Uttam Kumaran: all these issues, and these are very, very expensive policies, like, extremely expensive policies. Construction, right, he’s doing commercial construction insurance a lot of times, so it’s…
205 00:23:10.160 ⇒ 00:23:12.969 Mike Klaczynski: So, one thing on that process.
206 00:23:14.940 ⇒ 00:23:25.890 Mike Klaczynski: that we could help him with is, like, he’s got this form, and he goes around, and instead of him having to type it in or write it in, like, he could just… just have a conversation and say, there’s Dairy cow 7, right?
207 00:23:25.890 ⇒ 00:23:26.430 Uttam Kumaran: Yes.
208 00:23:26.430 ⇒ 00:23:37.809 Mike Klaczynski: and then we collect the transcript, and then out of the transcript, we pull it out and then feed it in. So, it could save them a little bit of time, because using transcripts is pretty easy. It’s something we have experience with.
209 00:23:37.810 ⇒ 00:23:40.539 Holly Condos: Yeah, so he wouldn’t write down everything on the form.
210 00:23:40.540 ⇒ 00:23:46.679 Mike Klaczynski: Yep. And then, like, let’s say he talks to the farmer, and the farmer’s like, oh, but I’ve got 2 more out in another field.
211 00:23:46.680 ⇒ 00:23:47.190 Holly Condos: Right.
212 00:23:47.190 ⇒ 00:23:51.890 Mike Klaczynski: like, the AI would be able to go through that and then tally it up, because it would see the whole transcript.
213 00:23:51.890 ⇒ 00:23:52.480 Holly Condos: Yep.
214 00:23:53.000 ⇒ 00:24:04.879 Uttam Kumaran: Yeah, so the thing I want to understand from him is, like, he goes and sources some information, like, who’s in the value chain between that and the quote getting done? And then, again, I want to understand, is that, is this something, like.
215 00:24:05.790 ⇒ 00:24:21.450 Uttam Kumaran: is this something where Brainforge comes in and builds for TCID? Is this something that we have to… we build sort of a managed service around? That’s, like, kind of what I want to gauge, like, what is… how… how do they buy IT, typically, and… and understand, like, what they would need. But, I mean, for us, totally.
216 00:24:21.610 ⇒ 00:24:26.580 Uttam Kumaran: Given the contextual tech, like, we can build a form factor,
217 00:24:26.690 ⇒ 00:24:37.410 Uttam Kumaran: a lot… again, a lot of their stuff is over phone and over email. Like, they’re not… there’s no modern tech in these places, in these insurance companies at all. So, they’re calling, they’re getting emailed these forms.
218 00:24:37.610 ⇒ 00:24:49.569 Uttam Kumaran: But basically, ideally, I’m like, hey, you should have a chat where you’re working on a client, you’re working on getting them a policy, and you should be able to upload new supplemental apps and have it filled out.
219 00:24:49.570 ⇒ 00:24:50.620 Mike Klaczynski: you know.
220 00:24:50.720 ⇒ 00:24:51.780 Uttam Kumaran: And…
221 00:24:52.030 ⇒ 00:24:59.870 Uttam Kumaran: That way, you can just start to get the quotes faster, and he works directly for his… for those clients, and again, these are, like, huge…
222 00:25:00.290 ⇒ 00:25:07.049 Uttam Kumaran: we’re, like, a very small… we only got insurance through him because I just know him, but yeah, we’re not, like, in his ICP at all.
223 00:25:07.100 ⇒ 00:25:18.380 Mike Klaczynski: So there’s two things here. One you mentioned is, like, renewals. So, like, he’s gonna fill this out once, and then next year there’s gonna be an update or a change. Correct. So imagine just being able to have an AI bot…
224 00:25:18.390 ⇒ 00:25:31.299 Mike Klaczynski: that has a conversation with the farmer, and literally goes down the list and says, has there been any changes? And the farmer can just say whatever, and then you collect the transcript and auto-update it, so there’s one. And the other thing that I think is really interesting, you said value chain.
225 00:25:31.440 ⇒ 00:25:38.289 Mike Klaczynski: So this is him inputting the data into this PDF, but there’s somebody at the insurance company on the receiving end that now takes.
226 00:25:38.290 ⇒ 00:25:38.670 Uttam Kumaran: Correct.
227 00:25:38.670 ⇒ 00:25:45.669 Mike Klaczynski: and has to fill it out. So, to your point, you know, we could work on building something that would be.
228 00:25:45.670 ⇒ 00:25:46.720 Uttam Kumaran: For both sides, yeah.
229 00:25:46.720 ⇒ 00:25:47.919 Mike Klaczynski: Both sides, yeah, yeah.
230 00:25:48.240 ⇒ 00:25:48.870 Uttam Kumaran: Yeah.
231 00:25:49.790 ⇒ 00:25:53.700 Uttam Kumaran: So, I think, I don’t know, Hannah, when did that call get… get booked?
232 00:25:53.700 ⇒ 00:26:09.509 Hannah Wang: Yeah, I wanted to talk about that, so he’s out all of next week, so I was thinking we can aim for the first week of December, but is this time, like, a good time for everyone? Should we just book it 30, 40 minutes worth this time, on that first week of December?
233 00:26:09.510 ⇒ 00:26:28.200 Uttam Kumaran: It’s gonna be… yeah, I’d be down to do an hour, and I will… I will send… I wrote down a lot about this. Like, I have some notes about this, so I can send in advance. And then Ian’s totally… he’s accessible over email and stuff, so… but I just want to try to extract from his mind everything that we need to produce a demo.
234 00:26:28.200 ⇒ 00:26:31.050 Mike Klaczynski: And to produce the materials around this, but…
235 00:26:31.420 ⇒ 00:26:35.359 Uttam Kumaran: Yeah, I think this is a great opportunity.
236 00:26:35.360 ⇒ 00:26:44.379 Hannah Wang: Okay, yeah, I’ll… I’ll book a time for everyone for a Wednesday somewhere in December, and then, yeah, everyone’s looped in, so feel free to just…
237 00:26:44.600 ⇒ 00:26:47.419 Hannah Wang: Reply in the thread with whatever you want to send.
238 00:26:47.720 ⇒ 00:26:48.330 Uttam Kumaran: Okay.
239 00:26:48.500 ⇒ 00:26:49.510 Mike Klaczynski: It’s a great use case.
240 00:26:50.140 ⇒ 00:26:55.579 Uttam Kumaran: The other opportunity I know we were, talking a bit about,
241 00:26:57.380 ⇒ 00:27:04.389 Uttam Kumaran: was, illegal. And so, on our side, like, we spent a little bit of time… we didn’t update the doc. I talked to…
242 00:27:04.640 ⇒ 00:27:08.419 Uttam Kumaran: Robert and our team, and Holly and I talked, and…
243 00:27:09.050 ⇒ 00:27:15.959 Uttam Kumaran: basically, there are some good use cases. I’ve worked… I’ve talked to… this is where maybe I’m, like, curious to…
244 00:27:16.150 ⇒ 00:27:25.599 Uttam Kumaran: see, like, kind of what the appetite is for AI. Some past legal companies we’ve gone to and proposed these, they were nervous about security.
245 00:27:25.730 ⇒ 00:27:31.199 Uttam Kumaran: So there is… so that’s where I think, like, I want to understand from…
246 00:27:31.510 ⇒ 00:27:35.920 Uttam Kumaran: someone who’s in the industry about, like, what is the appetite for AI tools
247 00:27:36.020 ⇒ 00:27:51.699 Uttam Kumaran: you know, that are cloud-based on documents, whether there’s, like, they need to sanitize, or… or basically, like, how that goes. Second is there’s certainly a lot of law that isn’t… that is more about, like, proactive compliance, and, like, policy… and, like, checking
248 00:27:51.810 ⇒ 00:27:57.659 Uttam Kumaran: backing things against policy, and so I feel like you, you, you kind of pick
249 00:27:57.790 ⇒ 00:28:09.880 Uttam Kumaran: picked on a couple there, but for example, like, understanding how regulation impacts existing policies, things like that, that I feel like if we identify a very timely thing here, we could totally do.
250 00:28:10.000 ⇒ 00:28:16.610 Uttam Kumaran: So, I think for me, at this point, in terms of legal.
251 00:28:17.000 ⇒ 00:28:25.809 Uttam Kumaran: And I… I was just talking to Rob about this last night, but he’s on a flight now. He gave me… he basically is speaking to a few other firms about what is…
252 00:28:25.910 ⇒ 00:28:31.530 Uttam Kumaran: like, what could be a great demo with contextual
253 00:28:31.570 ⇒ 00:28:48.559 Uttam Kumaran: I do think that there’s something around policy, internal policies, if we’re going internal, and then there’s maybe something around, litigation. I mean, another thing we… we talked to… he was… he met some people that were in, like, the court transcript business.
254 00:28:48.570 ⇒ 00:28:57.859 Uttam Kumaran: And we were thinking about something there as well, I need to look back at some notes, but there’s something here that I just want to focus in on, like, what exactly the demo is.
255 00:29:00.430 ⇒ 00:29:05.870 Mike Klaczynski: John or Raj, are you guys familiar with the Claywise case study?
256 00:29:08.550 ⇒ 00:29:15.369 Rajiv Shah: I mean, I’m very loosely in terms of… they have lots of patent documents that they’re using us for, yeah.
257 00:29:15.620 ⇒ 00:29:30.719 Mike Klaczynski: Yeah, so the challenge they have as a patent lawyer is, like, somebody comes to you and says, hey, I think I have a novel idea, and then you can spend weeks going through pre-existing documentation to say, hey, is this actually novel? So that’s what that ClaimWise example is.
258 00:29:32.390 ⇒ 00:29:33.040 Uttam Kumaran: Yeah.
259 00:29:33.690 ⇒ 00:29:40.010 Mike Klaczynski: But… I mean, there’s so much precedence in legal, everything is precedence-based.
260 00:29:40.320 ⇒ 00:29:51.020 Mike Klaczynski: Yeah. And I think a lot of that work is done by paralegals, so maybe, you know, that ICP is actually that paralegal is automating a lot of the manual and menial things that the paralegal does.
261 00:29:54.050 ⇒ 00:29:59.260 Holly Condos: Yeah, I think that is fair, having been one long ago.
262 00:29:59.740 ⇒ 00:30:04.380 Holly Condos: There are a lot of tools, but, you know, it’s still…
263 00:30:04.720 ⇒ 00:30:09.740 Holly Condos: It’s not something that the lawyer is charging his customer, his or her customer for, so…
264 00:30:10.860 ⇒ 00:30:11.580 Mike Klaczynski: Yep.
265 00:30:13.300 ⇒ 00:30:13.920 Mike Klaczynski: Yeah, so I…
266 00:30:13.920 ⇒ 00:30:15.390 Uttam Kumaran: I think… yeah, go ahead.
267 00:30:15.880 ⇒ 00:30:21.769 Mike Klaczynski: Yeah, I’m… I’m trying to think who internally is our insurance subject matter expert.
268 00:30:24.060 ⇒ 00:30:28.650 Mike Klaczynski: Raj, do you know if any of the other SCs or AEs have worked on that?
269 00:30:28.650 ⇒ 00:30:36.870 Rajiv Shah: I’m not sure off the top of my head. I mean, I know a bit from my days in insurance like that, but all this is… Yeah, and even if there’s anyone who’s, like.
270 00:30:37.300 ⇒ 00:30:44.380 Uttam Kumaran: Maybe talk to somebody in commercial insurance or any of the big insurance firms could be helpful for that call, too.
271 00:30:48.510 ⇒ 00:30:50.040 Mike Klaczynski: John, do you have anybody?
272 00:30:51.790 ⇒ 00:30:57.829 John Marini: I do not, I don’t know if NARC has any experience, I know finance, but I don’t know about insurance.
273 00:31:00.260 ⇒ 00:31:04.419 Holly Condos: I can take a look, I might know a person I do.
274 00:31:06.710 ⇒ 00:31:16.830 Mike Klaczynski: Yeah, on the insurance side, we could get Farzad to help. He’s worked with… he had a client that he built some stuff and materials out for, and I think Jason has experience on that, but on the legal side, not so much.
275 00:31:19.420 ⇒ 00:31:35.030 Uttam Kumaran: Yeah, on the legal side, we have some… I think this is where I just brought it up to Robert yesterday on my side, so I’ll ask him when he’s off today. But this is where I think he was explaining some things about the litigation process.
276 00:31:35.130 ⇒ 00:31:49.169 Uttam Kumaran: He did mention, like, things in patent law. And so, yeah, anything where, you know, this is also what we found in real estate, was oftentimes people are trying… like, in real estate, a good example, and actually this is where
277 00:31:49.690 ⇒ 00:31:53.760 Uttam Kumaran: I’m just gonna note this down. We should consider going back after them.
278 00:31:53.880 ⇒ 00:31:57.990 Uttam Kumaran: Which is… we went after this company called,
279 00:31:58.500 ⇒ 00:32:03.119 Uttam Kumaran: Hill Partners, they’re, one of the,
280 00:32:03.540 ⇒ 00:32:11.310 Uttam Kumaran: largest commercial real estate owners here in Austin. They own a lot of downtown Austin,
281 00:32:11.450 ⇒ 00:32:19.140 Uttam Kumaran: And we were working with their… the guy that runs the whole thing, and, their head of, leasing.
282 00:32:19.240 ⇒ 00:32:20.959 Mike Klaczynski: Like, leasing contracts.
283 00:32:20.960 ⇒ 00:32:24.539 Uttam Kumaran: And basically, their problem, and we did develop
284 00:32:24.970 ⇒ 00:32:31.770 Uttam Kumaran: We did develop… I mean, we actually did develop a contextual demo, but maybe we should update and go back to them. But basically, they were,
285 00:32:33.530 ⇒ 00:32:49.120 Uttam Kumaran: They have trouble understanding, like, what policies in a lease negotiation process are inbound and out of bounds, and so every lease negotiation process is almost, like, bespoke, and one guy who does a lot of them understands, sort of, like, what they can give and take on.
286 00:32:49.210 ⇒ 00:33:01.710 Uttam Kumaran: And I was like, look, that process takes a long time. You… and you should build sort of a scorecard agent that helps you understand the terms that your… that your… your tenant
287 00:33:02.070 ⇒ 00:33:21.020 Uttam Kumaran: you know, is basically negotiating on, and so you can say, yeah, we always are flexible here, we’re not flexible there, versus this is something this one guy is doing, and of course, it’s… it takes about 6 to 8 weeks to negotiate each lease, and they’re trying to do a lot more. So this is something where, like, I can actually… let me actually go look at,
288 00:33:21.110 ⇒ 00:33:33.279 Uttam Kumaran: we actually do have a demo, I think it’s sitting in our instance. I can wrap… I can… let me… so that may be even closer to the finish line of, like, wrapping something up into something that we can go to market with even faster.
289 00:33:33.400 ⇒ 00:33:35.160 Uttam Kumaran: Yeah.
290 00:33:36.230 ⇒ 00:33:41.820 Mike Klaczynski: Without getting too political,
291 00:33:42.370 ⇒ 00:33:56.759 Mike Klaczynski: there are some cities where the rules and regulations are changing as well, and so, like, imagine if you have a ton of leases, and now you’re in New York, and a new policy, and a new, administration comes in and says, hey, we’re gonna redo and change all this.
292 00:33:56.860 ⇒ 00:34:03.230 Mike Klaczynski: Now, imagine having to go through, you know, a thousand leasing contracts and have to adjust those based on new rules and regulations. I think that could.
293 00:34:03.230 ⇒ 00:34:04.000 Holly Condos: Right.
294 00:34:04.000 ⇒ 00:34:05.279 Mike Klaczynski: Same thing in Seattle.
295 00:34:05.380 ⇒ 00:34:08.850 Mike Klaczynski: Where John and I are familiar.
296 00:34:10.489 ⇒ 00:34:13.799 Uttam Kumaran: Yeah, so I almost wonder, like, in that situation.
297 00:34:14.079 ⇒ 00:34:19.189 Uttam Kumaran: Who in the real estate firm is tasked with Almost that, like…
298 00:34:19.679 ⇒ 00:34:23.509 Uttam Kumaran: Whether it’s com… if you consider that compliance or adherence…
299 00:34:23.510 ⇒ 00:34:23.960 John Marini: from…
300 00:34:23.960 ⇒ 00:34:24.960 Uttam Kumaran: regulation.
301 00:34:24.960 ⇒ 00:34:36.329 John Marini: Depends, usually it can be risk. So, like, this was during COVID, we did these projects at Snorkel, so during COVID, a lot of, like, loan things got suspended, like, certain kinds of interest, certain kinds of loans.
302 00:34:36.330 ⇒ 00:34:36.790 Uttam Kumaran: Awesome.
303 00:34:36.790 ⇒ 00:34:40.179 John Marini: We’re scrambling to figure out how to accommodate that, so…
304 00:34:40.449 ⇒ 00:34:48.910 John Marini: They built AI, basically, to go through that. They did it with predictive AI, because gen AI didn’t exist, in the way it does now, but…
305 00:34:49.420 ⇒ 00:34:52.730 John Marini: I would think, like, risk or contracts.
306 00:34:54.710 ⇒ 00:34:55.350 Holly Condos: Yeah.
307 00:34:56.020 ⇒ 00:34:56.810 Holly Condos: I agree.
308 00:34:56.810 ⇒ 00:35:01.110 Uttam Kumaran: Yeah, so one thing I think… I think there’s… so there’s something around risk.
309 00:35:01.600 ⇒ 00:35:04.869 Uttam Kumaran: mitigation or compliance. There’s also this, like.
310 00:35:05.090 ⇒ 00:35:19.629 Uttam Kumaran: I mean, I think a lot of people that are, doing these lease negotiations, they don’t have a principled mindset around it. Like, there are, again, there are some terms, like, whether you throw in tenant improvements, or whether you change, like, the loan term that are more fixed versus not.
311 00:35:19.630 ⇒ 00:35:31.139 Uttam Kumaran: And usually it’s, like, one person, even at the… these guys do hundreds of millions… they, like, they own tons of Texas, and they’re just, like, doing this one by one, like, on a piece of paper. And so, for… for me.
312 00:35:31.230 ⇒ 00:35:37.960 Uttam Kumaran: Yeah, if you go downtown, they own, like, half the buildings here. And I was talking to them, and it’s just, like.
313 00:35:37.970 ⇒ 00:35:45.419 Uttam Kumaran: They don’t have any, like, scorecard written down on how they negotiate these leases and, like, the impact of changing terms.
314 00:35:45.420 ⇒ 00:36:00.599 Uttam Kumaran: And so, one thing is we could just do, like, a lease negotiation agent, is really what we put together for them, but this was almost, like, a year and a half ago or so, so we built it. It’s kind of… it was a little bit janky. I think we could do it a little bit better. Not only go circle back with them at something more polished.
315 00:36:00.600 ⇒ 00:36:05.520 Uttam Kumaran: But I would just go hit every major commercial You know, real estate firm.
316 00:36:06.080 ⇒ 00:36:24.929 Uttam Kumaran: Here in Central Texas, because I can go see them. Like, we went to their office and did the whole demo. I just think the problem we face with them is… I think, again, I need to under… under… understand, like, where the budget is coming from and how to frame the savings. So that’s… that’s the biggest thing that we kind of, like, didn’t…
317 00:36:25.240 ⇒ 00:36:28.800 Uttam Kumaran: We didn’t nail, and we just didn’t have our stuff together at that point.
318 00:36:29.090 ⇒ 00:36:32.210 Uttam Kumaran: As much as we do now, so… let me think about that.
319 00:36:33.940 ⇒ 00:36:37.050 Mike Klaczynski: And our platform capabilities have come a long way as well, so…
320 00:36:37.050 ⇒ 00:36:38.370 Uttam Kumaran: Yes, totally.
321 00:36:38.370 ⇒ 00:36:41.419 Mike Klaczynski: We could definitely update… help you update that demo.
322 00:36:41.420 ⇒ 00:36:49.070 Uttam Kumaran: And so, in both situations, right, I think this is, like, in the insurance situation, there’s one person who does a lot of the brokering, but you have all these, like, supplemental staff.
323 00:36:49.170 ⇒ 00:37:02.269 Uttam Kumaran: Additionally, even another thing, actually, maybe that just came to mind is, in their office, they had almost, like, 20 people that all they do is take leases, build a lease abstract, and input it into another
324 00:37:02.440 ⇒ 00:37:08.669 Uttam Kumaran: software. But they have to get it right, because that handles the billing, right? The timing and the terms.
325 00:37:08.670 ⇒ 00:37:09.060 Holly Condos: Hmm.
326 00:37:09.060 ⇒ 00:37:12.100 Uttam Kumaran: But, again, that is a pure extraction job.
327 00:37:12.330 ⇒ 00:37:18.050 Uttam Kumaran: extraction and then hitting some endpoint into Yardi, or whatever they’re using for contract management, so…
328 00:37:18.560 ⇒ 00:37:24.769 Uttam Kumaran: that’s another thing where I think we should think about. So there were a couple… there were a couple demos squarely in real estate.
329 00:37:25.160 ⇒ 00:37:27.890 Uttam Kumaran: that we can… We can look to do.
330 00:37:27.890 ⇒ 00:37:34.480 Mike Klaczynski: I mean, that’s a perfect example. We can make those 20 people 10 times more productive, or reduce those 20 people.
331 00:37:34.480 ⇒ 00:37:47.810 Uttam Kumaran: No, yeah, and mainly they were like, look, we have… I don’t know, we can’t scale up the amount of leases for two reasons. One, one guy handled all the leases, and he’s like, yeah, and so… and he’s like, that’s one problem. And second is that
332 00:37:47.850 ⇒ 00:38:00.630 Uttam Kumaran: they’re making mistakes, actually, like, when they’re inputting. That was the more of the problem, is, like, they’re not getting it right, and they’re not doing it fast enough. Like, it’s taking weeks for it to get into the software, that’s causing delays in billing.
333 00:38:00.740 ⇒ 00:38:03.749 Uttam Kumaran: And so, they’re not able to scale up their operation.
334 00:38:03.980 ⇒ 00:38:05.220 Uttam Kumaran: We…
335 00:38:05.220 ⇒ 00:38:13.989 Mike Klaczynski: So that’s, I mean, that’s a perfect example. So they could continue using their current process, and then, like, one of the final steps is just to have the AI go through and double-check them, and flag anything.
336 00:38:13.990 ⇒ 00:38:14.680 Uttam Kumaran: Yes.
337 00:38:14.680 ⇒ 00:38:18.049 Mike Klaczynski: So that could be, like, a lightweight, non-intrusive.
338 00:38:18.290 ⇒ 00:38:24.970 Mike Klaczynski: Way to help them out. I mean, this is the same thing we saw at Palomar Insurance, which is the opportunity Farzad was pursuing in San Diego.
339 00:38:25.140 ⇒ 00:38:31.080 Mike Klaczynski: you know, they… I think their underwriters were doing, like, 5 policies a month, and they wanted to get to 40.
340 00:38:31.420 ⇒ 00:38:36.639 Mike Klaczynski: And so they’re scratching their head on, how do we automate a lot of these systems? So,
341 00:38:37.270 ⇒ 00:38:41.350 Mike Klaczynski: It sounds like they have the pain. They feel the pain. They want to scale up, but they just can’t.
342 00:38:41.870 ⇒ 00:38:51.239 Uttam Kumaran: Yeah, and another, actually, another… this is great that we’re just… I’m just trying to think about who’s… who emailed me. I’m talking to these folks,
343 00:38:51.670 ⇒ 00:38:53.390 Uttam Kumaran: Here, let me share this.
344 00:38:53.560 ⇒ 00:39:07.399 Uttam Kumaran: these guys are called 13th Floor Investments. They’re a Miami-based commercial real estate firm. We talked to them, like, several months ago. Let me just extend this.
345 00:39:08.050 ⇒ 00:39:18.199 Uttam Kumaran: And then they weren’t ready. This was, again, in June, something’s changed recently. I basically pitched them on, like, insurance, document processing, lease reporting.
346 00:39:18.440 ⇒ 00:39:22.690 Uttam Kumaran: And, he owes me a response back.
347 00:39:22.900 ⇒ 00:39:28.280 Uttam Kumaran: But again, another really great opportunity for us to… Send a demo to.
348 00:39:31.490 ⇒ 00:39:38.040 Uttam Kumaran: And then I just have a lot of experience in, like, commercial real estate data from working at WeWork, so these guys usually…
349 00:39:38.270 ⇒ 00:39:57.660 Uttam Kumaran: pick up the phone. So for them, I basically was like, one, we should do some type of just, like… we basically pitched them on two things. One, we need to do some discovery, because I just need to know, like, what are the heavy document-related processes, and then second, we pitched sort of some type of training. But totally, I basically said, like.
350 00:39:57.810 ⇒ 00:40:05.159 Uttam Kumaran: Something around leasing report consolidation and, like, document processing, is what they mentioned last, so…
351 00:40:05.550 ⇒ 00:40:10.899 Mike Klaczynski: So, I think what’d be really fun is to grab 30 minutes with each of these.
352 00:40:11.180 ⇒ 00:40:18.380 Uttam Kumaran: Yeah. Potential opportunities, and just interview them. Say, what are some documents you have? What are some immediate impacts that we could do to…
353 00:40:18.380 ⇒ 00:40:23.630 Mike Klaczynski: Either improve efficiency, or help reduce errors, or help you build new business.
354 00:40:23.700 ⇒ 00:40:32.429 Mike Klaczynski: And then we can just get together as a group and put together a demo that’ll tackle all those, and then go back to all of them and say, hey, here’s the capabilities we have, because…
355 00:40:32.430 ⇒ 00:40:44.630 Mike Klaczynski: you know, Raj is amazing at pushing the envelope of what’s possible, and especially with our new capabilities, like filling out forms and consuming stuff and searching the web, like, all those capabilities are there.
356 00:40:45.440 ⇒ 00:40:49.709 Uttam Kumaran: Okay, let me, let me send a note to these folks, too, and then…
357 00:40:50.430 ⇒ 00:41:05.170 Uttam Kumaran: Yeah, okay, perfect. So I think we have at least a couple of directions to go. We’ll wait to kind of get… since we have an insider on the insurance side, we’ll kind of wait for that call to happen, and then we can move towards a demo there, and he’ll be the…
358 00:41:05.330 ⇒ 00:41:12.479 Uttam Kumaran: he’ll be the guy to give us the stamp on, like, whether he would buy it or not for how much. And then a legal… on… in real estate.
359 00:41:12.940 ⇒ 00:41:28.019 Uttam Kumaran: I will also go spin back up our past demo. We could kind of dust it off and get some more, features into there based on the new platform, and then I’m probably going to send something to these guys and back to HPI.
360 00:41:28.290 ⇒ 00:41:31.080 Uttam Kumaran: I don’t know, maybe the week we’re back to, so…
361 00:41:31.260 ⇒ 00:41:36.440 Mike Klaczynski: Yeah. And what’s nice about it is it’s all related. Like, whether it’s real estate or legal.
362 00:41:36.440 ⇒ 00:41:37.230 Uttam Kumaran: Totally, totally.
363 00:41:37.230 ⇒ 00:41:48.839 Mike Klaczynski: For insurance, it’s all contracts. It’s all contractual language that’s got standardized, requirements, and there’s a ton of it, and it’s all very manual. So, yeah, perfect, perfect use case.
364 00:41:50.310 ⇒ 00:41:54.430 Uttam Kumaran: Okay, and then I have some active follow-ups to do on some of our existing clients.
365 00:41:54.780 ⇒ 00:41:56.260 Uttam Kumaran: I’ll let you know what I hear.
366 00:41:56.450 ⇒ 00:41:57.980 Uttam Kumaran: Okay.
367 00:41:58.500 ⇒ 00:42:09.819 Mike Klaczynski: I am… I’m updating the deal registration form, and so instead of having to have you do it, maybe what we’ll do is we can maybe grab 10 minutes or just do this over Slack. I want to get all these entered on your behalf, so they’re.
368 00:42:09.820 ⇒ 00:42:23.120 Uttam Kumaran: Yeah, and also, that’s mainly gonna be a me problem. The other… the rest of the team here is also way more organized, so they can, like, Hannah can totally help, or Holly can help to input that, if that just helps streamline, for sure.
369 00:42:23.120 ⇒ 00:42:27.660 Mike Klaczynski: Yeah, we’ll make it happen. Anyway, I want to make sure they’re tagged to you, so… Okay, cool.
370 00:42:28.150 ⇒ 00:42:33.339 Mike Klaczynski: Well, this has been awesome. I’m super excited to reconnect and get this going.
371 00:42:33.790 ⇒ 00:42:35.639 Uttam Kumaran: Okay, perfect. Thank you, guys.
372 00:42:35.640 ⇒ 00:42:36.769 Mike Klaczynski: Thanks, everyone. Bye.
373 00:42:37.080 ⇒ 00:42:38.309 Holly Condos: Talk to you soon.
374 00:42:38.570 ⇒ 00:42:39.110 Uttam Kumaran: Bye.