Meeting Title: Demos and Retros Date: 2025-01-10 Meeting participants: Luke Daque, Anne, Nicolas Sucari, Uttam Kumaran, Sahanaasokan, Ryan Brosas, Miguel De Veyra, Connor Fenn
WEBVTT
1 00:00:14.360 ⇒ 00:00:15.000 Luke Daque: Oh!
2 00:00:15.000 ⇒ 00:00:15.890 Uttam Kumaran: Hey! Dude.
3 00:00:16.900 ⇒ 00:00:17.620 Luke Daque: How’s it going.
4 00:00:19.760 ⇒ 00:00:20.780 Uttam Kumaran: Good.
5 00:00:28.080 ⇒ 00:00:29.100 Miguel de Veyra: Hey, everyone.
6 00:00:29.920 ⇒ 00:00:30.710 Uttam Kumaran: Hey.
7 00:01:11.400 ⇒ 00:01:13.389 Uttam Kumaran: Highest, Parob, Tiktok.
8 00:01:15.190 ⇒ 00:01:17.198 Payas Parab (Not TikTok): Whoops! That’s a little loud.
9 00:01:17.810 ⇒ 00:01:18.480 Payas Parab (Not TikTok): Oh, God!
10 00:01:19.565 ⇒ 00:01:19.880 Uttam Kumaran: Cool.
11 00:01:20.610 ⇒ 00:01:21.920 Luke Daque: That’s a good name.
12 00:01:24.840 ⇒ 00:01:27.859 Payas Parab (Not TikTok): Yeah. In case you didn’t know anyone didn’t know I was a tiktoker
13 00:01:28.210 ⇒ 00:01:31.019 Payas Parab (Not TikTok): used to do those dances before I switched the data.
14 00:01:32.810 ⇒ 00:01:37.840 Uttam Kumaran: Wow! Dude I why did you? Why did you decide to do come to such a boring life?
15 00:01:40.810 ⇒ 00:01:44.379 Payas Parab (Not TikTok): Just made sense. You know, it’s a natural transition.
16 00:01:44.780 ⇒ 00:01:45.340 Payas Parab (Not TikTok): Yeah.
17 00:01:56.170 ⇒ 00:01:56.950 Nicolas Sucari: Hey, guys.
18 00:01:58.640 ⇒ 00:02:06.179 Uttam Kumaran: Hey, cool. I think maybe we get started. I know maybe we’re missing a few people, but
19 00:02:06.470 ⇒ 00:02:20.509 Uttam Kumaran: it looks like we have like, I don’t know 10 or 15 people. So that’s perfect. Yeah. So I wanted to start running these sort of Friday meetings kind of been a long time coming. I know, typically we’ve done these meetings that are a little bit
20 00:02:20.740 ⇒ 00:02:34.309 Uttam Kumaran: sort of like whatever’s on my mind, and that’s probably not the best way to share what everything is going on. But also each week I’m kind of gonna probably just popcorn this to someone else on the team
21 00:02:34.860 ⇒ 00:02:47.283 Uttam Kumaran: done this, a bunch of companies, and it it just helps one to take a little bit of stuff off my plate, but also, more importantly, like, gives everyone a little bit of you know fun to have on Friday and collect sort of
22 00:02:48.082 ⇒ 00:02:55.160 Uttam Kumaran: stuff for this. And then also, you know, I have a couple of ideas that will add to this meeting to make this a little bit more
23 00:02:55.690 ⇒ 00:03:05.190 Uttam Kumaran: fun each week. But I think this is a good start. Yeah. So basically, kind of the structure of this, I just wanted to sort of go through every team and talk about
24 00:03:05.350 ⇒ 00:03:15.439 Uttam Kumaran: like wins. Talk about challenges. Talk about stuff that we’re working on, and I’ll probably just popcorn to someone on each team. To go through that
25 00:03:17.100 ⇒ 00:03:25.248 Uttam Kumaran: and then we can talk as a whole sort of like what worked this week, what didn’t work, and then can kind of leave it open to folks.
26 00:03:26.820 ⇒ 00:03:56.809 Uttam Kumaran: cool. So yeah, I mean, I think I wanted to just start by shouting a few people. And this by no means is everybody but from me and and other folks can go after this but for me, I think the couple of people that really helped out this week one was Miguel. We got done with this like really really significant demo for ABC. Home and I went in person. Met their Cfo one of their GM’s, their head of customer service. And they’re like Super blown away, and we pulled up the demo.
27 00:03:56.810 ⇒ 00:04:11.589 Uttam Kumaran: I should have taken a picture. But it’s I’m we’re in this like, I’m in this like eighties boardroom, and we have the demo page up on like this giant projector and we walked through it with everybody. So it was really, really, it was a big win this week.
28 00:04:11.961 ⇒ 00:04:30.640 Uttam Kumaran: I think the second person is Luke on the data side. I think you know. I don’t know. I just want to write Dbt. Code all day. That’s like all I want to do in in my dream state. And I’m excited to be able to work with Luke again on doing that for a new client. And yeah, that’s like
29 00:04:30.900 ⇒ 00:04:35.080 Uttam Kumaran: that sort of stuff we do in our sleep. So I’m excited to sort of like
30 00:04:35.340 ⇒ 00:04:45.039 Uttam Kumaran: crush that we’ve already migrated like 24 models to Dbt. We established Dbt and Github all basically in like 3 days.
31 00:04:45.440 ⇒ 00:04:54.689 Uttam Kumaran: Which is a lot. A lot of that is due to sort of the scripts we wrote before and decisions we made like over the past year on, how do we structure projects?
32 00:04:55.123 ⇒ 00:05:01.939 Uttam Kumaran: But yeah, I mean, we’re gonna make quick work out of the the core stuff for Eden. So I’m I’m really really excited there.
33 00:05:02.519 ⇒ 00:05:24.579 Uttam Kumaran: I think, Sahana, I also want to shout out. She joined. Sort of recently. She’s crushed a lot of the work for Arthelper and for Eden and done everything Async. Which has been really, really amazing. So thank you for that. I know it’s not easy jumping in and moving as fast as we typically move. And sometimes I think.
34 00:05:24.917 ⇒ 00:05:30.369 Uttam Kumaran: we kind of move, and we assume everybody knows how everything is going. So I appreciate you kind of picking stuff up. And
35 00:05:30.480 ⇒ 00:05:38.990 Uttam Kumaran: yeah, we’re working super super fast there. Yeah, that’s probably like all the shout outs from my side, but if anyone else has any they want to do.
36 00:05:41.290 ⇒ 00:05:43.200 Uttam Kumaran: Floor is everybody’s.
37 00:05:49.370 ⇒ 00:05:54.109 Uttam Kumaran: Everybody’s so depressed today nobody’s happy about working with each other at all.
38 00:05:58.540 ⇒ 00:06:04.779 Uttam Kumaran: You can do like. I didn’t like working for this with this person this week, for Xyz reason, too, but it’ll just be awkward.
39 00:06:08.580 ⇒ 00:06:12.460 Nicolas Sucari: Maybe also shout out to Connor, I don’t think he’s here.
40 00:06:13.590 ⇒ 00:06:17.690 Nicolas Sucari: but I think he’s been doing a lot of work on the sales side.
41 00:06:18.953 ⇒ 00:06:20.560 Miguel de Veyra: So yeah.
42 00:06:20.848 ⇒ 00:06:22.820 Nicolas Sucari: He just joined. So maybe sure.
43 00:06:23.060 ⇒ 00:06:24.666 Miguel de Veyra: Redo, that.
44 00:06:27.600 ⇒ 00:06:30.330 Nicolas Sucari: Hey, Connor, I was just shouting out for you
45 00:06:30.640 ⇒ 00:06:34.260 Nicolas Sucari: for all the work and the effort that you’re doing on the sales stuff
46 00:06:34.580 ⇒ 00:06:38.750 Nicolas Sucari: during these past weeks. So yeah, good job.
47 00:06:41.290 ⇒ 00:06:44.901 Uttam Kumaran: I was actually just on a call. So I was a little bit late.
48 00:06:45.160 ⇒ 00:06:45.560 Nicolas Sucari: But.
49 00:06:45.560 ⇒ 00:06:47.670 Uttam Kumaran: Yes, that’s what we want to hear.
50 00:06:50.930 ⇒ 00:06:52.680 Connor Fenn: On camera, real quick.
51 00:06:55.900 ⇒ 00:06:58.726 Uttam Kumaran: Cool! We’re just going through like shout outs,
52 00:06:59.430 ⇒ 00:07:01.369 Uttam Kumaran: If anyone has any more to do.
53 00:07:01.740 ⇒ 00:07:12.219 sahanaasokan: I can go next. Yeah, it’s my 1st week here so nice to meet everyone. Yeah. Shout out to Robert. I asked him a lot of questions this week, and he was very patient with me. So yeah, thank you.
54 00:07:14.170 ⇒ 00:07:15.790 Uttam Kumaran: Oh, yeah. Nice.
55 00:07:17.800 ⇒ 00:07:24.429 Miguel de Veyra: I guess I’ll go next. Shout out to Casey, you know, building those agents because I was doing like there was a lot of my plate
56 00:07:24.620 ⇒ 00:07:30.309 Miguel de Veyra: this week. So yeah, he kind of helped me out big time, especially on the agent for ABC, so yeah.
57 00:07:37.190 ⇒ 00:07:43.037 Connor Fenn: I mean, I’ll say, shout out to all you guys for building those agents. They’re pretty sick, in my opinion.
58 00:07:44.240 ⇒ 00:07:49.069 Uttam Kumaran: Yeah, we have some cool stuff that we’ll talk about in the AI sort of section today. But
59 00:07:49.930 ⇒ 00:07:55.840 Uttam Kumaran: a lot of this lot of stuff like a lot of backlog sort of like infrastructure stuff. Got done this past 2 weeks.
60 00:07:57.250 ⇒ 00:07:59.519 Uttam Kumaran: So we’re going to show some cool bots today.
61 00:08:06.210 ⇒ 00:08:07.115 Uttam Kumaran: Cool
62 00:08:08.670 ⇒ 00:08:19.710 Uttam Kumaran: if nobody else, or if anyone else wants to just raise your hand, I can call on you. But I guess let’s I just wanted to go through sort of each core team. And I’ll probably just pick on people to give
63 00:08:20.282 ⇒ 00:08:24.740 Uttam Kumaran: and update so maybe, Nico, do you want to give the data.
64 00:08:25.230 ⇒ 00:08:28.970 Uttam Kumaran: or maybe, like, broadly, like the client update yeah
65 00:08:30.450 ⇒ 00:08:34.140 Uttam Kumaran: notes. And there’s some notes in the notion. If you want to
66 00:08:34.340 ⇒ 00:08:39.249 Uttam Kumaran: use that, I know I just don’t want to miss anything that anyone from the team wrote. But yeah.
67 00:08:40.590 ⇒ 00:09:01.300 Nicolas Sucari: Yeah, I think, on the data side, we started working with Eden. And we, I think we finished up the audit, or we’re close to finish the audit for our helper. So yeah, 1st of all, Eden, look you already said that. But he’s been working on all of the Dbt stuff trying to migrate all of those scale queries from bigquery
68 00:09:01.300 ⇒ 00:09:18.469 Nicolas Sucari: to Dbt, it’s been a great kickoff week for us at Eden, working on all of that code, trying to understand and meet their data team, too. So we are getting involved a little bit more with with them and trying to understand what are their
69 00:09:18.490 ⇒ 00:09:21.599 Nicolas Sucari: the requirements so that we can help them
70 00:09:21.610 ⇒ 00:09:48.979 Nicolas Sucari: have better data there. So we are. Yeah, we’re working close to Bo is a data scientist from their team writing python scripts. We’re going to be helping him moving some data into some data modeling into Dbt, so that he can get updated stuff for his reports, and we hope to get that ready to deploy as soon as possible, so that we can get a quick win there
71 00:09:49.040 ⇒ 00:10:15.539 Nicolas Sucari: and then an art helper. Well, you already said that, too. So Hannah has been working with Robert really, really hard on that one, so that they can write that audit for them, and see if we can get some more requirements, and we can renew that contract moving forward. Apart from that, we still have pool parts. There pay us been doing some sick weather analysis and explaining the different correlations.
72 00:10:15.690 ⇒ 00:10:41.110 Nicolas Sucari: That’s a little bit difficult to explain from from me to you guys. But understanding different patterns of precipitation. I don’t know what else right now I can’t remember. But we explained that to Kim we had a meeting. We shared that with her so that we can start, we we can still work on that a little bit further so that we can identify some patterns on the sales side on revenue.
73 00:10:41.478 ⇒ 00:10:56.409 Nicolas Sucari: So, yeah, we are still working on on that one, too. And we have this queue analysis. We’re doing a a big work on understanding all of the skills that they have in all of their platforms and trying to consolidate and have a master list for them to use
74 00:10:56.721 ⇒ 00:11:18.200 Nicolas Sucari: not only to to understand what are all of the products that are selling across each platform, but also to understand the cost and where they are getting from each of those products. So yeah, there is a lot on the data side. And we hope we can get some more clients and keep keep increasing the amount of tasks that we have right now.
75 00:11:18.470 ⇒ 00:11:20.110 Nicolas Sucari: But yeah, it’s been a great week.
76 00:11:21.380 ⇒ 00:11:27.180 Uttam Kumaran: Cool. Yeah, I know this work on Eden’s doing really, really well, highest is sort of owning all the work on pool parts. And yeah, we
77 00:11:27.260 ⇒ 00:11:36.379 Uttam Kumaran: we basically provided to them something they wanted for a while, which is, does weather affect their sales. Pool parts is a pool
78 00:11:36.390 ⇒ 00:12:00.119 Uttam Kumaran: home goods like company. They sell pool pumps, pool covers, brushes. You name it related to pools, of course, very seasonal business during pool openings and closings, but also during storms. And so we produce you know, pies produced an analysis that’s like beyond my education level about like how sort of weather features, or get affected by sales. So
79 00:12:00.120 ⇒ 00:12:10.350 Uttam Kumaran: that was really really that was really really great, and I don’t think they fully like wrap their head around it. But I also want to send that to Dan Nico as well. But maybe I don’t know.
80 00:12:10.770 ⇒ 00:12:15.619 Uttam Kumaran: Find some some time when we didn’t get to last time we talked to him, but we should get that in front of him. He
81 00:12:16.060 ⇒ 00:12:21.226 Uttam Kumaran: it’s he’s gonna love that. So he’ll probably have a couple more ideas for us there!
82 00:12:22.240 ⇒ 00:12:23.440 Nicolas Sucari: Cool. Yeah.
83 00:12:23.440 ⇒ 00:12:24.040 Uttam Kumaran: Cool.
84 00:12:24.856 ⇒ 00:12:27.293 Uttam Kumaran: So yeah, I guess I will.
85 00:12:27.900 ⇒ 00:12:42.149 Uttam Kumaran: I’ll take the section for the AI team. Yeah, we had a lot of big wins this week. We’re not only in flight with several clients to pitch AI services, and for folks that aren’t as aware on the data side for what we do on the AI side.
86 00:12:42.484 ⇒ 00:13:01.879 Uttam Kumaran: And hopefully, this will kind of become more of a, you know, sort of meshed longer term. But we basically are building, we have kind of 2 services. We, we’re working on building AI automations to help people with sort of outbound lead generation outbound client facing customer facing stuff, and then also for internal operations, related automations
87 00:13:01.880 ⇒ 00:13:11.920 Uttam Kumaran: like meeting no transcriptions, internal agents, things like that. And so we are just going live with this sort of demo page that I’m going to share right now.
88 00:13:12.229 ⇒ 00:13:18.809 Uttam Kumaran: Which we have been using in several meetings. And we’re gonna actually thought we have a path towards getting this on the
89 00:13:19.110 ⇒ 00:13:38.359 Uttam Kumaran: brain forge AI website as well. But basically, here we have 3 agents. That we’re working on. A Csr assist co-pilot. This is these 2 agents we built for a potential client of ours ABC. Home and Commercial. They’re one of the biggest home and commercial services in Central Texas. I just mentioned, I think I
90 00:13:38.360 ⇒ 00:13:57.089 Uttam Kumaran: just mentioned, or maybe this, I just talking to their their whole team. Basically yesterday we had a great meeting. This agent basically sits with their customer service, reps and allows them to ask questions while they’re live on a call with a customer. For example, you can say I have a customer
91 00:13:57.810 ⇒ 00:14:04.420 Uttam Kumaran: that is about to churn because we missed going to their house.
92 00:14:04.710 ⇒ 00:14:19.400 Uttam Kumaran: What should I do. We have loaded the Csr with several different documents and processes? And then we can basically say, Tell me about our services. I can offer them
93 00:14:19.754 ⇒ 00:14:47.759 Uttam Kumaran: and so these customer service reps not only for sometimes these requests, they have to pass it to someone else. They don’t know how the details as a call back. There’s a lot of things they need to sort of deal with in live on a call. And we’re working with their team to basically deploy an AI agent to all of their I think they may have like a hundred or across, like several different business domains. Customer service reps and this is just a short demo, where we loaded several documents about their pest, related side of their business.
94 00:14:47.760 ⇒ 00:14:59.299 Uttam Kumaran: and the customer service reps can use this to sort of assist. With what services should I offer? Can you tell me about specific service and sort of what are the steps? How do our technicians work? What is our processes? Things like that?
95 00:14:59.850 ⇒ 00:15:04.300 Uttam Kumaran: We also did another demo, which is this knowledge creation agent
96 00:15:04.490 ⇒ 00:15:10.089 Uttam Kumaran: within ABC home as well. They have trainers, trainers are tasked with creating documentation.
97 00:15:10.520 ⇒ 00:15:33.519 Uttam Kumaran: we do a lot of documentation here as well. So we definitely know the struggle of creating those documents, making sure they’re up to date. Think I would say, we’re in a pretty good place compared to most companies. I worked at these guys. They have documents in Google sheets and their Crm in spreadsheets. And it’s a complete mess. And so one of the pro one of the tasks we’ll do for them initially is just to help clean a lot of that up.
98 00:15:33.520 ⇒ 00:15:46.319 Uttam Kumaran: And the second, we’re gonna we’re building an agent that basically helps their trainers create these documents. So, for example, I want to create a doc on how to upsell termite services.
99 00:15:46.750 ⇒ 00:16:11.399 Uttam Kumaran: This agent is actually going to start to interview the user and basically ask them questions on like, What part of this do you want to do? What scenarios do you want to add and walk them through creating the document in a much more fixed way, almost closer, like, if you were to fill out a Google form. But of course, talking like you would talk to chat. Gpt. This way. We guarantee sort of fixed structures for the documentation that comes out
100 00:16:11.790 ⇒ 00:16:16.840 Uttam Kumaran: but also trainers can use this way easier than like staring at blank page.
101 00:16:17.250 ⇒ 00:16:20.990 Uttam Kumaran: So this was was really a hit in the meeting yesterday.
102 00:16:21.890 ⇒ 00:16:27.380 Uttam Kumaran: And so of course, it’ll ask you like probably 10 or 15 questions and then produce a doc at the end that you could copy and paste.
103 00:16:28.650 ⇒ 00:16:35.910 Uttam Kumaran: The last demo. And I’ll actually want to show this in slack is. Where is
104 00:16:37.766 ⇒ 00:16:42.459 Uttam Kumaran: if you’re in our where is this? Is this in?
105 00:16:43.140 ⇒ 00:16:45.256 Uttam Kumaran: Oh, it’s an AI agents.
106 00:16:46.390 ⇒ 00:16:56.980 Uttam Kumaran: If you’re in our internal AI Agent channel, or if you’re not feel free to join, this is sort of where we’re going to start to build agents for ourselves. One of the 1st agents we built is this lead researcher agent?
107 00:16:57.496 ⇒ 00:17:09.559 Uttam Kumaran: Let’s say we’re looking to sell to Brainforge, for example. As. And let’s say I’m Connor fen I go and look up. I go on. Linkedin. I look up. Okay, I want to look up. Okay.
108 00:17:09.819 ⇒ 00:17:11.429 Uttam Kumaran: what’s brain forge?
109 00:17:11.560 ⇒ 00:17:33.069 Uttam Kumaran: Great this company in Austin? I want to look at what they are. I want to click on my profile. You didn’t want to go to Google. You want to search news. This is typically anywhere from like a 15 to 45 min sort of activity. But more than just understanding the company. You want to understand, sort of how we could help them right? And we get leads from all sorts of places. Someone
110 00:17:33.070 ⇒ 00:17:56.159 Uttam Kumaran: I may meet on the street. And it’s like, I need data. Someone may email, one of us and they want help with something. We quickly want to understand what their use cases and what their company is about. And so we built this lead researcher agent, for example, I think Connor is probably the heaviest user of these. But this is a company called Ct home. Basically, we’re pitching a Comp. We’re pitching one of their lead investors
111 00:17:56.160 ⇒ 00:18:03.469 Uttam Kumaran: to bring us into their company or the investment company. But you can see it gives the company overview the key contacts the opportunities.
112 00:18:03.470 ⇒ 00:18:14.270 Uttam Kumaran: It also helps with qualification and next steps, and also relevant case studies. The lovely thing about these now is that these are all getting pulled from.
113 00:18:15.790 ⇒ 00:18:18.330 Uttam Kumaran: These are all getting pulled from notion that
114 00:18:18.640 ⇒ 00:18:46.820 Uttam Kumaran: meaning we’re pulling our case studies from notion. We’re pulling our services from notion we’re pulling. All the details are actually pulling from those live documents. So these aren’t being these aren’t. This isn’t a fixed prompt anymore. Which is amazing. It’s kind of part of the work that Miguel did, which is, we now have a process where we’re writing a couple of key databases from notion into our vector database on a daily basis. So no longer are these like fixed, these are getting pulled.
115 00:18:47.246 ⇒ 00:19:05.889 Uttam Kumaran: These are getting pulled from notion live. So as we update notion that knowledge gets used by the AI agents, which is really, really amazing. So the 1st step of this is really just like, what is a company about? The second step is understanding. How can we? What can we sell to them? Right? Oh, they’re a data company, or they look like they have this much budget.
116 00:19:05.900 ⇒ 00:19:23.400 Uttam Kumaran: And then the 3rd thing is actually allowing, you know, whoever’s on the sales side to actually take action based on this. And so those are the phases of this agent which is like, do the research, then make a suggestion, then actually help, you know, with the actual action. So this is the 1st sort of agent that we’ve deployed pretty widely.
117 00:19:23.610 ⇒ 00:19:31.808 Uttam Kumaran: Feel free to just use this as much as you want. The one thing that I will say is, if you do use this at the end. Here you’ll see a you’ll see a
118 00:19:32.440 ⇒ 00:19:41.835 Uttam Kumaran: a feedback. And basically what you would do is if you have a concern with a message, please just give it a feedback. You can literally, just, I think, message and just say
119 00:19:42.560 ⇒ 00:20:02.330 Uttam Kumaran: as Connor did. Some of the news didn’t correspond to the right, John Burke. That feedback goes directly to the AI team, and then we improve the the bot that way. And you can just tag it and basically tell it that one more demo. I don’t want to hog up all the time, but this is a very out of all the teams. I feel like this is a very
120 00:20:02.780 ⇒ 00:20:08.369 Uttam Kumaran: demo heavy sort of week is this notion, Bot, that we worked on
121 00:20:09.412 ⇒ 00:20:12.739 Uttam Kumaran: and this for me, was like the culmination of like
122 00:20:12.980 ⇒ 00:20:19.210 Uttam Kumaran: I wanted this 6 months ago, and we didn’t have the people I didn’t know sort of like how to do this.
123 00:20:19.310 ⇒ 00:20:44.669 Uttam Kumaran: And we didn’t have the time either. So finally, we’re able to do this, and this is going to sort of set the stage for a lot of fundamental agents that we’re going to build for the company. This is a notion, Bot. Again I mentioned that we started loading a lot of data into notion. One of the key things that we loaded is our leads table. So, for example, if I want to say tell me about Stack, Blitz, and what we’re selling to them.
124 00:20:45.240 ⇒ 00:20:52.915 Uttam Kumaran: Stack Blitz is a lead that we’re selling to we have a bunch of info there from sales team about like what we’re talking to them about.
125 00:20:54.210 ⇒ 00:21:01.799 Uttam Kumaran: it looks like, basically we’re looking for a tool for data engineering. It looks like we’re still waiting a status update for their project. We’re offering data engineering.
126 00:21:02.276 ⇒ 00:21:04.703 Uttam Kumaran: I think if let’s talk, let’s take
127 00:21:05.320 ⇒ 00:21:10.889 Uttam Kumaran: How about cbre? What are we trying to do for them?
128 00:21:13.970 ⇒ 00:21:20.920 Uttam Kumaran: And so this is all coming from the knowledge that’s actually in notion. And you can see here we’re offering Cvres.
129 00:21:21.120 ⇒ 00:21:25.160 Uttam Kumaran: just streamline, lead generation, enhance market analysis, and represent the tenant.
130 00:21:25.842 ⇒ 00:21:31.980 Uttam Kumaran: These are all coming from our leads. Table in in notion here.
131 00:21:32.200 ⇒ 00:21:35.529 Uttam Kumaran: of which we have, like a, you know, bunch with a ton of data in.
132 00:21:35.760 ⇒ 00:21:50.889 Uttam Kumaran: So this is sort of getting to the stage where, like we have, we all write a lot in notion. The problem with documentation, historically, is like what percent of it actually is is utilized. And this is why we wanted to 1st build an AI bot that just helps broadly with talking to notion.
133 00:21:51.160 ⇒ 00:21:54.749 Uttam Kumaran: The sort of vision here is we’re gonna start to have
134 00:21:55.773 ⇒ 00:22:24.029 Uttam Kumaran: bots where necessary. For example, for the sales bot, we may help have a just a dedicated sort of sales. Assist bot. You could ask about clients. You could ask about how I should tackle this. You can paste in meetings and get feedback. The other thing that we’re gonna build next week is. And this is kind of towards the message I sent to Marianne in the team meeting is we have all these meetings per week right? And I’m not a particular fan of meetings for meetings sake.
135 00:22:24.361 ⇒ 00:22:46.630 Uttam Kumaran: I do feel like our meetings are pretty focused. But a lot of the time spent in a meeting is probably talking about notion. Are we organized there? And then? What are the action items? And then we probably get 50% done of what we talk about? Right? So there’s a huge drop off. One of the things we’re going to be building is basically an AI agent that takes all of our meetings and all the transcripts which we’re already doing
136 00:22:46.760 ⇒ 00:23:08.570 Uttam Kumaran: processes that per team. For example, if you take a client call, we want to provide feedback on like what we talked about what actions were need to be taken. The AI team calls a different meeting right where we may have specific tickets that need to created. So each meeting is basically gonna have a prompt associated with it. And not only the 1st thing will be just providing a more tailored summary. That’s better than like.
137 00:23:08.570 ⇒ 00:23:28.520 Uttam Kumaran: If you were to take the transcript and put it into Chat gpt, because we now have knowledge of who the people are in our team what we’re doing for a client, the services we offer right and that’s all. From this notion work. So each meeting will get a specific summary. For example, the sales meeting may have specific follow ups that need to happen. The second thing is, all this is going to get pumped into slack
138 00:23:28.872 ⇒ 00:23:55.609 Uttam Kumaran: and slack is really, I would say, out of all the tools we’re using, slack is where work happens for us and our company. So slack is really going to be where the interaction with this bot happens. So the 1st thing is just summarizing those. The second thing is actually giving key insights. Hey, this ticket needs to be created. This meeting needs to be booked assigning those to people. The 3rd layer is basically you’ll be able to tell the bot. Go ahead and book that meeting. Go ahead and create that ticket things like that.
139 00:23:55.830 ⇒ 00:23:59.119 Uttam Kumaran: So we’re kicking off a lot of that process next week
140 00:23:59.240 ⇒ 00:24:07.720 Uttam Kumaran: for just the meetings. But the main goal here is just reducing the loss that happens in every meeting, in every company which is like you come to the meeting.
141 00:24:07.980 ⇒ 00:24:26.199 Uttam Kumaran: Maybe you’re organized. Maybe you’re not for the most part, I would say we just come in and we we I look at my calendar. I’m like, what am I talking about right now and then. I I sort of go for it. But then, also, the actions being taken, we want just really have people come to meeting focus on talking about the topic, the stuff on the edges. We’re gonna hand off to AI.
142 00:24:28.660 ⇒ 00:24:33.559 Uttam Kumaran: Okay, I just talked for like a while there. So that’s everything. On AI.
143 00:24:34.300 ⇒ 00:24:35.810 Uttam Kumaran: Any questions.
144 00:24:35.930 ⇒ 00:24:41.640 Uttam Kumaran: I just took a lot of credit for a lot of work that Miguel and Casey are doing. By the way. So it’s all credit to them.
145 00:24:42.630 ⇒ 00:24:48.419 Connor Fenn: I have a quick question on the notion piece. So it’s pulling from notion.
146 00:24:48.700 ⇒ 00:24:54.909 Connor Fenn: does it? Just pull the writing from notion, or, as we like, attach documents and stuff. Will it review that, too?
147 00:24:55.420 ⇒ 00:25:06.960 Connor Fenn: So like eventually, could I ask? You know the bot, hey? I just prepared the scope of work for cbre. Tell me what it entailed again, and we’ll.
148 00:25:06.960 ⇒ 00:25:15.360 Uttam Kumaran: That’s a good. That’s a good point. Yeah, I think we should. We should. We could add that to the notes, Miguel. I don’t know we’ll have to think about if the SDK, we can get the file link and then
149 00:25:15.500 ⇒ 00:25:16.940 Uttam Kumaran: basically bring that in.
150 00:25:17.530 ⇒ 00:25:22.609 Uttam Kumaran: But that’s a really good point, because we don’t have the figma linked. We have the figma link, but that
151 00:25:22.720 ⇒ 00:25:25.169 Uttam Kumaran: we don’t have the content coming in from Figma. So.
152 00:25:25.880 ⇒ 00:25:36.650 Connor Fenn: I would say, too, it’d be cool, though I don’t know. This might be more down the road, but eventually you could start to interact with it where it’s like, Hey, like, I need this file. Can you give it to me?
153 00:25:36.650 ⇒ 00:25:40.159 Uttam Kumaran: Correct. Yeah, or like, tell me, another big thing is like.
154 00:25:40.410 ⇒ 00:26:02.590 Uttam Kumaran: I think again, I would say, compared to other companies, have been at our notions fairly organized. In fact, we have a person dedicated that Mary Ann is doing a solid job. But still it’s hard to find stuff, just because there’s hundreds of things. So one of the key things is just like, where do I go to get this right? And I think that’s a great example we can probably add for this demo. But, like.
155 00:26:02.690 ⇒ 00:26:13.109 Uttam Kumaran: where is this document? Or give me the notion, link for this? The other thing is writing back like, update this thing to include this right? So stuff like that, we’re totally gonna add with this great idea.
156 00:26:18.870 ⇒ 00:26:19.660 Uttam Kumaran: cool
157 00:26:20.147 ⇒ 00:26:27.050 Uttam Kumaran: next team is sales. So maybe, Connor, do you wanna talk about? I know that we were working on a bunch of clients. But
158 00:26:27.620 ⇒ 00:26:31.969 Uttam Kumaran: yeah, if you want to just take the overall stuff for sales. That’d be amazing.
159 00:26:32.570 ⇒ 00:26:43.880 Connor Fenn: Yeah for me, for I guess just the wins that I kinda had thought about was, you know, week 2 was able to get proposal out the door. It’s pretty happy about that. With cbre
160 00:26:44.466 ⇒ 00:26:57.539 Connor Fenn: we have 2 more pretty solid discovery calls, and a demo, and another proposal out the door. So kind of was just feeling good about that, getting a jump on things right away.
161 00:26:58.214 ⇒ 00:27:10.950 Connor Fenn: As far as challenges any and all of the email Linkedin Outreach I’ve done has not been very successful. So gonna try to work on that and tailor that a little bit more.
162 00:27:11.757 ⇒ 00:27:21.670 Connor Fenn: And then, yeah, just keep working on just overall service knowledge, you know. Obviously, I’d like to start doing all of these calls without you guys. But
163 00:27:22.090 ⇒ 00:27:24.050 Connor Fenn: yeah, we’ll get there.
164 00:27:25.820 ⇒ 00:27:26.710 Connor Fenn: But
165 00:27:27.130 ⇒ 00:27:30.759 Connor Fenn: yeah, that’s all I can really talk about on my side. I don’t know if you want to talk about more of the.
166 00:27:31.160 ⇒ 00:27:34.346 Uttam Kumaran: Yeah, I know we the I would say broadly.
167 00:27:34.830 ⇒ 00:27:42.400 Uttam Kumaran: in the last 2, 2 to 4 weeks we’ve in my in my history. This is the most proposals we’ve ever sent out.
168 00:27:43.172 ⇒ 00:27:52.790 Uttam Kumaran: Of course, like, we’re not going to win any. And of course, like I really don’t get, I’m not really happy at all until the check hits. Frankly. Otherwise, I’m like, I’m pretty much
169 00:27:53.070 ⇒ 00:27:56.169 Uttam Kumaran: like it doesn’t matter. But I will say, like
170 00:27:56.220 ⇒ 00:28:26.000 Uttam Kumaran: we are a data company. And if we know one thing, we know, things are a funnel. And so to see us not only take this many initial calls for stuff, this many things to get to the proposal stage means we’re really heading the right direction. You know what? What the next thing you know, what we’re gonna want to do on sales is really take a step back and look at. Why? Why do we lose some of these? Why are some of these not getting to the next stages. Understand whether it’s like this wasn’t the right client to start with, or something happened in the process. And those sort of retros will do longer term.
171 00:28:26.350 ⇒ 00:28:32.665 Uttam Kumaran: but it’s a huge win. I mean, we’re sending some of the biggest contracts we’ve sent out for some really really big names.
172 00:28:33.490 ⇒ 00:28:39.650 Uttam Kumaran: you know, and and really really excited to hopefully close some of these. So yeah,
173 00:28:40.980 ⇒ 00:28:45.009 Uttam Kumaran: cool. And then, maybe, Ann, I could pass it to you on the design side.
174 00:28:46.300 ⇒ 00:28:57.950 Anne: Yep, so for the design we just launched the Newsletter single page. Then we have the new design Newsletter section for blog page that’s for next week.
175 00:28:58.730 ⇒ 00:29:02.660 Anne: and then most of the documents and capabilities text
176 00:29:02.830 ⇒ 00:29:07.759 Anne: are done and for review, and I think the challenge for the design is
177 00:29:07.930 ⇒ 00:29:11.710 Anne: mostly copy for the documents
178 00:29:11.990 ⇒ 00:29:14.160 Anne: and then for next week.
179 00:29:15.268 ⇒ 00:29:23.831 Anne: We’ll be starting the design for the resources website, pages, logo animation for Ig reels and Youtube shorts, and then,
180 00:29:24.610 ⇒ 00:29:32.040 Anne: I’ll be having a meeting with Miguel for AI Demo showcase. That’s all from design team.
181 00:29:32.610 ⇒ 00:29:41.941 Uttam Kumaran: Cool. I just wanna double click on a few of these because Ann is not gonna promote herself. So I will promote her. We
182 00:29:42.900 ⇒ 00:29:46.699 Uttam Kumaran: we’ve been doing a lot of work on this sort of capabilities, deck format
183 00:29:46.880 ⇒ 00:29:50.420 Uttam Kumaran: and building sort of these key slides, I would say.
184 00:29:50.890 ⇒ 00:30:15.399 Uttam Kumaran: like, I’ve done a really good job giving enough content for this. But we have asked for basically like templated slides that we can start to use. One is transition sort of the deck that Robert from Pongo, like what what Pongo was doing in terms of these initial calls, or the proposal decks, or the stuff for the audits to this format. And we have several sort of formats here. Like again.
185 00:30:15.590 ⇒ 00:30:30.509 Uttam Kumaran: text, single text, single column entire. These sort of transition slides. We also have. You know, these sort of like timeline slides. We have these 3 pane slides we have team, I think, is this, though this might have been, though
186 00:30:30.710 ⇒ 00:30:32.579 Uttam Kumaran: this is the latest one. Right, Ann.
187 00:30:32.917 ⇒ 00:30:34.940 Anne: I think that’s the old one.
188 00:30:34.940 ⇒ 00:30:37.579 Uttam Kumaran: Oh, really, okay, hold on. Let me let me just go to the new one.
189 00:30:47.000 ⇒ 00:30:50.290 Uttam Kumaran: maybe I don’t have the link.
190 00:30:58.800 ⇒ 00:31:01.319 Uttam Kumaran: Okay. Here, okay, cool.
191 00:31:06.800 ⇒ 00:31:13.740 Uttam Kumaran: Okay, yeah. So basically creating each of these templated slides. Where we have
192 00:31:14.060 ⇒ 00:31:27.450 Uttam Kumaran: again several things that we can slot in and use, no matter who’s creating decks. Basically, we have now, like a place we go to get these slides and create them. We’re not a very deck, heavy company. At all. But of course, some
193 00:31:27.880 ⇒ 00:31:35.989 Uttam Kumaran: 2 things, once, some clients. We want to start doing decks, for. These are large, large contracts. So we want to start to act like
194 00:31:36.270 ⇒ 00:32:06.180 Uttam Kumaran: the people that we’re competing with. Like Accenture Deloitte, we want to start to show them that, like we, we do have this really formal process. The second thing is sometimes for clients that we talk to where there’s not really a great structure like they don’t give us enough information, or we’re sort of having a broad conversation. A deck is a great way to structure that. And basically until we get to the point where we can Riff, and then you can sort of pull the parachute out of this deck, but sometimes Deck is friendly. The 3rd thing I have a lot of people always ask me like, send me a deck about what you guys do.
195 00:32:06.683 ⇒ 00:32:08.399 Uttam Kumaran: And I would love to
196 00:32:08.580 ⇒ 00:32:14.289 Uttam Kumaran: do that and have them learn about us. So we have timelines, sort of like
197 00:32:14.600 ⇒ 00:32:21.900 Uttam Kumaran: table formats, 3 column table formats, or we’ve copied some of the table, some of the tables from our last
198 00:32:22.432 ⇒ 00:32:43.679 Uttam Kumaran: from some of the decks that I sent to Ann again really, great templates that we can reuse. We did a lot of inspo into snowflakes, decks by trans decks, bunch of Deloitte, Bcg. Decks that weren’t really ugly and tried to give as much of that as possible. We have our whole team on one slide now, which is great.
199 00:32:44.076 ⇒ 00:32:51.804 Uttam Kumaran: And so I’m really, really excited to leverage this, I’m going to be working on some of this tomorrow. So that’s 1 thing. The second thing is
200 00:32:52.360 ⇒ 00:33:16.510 Uttam Kumaran: we have several sales assets that are all sort of ready to use. We have one pagers on our services. We have sow docs that we’re using across the board for every single proposal which is great. I have never gotten something like this from any consultant that looks nearly this good how? How does this help us? 1%, 10%? I don’t know.
201 00:33:16.560 ⇒ 00:33:23.320 Uttam Kumaran: I think 10. It’s probably somewhere in the middle. But I love the way these look. And I think we come across really, really professional
202 00:33:23.813 ⇒ 00:33:29.290 Uttam Kumaran: and then, yeah, the newsletter, we’re starting slowly, starting to grow. And we’ve made some website updates for that. So
203 00:33:32.310 ⇒ 00:33:36.390 Uttam Kumaran: yeah, great and then maybe, Ryan, I can kick it to you for marketing stuff.
204 00:33:37.794 ⇒ 00:33:45.290 Ryan Brosas: I the win that we got is that we, on our 1st publish, or like asking like a sub like
205 00:33:45.370 ⇒ 00:34:07.500 Ryan Brosas: our our newsletter subscriber from Linkedin, or in other social. So for surprisingly we got like a 1 sub subscriber, which is, we just established just yesterday, and also we we we got we establish or publish our 1st video content, which is very huge for us.
206 00:34:07.500 ⇒ 00:34:22.649 Ryan Brosas: And we got like a huge impression on Instagram, and also we. I also structure like a technical draft blog which is from the from the documentation of Luke.
207 00:34:23.172 ⇒ 00:34:30.630 Ryan Brosas: which is more on synthetic data. And I’m asking for like waiting for the feedback for that and the the
208 00:34:30.760 ⇒ 00:34:52.767 Ryan Brosas: the struggles that I have is like the other stuff that I needed to, you know, tackle with our with our articles like, you know, more more like more technical stuff, which is a lack of documentation. Which is, I need some expertise on the on this specific tools. And I can, you know,
209 00:34:53.230 ⇒ 00:35:02.125 Ryan Brosas: I can, like extent, extend those materials or those those materials to other content. Also
210 00:35:02.710 ⇒ 00:35:08.640 Ryan Brosas: for our performance we got like a low performance, which is, I think.
211 00:35:09.250 ⇒ 00:35:11.566 Ryan Brosas: because this is from the
212 00:35:12.280 ⇒ 00:35:38.130 Ryan Brosas: the holidays, I guess. But we are doing well on SEO and the struggles that I have is when getting some meetings that that, we want to repurpose. We are starting that also. Because we want to make you know more content. And you know, pushing more content on our socials. And I think that’s for the struggles.
213 00:35:38.270 ⇒ 00:35:45.719 Ryan Brosas: And the next step that I want to establish for the marketing is increase volume in all
214 00:35:46.361 ⇒ 00:35:57.798 Ryan Brosas: in all channels like establish like a proper strategy for each channels, and also like figuring out what you know, a lead magnet that good that we can. You know,
215 00:35:58.620 ⇒ 00:36:03.310 Ryan Brosas: that we can cook some subs or potential leads.
216 00:36:03.460 ⇒ 00:36:10.440 Ryan Brosas: And also like publishing like the 1st audio for our, podcast I think that’s all for me.
217 00:36:12.070 ⇒ 00:36:38.480 Uttam Kumaran: Okay? Great. Yeah. In particular, for the meetings. I think definitely any of the sales meetings we have, which I think, Connor, that’s kind of the. I wanted to just make sure everything’s recorded because we’re gonna take our frames and sort of cut them up for video where we’re talking about a certain thing. And Ryan will basically cut that into like a Tiktok style video with with the caption. And it’s a quick cuts. We’re gonna use that to put on shorts, Tiktok and Instagram.
218 00:36:38.803 ⇒ 00:36:42.490 Uttam Kumaran: So as much of those that we we have a lot of stuff
219 00:36:42.560 ⇒ 00:37:05.960 Uttam Kumaran: in our in like from last year basically recorded. But I think now we that we have some consistent sort of like the way we talk about things, those will make great clips to basically put out so we’re gonna try to do some of this more passive video where we’re not really recording anything like just for marketing. But we can repurpose those. And then, yeah, I think as we go I think probably between me
220 00:37:06.345 ⇒ 00:37:17.849 Uttam Kumaran: the sales team and Ryan will think about some more marketing content. We want to do. We’re just probably at the point where I think on the AI side, we have a few key things that we can sell same on the data side.
221 00:37:17.960 ⇒ 00:37:34.889 Uttam Kumaran: So we want to kind of sort of take that distance, not only create sales assets like one pagers, but also start to create videos, get that onto the site and have just several ways that people can find out exactly what we do, how it benefits them and like how to get in contact with us.
222 00:37:36.100 ⇒ 00:37:38.820 Uttam Kumaran: So we’ll we’ll keep pushing there.
223 00:37:40.360 ⇒ 00:37:42.290 Connor Fenn: Cool like it.
224 00:37:43.370 ⇒ 00:37:57.290 Uttam Kumaran: Cool and then the last piece I had is basically on just like people. So we’re still sort of in process with a few more analysts.
225 00:37:57.765 ⇒ 00:38:18.239 Uttam Kumaran: Again, we interview sort of like passively all the time. So we always have. You know, people across De AI analytics, engineering and data analysis in process. I’ll kind of put this out there again. If if anybody has any smart friends that they think could be a good fit here on in doing anything.
226 00:38:18.774 ⇒ 00:38:30.810 Uttam Kumaran: Please introduce them to me. If we can find that way to fit them in, we totally will. We probably are in interview process with one analytics engineer, probably like 3 analysts.
227 00:38:31.322 ⇒ 00:39:00.370 Uttam Kumaran: And then probably 2 AI folks. Again, sort of as we grow. The biggest thing is, we always want to have great people available to sort of fit into our process. You don’t want to be reactively finding people, because that’s where we basically make we sort of lower our standards and bring in people that aren’t smarter than than each of us every time. So I’m always on the lookout, and we have a great recruiting notion where you can go see everybody that’s in process. There’s
228 00:39:02.070 ⇒ 00:39:12.639 Uttam Kumaran: cool. We’re coming up on time. I thought I mean I don’t know. I thought this meeting format was great. I probably took the most time on the AI stuff. I tried to talk very fast and get through that. But.
229 00:39:13.030 ⇒ 00:39:18.539 Uttam Kumaran: I don’t think we’ll have that many Demos next week. Anything else about this meeting
230 00:39:18.790 ⇒ 00:39:23.510 Uttam Kumaran: or things for this week, or questions the answer
231 00:39:27.840 ⇒ 00:39:41.629 Uttam Kumaran: cool. So for next week I’ll probably kick this to somebody like, probably by Monday. And that way you have a little bit of a week to kind of get ahead on this and get updates from everybody, and then we’ll think of something else fun to do during this meeting
232 00:39:41.860 ⇒ 00:39:48.210 Uttam Kumaran: to kind of break up the the boringness. But cool.
233 00:39:48.320 ⇒ 00:39:52.319 Uttam Kumaran: Okay, guys, thank you so much. This is a great week. And then, yeah, looking forward to
234 00:39:52.550 ⇒ 00:39:55.489 Uttam Kumaran: to chatting either on slack or next week on Monday.
235 00:39:57.020 ⇒ 00:39:57.970 Miguel de Veyra: Thanks. Everyone.
236 00:39:58.620 ⇒ 00:40:00.220 Anne: Everyone guys, happy.
237 00:40:00.220 ⇒ 00:40:00.650 Uttam Kumaran: Thank you.
238 00:40:01.870 ⇒ 00:40:03.099 Nicolas Sucari: Thanks, guys. Bye-bye.