Meeting Title: Brainforge x Afterpay Automation Consultation Date: 2025-10-14 Meeting participants: Luke, Uttam Kumaran
WEBVTT
1 00:00:46.860 ⇒ 00:00:48.530 Uttam Kumaran: Hey, Luke, can you hear me?
2 00:00:48.980 ⇒ 00:00:51.129 Luke: Yeah, I can hear you. How’s it going?
3 00:00:51.730 ⇒ 00:00:53.149 Uttam Kumaran: Hey, good, how are you?
4 00:00:53.460 ⇒ 00:00:56.559 Luke: Yeah, really well. So, how do I pronounce your first name?
5 00:00:57.140 ⇒ 00:00:58.100 Uttam Kumaran: Who, Tom?
6 00:00:58.100 ⇒ 00:01:01.070 Luke: Water, okay. Nice to meet you. Yeah, nice to meet you.
7 00:01:01.070 ⇒ 00:01:03.789 Uttam Kumaran: Yeah, nice to meet you. Thanks for taking the time.
8 00:01:04.120 ⇒ 00:01:14.060 Luke: No, awesome, yeah, pleasure, to, yeah, to chat to you. Where do you want to begin? Where are you guys based?
9 00:01:14.930 ⇒ 00:01:28.000 Uttam Kumaran: Yeah, I’m here in Austin, in Texas, in the States. So my background is in data engineering. I worked as a data engineer for a while, and then I started Brainforge about…
10 00:01:28.170 ⇒ 00:01:39.859 Uttam Kumaran: two and a half years ago. Quit my last job and just started seeing, like, okay, can I go off on my own and start building data systems for people? That’s what I did. So, built data teams, led data.
11 00:01:39.860 ⇒ 00:01:49.630 Uttam Kumaran: different product companies, and then, you know, slowly just bootstrapped and grew the business, and during that whole time, I was using AI to actually build our company.
12 00:01:50.200 ⇒ 00:01:50.650 Luke: Oh, listen.
13 00:01:50.650 ⇒ 00:02:08.389 Uttam Kumaran: And so that’s where I… that’s where I learned, actually, like, how to do this, and it’s not, like, vaporware. Like, actually how to use clay and NNN and all these things, because I learned the hard way, like, what it was like to build, like, an internal AI team more than a year ago, you know? So, naturally, I think in the last year.
14 00:02:08.419 ⇒ 00:02:17.549 Uttam Kumaran: after doing a lot of that internally, we’re like, hey, we should offer this to our clients. And so our team, we’re global, but we have, like, probably half of our team is here in the States.
15 00:02:17.580 ⇒ 00:02:30.039 Uttam Kumaran: half our team is kind of, like, scattered. We don’t have, like, a consistent, like, global strategy, just where I can find smart engineers, really. But all of our team is mostly engineers, everybody is, like.
16 00:02:30.210 ⇒ 00:02:46.460 Uttam Kumaran: just loves working on AI and data problems. But we’re a bootstrap business, so it partly helps to find some people globally, but we also have a bunch of people here in the States, like sales, marketing. My business partner is in New York. I used to live in New York as well, so… kind of all over the place.
17 00:02:46.830 ⇒ 00:02:57.280 Luke: No, that’s awesome. Yeah, sounds like, an exciting, kind of, couple of years for you building the business. I had a look at you guys. You guys look, look, look awesome, so…
18 00:02:57.280 ⇒ 00:02:58.360 Uttam Kumaran: Appreciate it.
19 00:02:58.880 ⇒ 00:03:06.419 Luke: Yeah, yeah, nice. I bet you it’s hard to find good engineers as well, right? Are they… are they scarce, or what? Is a lot of them around?
20 00:03:06.420 ⇒ 00:03:14.610 Uttam Kumaran: Yeah, well, this is where, like, my secret is, like, I was an engineer my whole career, so… I feel like I…
21 00:03:14.730 ⇒ 00:03:34.060 Uttam Kumaran: search for people that are kind of, like, weirdo engineers, and I’m able to turn them into, like, great client-facing engineers. That’s what I did. But I think my… I have a lot of talent in, like, engineering recruiting, so we’ve never struggled to find great people. I think where we’ve struggled is more on just, like.
22 00:03:34.110 ⇒ 00:03:43.940 Uttam Kumaran: the business and the sales and the project management side, I think it’s always hard to find great, like, project managers that have technical experience. They’re not just, like, pencil pushers, you know?
23 00:03:43.940 ⇒ 00:03:57.119 Uttam Kumaran: But we’ve gotten lucky and found really, really awesome engineers. I think, like, that luck is slowly running out as I’m, like, growing to be a bigger operation, but that’s been the fun, like, the fun in this… I always loved working with clients, and…
24 00:03:57.210 ⇒ 00:04:06.089 Uttam Kumaran: I love developing new solutions, and for us, it’s like, we now have, like, 10 or 15 different clients, and it’s so awesome getting to, like.
25 00:04:06.190 ⇒ 00:04:09.890 Uttam Kumaran: Bring our knowledge of, like, new tools and things to each of them.
26 00:04:09.910 ⇒ 00:04:22.250 Uttam Kumaran: Versus when I was just an engineer at one company, you know, I felt like the company was only monetizing part of me, and I was getting bored, and now we have a lot of people who have that similar itch, where they want to work on multiple problems in parallel, and…
27 00:04:22.260 ⇒ 00:04:31.420 Uttam Kumaran: We’re a bootstrap company, so it’s not, like, a crazy startup or anything. That’s… that’s sort of my background, is working in… in startup world, so it’s…
28 00:04:31.420 ⇒ 00:04:44.420 Uttam Kumaran: I would say for the rest of the company, it’s a pretty chill lifestyle, like, we mainly just work on tough problems. For me, it’s not… it’s the opposite, but for them, I would say try to keep it fairly, fairly relaxed, so, yeah.
29 00:04:44.660 ⇒ 00:04:56.370 Luke: That’s, that’s awesome. Always, yeah, always gonna be pretty crazy in the first couple years of starting a new company, so, yeah, I hope, hope it, keeps working out for you.
30 00:04:56.370 ⇒ 00:05:00.930 Uttam Kumaran: I appreciate it, man. Yeah, tell me about your background and your business. We’d love to hear about it.
31 00:05:01.130 ⇒ 00:05:18.050 Luke: Yeah, yeah, for sure. So, my 9 to 5 is in, kind of, digital and, kind of, tech consulting, so, and I work with brands like… do you know Cash App and, Afterpay? They’re in my block, so my client.
32 00:05:18.050 ⇒ 00:05:18.480 Uttam Kumaran: Yes.
33 00:05:18.480 ⇒ 00:05:34.009 Luke: is actually Afterpay, so I work with them on a whole bunch of different projects, you know, web platforms, we’re building their website. We do a lot of, kind of, data analytics stuff for them, a lot of engineering,
34 00:05:34.010 ⇒ 00:05:48.240 Luke: A lot of CRM, so we build emails and build their workflows and stuff like that, some SEO, so a bunch of, kind of, you know, tech, marketing, and kind of digital data projects for those guys.
35 00:05:48.240 ⇒ 00:05:50.850 Luke: So that’s… that’s my 9 to 5.
36 00:05:50.980 ⇒ 00:05:54.970 Luke: the problems that I’m trying to solve now, and hopefully you guys can help.
37 00:05:54.970 ⇒ 00:06:12.019 Luke: is I’m trying to start a new company, in the property investment space, so basically like a buyer’s agency, where we help clients, we help them find, and purchase investment properties, all around Australia.
38 00:06:12.020 ⇒ 00:06:27.129 Luke: So I’m trying to actually set up, a lead, or a lead automation workflow, that will help me with, I suppose, yeah, generate new business and generate new sales leads.
39 00:06:27.530 ⇒ 00:06:36.040 Luke: And the segment that I’m focusing on is B2B, so trying to generate referrals from
40 00:06:36.040 ⇒ 00:07:00.339 Luke: people like mortgage brokers, financial planners, and so on, and so forth, because they are talking to clients who might want to diversify their, their wealth, they might want to purchase a property, and then they’ll refer them to me. So I feel like that’s probably a good place to start in trying to automate that flow.
41 00:07:00.340 ⇒ 00:07:16.009 Luke: Using tools like, I don’t know, Clay, Apollo, Make, NAN, I don’t really… I’m not tied into any of those. It would be whatever you recommend. I have been recommended N-A-N, is that how you say it? I’m not sure.
42 00:07:16.360 ⇒ 00:07:36.359 Luke: And so maybe using that as kind of the wrapper, or the automation, platform, and then plugging other tools in around that. So yeah, I just want to get this kind of company up and running as quickly as possible, and try and get some… get some meetings booked in the calendar. So…
43 00:07:36.360 ⇒ 00:07:40.579 Luke: Yeah. Yeah, that’s… that’s problem number one.
44 00:07:40.770 ⇒ 00:07:46.330 Luke: And then I suppose the other part of that is, how do I then take that.
45 00:07:46.600 ⇒ 00:07:55.300 Luke: that and productize that, right? So I want to then be able to consult to other companies and help them with the same problem.
46 00:07:55.300 ⇒ 00:08:20.079 Luke: Right? So, whether that is stitching these tools together into a workflow, and then just documenting that into, like, an SOP or something, and then helping other companies do that, I think let’s get the workflow tested and validated first, and then if it works for me and my company, I want to then be able to help other companies do something similar.
47 00:08:20.080 ⇒ 00:08:22.610 Luke: in whatever niche it might be, right?
48 00:08:22.610 ⇒ 00:08:23.100 Uttam Kumaran: Totally.
49 00:08:23.100 ⇒ 00:08:34.439 Luke: property, it can… I… just whatever ICP, like, I might want to change the target, right, for someone else, but just the same workflow, yeah? Cool. Does that make sense?
50 00:08:34.440 ⇒ 00:08:38.999 Uttam Kumaran: I mean, makes sense. I mean, look, it’s a… it’s a very common… I would say the…
51 00:08:39.120 ⇒ 00:08:53.510 Uttam Kumaran: the automation that you’re looking for is very common. I mean, in our business, we do this a lot, so we use… to give you a sense of how we do this, we have several different, like, ICPs we go after, right? And so, of course, like, for us, for example, we typically work with
52 00:08:53.550 ⇒ 00:09:13.489 Uttam Kumaran: pretty large, like, 20 to 100 million dollar mid-sized private businesses that are building, sort of, AI and data systems, right? That’s, like, one class of folks. So that, generally, we have, like, revenue, we have… and then we kind of look at, like, what’s the persona, and then we look at geography, and then we look at the industry, right? So you have these different cuts, and then, of course.
53 00:09:13.720 ⇒ 00:09:25.440 Uttam Kumaran: what we recommend is, like, you try to make that ICPS constrained as possible, and then we help you sort of generate at what level of automation you want. Like, you can go all the way in terms of automating
54 00:09:25.440 ⇒ 00:09:33.989 Uttam Kumaran: the generation of, like, a LinkedIn DM, an email, having it auto-fire. You could also go as little as, like, just produce it
55 00:09:34.040 ⇒ 00:09:42.639 Uttam Kumaran: for me, so that I can take that, and then I can tweak it a little bit and go. So we’ve helped people do, like, the gamut. I would say.
56 00:09:42.670 ⇒ 00:09:59.519 Uttam Kumaran: we… I had more success, and we’re in B2B as well, so in our business, I have more success. I don’t care, actually, that the system fires everything off. I just… I actually care that… can we just automate, like, 60-80% of it? Because then I can tweak it, I can make it just perfect, and I can send it out.
57 00:09:59.520 ⇒ 00:10:11.979 Uttam Kumaran: And we’re not sending, like, hundreds of thousands, right? We may send, like, 10, 20, 30 a week. And I… I do want the AI to help me with the research. I want the AI to help me with identifying the leads, qualifying the ICP,
58 00:10:12.030 ⇒ 00:10:30.540 Uttam Kumaran: But I don’t need it to generate AI slot for the message, you know? I could probably, like, take something, add one or two lines, and fire that. That’s easy for me. So that’s, like, sort of the range of things we could do. I think probably the crucial things here is just having a very well-defined ICP.
59 00:10:30.580 ⇒ 00:10:36.169 Uttam Kumaran: Second is, just making sure that you…
60 00:10:36.260 ⇒ 00:10:51.150 Uttam Kumaran: like, have some understanding of, like, what factors matter in the scoring system, right? So, you should… you should know, like, okay, does revenue matter more? Does a certain other score matter more than another? So that’s… that’s, like.
61 00:10:51.150 ⇒ 00:10:59.379 Uttam Kumaran: you know, what I would… what I would recommend. Have you tried anything so far, or tried anything in practice that’s worked or been more difficult?
62 00:10:59.610 ⇒ 00:11:04.660 Luke: No, I haven’t… you’re actually… I can still hear you, but your screen’s actually frozen. Can you see me?
63 00:11:04.660 ⇒ 00:11:09.179 Uttam Kumaran: Oh, here. Yeah, I can see you, I can see you. Let me just… Yeah, yeah, cool.
64 00:11:09.510 ⇒ 00:11:13.029 Luke: Yeah, I got you, I got you.
65 00:11:13.030 ⇒ 00:11:34.470 Luke: No, I haven’t actually tried any process yet. This will be the first one, and I, yeah, I kind of, agree with what you’re saying about not having to really automate 100% of it. I mean, ideally, that would be nice, like, if the meetings got booked into my calendar for me, and I just showed up to the call,
66 00:11:34.470 ⇒ 00:11:35.020 Uttam Kumaran: Yeah.
67 00:11:35.020 ⇒ 00:11:47.099 Luke: But I don’t know whether the AI outreach, like, the cold email outreach, is it effective yet? I mean, in theory, right, it sounds like it’d be amazing.
68 00:11:47.100 ⇒ 00:11:56.189 Uttam Kumaran: It depends on your buyer. It totally depends on your buyer. Like, we have some people… some of our… some of our ICP are used to emails, some of our ICP
69 00:11:56.250 ⇒ 00:12:03.599 Uttam Kumaran: like, for example, me, I’m in tech services here, I get spam, so I’m not checking anything anymore, because it’s all, like, AI spam.
70 00:12:03.650 ⇒ 00:12:18.330 Uttam Kumaran: So it does really depend on your… on your buyer. You know, on not only the messaging medium, like, whether it’s email, whether that is SMS, whether that is cold phone, whether that’s LinkedIn,
71 00:12:18.600 ⇒ 00:12:33.939 Uttam Kumaran: But that’s, again, it’s just… it’s something you don’t want to have a one-size-fits-all. Like, if you’re focused on… and this is where you’ll know best on brokers and financial planners, in the States here, like, most… all those people are on email, right? That’s their primary domain. They’re not…
72 00:12:33.940 ⇒ 00:12:43.089 Uttam Kumaran: They may or may not be using LinkedIn much, but a lot of them are also, like, phone, right? They’re used to getting called on the phone, and so you may have some success
73 00:12:43.290 ⇒ 00:12:59.929 Uttam Kumaran: just having a very, very great ICP score that gives you, like, 20 people to call, gives you, like, basically what to say on the call, or, like, who this person is, and that may be… that may lead to higher scores, versus if you were to try to go the distance and have, like, AI call them, or, like, have everything AI
74 00:12:59.990 ⇒ 00:13:03.520 Uttam Kumaran: You may find the results to be tough, just because, like.
75 00:13:03.720 ⇒ 00:13:06.069 Uttam Kumaran: There’s still a big human component.
76 00:13:06.070 ⇒ 00:13:07.340 Luke: Yeah. You know, I think…
77 00:13:07.560 ⇒ 00:13:08.210 Uttam Kumaran: Yeah.
78 00:13:08.540 ⇒ 00:13:23.410 Luke: I definitely, yeah, I definitely wouldn’t have… I don’t think AI is there yet for calls, but I would potentially want to trial email, so I’m just wondering, as part of this flow that we can set up.
79 00:13:23.410 ⇒ 00:13:29.680 Luke: Are we able to test and trial, like, am I able to customize whether I want
80 00:13:29.700 ⇒ 00:13:33.110 Luke: it 100% or 80%, or is it…
81 00:13:33.110 ⇒ 00:13:34.870 Uttam Kumaran: Totally, yeah, so every…
82 00:13:35.120 ⇒ 00:13:46.829 Uttam Kumaran: No, it’s actually quite easy, and in fact, most of this flow, you actually may not need N8N. I think you could probably accomplish all of this in clay, just given the initial scope.
83 00:13:46.830 ⇒ 00:13:59.049 Uttam Kumaran: And what does accomplishing this mean? It means, okay, like, let’s say you have Apollo, and you have a bunch of Apollo credits, great, so find me in this geo, find me, like, these people with this title, this industry, great.
84 00:13:59.070 ⇒ 00:14:11.710 Uttam Kumaran: you can set up your, like, lead scoring qualification in Clay. What does that mean? It could be literally an LLM prompt that says, hey, take these 5 dimensions, score this from 1 to 5,
85 00:14:11.710 ⇒ 00:14:19.120 Uttam Kumaran: and then only move 3 and up to the next waterfall stage, right? The next stage is write the email, so…
86 00:14:19.120 ⇒ 00:14:31.739 Uttam Kumaran: do some Google searching, use perplexity search, draft an email, and then you can then also decide at that point whether you want to review them, or you want it to just get put into your inbox, or you want it to actually get sent off.
87 00:14:31.740 ⇒ 00:14:39.849 Uttam Kumaran: So there’s, there’s a bunch of also tricky things on, like, for example, if you do send, with email.
88 00:14:39.920 ⇒ 00:14:44.149 Uttam Kumaran: There’s… there’s a couple things to make sure, like, you don’t get caught by spam and things like that.
89 00:14:44.150 ⇒ 00:14:44.760 Luke: Yeah, yeah.
90 00:14:44.760 ⇒ 00:14:52.980 Uttam Kumaran: what we found in our… in my business is that I just prefer it get drafted in my inbox, because then I can send it, but again, we’re not doing…
91 00:14:53.250 ⇒ 00:15:09.060 Uttam Kumaran: we’re not doing thousands and thousands, we’re maybe doing 10 or 20 a week, and so I can review that and publish it. But you’re… you’re on point in that at each step, there can be a human in the loop, and then you can slowly remove those as you get more comfortable.
92 00:15:09.060 ⇒ 00:15:09.740 Luke: Yeah, okay.
93 00:15:09.740 ⇒ 00:15:11.820 Uttam Kumaran: With the outputs, yeah.
94 00:15:11.820 ⇒ 00:15:23.059 Luke: Yeah, awesome. Yeah, that sounds… yeah, that sounds good. And then would you be using Airtable at all? Or, I mean, I’m just curious to hear from you, like, what are the essential tools in this stack?
95 00:15:23.990 ⇒ 00:15:29.709 Uttam Kumaran: Yeah, so I don’t… have you seen Clay before, or have you logged in and seen it? Because I can even just show you, like.
96 00:15:29.930 ⇒ 00:15:31.340 Uttam Kumaran: An example of, like, a one.
97 00:15:31.340 ⇒ 00:15:32.209 Luke: Yeah, no, I was…
98 00:15:32.210 ⇒ 00:15:32.920 Uttam Kumaran: And that makes…
99 00:15:32.920 ⇒ 00:15:36.019 Luke: Yeah, consider me a bit of a rookie, yeah.
100 00:15:36.460 ⇒ 00:15:41.420 Uttam Kumaran: Okay, so let me just show you this. Clay is, like… It’s like Google Sheets plus
101 00:15:42.100 ⇒ 00:15:59.360 Uttam Kumaran: like, an automation tool. So I would say you wouldn’t need an Airtable. Everything in Clay is pretty tabular. So, you would have rows for, each of your… your people, and they would sort of get,
102 00:15:59.860 ⇒ 00:16:06.680 Uttam Kumaran: they would get, what do you call it, like, enriched, and then qualified, and so let me just share…
103 00:16:06.990 ⇒ 00:16:08.889 Uttam Kumaran: Let me share this with you.
104 00:16:14.360 ⇒ 00:16:16.310 Luke: I think your screen’s frozen again.
105 00:16:21.260 ⇒ 00:16:21.990 Uttam Kumaran: This is okay.
106 00:16:26.700 ⇒ 00:16:28.200 Uttam Kumaran: This loads…
107 00:16:29.710 ⇒ 00:16:31.109 Luke: Yeah, I can see that.
108 00:16:32.290 ⇒ 00:16:33.200 Uttam Kumaran: Okay.
109 00:16:36.810 ⇒ 00:16:40.430 Uttam Kumaran: Great, so let me just show you,
110 00:16:41.250 ⇒ 00:16:45.010 Uttam Kumaran: Let’s see… it’s a good… Hold on.
111 00:16:46.450 ⇒ 00:16:50.949 Uttam Kumaran: Let me show you one that we did. Again, these are just ours, so I’ll show you one that we did.
112 00:16:51.060 ⇒ 00:16:54.720 Uttam Kumaran: Last year for the long side.
113 00:16:57.020 ⇒ 00:17:03.930 Uttam Kumaran: So, this is, like, we did a lookalike campaign for… actually, here’s… I have a better one.
114 00:17:04.290 ⇒ 00:17:19.819 Uttam Kumaran: So one of our clients, was a coconut water company, and so I tasked my team with saying, hey, let’s go find other beverage companies, you know, to… to go work with. And so, what you’ll find in this table
115 00:17:19.819 ⇒ 00:17:38.639 Uttam Kumaran: is basically, we are looking at, I was like, go through Amazon, find all the beverage companies on Amazon, and then find me the people to reach out to, because our solution at the time was related to, like, selling things, selling beverage products, e-commerce. And so what you’ll see here, if you use Airtable or Google Sheets, just a spreadsheet.
116 00:17:38.650 ⇒ 00:17:49.860 Uttam Kumaran: Right? But what you’re seeing is you’re seeing all these, like, nifty features on top. And so, what this is, is we pulled all this from Apollo directly into Clay, and then there are helpful things we’re running here.
117 00:17:49.870 ⇒ 00:18:02.460 Uttam Kumaran: So, for example, this is, like, a cleanup, so this just takes the company name and cleans it up, so you don’t get, like, misspellings or hyphens or things like that. We… we use, another
118 00:18:02.460 ⇒ 00:18:10.689 Uttam Kumaran: Enrichment service to kind of get the person’s name, from their title, to get their email, and then we…
119 00:18:10.720 ⇒ 00:18:16.139 Uttam Kumaran: We check, like, if the email’s gonna bounce or not. We then write the sales email here.
120 00:18:16.190 ⇒ 00:18:22.060 Uttam Kumaran: So for example, this is the email that the AI wrote. Hey, products out of stock aren’t updated.
121 00:18:22.190 ⇒ 00:18:37.240 Uttam Kumaran: blah blah blah, like, it’s using their company, their title, and then finally, it’s pushing this into a tool called HeyReach that we use for LinkedIn outbound. And so this is, like, sort of an end-to-end thing where there’s no…
122 00:18:37.390 ⇒ 00:18:54.239 Uttam Kumaran: there was only a human involved in, like… for example, I got told, hey, go look up… go look at this list and just make sure that all these people are valid, right? And there’s only 150, so I can easily look through this list, and as a business owner, I can be like, yeah, these are all folks that
123 00:18:54.240 ⇒ 00:19:07.029 Uttam Kumaran: I would like to go after. And for me, look, think about how much time it would have taken me to go find all these people and draft the email to. And so, we sent this, and out of this, I probably got, like, 5 or 10 different
124 00:19:07.070 ⇒ 00:19:08.940 Uttam Kumaran: message back, again, like.
125 00:19:09.190 ⇒ 00:19:13.890 Uttam Kumaran: it depends on your ICP, like, how much they’re getting, message volume, things like that, but…
126 00:19:13.920 ⇒ 00:19:31.159 Uttam Kumaran: this is, like, an example of an end-to-end play flow that is very, very easy for us to do. And in this case, we didn’t send an email, we sent a LinkedIn message, but it would be pretty trivial. Actually, we did send an email, so this is an instant lead, is our email outbound.
127 00:19:31.360 ⇒ 00:19:39.430 Uttam Kumaran: And so we… we added this to an Instantly campaign that then we went into Instantly and triggered. This is for LinkedIn outbound.
128 00:19:39.700 ⇒ 00:19:48.490 Uttam Kumaran: And so, you actually, I think for your use case, it’s very similar to this. You wouldn’t have to leave Clay, most likely, at all. The only things you would need is
129 00:19:48.830 ⇒ 00:19:55.980 Uttam Kumaran: you would definitely need API keys for Apollo, and then we would have to decide on, like, what you’re gonna use to
130 00:19:56.130 ⇒ 00:20:01.530 Uttam Kumaran: If you’re gonna send the email yourself, or if you’re gonna want to use it instantly, or something similar for email offline.
131 00:20:02.150 ⇒ 00:20:06.000 Luke: Okay, so you could plug in Gmail there instead of instantly, or what?
132 00:20:06.600 ⇒ 00:20:14.039 Uttam Kumaran: Yeah, so it depends… so, because we were sending, you know, we’re sending… we were sending thousands of emails, you don’t want to use your own Gmail, because…
133 00:20:14.040 ⇒ 00:20:15.029 Luke: Gotcha, gotcha.
134 00:20:15.030 ⇒ 00:20:22.760 Uttam Kumaran: They’ll ban you, and you’re kind of cooked at that point. So you want to use, sort of additional domains that…
135 00:20:23.420 ⇒ 00:20:36.899 Uttam Kumaran: you know, you would… you would just buy, and you can buy those instantly. Instantly, it was our, kind of, like, tool of choice. Still, it’s really, really good and pretty cost-effective. AReach for LinkedIn is also really, really good, cost-effective.
136 00:20:37.030 ⇒ 00:20:39.010 Uttam Kumaran: And then this is using Twain.
137 00:20:39.600 ⇒ 00:20:50.789 Uttam Kumaran: again, it’s… it’s sort of… you could use ChatGPT here and just put in a prompt, like, hey, given these 5 properties, write me this type of email. Twain, is just… I can just show you,
138 00:20:51.070 ⇒ 00:20:55.619 Uttam Kumaran: Twain just… there’s a lot of things you can do in Twain, like change the,
139 00:20:55.840 ⇒ 00:21:02.990 Uttam Kumaran: like, tone, like, you could just… it’s just, like, really, really good at writing. And so…
140 00:21:03.330 ⇒ 00:21:06.729 Uttam Kumaran: This is our, kind of, tool of choice to write RDs.
141 00:21:08.130 ⇒ 00:21:15.990 Uttam Kumaran: So this is something end-to-end, like, again, you mentioned sort of a week. I feel like it’s sort of being the ballpark. I think the biggest thing I would mention is
142 00:21:16.110 ⇒ 00:21:19.919 Uttam Kumaran: You just… it’s just gonna take some time to nail, is what I’m saying. Yeah.
143 00:21:20.230 ⇒ 00:21:20.550 Luke: Like.
144 00:21:20.550 ⇒ 00:21:24.710 Uttam Kumaran: You know, so that’s probably the biggest thing to just be aware of, is like.
145 00:21:24.870 ⇒ 00:21:29.270 Uttam Kumaran: You may… it’s gonna take some tweaking to get the email that you’re proud of sending.
146 00:21:29.450 ⇒ 00:21:37.749 Uttam Kumaran: to get your ICP, you know, and then, again, if you’re gonna have to… it’s just like sales, like, you have to figure out what’s working and then double down.
147 00:21:37.990 ⇒ 00:21:40.120 Uttam Kumaran: You know, kind of over time, but…
148 00:21:40.260 ⇒ 00:21:58.839 Uttam Kumaran: I feel like this is something that if we set up the first version of, you could totally maintain and continue to run, and again, like, if you’re gonna go sell this to other folks, very easy to build off of an initial clay table and, you know, reuse it for folks.
149 00:22:00.200 ⇒ 00:22:13.489 Uttam Kumaran: Okay, so… and would you be able to kind of provide, like, some documentation in terms of how you guys set it up? Like, some kind of, like, workflow, or SOP or something like that?
150 00:22:14.430 ⇒ 00:22:20.759 Uttam Kumaran: Yeah, definitely, yeah, we could provide you all the documentation on, like, how to set up an initial version,
151 00:22:21.130 ⇒ 00:22:29.549 Uttam Kumaran: And then, of course, like, we can sort of stay available if we… if we only work for a week or so. We could stay available if you need us.
152 00:22:29.720 ⇒ 00:22:33.599 Uttam Kumaran: You know, for additional hours, but yeah, we can document the whole thing.
153 00:22:33.700 ⇒ 00:22:51.949 Luke: Yeah, awesome. Just, quickly, so, like, you… that was all in clay. Like, what… Yes. Is Apollo just another version of Clay, or does it have a different use case? And then, obviously, I know that Make and then N8N are kind of… serve a similar use case, right?
154 00:22:52.590 ⇒ 00:23:01.270 Uttam Kumaran: Yeah, so, Apollo, is… Apollo is like a person lookup tool. It’s like a people enrichment tool. Okay, okay.
155 00:23:01.440 ⇒ 00:23:10.789 Uttam Kumaran: that, like, Apollo, Clearbit, ZoomInfo, those are all, like, hey, I have this person, give me everything about them, right? So there’s a lot of vendors. You actually can…
156 00:23:10.950 ⇒ 00:23:15.519 Uttam Kumaran: So, Clay is really nice because you can get Apollo data through Clay.
157 00:23:15.520 ⇒ 00:23:19.310 Luke: They just take a little bit of, like, a premium. So Clay has credits.
158 00:23:19.350 ⇒ 00:23:28.179 Uttam Kumaran: And so it’s, like, one credit for one person enrichment, versus if you went to Apollo directly, they’re all just, like, brokering data, basically.
159 00:23:28.180 ⇒ 00:23:28.810 Luke: Gotcha.
160 00:23:28.810 ⇒ 00:23:42.510 Uttam Kumaran: So you actually don’t… you actually don’t need to go, like, find the best enrichment source. You can procure all that through Clay. And then N8N and Clay are probably closer competitors. I would say…
161 00:23:42.580 ⇒ 00:23:49.780 Uttam Kumaran: You could build this in N8N, but I would say for your use case, it’s very easy to just do this. You’re not running
162 00:23:49.860 ⇒ 00:23:58.629 Uttam Kumaran: like, JavaScript or Python, you’re not, like, talking to multiple APIs. You’re mainly just trying to do a lot of, like, text-based waterfalls.
163 00:23:58.820 ⇒ 00:24:03.220 Uttam Kumaran: And so, I don’t know, I would… bang for your buck, like, and…
164 00:24:03.400 ⇒ 00:24:06.420 Uttam Kumaran: like, ease of use, I would just suggest using
165 00:24:06.680 ⇒ 00:24:13.909 Uttam Kumaran: Clay, and they have pretty generous, like, trials and things like that. You know, so…
166 00:24:14.550 ⇒ 00:24:25.210 Luke: Yeah, awesome. Okay, so that all sounds pretty good. So, like, what are your, like, what are your… what is your pricing like, or what… what would a cost look like for this, indicatively?
167 00:24:26.360 ⇒ 00:24:30.900 Uttam Kumaran: Yeah, I mean, it’s… it sort of depends, so, like, I would say we…
168 00:24:31.620 ⇒ 00:24:46.819 Uttam Kumaran: of course, we try to work on, like, long-term relationships with people. It seems like this is sort of… you’re kind of thinking of it more of, like, hey, just come in and see if you can rip one of these, and then, like, kind of hand it off. I could go back with my team. I mean, I think it’s probably at least…
169 00:24:46.870 ⇒ 00:25:00.800 Uttam Kumaran: 10 hours of work for us to just build a first version of it. And then, like, it would sort of depend on, like, what tweaks you would need. Again, if I was just to say it back out, it’s like, okay.
170 00:25:01.190 ⇒ 00:25:08.439 Uttam Kumaran: It’s able to find the ICP, like, enrich them, build the email, and pass it to, like, whatever’s gonna send it.
171 00:25:08.770 ⇒ 00:25:18.689 Uttam Kumaran: It’s probably, like, at least… it’s probably just, like, a week or two of work, and not… not, like, full-time or anything, but I would probably budget for that. Like, our typical…
172 00:25:19.320 ⇒ 00:25:19.890 Luke: Like, what.
173 00:25:19.890 ⇒ 00:25:21.290 Uttam Kumaran: Yeah, like, with its… with…
174 00:25:21.700 ⇒ 00:25:31.299 Uttam Kumaran: Exactly, so I will… I will say, like, we can get it done pretty quick, but I usually just try to say, like, there’s gonna be some tweaks, so probably, like, one or two weeks is probably fair.
175 00:25:31.300 ⇒ 00:25:32.580 Luke: Yeah.
176 00:25:33.190 ⇒ 00:25:45.000 Uttam Kumaran: And then, yeah, I mean, like, I would say our, like, for the most part, our typical… we start this type of really light automation work, usually, like, 1.50 an hour, so it sort of depends on…
177 00:25:45.420 ⇒ 00:26:00.929 Uttam Kumaran: like, on what your budget is, and if that’s something that’s in your range. I know you reached out on Contra, so for me, it’s like, I just was getting set up on Contra, so I would probably like to do the whole thing on there, that way I can get… we can hopefully nail it and get a good review from y’all. Yeah.
178 00:26:00.930 ⇒ 00:26:01.500 Luke: Yep.
179 00:26:01.500 ⇒ 00:26:18.879 Uttam Kumaran: And so… and then again, like, we’re… again, like, my background is not in consulting, and I’ve hired a lot of bad consultants, so we try to do the job through the reality. We hope that, like, you’ll… you’ll get a good win, you’ll work with us in the future, or refer us more business, so that’s kind of how we operate, so…
180 00:26:19.280 ⇒ 00:26:32.500 Luke: So that’s… yeah, that sounds great. One thing I would say that is, yes, this specific use case for my company will be, almost like a single-time project, and if it works…
181 00:26:32.500 ⇒ 00:26:57.330 Luke: then there will be, obviously, potentially more examples of, you know, selling that to other companies and potentially tweaking and evolving that use case in the future. The other, I suppose, side of that is, you know, how would you be able to, say, plug in to the work that we do in my agency, my 9 to
182 00:26:57.330 ⇒ 00:27:06.229 Luke: To help kind of consult, or help kind of advise us with our clients, right? So, for example, cash.
183 00:27:06.230 ⇒ 00:27:06.700 Uttam Kumaran: Yeah.
184 00:27:06.700 ⇒ 00:27:19.110 Luke: Afterpay. I know that there’s a strong appetite to implement a similar system, although it’ll probably be more complex, because they’ve got a bigger, Martech stack. There’s a lot.
185 00:27:19.110 ⇒ 00:27:35.979 Uttam Kumaran: tools, like, they use Snowflake, they’ve got, probably they’ll need… Yeah, so that’s actually, like, the majority of our business, actually, is all marketing data analytics. So that’s… so the automation stuff and these sort of small automations, we sort of do, but the majority of our business is actually quite heavy.
186 00:27:35.980 ⇒ 00:27:43.860 Uttam Kumaran: marketing data analytics. Like, we work with a lot of multi-hundred-million dollar businesses here in the States, setting up Snowflake.
187 00:27:43.860 ⇒ 00:28:03.640 Uttam Kumaran: amplitude mix panel segment, like, different CDPs, different, like, product analytics, different, like, pixel events, like, a lot of stuff for e-comm and for digital B2B, so totally up our wheelhouse, too. And yeah, definitely, like, way bigger. Way bigger.
188 00:28:03.640 ⇒ 00:28:22.199 Luke: Yeah, yeah, for sure. So, I just had a call with a client this morning who’s really keen to kind of experiment and trial with a similar workflow that is based on account-based marketing. So, really, an example would be getting Afterpay
189 00:28:22.250 ⇒ 00:28:39.180 Luke: brands, so think of an e-commerce brand, getting them to actually, use Afterpay in their business, right? And so that’s a B2B play, that they struggle with enrichment. I think they use Sixth Sense, do you know Sixth Sense?
190 00:28:39.440 ⇒ 00:28:40.840 Uttam Kumaran: Yes, I do. Yeah, yeah, yeah.
191 00:28:40.840 ⇒ 00:28:55.599 Luke: Yeah, and they weren’t very happy with that, so they were kind of wanting to experiment with, like, you know, a bespoke workflow using AI to help generate these kind of leads that are more enriched and help their sales team. So, I think.
192 00:28:55.600 ⇒ 00:28:56.360 Uttam Kumaran: Yeah.
193 00:28:56.360 ⇒ 00:28:58.969 Luke: Yeah, there’s definitely, you know, potential.
194 00:28:58.970 ⇒ 00:29:04.240 Uttam Kumaran: Yeah, I mean, I think you should… yeah, you should get a sense of, like, I think if we work together, you’ll kind of see, like.
195 00:29:04.250 ⇒ 00:29:19.050 Uttam Kumaran: how we work, and kind of a couple options, and then we can equip you with, you know, we’ve done this sort of, like, setting up account-based marketing flows, enrichment and qualification flows using Clay, N8M, a bunch. Like, I can give you some past case studies and stuff, and…
196 00:29:19.290 ⇒ 00:29:19.720 Luke: I think…
197 00:29:19.720 ⇒ 00:29:23.360 Uttam Kumaran: We can kind of equip you with enough to sort of sell that, too, so…
198 00:29:23.720 ⇒ 00:29:27.570 Luke: Yeah, and then we’d get you guys to help with the implementation, right?
199 00:29:28.450 ⇒ 00:29:29.800 Uttam Kumaran: Perfect, yeah, yeah, yeah.
200 00:29:29.800 ⇒ 00:29:52.490 Luke: Yeah, yeah, awesome, that sounds good. Alright, mate, I’ve got to run, but yeah, really nice to chat with you. If you wouldn’t mind just, if you want to use, Contra and just respond with just an estimate of what you think it’d cost to complete, my, my project, that would be awesome, and then we can, go from there.
201 00:29:53.230 ⇒ 00:30:02.469 Uttam Kumaran: Okay, okay, perfect, and sorry about the video, it just happened to come out, and my… my old MacBook Air is crapping out, you know. I… everybody in my company gets nice laptops except for…
202 00:30:02.470 ⇒ 00:30:06.770 Luke: Yeah, you gotta… We’re gonna keep your costs down as well, right? Yeah.
203 00:30:06.770 ⇒ 00:30:13.829 Uttam Kumaran: Okay, perfect. So I’ll, I’ll get back to you on Quonsha, and again, I appreciate the time, and yeah, hopefully talk soon.
204 00:30:14.120 ⇒ 00:30:15.849 Luke: Thanks, Utah. Nice to meet you. Bye.
205 00:30:15.850 ⇒ 00:30:17.689 Uttam Kumaran: Okay, thank you so much, bye.