Meeting Title: Aditya Bahl x Robert Tseng Partnership Discussion Date: 2025-06-26 Meeting participants: Aditya Bahl, Robert Tseng
WEBVTT
1 00:00:29.820 ⇒ 00:00:31.469 Aditya Bahl: Hey, Robert, how are you?
2 00:00:31.470 ⇒ 00:00:33.219 Robert Tseng: Hey, Dtia! Good! How are you?
3 00:00:33.730 ⇒ 00:00:39.549 Aditya Bahl: Doing well. Sorry I missed your Linkedin message a little while back. I know we were trying to connect, and I just kind of came out.
4 00:00:39.550 ⇒ 00:00:40.209 Robert Tseng: Oh, yeah.
5 00:00:40.380 ⇒ 00:00:41.769 Aditya Bahl: Schedule some time, you know.
6 00:00:42.190 ⇒ 00:00:43.379 Robert Tseng: No worries.
7 00:00:43.540 ⇒ 00:00:44.570 Robert Tseng: How you been.
8 00:00:45.070 ⇒ 00:00:50.600 Aditya Bahl: Things have been good, quite busy, but good! And how has the move to New York been.
9 00:00:52.096 ⇒ 00:00:57.763 Robert Tseng: Yeah, no. New New York’s been good. I’ve been. Oh, I guess I mentioned I was in Jersey and moved to New York.
10 00:00:58.330 ⇒ 00:01:00.160 Robert Tseng: yeah, pretty settled in now.
11 00:01:00.260 ⇒ 00:01:11.310 Robert Tseng: I’ve been there for like almost 3 months. Kind of time flies. But it’s good. My business partner is here in town. So actually at a client office right now. And yeah, business is going well.
12 00:01:11.836 ⇒ 00:01:17.650 Robert Tseng: and yes, I guess the the summer in New York is always very exciting. A lot of stuff going on
13 00:01:17.770 ⇒ 00:01:26.880 Robert Tseng: Nba draft yesterday, so got to have a couple of cool sightings of folks around town. And yeah, so all, all’s well.
14 00:01:27.380 ⇒ 00:01:41.929 Aditya Bahl: Nice, nice, very cool and then, so right now, just someone get a little more context to for you. Are you primarily like a consulting service. Or do you you guys have like a platform you guys have built or like, what? Exactly, give me the rundown. Yeah.
15 00:01:42.220 ⇒ 00:01:46.098 Robert Tseng: Yeah, yeah. So we we’re we’re a services company.
16 00:01:46.720 ⇒ 00:01:51.387 Robert Tseng: I think maybe you’ve noticed in some of our content. I don’t know what gave you the idea that we’re building product. But
17 00:01:51.830 ⇒ 00:02:07.985 Robert Tseng: I mean, we do build. We have an internal data platform. That helps us to scale like our services across our clients. And so I think we’ve been sharing more about like both kind of broadly about like how the solvent was made, and some of the cool stuff that we’ve built.
18 00:02:08.430 ⇒ 00:02:20.129 Robert Tseng: So yeah, we we have built a lot of like internal tooling for ourselves, and we’ve been deploying it after science as well. So yeah, I think since last we spoke, maybe I kinda
19 00:02:20.340 ⇒ 00:02:32.799 Robert Tseng: yeah, the data and AI piece were kinda kind of felt like 2 different like, go to market strap like kind of initiatives for us. But I think we’re really trying to unify it a lot more where I think the the more cohesive narrative is like.
20 00:02:33.265 ⇒ 00:02:45.304 Robert Tseng: yeah, like being, but still still in the, in the still, in the, in the vein of like transforming raw data into actual insights. I mean, that’s still the case. But we do so with like,
21 00:02:46.290 ⇒ 00:02:57.369 Robert Tseng: AI enabled kind of like kind of data data work, I guess. So. Yeah, I think. There’s, I mean, there’s just a lot of things that kind of.
22 00:02:57.730 ⇒ 00:03:16.950 Robert Tseng: I think the the speed that we’ve moved that has definitely gone faster. And then, yeah, like, we’ve been able to share some of the same processes, and embed that into various clients at this point. So trying to figure out how to like kind of package that into like our
23 00:03:17.150 ⇒ 00:03:23.329 Robert Tseng: offering a bit more clearly. But yeah, that’s kind of where I I think the second half of the year is gonna go into.
24 00:03:23.670 ⇒ 00:03:31.799 Aditya Bahl: Yeah, no, I think that’s from my understanding. I was like, I think you guys are Services company. But then I saw your Linkedin post I didn’t like. I just that’s why I kind of like came across your message, too.
25 00:03:31.800 ⇒ 00:03:32.130 Robert Tseng: Oh, yeah.
26 00:03:32.130 ⇒ 00:03:42.289 Aditya Bahl: And then I think it was like the company really loved your guys like data insights or some sentence. And I was like, Oh, like, maybe it’s like you guys are building something to internally or like that came from.
27 00:03:42.830 ⇒ 00:03:43.420 Robert Tseng: Yeah.
28 00:03:43.769 ⇒ 00:03:47.969 Aditya Bahl: But what sort of clients are you like? Servicing now.
29 00:03:49.386 ⇒ 00:03:53.650 Robert Tseng: So yeah, I think we are mostly working with, like.
30 00:03:55.000 ⇒ 00:03:57.391 Robert Tseng: mostly Sas at this point.
31 00:03:58.570 ⇒ 00:04:15.499 Robert Tseng: yeah, we still have a couple like old like legacy clients that are. I mean, it’s been with us for a while, and they’re more like ecom telehealth clients. But all the new ones from this past quarter have all been sas, they’ve like, either just raised series B or yeah, I think we just
32 00:04:16.350 ⇒ 00:04:19.890 Robert Tseng: yeah it that they are like the whole.
33 00:04:20.240 ⇒ 00:04:50.080 Robert Tseng: go to market motion for b 2 b Saas companies, as they’re trying to one build out their like plg strategy, doing self serve kind of product analytics driven like, that’s my expertise. So I think I’ve been able to like get clients through that. And then my business partner. He was formerly we work. And so he kind of knows how to work like a lot better with just more traditional Saas go to market teams where they have territories. They’re going after accounts. And it’s a longer kind of sales cycle.
34 00:04:50.419 ⇒ 00:04:54.039 Robert Tseng: But yeah, so I think this shift has really just been
35 00:04:55.280 ⇒ 00:05:02.009 Robert Tseng: yeah, just work. I think we’re just working working better with with Sas companies. At this point.
36 00:05:02.636 ⇒ 00:05:06.310 Robert Tseng: But yeah, we’re still kind of testing different verticals along the way.
37 00:05:06.780 ⇒ 00:05:08.950 Aditya Bahl: Gotcha gotcha. Okay. Nice. Great.
38 00:05:11.340 ⇒ 00:05:16.140 Robert Tseng: How about you? How are things going on? The AI front.
39 00:05:16.340 ⇒ 00:05:32.080 Aditya Bahl: I’m happy to just give you a quick like. Tldr. Can I give you show you like a quick demo to just of our platform. So you have some idea of what we’re building and some way we can kind of have, like a partnership or kind of figure out. How things could work.
40 00:05:33.640 ⇒ 00:05:34.679 Aditya Bahl: Can you see it?
41 00:05:35.910 ⇒ 00:05:37.050 Robert Tseng: Yes, I can see it.
42 00:05:37.540 ⇒ 00:05:56.630 Aditya Bahl: So what you’re looking at is our voice agents, platform and then these are a bunch of agents I’ve created for, like, you know just demo agents for a bunch of companies, whether it’s like debt, collection agent, inbound lead, qualification, calendar, booking, lead, generation, energy company, billing, medical info collector, customer support, dentist, receptionist, scheduling
43 00:05:56.946 ⇒ 00:06:08.649 Aditya Bahl: debt collection agent for utilities like law agent 24, 7. Receptionist. You kind of get the idea. You can customize these agents for, like anything like inbound outbound calling and then you come in here.
44 00:06:08.870 ⇒ 00:06:16.660 Aditya Bahl: and then you can select any model that you want. There’s like, you know different voices that we have. They’re like multilingual support.
45 00:06:16.860 ⇒ 00:06:21.449 Aditya Bahl: And this is like a I’m I’m sure you’ve done like prompt engineering.
46 00:06:21.940 ⇒ 00:06:22.480 Robert Tseng: Yeah.
47 00:06:22.940 ⇒ 00:06:40.529 Aditya Bahl: So this, like the system from basically, you know, it’s like you, you work at a utilities company. The these are your responsibilities. XYZ. But a logic, a lot of the magic really happens in the decision tree. And over here this is where, like, you know, the whole like a flow can go. And then for this one, for example, there’s just like.
48 00:06:40.530 ⇒ 00:06:56.819 Aditya Bahl: you know, do the introduction identity verification, like basically the structure of the call and then there is a tool calling. So you know, do you want to end the call? Transfer the call like taking the action per se, like book on the calendar. It can navigate ivr. And
49 00:06:57.270 ⇒ 00:07:10.699 Aditya Bahl: yeah. So we’ve had some pretty good feedback, like a lot of Demos where people don’t know they’re talking to an AI. So which is a little bit scary. But I think that’s kind of the point where we’re at show you like a little quick teaser.
50 00:07:13.180 ⇒ 00:07:21.680 Aditya Bahl: Hi, there! This is Taylor calling from the Los Angeles Power Company. I’m reaching out regarding your utility account. May I speak with Aditya? Please? Yes, this is him.
51 00:07:22.940 ⇒ 00:07:35.890 Aditya Bahl: Great. Thank you. To make sure I’m speaking with the correct person. Could you please confirm the street address listed on your account? Yes, that is 7, 3, 3 North Kings Road, Los Angeles 9 0 0 6 9.
52 00:07:36.750 ⇒ 00:07:58.100 Aditya Bahl: Thank you for verifying that, Aditya. I am calling because our records show you have an outstanding balance of $1,000 on your utility account. Please note this call is an attempt to collect a debt, and any information obtained will be used for that purpose. Actually, I’m kind of going through a difficult time. Is it possible to like split it up into some sort of payments or something like that.
53 00:07:59.270 ⇒ 00:08:19.629 Aditya Bahl: I’m really sorry to hear that, Aditya. I completely understand that times can be tough, and I’m here to help if paying the full 1,000 into 4 monthly payments of about $250 each. Would that make things a bit easier for you? Yeah, that would be very, very helpful. Thank you.
54 00:08:20.940 ⇒ 00:08:29.248 Aditya Bahl: I’m glad we so you kind of get the idea, you know, and there’s like other demos that I posted on Linkedin, where it’s like you can easily interrupt them. It’s very conversational.
55 00:08:29.630 ⇒ 00:08:31.060 Aditya Bahl: and
56 00:08:31.890 ⇒ 00:08:53.459 Aditya Bahl: they can take action essentially. And for any custom integrations that we need for different customers, we can build that out. So these are a bunch of agents. There’s like a knowledge base you can connect to. There’s phone numbers. And then there’s Batch calling. If you want to do like hundreds. This was like a telecom company to like upsell services to like your existing customers. So you come in here just need to call them a bunch of phone numbers column B customer names.
57 00:08:53.630 ⇒ 00:08:56.626 Aditya Bahl: And then there’s a call history. So this was
58 00:08:57.150 ⇒ 00:09:15.411 Aditya Bahl: like a warehouse scheduling agent. So this is a transcript you call. I have a shipment coming to la what are the least terms? You ask questions, and it makes an Api call, and it books like on like Cal com, basically. And you get a notification. This was like I was on a call before. So just kind of did it in real time.
59 00:09:17.110 ⇒ 00:09:40.252 Aditya Bahl: yeah. So this is what we built. There’s so many use cases. Obviously, like, right now, we’re kind of focused on like call centers personal injury, law firms, telecom and utilities. But e-commerce. There’s been a use case recently as well. There’s a distributor or no. There’s this company called Bim bamboo. They sell toilet paper to whole foods
60 00:09:41.020 ⇒ 00:10:09.819 Aditya Bahl: and then they sell it to a distributor. But they wanna like call all the whole foods to make sure it’s like the toilet paper is there? But for outbound calling you can’t solicit, you know, like cold calls. But you can upsell to your existing customers, or it could be an informational call. So that’s okay. So that’s why it’s just calling. It’s like, Hey, is there toilet paper in stock like yes or no, and then, if it’s out of stock, when will it be back? And if the dates unknown, do you have like the name and contact details of the purchasing manager.
61 00:10:10.175 ⇒ 00:10:16.229 Aditya Bahl: So there’s a bunch of things you can do for like inbound, outbound calling. That’s essentially, you know, like our primary focus.
62 00:10:17.700 ⇒ 00:10:18.359 Robert Tseng: Got it.
63 00:10:18.970 ⇒ 00:10:19.700 Aditya Bahl: Yeah.
64 00:10:21.360 ⇒ 00:10:26.749 Aditya Bahl: have you guys done anything like with voice agents? Or you know, do you think there could be a potential
65 00:10:27.960 ⇒ 00:10:29.000 Aditya Bahl: use case.
66 00:10:30.340 ⇒ 00:10:31.631 Robert Tseng: Yeah, I mean, I think,
67 00:10:32.890 ⇒ 00:10:38.708 Robert Tseng: we so we’ve done like internal.
68 00:10:40.830 ⇒ 00:10:48.639 Robert Tseng: yeah, we’ve done like internal knowledge base build outs, like, I think, for us like, why, we kind of play nicely in the in the AI world is that
69 00:10:49.124 ⇒ 00:11:00.460 Robert Tseng: we think that you know, obviously model selection. And some of this, like observability aside, like the most important thing, is having the context all kind of like accessible to to
70 00:11:00.978 ⇒ 00:11:06.560 Robert Tseng: I guess whatever co-pilot automation or agent that you’re you’re building out. And so
71 00:11:06.921 ⇒ 00:11:10.939 Robert Tseng: there’s a couple of clients that we work with that are more just kind of like
72 00:11:11.120 ⇒ 00:11:17.370 Robert Tseng: they’re like home services, business or pest control, or whatever. And we’ve basically just equipped their
73 00:11:17.600 ⇒ 00:11:37.529 Robert Tseng: existing like Cs reps with like a tool that they could chat with, so that they know how to like pull from like their existing knowledge base. When you know handling objections, then most importantly doing upsells. And so I mean, there’s probably a world where, like, yeah, we should also introduce that. And like, help them to
74 00:11:37.690 ⇒ 00:11:47.789 Robert Tseng: make you like cut down head, count on their number of reps, too, and be like, Hey, like this is where the voice voice AI technologies is there? So I think. It’s it’s
75 00:11:47.890 ⇒ 00:11:52.409 Robert Tseng: I don’t think it would be happen that would happen right away. I don’t know what about you, but like I feel like
76 00:11:52.740 ⇒ 00:12:10.880 Robert Tseng: we’ve we’ve thought about going the voice AI route. But I think there is resistance to like being like, okay, well, you implement this. You’re gonna you get to fire half your your full force or whatever. So I think that’s why we opted to do this like enablement approach first, st which inevitably is showing inefficiencies within their
77 00:12:11.060 ⇒ 00:12:21.410 Robert Tseng: within their reps. And then. Now we’re gonna help with some of the restructuring as well. And I think that naturally lends itself to like, okay, well, maybe you should replace them with the voice so.
78 00:12:21.410 ⇒ 00:12:24.509 Aditya Bahl: That’s very interesting. So this is for a home services business.
79 00:12:24.730 ⇒ 00:12:26.329 Robert Tseng: Yeah, home services, places.
80 00:12:26.668 ⇒ 00:12:29.379 Aditya Bahl: What like? What home services are they providing.
81 00:12:30.285 ⇒ 00:12:41.644 Robert Tseng: So every anything from like pest control to plumbing, or whatever they’re like the largest home service provider in Texas. So they just kind of like kind of run. Run all over the place.
82 00:12:42.150 ⇒ 00:12:45.369 Aditya Bahl: Got it. So they have people doing inbound calling or outbound.
83 00:12:46.270 ⇒ 00:12:56.999 Robert Tseng: They they have people doing inbound, or it’s both so on the inbound side. It’s like customers, or like clients will call them and be like, I have a skunk. I don’t know what to do
84 00:12:57.180 ⇒ 00:13:22.550 Robert Tseng: right? And like you help the sales. Rep may kind of be like, Okay, triage the situation like, Hey, it’s actually not a skunk. It’s a black cat like you’re fine. We’re not gonna send anyone out. But maybe it’s like, okay. It’s actually a skunk. We’ll send someone out. And actually, hey, while you’re at it like skunks, like I don’t know. Maybe they’re friends with like porcupines, or whatever. So we should. You should go in. We should. You should pay an additional to go and like, get there to go get the porcupine checked out as well, or whatever
85 00:13:22.650 ⇒ 00:13:23.210 Robert Tseng: you know.
86 00:13:23.577 ⇒ 00:13:29.820 Aditya Bahl: You’ve been dealing with, too. It’s so interesting. And I was like, Oh, wow! Now I know.
87 00:13:31.500 ⇒ 00:13:36.569 Aditya Bahl: No. But it’s so. The issue that they’re having is like inconsistency in customer service. Right?
88 00:13:36.570 ⇒ 00:13:37.770 Robert Tseng: Totally. Yeah.
89 00:13:37.770 ⇒ 00:13:42.140 Robert Tseng: And that’s due to what’s the reason they’re like training? Or is it like, turnover?
90 00:13:42.900 ⇒ 00:13:47.829 Robert Tseng: Yeah, training turnover. Yeah, these are just shops like, I think the turnover is really high. Then.
91 00:13:48.370 ⇒ 00:13:58.100 Robert Tseng: yeah, I think oftentimes. Well, yeah, just human triaging is quite limited. I think they they use to whatever script that you give them. And so yeah, I think
92 00:13:58.930 ⇒ 00:14:14.960 Robert Tseng: I don’t know. We we kind of have the thesis that like, yeah. Obviously, by making the knowledge base more accessible. You kind of stretch everybody’s expertise a bit more rather than like Sally being like, hey? I don’t know the answer. Let me go. Put you on hold and call, John. They can just like ask
93 00:14:15.500 ⇒ 00:14:35.939 Robert Tseng: the agents, and like it’ll kind of give them. Give them the answer and then, because it’s like always listening to their conversations as well. Yeah, we it within the kind of like chat feature that we have with them. They also be like, Hey, like for the customer mentioned this. Maybe you should bring up like this upsell opportunity. Kind of thing just to like prompt them to try to
94 00:14:37.030 ⇒ 00:14:41.900 Robert Tseng: make better kind of take the conversation in better directions. On the call.
95 00:14:42.790 ⇒ 00:14:45.085 Aditya Bahl: Yeah, yeah, no, definitely.
96 00:14:46.260 ⇒ 00:14:58.929 Aditya Bahl: yeah. I think that’s kind of initially, like, I didn’t show you this other piece. What we’ve also built was. This is where we started. So we pivoted to voice agents. But we have a video generation platform, too, like if you’ve seen synthesia or Hagen by any chance
97 00:15:00.510 ⇒ 00:15:04.400 Aditya Bahl: for like training. Basically, it’s like you have an AI avatar. So like, you know, this
98 00:15:04.910 ⇒ 00:15:24.266 Aditya Bahl: use case, you know, you can just have it. You’re kind of just like whatever text and the output, you just designed a bunch of slides. It’s like a video. But the really cool feature that we have is like video search. So it’s like someone in the call center. For example, if they’re like, how do I do like email format for subject lines, right?
99 00:15:26.540 ⇒ 00:15:33.470 Aditya Bahl: for law firms, you know, whatever that might be, it returns like a video and text guiding in the right direction. So for like a training perspective, because people are always quitting right?
100 00:15:34.620 ⇒ 00:15:43.630 Aditya Bahl: That’s there. But I think, like majority of like our business and revenue is really like focus on like voice agents that’s like a bigger opportunity cause. Like the thing. Video training is also like.
101 00:15:44.710 ⇒ 00:15:54.589 Aditya Bahl: like, personally, I think we probably need to invest like 30 more K to get the avatars better. But even like the cutting edge stuff with Haitian and Syndesia, it’s still like a little bit gimmicky, you know. It’s like
102 00:15:54.980 ⇒ 00:15:59.796 Aditya Bahl: it’s like we’re getting there. But it’s just like we’re not there just yet.
103 00:16:00.670 ⇒ 00:16:12.799 Aditya Bahl: yeah, nobody. I would love to just see if there’s a way for us to kind of like, maybe some sort of like revenue share, or whatever that works for, like anywhere. You know, there’s a use case for like voice agents, and we can kind of, you know, figure something out there.
104 00:16:13.700 ⇒ 00:16:31.950 Robert Tseng: Yeah, yeah. So I think, like, definitely. So, yeah, I guess you said personal injury laws like the use cases that you’ve been able to do. I mean, we we work with a couple of personal injury law firms as well. We do more like kind of medical record extraction for them. So it’s kind of like after they get some requests to go in like they have to.
105 00:16:32.390 ⇒ 00:16:41.279 Robert Tseng: I mean, they get sent, like, you know, 50 to 100 pages worth of stuff. We can kind of just reformat it all into like a clean Pdf, with all the like things that the
106 00:16:41.710 ⇒ 00:16:53.659 Robert Tseng: I guess the attorney would want to see. So like, that’s kind of our. And so I I see a world where, like, yeah, if there’s like a opportunity to sell like voice AI like I mean, we don’t want to build it in house like we could definitely consider like having
107 00:16:53.810 ⇒ 00:17:05.539 Robert Tseng: kind of selling build now. But then, even on your side, like, I don’t know if you’re kind of privy to the types of data problems that your team kind of faces sometimes. If if your clients your customers are like.
108 00:17:06.210 ⇒ 00:17:18.049 Robert Tseng: I don’t know their knowledge base is like kind of all over the place, and they just even need to get more data into like a centralized place. Then we could be a partner to kind of help them to be ready for AI right?
109 00:17:18.050 ⇒ 00:17:34.349 Aditya Bahl: That’s actually a very good point, because I think we could cross sell definitely. There’s opportunity like a law firm. There was a pi firm here in La. I had an hour and a half meeting with, and they’re very interested in like the voice agents. But then they were like, Oh, this is also another big problem of like something with like extraction or kind of there was something talking
110 00:17:34.710 ⇒ 00:17:39.070 Aditya Bahl: is the deal deal letter, or what’s the term offer letter.
111 00:17:39.910 ⇒ 00:17:57.920 Aditya Bahl: I think it’s 1 of those things, but it needs to be like a bunch of information you get to like from that entry. You create this letter right? And that’s something it’s like, I was like, there’s a lot of things we can do. But it’s like, I want to stick to like voice agents, you know. It’s like, but I would just like, Hey, I know, like, you know, like Robert and his team, they can kind of take care of this. Right? So it’s like.
112 00:17:58.870 ⇒ 00:18:10.959 Aditya Bahl: yeah, there could definitely be like an opportunity kind of there. And I think it’s like we can kind of figure out something of like either way, you know, because, like a lot of my customers, I’m sure we’ll need other help over time. And it’s just like, Oh, like, you know, like a win win situation for everyone. Basically.
113 00:18:11.530 ⇒ 00:18:12.140 Robert Tseng: Yeah.
114 00:18:12.600 ⇒ 00:18:22.560 Robert Tseng: no, that’s a good one, I think. If you’re trying to break into Ecom, I do have like the E-com client. They’re relatively small to do, probably like 5 million a year right now. But yeah, I mean, they don’t really have like
115 00:18:23.110 ⇒ 00:18:38.890 Robert Tseng: they don’t have a big C like, you know, Cs force. And so they’re they’re interested, I mean, they the idea of like, Hey, voice AI like Cs reps like, has has. They’ve asked for something like that before. So I think it could be a good kind of opportunity for us to be like, Hey.
116 00:18:39.670 ⇒ 00:18:45.399 Robert Tseng: you know, maybe we should actually like Demo something with you. Now like try, try, try, build now. So.
117 00:18:45.910 ⇒ 00:18:50.730 Aditya Bahl: Yeah, yeah, it’s is it the Jovi coffee? Or is that someone, or like a different client.
118 00:18:50.910 ⇒ 00:18:58.009 Robert Tseng: Oh, no, Javi, I mean, they’re big. They’re like they do like 120 million a year or whatever. So? Yeah, no, this is this is difficult.
119 00:18:59.270 ⇒ 00:19:07.520 Aditya Bahl: Got it. Got it? Yeah, it’s like, if you to like schedule a call with them and see if they’re interested. And you know, yeah, that would. That would be greatly appreciated.
120 00:19:08.060 ⇒ 00:19:08.630 Robert Tseng: Okay.
121 00:19:08.930 ⇒ 00:19:09.660 Robert Tseng: Cool.
122 00:19:09.950 ⇒ 00:19:10.640 Aditya Bahl: Yeah.
123 00:19:10.640 ⇒ 00:19:20.208 Robert Tseng: Yeah, I’ll send you over some of like our materials. So you can better like, understand? Like, how to sell with us for like, how to partner with us. Yeah. And we we don’t really have like
124 00:19:21.370 ⇒ 00:19:25.670 Robert Tseng: winning horse in this race. We just kind of pick whatever vendors that we.
125 00:19:25.670 ⇒ 00:19:26.810 Aditya Bahl: This, yeah.
126 00:19:26.810 ⇒ 00:19:30.636 Robert Tseng: Kind of like kind of horizontally give us the most coverage. So I think,
127 00:19:30.910 ⇒ 00:19:31.580 Aditya Bahl: Yeah, we don’t.
128 00:19:31.580 ⇒ 00:19:33.429 Robert Tseng: I’m a voice. AI, partner, right now go ahead.
129 00:19:33.430 ⇒ 00:19:41.859 Aditya Bahl: I think that’d be great. But like selling your guys services like if you were to say 3 things, I’m just gonna make a note of it like, what would you say? It’s like, you know. Robert and his team can handle.
130 00:19:44.112 ⇒ 00:19:48.467 Robert Tseng: Yeah. So if there’s anything or pain point about like, oh, like,
131 00:19:49.960 ⇒ 00:20:02.339 Robert Tseng: data is like, don’t, don’t. They? Don’t trust the data. They’re like they’re or they’re like missing gaps or something something around like data trust or data quality. I think that’s that’s like a usually a signal for an Icp that would pick up on
132 00:20:02.835 ⇒ 00:20:24.664 Robert Tseng: another one is like, yeah, we spend so much time like, kind of like on document, like, kind of like pushing around like pencil pushing right? It’s just like translating this to that like this code to that code, or, like, you know, drafting these things like anything around like kind of that type of like information summarization. We think we do. Very, very well.
133 00:20:25.210 ⇒ 00:20:30.189 Robert Tseng: and then, yeah, if everybody’s asking for like, it’d be great if I could see like
134 00:20:30.640 ⇒ 00:20:37.560 Robert Tseng: this kind of chart, or whatever like it, just in general, like data data, viz. Kind of like questions.
135 00:20:37.964 ⇒ 00:20:45.909 Robert Tseng: I think those are kind of the 3. Those are like the 3, like in the leading indicators. So like, Hey, maybe there’s an opportunity for us here. Yeah.
136 00:20:46.390 ⇒ 00:20:55.650 Aditya Bahl: Okay, I didn’t know that that’s very helpful. Yeah, just send over like some content that, you have any conversations I’m having. And if, like I come across that I’ll just kind of send that over your way.
137 00:20:56.370 ⇒ 00:21:15.920 Robert Tseng: Okay, cool. Yeah, I’ll put you in touch with our. We have a partnerships person on our team. I think you met. I mean she kind of stretches, and she does marketing. And I’m having actually you met Hannah, I think maybe at the la thing that I threw she’s kind of helping a lot more on partnership side. So I’ll probably kind of connect to you with her over email. And then she’ll just like fire everything over to you.
138 00:21:16.260 ⇒ 00:21:18.259 Aditya Bahl: Gotcha gotcha. Yeah, I think that’s
139 00:21:18.460 ⇒ 00:21:35.229 Aditya Bahl: helpful. And then it’s kind of like as an ongoing purpose. I think. Just like what I’ve been kind of offering everyone else here is just like 10% commission for, like any introductions you make, and it kind of closes and then it’s like, I’ll just do all the heavy lifting. So that’s just kind of you’re just making the intro and kind of, you know, like, yeah.
140 00:21:35.810 ⇒ 00:21:38.519 Robert Tseng: Sure, okay, yeah, that sounds good.
141 00:21:39.220 ⇒ 00:21:42.620 Aditya Bahl: Alright! Is there anything else I can help you out with these days?
142 00:21:45.110 ⇒ 00:21:57.495 Robert Tseng: Well, I’m coming back to La in like a month. So yeah, maybe we could like kind of well, now that we’re kind of reconnected. I’ll message you people I’m looking to, maybe connect with. And then
143 00:21:58.250 ⇒ 00:22:03.000 Robert Tseng: yeah, I mean, if you’re going after call centers and pi firms right now.
144 00:22:03.600 ⇒ 00:22:16.559 Robert Tseng: I mean, I just have a friend who was in the like medical voice agent space, and he like he left it. I think he just. He went and joined 11 x or whatever. So yeah. So I don’t know if you’re I don’t really know what the
145 00:22:16.860 ⇒ 00:22:23.740 Robert Tseng: like. Why, there’s consolidation happening in the voice issue. I mean, I’m I’m curious. But yeah, I think, yeah.
146 00:22:23.740 ⇒ 00:22:24.290 Aditya Bahl: We get a call.
147 00:22:24.290 ⇒ 00:22:25.550 Robert Tseng: Yeah, 11.
148 00:22:25.550 ⇒ 00:22:26.270 Aditya Bahl: I have an x.
149 00:22:26.580 ⇒ 00:22:27.340 Robert Tseng: Yeah.
150 00:22:27.340 ⇒ 00:22:43.770 Aditya Bahl: Okay, we we got like 2. That’s that’s good. That’s so funny to hear. It’s like we got 2 acquisition interest to already. One was from a telecom company and then other ones from the utility space. That’s like I, right now, it’s just like growing the top line and the higher multiple we can get. I think that’s kind of like, you know.
151 00:22:43.770 ⇒ 00:22:44.740 Robert Tseng: 1st of all, yeah.
152 00:22:44.740 ⇒ 00:22:48.480 Aditya Bahl: Yeah, yeah, I don’t know. Just like for us, like, right now.
153 00:22:48.930 ⇒ 00:22:57.889 Aditya Bahl: yeah, that that’s a very good question, because it’s like, I’ve pretty much bootstrapped it. For the most part I’m only taking like very little angel funding. But I’m looking to get a little bit more, and then
154 00:22:59.120 ⇒ 00:23:05.690 Aditya Bahl: see if I raise like a proper round, then it’s like, you know, you promised 40% growth last quarter you grew 25. You know, it’s like those are like
155 00:23:06.010 ⇒ 00:23:07.390 Aditya Bahl: I have versus like.
156 00:23:07.390 ⇒ 00:23:07.730 Robert Tseng: Yeah.
157 00:23:07.780 ⇒ 00:23:22.829 Aditya Bahl: Right now, like you’re running like a nice business, you know, and it’s like it’s probably like life’s good, you know, but it’s like as soon as you take venture dollars. Then it’s like, looks like triple triple double double. And it’s just like, you know, it’s like, Oh, my God, yeah. But yeah.
158 00:23:22.990 ⇒ 00:23:31.860 Robert Tseng: Yeah, no, yeah. You you keep going. I think it’s you’re in a good space. I mean, I feel like there’s a lot of transactions happening in that world. So hope you.
159 00:23:31.960 ⇒ 00:23:36.879 Aditya Bahl: Yeah, no out of like did he take venture, or he was bootstrapped. That person.
160 00:23:37.720 ⇒ 00:23:39.360 Robert Tseng: They were Yc. Backed.
161 00:23:39.720 ⇒ 00:23:40.490 Aditya Bahl: Oh, that makes you back.
162 00:23:40.790 ⇒ 00:23:45.289 Robert Tseng: Yeah, they pivoted, or they kind of just like narrowed down into that. And then
163 00:23:45.640 ⇒ 00:24:04.340 Robert Tseng: I mean for them they were. I think they just growth slowed. Obviously, like you said invest pressure or whatever. So they were just being they were. They were pushed to make an exit. And so you know, 11 X is a Yc company, too. So that’s usually just what happens. It’s just like, Hey, you’re growing faster than them like you want to buy them out, and everything so.
164 00:24:04.510 ⇒ 00:24:06.380 Aditya Bahl: Were they happy with the exit, or.
165 00:24:06.810 ⇒ 00:24:15.110 Robert Tseng: Yeah, I mean, I guess he and his co-founder. They both work at 11 next now, so I don’t know how much they really got out of it if they’re both just grinding on another start.
166 00:24:15.378 ⇒ 00:24:26.119 Aditya Bahl: It’s like, is that the end goal? Or you know, it’s like out, because there’s like, you know, like valuation, like liquidation preferences. So it’s like sometimes, even after like all that, it’s like, what are you walking away from?
167 00:24:26.120 ⇒ 00:24:28.879 Robert Tseng: I’m sure they didn’t have much leverage. That’s kind of what I think. So.
168 00:24:28.880 ⇒ 00:24:29.750 Aditya Bahl: Okay.
169 00:24:29.930 ⇒ 00:24:30.830 Aditya Bahl: Fair enough.
170 00:24:30.830 ⇒ 00:24:34.429 Robert Tseng: I’m sure they got something out like I’m sure it was. It wasn’t that. Yeah, yeah.
171 00:24:34.430 ⇒ 00:24:39.889 Aditya Bahl: Cool. But yeah, yeah, looking forward to keeping in touch. And then, yeah, if you could make that intro to that
172 00:24:40.800 ⇒ 00:24:44.270 Aditya Bahl: consumer company, that would be yeah.
173 00:24:44.270 ⇒ 00:24:47.910 Robert Tseng: Sure, cool, alright, well, good, reconnecting.
174 00:24:47.910 ⇒ 00:24:53.780 Aditya Bahl: Everything. Enjoy New York. I hear it’s a lot of fun. It’s not too hot for you. So yeah.
175 00:24:53.780 ⇒ 00:24:59.740 Robert Tseng: Yeah, thank you. And I hope, stay safe in la, you know, it seems kind of crazy over there. Yeah.
176 00:25:00.510 ⇒ 00:25:03.489 Robert Tseng: Okay. Alright. Talk to you later. See ya.