Meeting Title: Contextual AI Project Planning Sync Date: 2026-03-09 Meeting participants: Pranav Narahari, Robert Tseng, iPhone (227)
WEBVTT
1 00:00:48.870 ⇒ 00:00:49.640 Pranav Narahari: Hey, Robert.
2 00:00:49.640 ⇒ 00:00:51.350 Robert Tseng: Apronounce.
3 00:00:54.320 ⇒ 00:01:01.429 Robert Tseng: Right. Alright, we’ll just jump into it, because I don’t know how much… how much more data I’m gonna have.
4 00:01:01.440 ⇒ 00:01:02.510 Pranav Narahari: That’s do it.
5 00:01:02.840 ⇒ 00:01:08.349 Robert Tseng: Do you have the doc pulled up? Because that’s probably what he’s gonna reference. You want to just go to that email chain and catch up?
6 00:01:09.240 ⇒ 00:01:11.069 Pranav Narahari: Yes, I will do that right now.
7 00:01:11.310 ⇒ 00:01:12.080 Robert Tseng: today.
8 00:01:12.940 ⇒ 00:01:19.260 Robert Tseng: Yeah, I think it’d be best if you… you pull it up, and you kind of, like, poke around at where you… and then we… I can… I can…
9 00:01:19.440 ⇒ 00:01:27.229 Robert Tseng: point out where I think he’s gonna get hung up on, but, I think otherwise, like, it seems like he’s… he’s… he’s good with…
10 00:01:28.170 ⇒ 00:01:35.660 Robert Tseng: I mean, he already had a couple questions, and we responded in an email, so I… I think he’s… I think he’s pretty…
11 00:01:36.390 ⇒ 00:01:37.080 Robert Tseng: You…
12 00:01:37.200 ⇒ 00:01:47.570 Robert Tseng: I’m pretty sure he wants… he wants to move forward, and he probably just wants to… it’s really just kind of like… I don’t want to call it an interview for you, but, like, he just kind of… he’ll probably ask you some questions, and…
13 00:01:47.570 ⇒ 00:01:58.329 Robert Tseng: wanna… I just want you to be able to feel comfortable talking to him, and, you know, once he… once he knows that, like, okay, like, Pranav’s good, then, like, I think he’ll… he’ll…
14 00:01:58.860 ⇒ 00:01:59.899 Pranav Narahari: 100%, sound good.
15 00:01:59.900 ⇒ 00:02:00.520 Robert Tseng: Yeah.
16 00:02:02.390 ⇒ 00:02:11.000 Robert Tseng: Okay, and then I’m actually gonna just share this with him as well. If we can bring him on now, that would be ideal, but…
17 00:02:11.009 ⇒ 00:02:11.469 Pranav Narahari: Yeah.
18 00:02:11.470 ⇒ 00:02:15.269 Robert Tseng: Can I send them the same link that you sent me? Okay, actually, I got it, yeah.
19 00:02:15.270 ⇒ 00:02:16.090 Pranav Narahari: Good work, yeah.
20 00:02:16.090 ⇒ 00:02:18.590 Robert Tseng: Yeah, yeah, okay, sweet.
21 00:02:19.850 ⇒ 00:02:23.309 Pranav Narahari: I’ll just let him know.
22 00:02:25.610 ⇒ 00:02:32.479 Robert Tseng: We are here if… Keep trying to… Oh, rush.
23 00:02:32.480 ⇒ 00:02:41.250 Pranav Narahari: Yeah, and if he’s a little bit late, too, and we go past the 30 minutes, like, I’m just kind of a fly on the wall on the client meeting, so I can just let Amber know.
24 00:02:43.460 ⇒ 00:02:44.140 Robert Tseng: Okay.
25 00:02:45.690 ⇒ 00:02:52.169 Robert Tseng: Yeah, we’ll just carry on with him. If he jumps in, he jumps in. They’re pretty, like, not… they’re pretty ad hoc, so I’ll just…
26 00:02:52.270 ⇒ 00:02:54.159 Robert Tseng: That’s just kind of how this clock is.
27 00:02:54.260 ⇒ 00:02:55.040 Pranav Narahari: Perfect.
28 00:02:59.390 ⇒ 00:03:00.140 Robert Tseng: Flipping it.
29 00:03:05.270 ⇒ 00:03:10.050 Robert Tseng: Okay, so, yeah, I guess, like…
30 00:03:10.370 ⇒ 00:03:16.680 Robert Tseng: We made a couple adjustments based on last conversation.
31 00:03:19.670 ⇒ 00:03:32.919 Robert Tseng: Yeah, so I took… I basically took the feedback that you gave me, and I tried to simplify the proposal. We kind of stripped out, like, all the kind of technical complexity that they added in. Like, they don’t need complex ML stuff or whatever, so that was kind of, like.
32 00:03:33.370 ⇒ 00:03:39.989 Robert Tseng: the angle I went at, it was like… I mean, if you saw the email that I sent to him, I think you were in thread as well, so it was like…
33 00:03:40.420 ⇒ 00:03:51.280 Robert Tseng: hey, you know, work with us, Pranav’s already integrated with our team, and also, it’s not going to be as complicated as you think it is. We already have a lot of the data, kind of, there, and I think
34 00:03:51.390 ⇒ 00:04:06.769 Robert Tseng: most of the work is really gonna be, like, kind of, calibrated for the outputs on, like, defining what goods looks like, so I feel like that’s probably where… I would just use it as, like, a requirements gathering session when you do talk to him.
35 00:04:06.910 ⇒ 00:04:08.429 Robert Tseng: You know.
36 00:04:08.640 ⇒ 00:04:19.529 Robert Tseng: if we broke it up into three phases, you’re kind of assuming that, like, you know, phase one’s gonna pretty go smoothly, we’re gonna get access to whatever we want. If you have any questions about specific integrations, like.
37 00:04:19.649 ⇒ 00:04:27.729 Robert Tseng: I mean, I would… I would probably run that by our team first, because, like, we’re… we… I don’t think Danny would… would know. I think we would…
38 00:04:28.420 ⇒ 00:04:29.760 Robert Tseng: Oh, hey Danny.
39 00:04:30.150 ⇒ 00:04:30.820 Pranav Narahari: Hey, honey.
40 00:04:31.080 ⇒ 00:04:34.750 iPhone (227): Just hopping on you now, sorry, I didn’t, see your message for a couple minutes.
41 00:04:35.190 ⇒ 00:04:48.979 Robert Tseng: No, all good. Thanks for jumping on on short notice. I wasn’t really sure if… yeah, I wasn’t expecting it. I was like, I’ll just prep for Nav anyway, and then he’ll… he’ll talk to you, because, I may… I may not be available when… when you guys are. Oh, good. Where are you heading, Robert?
42 00:04:49.230 ⇒ 00:04:51.340 Robert Tseng: I’m in Panama right now, actually.
43 00:04:52.440 ⇒ 00:04:54.689 iPhone (227): My, my brother’s down in Panama.
44 00:04:55.370 ⇒ 00:04:58.619 Robert Tseng: Oh, no way! What is he doing down here?
45 00:04:58.620 ⇒ 00:05:04.549 iPhone (227): Him and his wife have a little condo in… just somewhere outside Panama City, so they’re down there for a month.
46 00:05:05.580 ⇒ 00:05:06.140 Robert Tseng: Cool.
47 00:05:06.340 ⇒ 00:05:08.350 iPhone (227): Yeah, man, looks, looks, looks fun.
48 00:05:08.820 ⇒ 00:05:15.160 iPhone (227): So, I was thinking about this little project, and I really like it. I think it’s really cool.
49 00:05:15.880 ⇒ 00:05:18.970 iPhone (227): I, I want to wrap my head around…
50 00:05:19.820 ⇒ 00:05:23.250 iPhone (227): Obviously, this is, you know, not an…
51 00:05:23.500 ⇒ 00:05:39.309 iPhone (227): not a de minimis budget request, and nothing we can squish into our current workflows, so… I’ve got to figure out a way to somehow get this sprint lined up, and I think part of what’ll help on this is if I have an understanding of really the full possibilities of this model.
52 00:05:39.790 ⇒ 00:05:54.790 iPhone (227): So I put it together really selfishly as, like, Mr. COO trying to go in here, but I realized that this is… this is really part of my larger augmented workforce kind of plan. I don’t know if I ever sent you guys that deck.
53 00:05:55.430 ⇒ 00:05:56.520 iPhone (227): No.
54 00:05:56.660 ⇒ 00:05:57.670 Robert Tseng: But, yeah.
55 00:05:58.130 ⇒ 00:06:03.230 iPhone (227): But, in essence, this will create the repositories for all this. Is that kind of what I’m seeing pours through the trees here?
56 00:06:04.900 ⇒ 00:06:19.890 Robert Tseng: Yeah, I think, like, the use case you described, like I was saying over email, is probably more narrow than you realize, but, like, the work that we would do to kind of prep all this, I mean, there’s just so many different applications you can kind of… you can have afterwards, so,
57 00:06:19.890 ⇒ 00:06:28.850 Robert Tseng: yeah, I think, like, you know, for now, I think it’s good to kind of… to keep it smaller, just so we can sprint towards something where you, you know.
58 00:06:28.920 ⇒ 00:06:35.190 Robert Tseng: ultimately, it’s gonna be, like, your own, kind of, like, yeah, COO, chief… I call it a chief of staff agent, or whatever.
59 00:06:35.190 ⇒ 00:06:49.449 Robert Tseng: But yeah, I mean, we want you to be thrilled with the outputs, like, and really kind of see how, you know, we’re harnessing kind of the… both the data platform that we already have, plus all this, like, context engineering work that, like, Pranav is an expert on.
60 00:06:49.450 ⇒ 00:07:01.110 Robert Tseng: And we’ve kind of done a few use cases of this already, so, like, we… both internally, we do this, but also for other clients, so I thought it’d be a good project for… to bring Pranav onto.
61 00:07:01.110 ⇒ 00:07:12.149 iPhone (227): Yeah, this is awesome, Pranav, excited to see it. I met with a couple off-the-shelf groups, including one called Worklytics this last week, if you guys have seen that platform.
62 00:07:12.570 ⇒ 00:07:16.050 iPhone (227): They pretty much do exactly this, they pipe in
63 00:07:16.190 ⇒ 00:07:32.959 iPhone (227): Slacks and, you know, emails, and they try to kind of build out, like, a how your company is working map, and honestly, after walking through their demo and everything else, I think this is cooler. I don’t think they really had set up anything novel there other than just
64 00:07:33.110 ⇒ 00:07:37.779 iPhone (227): showing which teams work with which teams. At this point, you know.
65 00:07:38.550 ⇒ 00:07:46.329 iPhone (227): contextual AI can pretty much figure out, hey, this team’s working with that team, and present a mind map easier than any built off-the-shelf tool.
66 00:07:46.690 ⇒ 00:07:47.440 iPhone (227): You guys are talking?
67 00:07:47.440 ⇒ 00:07:49.639 Robert Tseng: at Contextual AI, they’re a partner of ours.
68 00:07:50.070 ⇒ 00:07:57.180 iPhone (227): No, not… no, I meant, like, like… Oh, okay, just like, background context, I meant semantically.
69 00:07:57.180 ⇒ 00:07:57.570 Robert Tseng: Yeah, yeah.
70 00:07:57.570 ⇒ 00:07:58.850 iPhone (227): Okay, okay, bye.
71 00:07:58.850 ⇒ 00:07:59.310 Robert Tseng: Nope.
72 00:07:59.310 ⇒ 00:08:00.580 iPhone (227): Although, cool, good to know.
73 00:08:00.900 ⇒ 00:08:01.550 Robert Tseng: Yeah.
74 00:08:01.550 ⇒ 00:08:14.349 iPhone (227): So that’s my thought, is this is probably a better way to go. They were quoting about $32 for the year, like an annual subscription just to Worklytics, so that kind of helped me understand a little bit of this value.
75 00:08:14.530 ⇒ 00:08:28.899 iPhone (227): And then beyond that, you know, there was… there was no LLM functionality really on there. I mean, you could ask it a little bit about how your team’s working, but you couldn’t really ask it to go deep dive or find specific documentation, things like that.
76 00:08:29.580 ⇒ 00:08:37.540 iPhone (227): So, all in all, I think this is a pretty good value, we just gotta figure out how to present a return, and then I gotta figure out how to plan some cash flows to get you guys started on this.
77 00:08:38.940 ⇒ 00:08:44.930 Robert Tseng: Sweet. So yeah, what do you, what do you feel like you need in order, like, what would be a good kind of,
78 00:08:45.470 ⇒ 00:08:47.260 Robert Tseng: ROI kind of pitch.
79 00:08:47.830 ⇒ 00:08:58.840 iPhone (227): Yeah, that’s kind of what I was trying to figure out, because I’ve thought about it for myself. So, first of all, on the cash flow side, I mean, the question is going to be how much can we include in existing sprints? So, is the answer to this point just zero?
80 00:08:59.640 ⇒ 00:09:14.150 Robert Tseng: No, no, so we broke it up into a few phases, so, like, I would say, like, I mean, we call it… I figure out if we call them Phase 0 to Phase 2, or Phase 1 to Phase 3, but the first one is really, like, that’s… that’s just kind of getting the integrations in. We can rely on existing resources for that.
81 00:09:14.400 ⇒ 00:09:31.179 Robert Tseng: What you’re really paying for is kind of Pranav’s time, I think, on… and obviously my time, too, and, like, kind of nailing the requirements, and then kind of… Pranav, I’ll let you speak to, kind of, like, phase two, like, what you… what that entails, and kind of why… why you scoped it in terms of how long that would take.
82 00:09:32.490 ⇒ 00:09:43.050 Pranav Narahari: Yeah, yeah, so, yeah, like Robert just said, Phase 1 is kind of getting the data in. Phase two is actually kind of what you talked about, Danny, like, creating that, like, context, like, graph.
83 00:09:43.070 ⇒ 00:09:54.480 Pranav Narahari: About, okay, using all this data, how do we kind of get, like, drive real insights, not just, like, who’s working with who, like, really just, like, that really, like, shallow level of insights?
84 00:09:54.480 ⇒ 00:10:03.379 Pranav Narahari: And what’s good here is that we can create something specific to you guys, and we kind of had a little… a few options that we listed, but we’ll talk to you about, like.
85 00:10:03.410 ⇒ 00:10:12.499 Pranav Narahari: what do you want in, like, a weekly update or a daily update, whatever. And then we can kind of create a context graph that can, of course.
86 00:10:12.570 ⇒ 00:10:20.349 Pranav Narahari: follow suit and deliver on what you guys are looking for exactly there, but then I think the idea here, too, is, like, this context graph can
87 00:10:20.350 ⇒ 00:10:34.100 Pranav Narahari: will exist, and then you can build additional agents potentially on top of this for sending reports, you know, even in real time. But then that’s down the line. So, like, when Robert was talking about how we’re, like.
88 00:10:34.100 ⇒ 00:10:40.699 Pranav Narahari: really narrowing the scope here. Like, that’s by design, but then after… after this, we can kind of talk about,
89 00:10:41.100 ⇒ 00:10:46.469 Pranav Narahari: like, how we can create additional type of reports after the fact.
90 00:10:46.570 ⇒ 00:10:50.959 Pranav Narahari: But, yeah, to answer your question, like, yeah, this Phase 2 is…
91 00:10:51.120 ⇒ 00:11:01.050 Pranav Narahari: Kind of the real meat of the project, which is, yeah, how do we take in all this context and organize it in a way to drive, like, these really powerful insights?
92 00:11:02.140 ⇒ 00:11:06.370 iPhone (227): This is, this is good, and…
93 00:11:06.900 ⇒ 00:11:10.490 iPhone (227): I think, as a business person.
94 00:11:10.610 ⇒ 00:11:14.890 iPhone (227): I almost got too excited about trying to, like,
95 00:11:15.620 ⇒ 00:11:33.150 iPhone (227): have some white space thinking about what I could do with such a tool, with forgetting the fact that the whole kind of point of this is just building a foundational layer that allows us to do a million things. And so I realized when I put this together, I was probably being way too narrow in how I approached this, and really, it’s kind of the backbone of the
96 00:11:33.150 ⇒ 00:11:39.480 iPhone (227): how I’m starting to see the new way of working. In essence, I could eventually see a tool like that
97 00:11:39.580 ⇒ 00:11:45.630 iPhone (227): Like this, being, in essence, the core dashboard for the way of working across the whole company, right?
98 00:11:45.720 ⇒ 00:11:55.699 iPhone (227): Yeah. So instead of accessing any individual application or tool, this basically… this command center becomes the central hub for working, for sending notes. I mean, I have…
99 00:11:55.700 ⇒ 00:12:06.190 iPhone (227): some contextual, you know, understanding of this, because NVIDIA kind of just rolled out this, basically, like, you know, command center approach to working. So I’ve watched Sky get, you know, that
100 00:12:06.390 ⇒ 00:12:21.000 iPhone (227): unleashed, and what power that drives. Everything from payroll data to, you know, their, agents to say, hey, help me draft an email for this team, and they put together things, or help me schedule, like, it’s crazy advanced.
101 00:12:21.000 ⇒ 00:12:34.609 iPhone (227): So I want to start by mostly focusing on how do we just get these data layers to the point where I can get, like, Josh, Adam, and myself really working with Gemini, sort of as the front-end command center tool for how we do, our way of working.
102 00:12:35.340 ⇒ 00:12:36.470 iPhone (227): Does that make sense? Yeah.
103 00:12:36.680 ⇒ 00:12:40.140 Pranav Narahari: Yeah, that makes sense. I think,
104 00:12:40.560 ⇒ 00:12:48.379 Pranav Narahari: what’s going to drive creating that, how we’re talking about it, kind of… we’re kind of saying it in, like, different words, like, just that…
105 00:12:48.600 ⇒ 00:13:05.700 Pranav Narahari: contextual graph, just, like, the brain that pulls in all this data. Like, you mentioned how, like, you know, the data we can pull in is endless, like, you talked about, like, within video, like, bringing in even payroll data. But the idea here with, like, this Phase 2 is to…
106 00:13:05.840 ⇒ 00:13:19.010 Pranav Narahari: bring in that data to actually drive, like, actionable insights. It’s not just an additional thing that we throw to… as context to the AI, and just, like, copy and paste. How do we actually contextualize it?
107 00:13:19.380 ⇒ 00:13:24.649 Pranav Narahari: And so, I think the best way to do that is to, like, have some defined deliverables.
108 00:13:24.650 ⇒ 00:13:28.270 Pranav Narahari: for what we want it to be able to provide.
109 00:13:28.270 ⇒ 00:13:49.829 Pranav Narahari: So, we don’t build this whole graph, but then it’s actually not able to drive the exact use case that you guys see for maybe being the biggest value add off the bat. So, that’s probably something I’d want to talk to you about, just to kind of, like, fully define, like, okay, what are, like, maybe the first couple, two, three, whatever many things that we want this
110 00:13:49.930 ⇒ 00:13:56.749 Pranav Narahari: This application to be able to provide, and so, yeah, like that…
111 00:13:57.290 ⇒ 00:14:00.930 Pranav Narahari: Week in a… week in a nutshell type of,
112 00:14:01.320 ⇒ 00:14:04.869 Pranav Narahari: like, report, I think, is a great one.
113 00:14:05.260 ⇒ 00:14:12.760 Pranav Narahari: And so, we can just kind of define, too, like, what exactly is going to go into that report. Does that kind of make sense? Like, how I’m thinking about this? Yeah. Okay.
114 00:14:12.760 ⇒ 00:14:27.910 iPhone (227): Yeah, because then it’ll allow us to define, sort of, what are the key data pipelines and that kind of thing to maintain. I mean, obviously, this can be iterative, too, so maybe the first step is access to data, and we set up… I’m having, in the background.
115 00:14:27.910 ⇒ 00:14:33.100 iPhone (227): Google Drive recategorized and implementing some things, like no closed Slack channels.
116 00:14:33.280 ⇒ 00:14:39.069 Pranav Narahari: Really trying to drive that open architecture so that when we pipe in data, we can use the backend credentialing.
117 00:14:39.070 ⇒ 00:14:55.400 iPhone (227): That things like Google Drive provide through the, you know, like an SSO type. Gemini’s really good at having that fully built in, so I love that as a front-end tool. So, in essence, I’ve got two folks who are just working on categorizing our Google Drive with appropriate, permissions and credentials right now.
118 00:14:55.540 ⇒ 00:15:03.269 iPhone (227): Because then at that point, you guys can basically plug and play, and we can eventually roll this out to other folks, too, based on that credentialing environment.
119 00:15:03.980 ⇒ 00:15:05.609 Pranav Narahari: Yeah, yeah, definitely.
120 00:15:06.530 ⇒ 00:15:09.089 iPhone (227): Cool, so, so how do you…
121 00:15:09.090 ⇒ 00:15:28.819 Robert Tseng: Danny, if I can just, like, kind of say one thing, something that you don’t even realize we’re already doing, which is, like, with all the resources that we’ve built for Eden, all the slide decks, the spreadsheets, like, we have, like, a… we have a Google Drive MCP kind of running, where basically, as I do these bi-weekly deck builds, like, I’m not gonna do it this week because I’m out of town, but Greg is gonna be using it.
122 00:15:28.890 ⇒ 00:15:44.499 Robert Tseng: he’s basically pulling the same data… I mean, the slide templates are all there, but he’s… but based on all the transcripts that we have on our internal meetings on Zoom, the work, the PRs that we’re pushing, we have all that context already, and it’s pulling those summaries into the slides already.
123 00:15:44.500 ⇒ 00:16:02.670 Robert Tseng: And so, I mean, we’re already kind of doing this type of… we’re already using these types of tools for you on the… on the data side, and I think we’re just kind of expanding it and giving it to you guys to use as well. So, like, I think, to me, what this is gonna prove is that, like.
124 00:16:02.670 ⇒ 00:16:18.800 Robert Tseng: any senior client-facing person on the Eden client is going to be able to use context that we have, and we’ve already kind of structured in a good way to be able to provide, like, you know, C-level kind of updates. And so I’m excited to see what
125 00:16:18.800 ⇒ 00:16:28.700 Robert Tseng: what the guys put together this week without me being… without me being there. But, you know, we’re already… we’re doing… we’re already doing that level of, kind of integration on… on the… on the data side.
126 00:16:29.210 ⇒ 00:16:45.760 iPhone (227): Alright, this is great. Love to hear that, and I think it’ll be a big unlock. So my first request would be, A, let’s try and… I’ll try and get some additional resources, Robert. I’m not sure how I’ll do that, or what it looks like, or whatever, but let’s unlock Pranav in there as soon as it’s appropriate, which sounds like we’re pretty close to.
127 00:16:45.840 ⇒ 00:16:57.170 iPhone (227): And… and in addition to that, let’s just keep this tight-knit. I really don’t want this to become, like, a full ELT project where we’re QAing before we’re even halfway to the finish line.
128 00:16:57.520 ⇒ 00:16:59.630 iPhone (227): So if there’s any way we can just, like.
129 00:17:00.250 ⇒ 00:17:06.559 iPhone (227): get to a point where what we hand over is really powerful, really cool. That’s kind of the impression I want to make internally.
130 00:17:06.950 ⇒ 00:17:14.459 iPhone (227): To… to eventually drive towards just shifting that full approach to how we access our data in the first place.
131 00:17:16.730 ⇒ 00:17:29.170 Pranav Narahari: Gotcha. Yeah, that makes a lot of sense to me. I think that, the thing that I mentioned before, just kind of like, let’s define exactly what that deliverable looks like, so we know exactly what success looks like, and then kind of work backwards from there would be.
132 00:17:29.170 ⇒ 00:17:34.080 iPhone (227): So, when do you want… when do you want to do that and talk through… I mean, is… do you want to go through that right now?
133 00:17:34.670 ⇒ 00:17:41.420 Pranav Narahari: So that might be… so I have a hard cut at, like, in 9 minutes. If we can maybe… Yeah.
134 00:17:41.420 ⇒ 00:17:54.880 iPhone (227): Why don’t you and I set up some time just to chat whitespace on this, and just take some notes and stuff? Like, we can figure some things out, hop on camera, and just go through some concepts. So, let’s, let’s get a time to do that.
135 00:17:54.880 ⇒ 00:17:56.109 Pranav Narahari: That sounds perfect.
136 00:17:56.110 ⇒ 00:17:59.220 iPhone (227): Are you a PT? Or ET?
137 00:17:59.220 ⇒ 00:18:01.079 Pranav Narahari: I’m, ESD, yeah.
138 00:18:01.250 ⇒ 00:18:03.890 iPhone (227): I’ve got tomorrow at 1, ET.
139 00:18:04.080 ⇒ 00:18:10.330 Pranav Narahari: Tomorrow one, let me just check that for you. That’s perfect. Let’s do it.
140 00:18:10.480 ⇒ 00:18:11.310 iPhone (227): Let’s do it.
141 00:18:11.710 ⇒ 00:18:14.520 Pranav Narahari: Cool. I’ll… I’ll set that up with you.
142 00:18:14.970 ⇒ 00:18:15.530 iPhone (227): Yeah.
143 00:18:15.550 ⇒ 00:18:17.430 Pranav Narahari: Or I can sense them over right now.
144 00:18:17.600 ⇒ 00:18:19.710 Pranav Narahari: Oh yeah, if you have it ready right now, that works.
145 00:18:20.190 ⇒ 00:18:20.990 iPhone (227): Cool.
146 00:18:21.480 ⇒ 00:18:22.100 Pranav Narahari: Cool.
147 00:18:22.640 ⇒ 00:18:28.279 iPhone (227): Alright, I’m excited, that’s awesome. Thanks, Robert, for, diving into that, and for Nov. Happy to have you, jump in here.
148 00:18:28.520 ⇒ 00:18:30.350 Pranav Narahari: Yeah, that sounds great. Talk to you tomorrow.
149 00:18:30.350 ⇒ 00:18:31.339 iPhone (227): Alright, see ya.
150 00:18:31.530 ⇒ 00:18:32.599 Pranav Narahari: Cool. See you guys.