Meeting Title: Internal AI Team | Standup Date: 2025-04-07 Meeting participants: Amber Lin, Patrik, Miguel De Veyra, Casie Aviles
WEBVTT
1 00:00:37.510 ⇒ 00:00:38.880 Amber Lin: Hello!
2 00:00:40.660 ⇒ 00:00:41.180 Casie Aviles: Hey!
3 00:00:41.785 ⇒ 00:00:47.319 Amber Lin: I think, Patrick said. He’ll be there. I’ll just wait a little bit. I’ll ping him. Now.
4 00:00:50.480 ⇒ 00:00:52.990 Amber Lin: what questions are we? Gonna ask him.
5 00:00:54.810 ⇒ 00:00:58.920 Casie Aviles: Oh, for Patrick! I think we could ask him about.
6 00:00:59.510 ⇒ 00:01:03.600 Casie Aviles: There’s this tool that Utam’s recommending us to work on, so
7 00:01:03.920 ⇒ 00:01:05.760 Casie Aviles: maybe we could ask him about that.
8 00:01:06.410 ⇒ 00:01:07.130 Amber Lin: Oh, okay.
9 00:01:09.190 ⇒ 00:01:10.590 Miguel de Veyra: It’s a dlt of them.
10 00:01:12.010 ⇒ 00:01:12.680 Casie Aviles: Yes.
11 00:01:12.680 ⇒ 00:01:16.939 Miguel de Veyra: Yeah, that one, because Patrick told us he has experience in a number. So.
12 00:01:16.940 ⇒ 00:01:17.510 Amber Lin: Oh!
13 00:01:17.510 ⇒ 00:01:19.700 Miguel de Veyra: Probably the only person that can do it.
14 00:01:19.990 ⇒ 00:01:24.260 Amber Lin: Okay, that’s awesome. As as long as we have someone that knows
15 00:01:26.037 ⇒ 00:01:33.130 Amber Lin: for the AI team. I know we have a official planning meeting for the AI team tomorrow.
16 00:01:33.750 ⇒ 00:01:37.730 Amber Lin: But what do we wanna work on today?
17 00:01:40.330 ⇒ 00:01:43.080 Miguel de Veyra: Sorry you meant. Do we have another AI planning meeting.
18 00:01:44.890 ⇒ 00:01:45.869 Amber Lin: Tomorrow, yeah.
19 00:01:46.320 ⇒ 00:01:49.949 Miguel de Veyra: Oh, okay, wait. Let me just pull up my linear.
20 00:01:52.290 ⇒ 00:01:55.179 Miguel de Veyra: Well, I guess it depends on what will happen with Patrick today.
21 00:01:56.610 ⇒ 00:02:00.010 Miguel de Veyra: Yeah. But I think the other thing we can discuss is the slack thing.
22 00:02:00.310 ⇒ 00:02:01.890 Miguel de Veyra: How are we doing that, Casey?
23 00:02:03.807 ⇒ 00:02:17.790 Casie Aviles: I don’t have any progress right now. Aside from just last Friday, which is just, you know, using polyatomic to transfer the messages and all the other data to Snowflake. That’s pretty much where I left off.
24 00:02:18.230 ⇒ 00:02:18.850 Miguel de Veyra: Okay.
25 00:02:23.800 ⇒ 00:02:29.240 Miguel de Veyra: Amber, there’s a couple of stuff ready and ready for development. Do you want to share screen? Maybe we can look at this.
26 00:02:33.210 ⇒ 00:02:43.839 Amber Lin: I’m ready for development, I mean, do you want to square share screen? Because I I really don’t know she’s because of me, so it’ll be if you guys just look at it.
27 00:02:48.770 ⇒ 00:02:53.160 Miguel de Veyra: Okay, yeah. Can you guys see, see linear.
28 00:02:54.510 ⇒ 00:02:55.210 Amber Lin: Me!
29 00:02:55.753 ⇒ 00:03:04.140 Miguel de Veyra: Yeah, so it’s basically this 3. But again, this is blocked by this is blocked by Dlt.
30 00:03:05.060 ⇒ 00:03:06.312 Miguel de Veyra: which is factory.
31 00:03:07.120 ⇒ 00:03:12.369 Miguel de Veyra: And then zoom assets to S. 3. I believe we’re already doing. We just have to structure it right.
32 00:03:14.270 ⇒ 00:03:15.210 Miguel de Veyra: Oh, hey! Patrick!
33 00:03:16.220 ⇒ 00:03:16.970 Amber Lin: Okay. Thank you.
34 00:03:17.220 ⇒ 00:03:18.300 Patrik: Here, you guys.
35 00:03:20.758 ⇒ 00:03:22.610 Miguel de Veyra: Casey. I think you’d take over.
36 00:03:24.350 ⇒ 00:03:32.210 Casie Aviles: Hey, Patrick? So yeah, I guess we just have, like we just want to ask for some guidance on, like
37 00:03:32.380 ⇒ 00:03:33.619 Casie Aviles: how we could set up
38 00:03:34.658 ⇒ 00:03:40.649 Casie Aviles: how or how we could leverage the Lt Hub for loading our data.
39 00:03:43.680 ⇒ 00:03:46.453 Patrik: Yeah, yeah. I mean, dl, 2 is great.
40 00:03:47.870 ⇒ 00:03:53.490 Patrik: have you like, how far have you gotten already with it?
41 00:03:54.884 ⇒ 00:04:00.679 Casie Aviles: I’ve only tried it once before with I tried getting slack messages, but
42 00:04:01.574 ⇒ 00:04:07.610 Casie Aviles: I I transferred it to super base, but I guess
43 00:04:08.320 ⇒ 00:04:15.039 Casie Aviles: the Re. The issue there was. I couldn’t get the messages. I only have, like channel data and users so.
44 00:04:15.590 ⇒ 00:04:20.420 Casie Aviles: But I guess we want to use the Lt Hub for all of our data sources like.
45 00:04:21.622 ⇒ 00:04:24.830 Casie Aviles: Like, zoom slack linear.
46 00:04:25.150 ⇒ 00:04:26.040 Casie Aviles: Yeah.
47 00:04:26.430 ⇒ 00:04:32.750 Casie Aviles: So not sure how we want to set that up and like if there’s like a best way to do it, or.
48 00:04:33.560 ⇒ 00:04:38.930 Miguel de Veyra: The priority is zoom and slack like those are the 2 things that we want to prioritize moving.
49 00:04:43.210 ⇒ 00:04:48.000 Patrik: Yeah, I mean, so dlt like, fairly
50 00:04:48.650 ⇒ 00:04:55.769 Patrik: fairly like new new software. They’ve released a bunch of
51 00:04:56.860 ⇒ 00:05:01.150 Patrik: these kind of like, like, dlt supported sources.
52 00:05:01.808 ⇒ 00:05:08.480 Patrik: slack being one of them. Sure, that’s the one that you used, Cassie. But then there’s like
53 00:05:08.960 ⇒ 00:05:16.289 Patrik: kind of these generic wrappers, right for like anything anything that you would.
54 00:05:17.030 ⇒ 00:05:24.429 Patrik: that’s not supported by dlt natively and that’s like a rest wrapper. I think they have, like a few other ones.
55 00:05:27.020 ⇒ 00:05:29.209 Patrik: So yeah, I mean, like.
56 00:05:29.530 ⇒ 00:05:38.320 Patrik: it’s a good choice, because it’s nice and flexible. It’s like low cost. You know, everything runs on on
57 00:05:38.740 ⇒ 00:05:41.089 Patrik: our infrastructure.
58 00:05:41.280 ⇒ 00:05:43.960 Patrik: We can deploy it, how we want to deploy it.
59 00:05:44.780 ⇒ 00:05:47.280 Patrik: And I guess the.
60 00:05:47.280 ⇒ 00:05:49.020 Miguel de Veyra: Can we deploy it on, too.
61 00:05:51.469 ⇒ 00:05:52.489 Patrik: Maybe
62 00:05:53.890 ⇒ 00:05:57.679 Miguel de Veyra: Or is that it has to be in something like aws or hero.
63 00:05:59.960 ⇒ 00:06:03.580 Patrik: You, you might be able to deploy it on windmill. I mean you.
64 00:06:03.970 ⇒ 00:06:09.633 Patrik: you probably can. It’s just like, yeah, you can actually
65 00:06:11.170 ⇒ 00:06:14.980 Patrik: Is there a reason why you would want to use Woodmill.
66 00:06:16.797 ⇒ 00:06:21.790 Miguel de Veyra: Cause. That’s I guess, the only other option. The only other thing we use right now is Hero.
67 00:06:22.880 ⇒ 00:06:26.510 Miguel de Veyra: and the windmill, I guess, is a bit easier for to deploy stuff into.
68 00:06:30.170 ⇒ 00:06:32.229 Patrik: Yeah, I mean, I would.
69 00:06:32.760 ⇒ 00:06:39.910 Patrik: you might want to think about, have you guys talked about using like an orchestration tool, like Dagster, or
70 00:06:41.407 ⇒ 00:06:43.080 Patrik: airflow, I mean.
71 00:06:43.080 ⇒ 00:06:43.570 Casie Aviles: Those links.
72 00:06:43.570 ⇒ 00:06:54.490 Patrik: Old. But if things like Dexter prefect, that’s like
73 00:06:54.850 ⇒ 00:07:01.740 Patrik: typically the standard. For like any sort of like data engineering like workflow.
74 00:07:04.440 ⇒ 00:07:09.959 Miguel de Veyra: Oh, okay, we probably have to ask the data team. Then on receipt.
75 00:07:09.960 ⇒ 00:07:16.490 Patrik: Dlt dlt integrates really like. So just for some context like.
76 00:07:17.120 ⇒ 00:07:22.304 Patrik: I’ve used dlt in 2 2 ways. One was
77 00:07:23.230 ⇒ 00:07:28.320 Patrik: one was using it with dag like orchestrating with Daxter. So daxter handles like
78 00:07:29.368 ⇒ 00:07:38.819 Patrik: essentially like the deployment of the server that’s gonna run the the Etl job. And then
79 00:07:41.350 ⇒ 00:07:46.789 Patrik: it also handles like Retries. It’s like, you can
80 00:07:46.960 ⇒ 00:07:52.319 Patrik: hook up other jobs associated to the success of like your Sync.
81 00:07:54.256 ⇒ 00:08:02.799 Patrik: so what we did was like we would sync data. And then on success of that, there’d be some other job that would trigger
82 00:08:03.294 ⇒ 00:08:07.195 Patrik: and so you can create these really nice like data pipelines.
83 00:08:07.860 ⇒ 00:08:10.090 Patrik: I’m not sure like what the extent of
84 00:08:11.120 ⇒ 00:08:13.390 Patrik: how you guys are thinking about.
85 00:08:13.950 ⇒ 00:08:18.070 Patrik: you know, moving data from these sources into into the destination. But
86 00:08:18.450 ⇒ 00:08:24.849 Patrik: you know, if there’s like down downstream dependencies, it it might be something to like. Consider.
87 00:08:26.010 ⇒ 00:08:26.409 Patrik: See you later.
88 00:08:26.410 ⇒ 00:08:28.239 Miguel de Veyra: Right now we have a working version.
89 00:08:30.520 ⇒ 00:08:32.270 Miguel de Veyra: But it’s all in windmill.
90 00:08:33.350 ⇒ 00:08:38.219 Patrik: Yeah, yeah, yeah, yeah. I mean, yeah. So so the other like.
91 00:08:39.280 ⇒ 00:08:43.679 Patrik: like, I’m I’m assuming like you, you would just run it on like a cron schedule.
92 00:08:43.900 ⇒ 00:08:44.380 Miguel de Veyra: Yeah, yeah.
93 00:08:44.380 ⇒ 00:08:44.890 Casie Aviles: Yeah.
94 00:08:44.890 ⇒ 00:08:45.790 Miguel de Veyra: Exactly.
95 00:08:46.160 ⇒ 00:08:55.669 Casie Aviles: And we use like, yes, the the Apis Sdks to pull the data from the sources. So we use custom python scripts. That’s what we have right now.
96 00:08:56.380 ⇒ 00:09:00.379 Miguel de Veyra: I guess that’s also the thing that utham wants to move away from.
97 00:09:00.590 ⇒ 00:09:03.480 Miguel de Veyra: because by basically right now, everything is cost of.
98 00:09:04.330 ⇒ 00:09:05.619 Patrik: Yeah, yeah, yeah.
99 00:09:06.430 ⇒ 00:09:11.420 Patrik: And it’s not. Is there a version control behind any of that stuff? Or is it just living? But no.
100 00:09:12.110 ⇒ 00:09:14.369 Miguel de Veyra: I don’t think there’s a version control. No case.
101 00:09:14.370 ⇒ 00:09:14.790 Patrik: Yeah.
102 00:09:14.790 ⇒ 00:09:16.710 Casie Aviles: Just yet, just on windmill.
103 00:09:19.400 ⇒ 00:09:26.509 Patrik: Yeah, I mean, I would consider. Probably I mean, I I like the exer. It’s it’s relatively new. It’s kind of get this like
104 00:09:26.620 ⇒ 00:09:29.553 Patrik: a little bit more of a modern architecture.
105 00:09:31.000 ⇒ 00:09:33.790 Patrik: I would I would deploy it on Dexter.
106 00:09:34.120 ⇒ 00:09:39.209 Patrik: Then you can set up assets for all of the
107 00:09:39.880 ⇒ 00:09:47.380 Patrik: we call like Daxter assets and what they basically are like, it’s like a dlt supported integration.
108 00:09:47.910 ⇒ 00:10:14.339 Patrik: And it’ll handle syncing from like a source to to the destination. Kind of handle the the nuts and bolts behind that infrastructure as well. But yeah, I mean, you still, like you still run into the problem with Dlt is like you’re not getting the same supported sources that you would with like an air bite, for example, like air bytes got like 300 400 plus like.
109 00:10:14.660 ⇒ 00:10:17.640 Patrik: you know, community driven sources dlts like.
110 00:10:17.830 ⇒ 00:10:20.170 Patrik: you know. Still, a little early on that front.
111 00:10:22.390 ⇒ 00:10:23.360 Miguel de Veyra: Yeah, I think so.
112 00:10:23.360 ⇒ 00:10:28.990 Miguel de Veyra: Yeah, I mean, I think we can definitely, this one is a bit we have to ask the data team.
113 00:10:29.980 ⇒ 00:10:36.049 Miguel de Veyra: But I think the bigger problem we still have right now is basically because this is deploying dlt right?
114 00:10:37.750 ⇒ 00:10:49.999 Miguel de Veyra: But we haven’t really built the Dlt hub, for I mean the Dlt automations for, or the Dlt code, whatever we call it, for the zoom stuff and slack stuff. So I guess that’s what we want. Your help with Patrick.
115 00:10:51.680 ⇒ 00:11:00.939 Miguel de Veyra: Yeah, because we have no idea how to use dlt like Casey tried it, and he was blocked. And our rule is, if you’re blocked more than one day. Let’s try to find alternative.
116 00:11:01.806 ⇒ 00:11:07.039 Patrik: Yeah, yeah, yeah. Let’s see.
117 00:11:09.800 ⇒ 00:11:13.540 Patrik: yeah. And you can with windmill you can deploy
118 00:11:15.380 ⇒ 00:11:18.210 Patrik: you can you can deploy anything right like it doesn’t.
119 00:11:18.450 ⇒ 00:11:20.549 Miguel de Veyra: You don’t have to write it in.
120 00:11:20.930 ⇒ 00:11:23.280 Patrik: In in the windmill. Ui.
121 00:11:23.280 ⇒ 00:11:37.460 Miguel de Veyra: I think what I would do is we’ll let’s work on getting the data, basically using dlt just to extract data from both slack and zoom, and then we can consider Dagster like, you know, I think this is a completely new ticket on where to deploy. It.
122 00:11:38.740 ⇒ 00:11:40.619 Patrik: Yeah, yeah, yeah, I agree.
123 00:11:43.110 ⇒ 00:11:47.389 Patrik: Yeah. So like, I would write it as a cli tool.
124 00:11:48.393 ⇒ 00:11:52.719 Patrik: And then you have, like a variable that determines the source.
125 00:11:53.450 ⇒ 00:12:02.540 Patrik: So you basically have, like, you’d have one oh.
126 00:12:02.870 ⇒ 00:12:14.210 Patrik: one like destination, right? You’re thinking to, was it? S. 3, or you know to super base. Yeah. So you have. You have a like a destination so like dlt is kind of split up into
127 00:12:15.020 ⇒ 00:12:16.060 Patrik: to
128 00:12:17.510 ⇒ 00:12:18.339 Miguel de Veyra: Or zoom, desktop.
129 00:12:18.340 ⇒ 00:12:39.470 Patrik: 2. Yeah, yeah, exactly like 2 things like, and and that’s the code you have to write everything. It’s gonna handle. So like the way Dlc works is it grabs all the data, then it puts it into like an In memory buffer where it defines the schema transforms. Maybe it moves it into like a different file type. It’s going from like Json to parquet, for example.
130 00:12:40.313 ⇒ 00:12:41.679 Patrik: And then
131 00:12:42.450 ⇒ 00:12:53.240 Patrik: it stores that locally on the disk. So the server that’s running dlt needs to have access to some sort of like persistent state.
132 00:12:53.240 ⇒ 00:12:53.850 Miguel de Veyra: Yeah.
133 00:12:53.850 ⇒ 00:12:54.860 Patrik: And then.
134 00:12:55.300 ⇒ 00:12:58.970 Miguel de Veyra: Does the windmill have persistence data? I don’t think they do.
135 00:13:00.370 ⇒ 00:13:04.120 Patrik: As long as the as long as it’s not a serverless machine it should be fine.
136 00:13:04.380 ⇒ 00:13:05.080 Miguel de Veyra: Okay.
137 00:13:07.370 ⇒ 00:13:07.840 Miguel de Veyra: Yes, yes.
138 00:13:07.840 ⇒ 00:13:11.489 Patrik: So yeah, as long as it’s not like a lambda or something like that.
139 00:13:11.740 ⇒ 00:13:12.569 Miguel de Veyra: Yeah, okay.
140 00:13:13.816 ⇒ 00:13:14.909 Patrik: And then
141 00:13:15.350 ⇒ 00:13:22.380 Patrik: so then it like it moves on to the destination part. Forget the actual name
142 00:13:22.970 ⇒ 00:13:33.280 Patrik: just like a what’s called, yeah. Well, anyways, that part like then reads from that local file system
143 00:13:33.910 ⇒ 00:13:41.250 Patrik: and handles writing out to the destination. So you can write. So if you just put together, one like S. 3. Writer.
144 00:13:41.460 ⇒ 00:13:42.110 Miguel de Veyra: Yeah.
145 00:13:42.110 ⇒ 00:13:48.690 Patrik: And then you have your cli essentially taking an argument that says, I want to sync data from.
146 00:13:49.000 ⇒ 00:13:49.929 Patrik: you know.
147 00:13:50.990 ⇒ 00:14:08.069 Patrik: like slack zoom, whatever and then you could define your function like your kind of functions that way. I think that’d be a good way, because you could package that up and just plop it into windmill, and you can also run it locally as well, pretty easily.
148 00:14:08.070 ⇒ 00:14:17.549 Miguel de Veyra: I think, though, like the thing we are, the problem, I think I believe Casey is, we can’t even extract the data from Zoom. I think that’s where we’re blocked. So
149 00:14:17.670 ⇒ 00:14:24.060 Miguel de Veyra: writing and stuff, I think, should be pretty easy. It’s like we get. The main issue we have is we can’t extract the data from Zoom.
150 00:14:25.130 ⇒ 00:14:26.520 Patrik: Was there no?
151 00:14:28.280 ⇒ 00:14:30.100 Patrik: Is there no Api.
152 00:14:33.150 ⇒ 00:14:33.850 Casie Aviles: There you go!
153 00:14:33.850 ⇒ 00:14:34.640 Casie Aviles: You’re not.
154 00:14:35.950 ⇒ 00:14:40.359 Patrik: As long as there’s an Api. Then then you can extract data.
155 00:14:42.800 ⇒ 00:14:43.210 Casie Aviles: Okay.
156 00:14:43.210 ⇒ 00:14:45.040 Patrik: So I guess, where? Where are you?
157 00:14:46.230 ⇒ 00:14:47.820 Patrik: What’s what’s the issue?
158 00:14:48.500 ⇒ 00:14:56.880 Patrik: If there’s no Api, then you’re kind of like, there’s no zoom doesn’t expose the data, so there’s no way to get it. If there’s an Api, then you then you.
159 00:14:57.150 ⇒ 00:14:59.310 Patrik: you know, Dlt, is not the problem.
160 00:14:59.940 ⇒ 00:15:00.760 Miguel de Veyra: Okay.
161 00:15:04.490 ⇒ 00:15:09.069 Miguel de Veyra: is it? We were blocked somewhere. Right? I think this is a great time to ask it.
162 00:15:12.060 ⇒ 00:15:14.291 Casie Aviles: Yeah. And that was the blocker, like with
163 00:15:16.340 ⇒ 00:15:22.229 Casie Aviles: with like the with dlt, can we like extract, even like the meeting recordings? Like.
164 00:15:22.610 ⇒ 00:15:26.020 Casie Aviles: I think that’s 1 of our concerns like yes.
165 00:15:26.430 ⇒ 00:15:29.180 Patrik: To answer those questions. You have to go
166 00:15:29.850 ⇒ 00:15:33.959 Patrik: like, go to the documentation of of the Api that you’re pulling.
167 00:15:34.680 ⇒ 00:15:36.810 Patrik: So if you’re working with slack.
168 00:15:37.840 ⇒ 00:15:46.470 Patrik: I would, for like in that example, like, I think the workflow is like, go to the dlt docs, check if there’s a native like source.
169 00:15:47.040 ⇒ 00:15:58.619 Patrik: and then from there check check what’s supported in that native source. So in slack you have like, there’s there’s some like can solutions already. Maybe that’s not all the solutions, but you might be able to leverage some of them.
170 00:15:59.030 ⇒ 00:16:07.600 Patrik: and then and then from there you can then go to the slack like Api docs
171 00:16:08.150 ⇒ 00:16:14.440 Patrik: and figure out which endpoints you need to hit, to extract the data that you’re looking for.
172 00:16:16.360 ⇒ 00:16:19.459 Patrik: and once you have determined that
173 00:16:19.750 ⇒ 00:16:25.470 Patrik: you can write your own sort of like wrapper around that Api.
174 00:16:26.109 ⇒ 00:16:30.299 Patrik: Did you take a look at the rest client like the rest, wrapper and dlt.
175 00:16:33.485 ⇒ 00:16:33.970 Casie Aviles: No.
176 00:16:35.010 ⇒ 00:16:36.900 Patrik: So go to the go to the docs here.
177 00:16:37.490 ⇒ 00:16:38.910 Casie Aviles: We go to sources.
178 00:16:43.690 ⇒ 00:16:46.770 Patrik: Yeah, go to that and then rest. Apis
179 00:16:48.580 ⇒ 00:16:54.030 Patrik: rest. I think recipi source. Or it might be, yeah.
180 00:16:56.860 ⇒ 00:17:02.929 Patrik: So it’s kind of split up into a few things you define like, where, like.
181 00:17:03.800 ⇒ 00:17:07.709 Patrik: where you’re getting the data from, how you’re authenticating to it.
182 00:17:08.619 ⇒ 00:17:16.200 Patrik: Which is typically the Dlt secrets is just kind of like a fancy way of extracting from your environment variables.
183 00:17:16.410 ⇒ 00:17:20.020 Patrik: And then you need a Paginator which
184 00:17:20.390 ⇒ 00:17:24.300 Patrik: any sort of like list Api from these like, major
185 00:17:26.456 ⇒ 00:17:30.320 Patrik: these major places are gonna have like a paginated response.
186 00:17:30.610 ⇒ 00:17:36.179 Patrik: But you have to figure out there. You’re essentially telling it. How do I get that like next page token.
187 00:17:39.430 ⇒ 00:17:42.450 Patrik: You’re kind of just like writing something like this.
188 00:17:48.930 ⇒ 00:17:52.330 Patrik: And then this.
189 00:17:52.470 ⇒ 00:18:00.830 Patrik: yeah, this gets hooked up as like a dlt source the same way any of their like can solutions would as well.
190 00:18:02.630 ⇒ 00:18:06.139 Miguel de Veyra: Okay, yeah, I think I got I get it pretty much, Casey, same in your end.
191 00:18:09.690 ⇒ 00:18:16.279 Casie Aviles: So yeah, as long as there’s an Api, we could use the this rest. Rest. Api, right.
192 00:18:16.460 ⇒ 00:18:20.401 Miguel de Veyra: The way we’re currently doing it, Casey, we use Api right.
193 00:18:21.150 ⇒ 00:18:29.909 Casie Aviles: Yeah, we use a custom script. Yeah, we we use Zoom Api and aws, yeah, like this one.
194 00:18:30.040 ⇒ 00:18:31.580 Casie Aviles: Doc, S, 3. Client.
195 00:18:31.580 ⇒ 00:18:37.889 Patrik: Yeah. So how do you? How do you know where you left off?
196 00:18:41.298 ⇒ 00:18:46.231 Casie Aviles: How this script works is. Basically, it takes like
197 00:18:47.030 ⇒ 00:18:54.273 Casie Aviles: the incoming recording. So there’s like, I have like a event script that I guess.
198 00:18:55.490 ⇒ 00:19:00.770 Casie Aviles: receives like an event. Where, for example, we get here like
199 00:19:01.170 ⇒ 00:19:05.750 Casie Aviles: we get these Zoom Events right? So once a recording is completed.
200 00:19:06.682 ⇒ 00:19:12.320 Casie Aviles: it then takes the download Urls of the recording files and.
201 00:19:13.390 ⇒ 00:19:18.240 Casie Aviles: Yeah. And then it uploads to S 3, basically, that’s how it works.
202 00:19:19.590 ⇒ 00:19:24.530 Casie Aviles: So it’s only for for yeah, for in incoming meetings, yeah.
203 00:19:25.410 ⇒ 00:19:33.380 Patrik: Gotcha. So if you you can’t like backfill meetings, or if you say this like failed like, how do you rerun it?
204 00:19:36.710 ⇒ 00:19:39.269 Casie Aviles: Sorry. What do you mean by backfill? Sorry?
205 00:19:40.300 ⇒ 00:19:45.149 Patrik: Like you know, if it missed the meeting, or
206 00:19:45.640 ⇒ 00:19:51.610 Patrik: you know, if you wanna if you wanna potentially like pull additional data.
207 00:19:51.920 ⇒ 00:19:54.819 Patrik: you would need to like, rewrite, or rerun all this.
208 00:19:55.200 ⇒ 00:19:58.789 Casie Aviles: Yeah, something like, yeah, we’ll have to rewrite this or.
209 00:19:59.760 ⇒ 00:20:00.659 Patrik: So how do you?
210 00:20:00.800 ⇒ 00:20:08.420 Patrik: If it’s just event based? I’m saying like, how do you handle like making it more durable?
211 00:20:09.830 ⇒ 00:20:10.220 Miguel de Veyra: I don’t.
212 00:20:10.220 ⇒ 00:20:14.099 Casie Aviles: Yeah, we don’t. We don’t. Just. It’s just really just this one, the event.
213 00:20:14.100 ⇒ 00:20:15.009 Casie Aviles: This is.
214 00:20:15.390 ⇒ 00:20:16.360 Patrik: Gotcha.
215 00:20:17.340 ⇒ 00:20:23.810 Patrik: Yeah. So I guess the like one of the benefits from using dlt is you? It stores the state
216 00:20:24.660 ⇒ 00:20:29.690 Patrik: of where where it left off.
217 00:20:30.410 ⇒ 00:20:37.820 Patrik: So it’ll it like incrementally syncs. It’s only syncing like new stuff coming from the Api.
218 00:20:38.996 ⇒ 00:20:47.889 Patrik: But you can always like rebuild. You know your data set or your source of truth. Cause. You can just say, I want to sync from the beginning.
219 00:20:49.540 ⇒ 00:20:53.790 Patrik: potentially, if there’s some some data that’s like malformed or or whatnot.
220 00:20:56.110 ⇒ 00:20:59.190 Patrik: So it kind of has that like built into it, which is nice.
221 00:21:02.030 ⇒ 00:21:07.919 Patrik: Okay? But yeah, I mean, it sounds like you have some
222 00:21:11.162 ⇒ 00:21:17.300 Patrik: it sounds like you have some boilerplate code here that you can leverage since you have, like some of the
223 00:21:18.380 ⇒ 00:21:24.540 Patrik: some of the destination. Like, yeah, if you scroll down, yeah, you have this, get access token stuff.
224 00:21:24.960 ⇒ 00:21:31.739 Miguel de Veyra: He’s the one that actually gets the get the data, is it that actually.
225 00:21:32.237 ⇒ 00:21:36.710 Patrik: Recording data. Yeah, Parses, the Zoom recording data. So.
226 00:21:36.710 ⇒ 00:21:39.390 Miguel de Veyra: Get zoom. Oh, is it this get zoom recording?
227 00:21:42.650 ⇒ 00:21:45.229 Miguel de Veyra: Yeah, I guess we can just use this and then integrate it with.
228 00:21:45.230 ⇒ 00:21:51.690 Patrik: Yeah. Good zoom recording. Yeah. So you got this Api zoom, USB, 2 meetings endpoint for recordings.
229 00:21:51.870 ⇒ 00:21:54.130 Patrik: and then you pass it. The bearer token.
230 00:21:55.440 ⇒ 00:21:56.170 Miguel de Veyra: Okay.
231 00:21:57.232 ⇒ 00:21:58.440 Patrik: Yeah. I mean.
232 00:21:58.440 ⇒ 00:22:02.219 Miguel de Veyra: Is there a Youtube video, Patrick, that you could recommend that we look into.
233 00:22:03.260 ⇒ 00:22:04.400 Patrik: Hmm!
234 00:22:05.090 ⇒ 00:22:12.890 Patrik: I think there probably is one I could. You could probably also like dump this whole thing into.
235 00:22:12.890 ⇒ 00:22:13.810 Miguel de Veyra: Gpt.
236 00:22:14.220 ⇒ 00:22:23.099 Patrik: Gpt and and ask it to spit out like. Give it! Give it that like prompt of like I’m working with Dlt. Here’s what a rest wrapper. Looks like
237 00:22:23.908 ⇒ 00:22:28.501 Patrik: that. Might that might get you like 75% of the way there.
238 00:22:30.640 ⇒ 00:22:35.910 Patrik: But yeah, I can, I can dig up like a video or something as well. I think maybe that’s helpful.
239 00:22:36.360 ⇒ 00:22:37.280 Miguel de Veyra: Okay, sure.
240 00:22:37.530 ⇒ 00:22:38.210 Miguel de Veyra: But this is.
241 00:22:38.210 ⇒ 00:22:43.459 Patrik: That’s good, like I would use. I would leverage some of this like existing code, for sure.
242 00:22:43.460 ⇒ 00:22:48.340 Miguel de Veyra: Okay, we’ll try this out and then see where it goes.
243 00:22:48.580 ⇒ 00:22:52.170 Miguel de Veyra: If we do. If we met, if we don’t message you probably working.
244 00:22:53.434 ⇒ 00:22:56.200 Patrik: Yeah, yeah, I mean, let me know. Shoot me. Shoot me a message. I’ll be.
245 00:22:56.200 ⇒ 00:22:57.309 Miguel de Veyra: Yes. Yes. Okay.
246 00:22:57.310 ⇒ 00:22:58.189 Patrik: At least at least my.
247 00:22:58.423 ⇒ 00:23:00.519 Miguel de Veyra: Probably ask you to run a spike in it.
248 00:23:01.570 ⇒ 00:23:06.327 Patrik: That’s that’s good. Yeah, just for it’s it sounds like
249 00:23:09.220 ⇒ 00:23:15.170 Patrik: boot time suggested. I do like one to 2 like 2 h working sessions.
250 00:23:15.690 ⇒ 00:23:16.200 Miguel de Veyra: Oh, okay.
251 00:23:16.516 ⇒ 00:23:19.993 Patrik: Cause, cause my hours are like pretty limited with you guys.
252 00:23:20.570 ⇒ 00:23:21.509 Miguel de Veyra: What’s the best.
253 00:23:21.510 ⇒ 00:23:21.860 Patrik: For you.
254 00:23:21.860 ⇒ 00:23:24.369 Miguel de Veyra: With you like on a working session.
255 00:23:27.420 ⇒ 00:23:30.470 Patrik: Yeah. Tuesdays, Wednesdays or Thursdays.
256 00:23:30.750 ⇒ 00:23:31.590 Miguel de Veyra: Okay.
257 00:23:32.010 ⇒ 00:23:37.419 Patrik: Just, I just need to know, like ahead of time. But yeah, I’m happy to like we could do like an hour or 2
258 00:23:38.423 ⇒ 00:23:42.310 Patrik: together, and just kinda like jam on on one of these projects.
259 00:23:42.630 ⇒ 00:23:59.700 Miguel de Veyra: Okay, okay, sure. We’ll try to figure it out today. So hopefully tomorrow, because Wednesday, Philippines is on holiday. So if we can figure it out today. We’ll probably hop on tomorrow or Thursday, and then if we could, and then, you know, it’s a different session. I guess.
260 00:24:00.550 ⇒ 00:24:02.249 Patrik: Yeah, that sounds great.
261 00:24:02.500 ⇒ 00:24:03.170 Miguel de Veyra: Okay.
262 00:24:03.936 ⇒ 00:24:08.530 Miguel de Veyra: I think that’s pretty much it, Amber. Is there anything else we need to discuss.
263 00:24:10.450 ⇒ 00:24:19.969 Amber Lin: I think that will be all for Patrick. So, Patrick, if you have something else to do, you can hop off. I’ll just talk to the team a little bit about like other stuff. We need to do.
264 00:24:20.520 ⇒ 00:24:22.459 Patrik: Awesome. Alright, thanks, guys. Appreciate it.
265 00:24:22.460 ⇒ 00:24:23.150 Amber Lin: Yeah.
266 00:24:23.150 ⇒ 00:24:23.740 Casie Aviles: Thank you.
267 00:24:23.740 ⇒ 00:24:24.120 Patrik: You too.
268 00:24:24.573 ⇒ 00:24:25.480 Amber Lin: Take care.
269 00:24:28.420 ⇒ 00:24:29.609 Amber Lin: Yeah, I think
270 00:24:30.356 ⇒ 00:24:50.800 Amber Lin: apart from the foundational stuff, which is very, very important. We do want to give like, give the company. Some easy wins. Right? So I think, do you guys have stuff to work on today? If if you do, I’ll I’ll make those tickets, and then tomorrow we can discuss what kind of easy ones we can do.
271 00:24:51.414 ⇒ 00:24:56.149 Miguel de Veyra: Yeah, because today, I think Casey is gonna be working on this stuff.
272 00:24:57.510 ⇒ 00:24:58.790 Miguel de Veyra: And then
273 00:24:59.780 ⇒ 00:25:05.920 Miguel de Veyra: I have the recruitment shit going on, and then there’s pro. Are there some tickets? Wait, let me share screen.
274 00:25:06.790 ⇒ 00:25:15.175 Miguel de Veyra: cause cause I think there’s some tickets. I have to write technical stuff for, okay.
275 00:25:18.330 ⇒ 00:25:20.520 Miguel de Veyra: display, empty con.
276 00:25:22.820 ⇒ 00:25:25.790 Miguel de Veyra: What happened to phone issues?
277 00:25:28.190 ⇒ 00:25:30.060 Miguel de Veyra: Requirements started?
278 00:25:35.430 ⇒ 00:25:37.010 Miguel de Veyra: None active meetings.
279 00:25:37.260 ⇒ 00:25:42.819 Miguel de Veyra: Is there something here? Wait, let me just move. This stuff, I guess. In the bottom
280 00:25:43.610 ⇒ 00:25:51.129 Miguel de Veyra: is, are there some stuff here, Amber, that you might want me to prioritize writing tech stuff for technical requirements.
281 00:25:51.605 ⇒ 00:26:01.120 Amber Lin: I I feel like the ones that, I added, are not that technical? I would say. You know the
282 00:26:01.290 ⇒ 00:26:14.350 Amber Lin: more of one for the data team, one for the sales team, right? So probably the most important part for the sales team is to do the follow up tracker. So a 97,
283 00:26:14.450 ⇒ 00:26:18.390 Amber Lin: if you can look more into that, I think Utam also gave a few comments
284 00:26:18.670 ⇒ 00:26:28.219 Amber Lin: less of an AI thing, more of an automation thing. But this is, I know this is really important for Robert, and it doesn’t take us too much time.
285 00:26:28.350 ⇒ 00:26:33.549 Amber Lin: Say you can select them all and then convert them to checklist. If you want.
286 00:26:35.330 ⇒ 00:26:35.660 Miguel de Veyra: Yeah.
287 00:26:36.570 ⇒ 00:26:44.980 Amber Lin: So if you select all of them and you go. Yeah, dropdown menu. You know the icons. No, they’re on the other side.
288 00:26:44.980 ⇒ 00:26:45.540 Miguel de Veyra: Here.
289 00:26:45.540 ⇒ 00:26:48.120 Amber Lin: That one checklist checklist.
290 00:26:48.120 ⇒ 00:26:49.270 Miguel de Veyra: Oh, okay. Nice.
291 00:26:49.270 ⇒ 00:26:49.850 Amber Lin: Yeah.
292 00:26:49.850 ⇒ 00:26:51.760 Miguel de Veyra: How do you use notion, structure.
293 00:26:53.318 ⇒ 00:27:00.840 Amber Lin: so that’s for sales and for the data team.
294 00:27:00.840 ⇒ 00:27:03.030 Miguel de Veyra: This one is going to be a bit complicated.
295 00:27:03.030 ⇒ 00:27:04.470 Amber Lin: Oh, really? -Oh.
296 00:27:04.950 ⇒ 00:27:09.230 Miguel de Veyra: Cause cause notion, notion. Databases are not simple.
297 00:27:10.130 ⇒ 00:27:18.720 Miguel de Veyra: I have an entire code that basically, I think right now, I haven’t even introduced the case for sales.
298 00:27:19.420 ⇒ 00:27:20.660 Miguel de Veyra: I hear sales.
299 00:27:23.060 ⇒ 00:27:24.330 Miguel de Veyra: Where is the table?
300 00:27:25.870 ⇒ 00:27:27.869 Miguel de Veyra: So where’s the table for sales?
301 00:27:29.655 ⇒ 00:27:36.199 Amber Lin: Good question. Let’s see if we scroll up a little bit. Maybe search leads.
302 00:27:37.210 ⇒ 00:27:44.320 Miguel de Veyra: Oh, yeah, yeah, maybe it’s wait. Sorry, interrupt Weeks.
303 00:27:45.110 ⇒ 00:27:47.309 Miguel de Veyra: Oh, maybe it’s different. I don’t know.
304 00:27:49.710 ⇒ 00:27:50.440 Amber Lin: So it’s
305 00:27:50.560 ⇒ 00:27:58.630 Amber Lin: I think it’s all in this database. So I think we’re just working off a singular database which might make things a lot easier.
306 00:28:02.630 ⇒ 00:28:05.149 Miguel de Veyra: Alright, can you guys see this screen like this?
307 00:28:05.150 ⇒ 00:28:08.539 Amber Lin: Yeah, I can see notes in the lease. Page.
308 00:28:08.770 ⇒ 00:28:10.200 Miguel de Veyra: Internal.
309 00:28:14.130 ⇒ 00:28:16.990 Miguel de Veyra: Where is this one? Why is internal empty?
310 00:28:17.330 ⇒ 00:28:19.180 Miguel de Veyra: A. IJS. Back end.
311 00:28:19.390 ⇒ 00:28:23.359 Miguel de Veyra: Wait. Sorry. I think it’s get notion. Sync, let me just open that up.
312 00:28:24.500 ⇒ 00:28:30.009 Miguel de Veyra: Get notion. Sync, yeah, it’s a bit complicated. Super, long
313 00:28:34.160 ⇒ 00:28:36.509 Miguel de Veyra: scripts, I think.
314 00:28:37.010 ⇒ 00:28:43.680 Miguel de Veyra: Here, you source. Where was the one the service offerings?
315 00:28:44.670 ⇒ 00:28:49.780 Miguel de Veyra: Let me see if I we already have that services, Doc. Service should. We don’t have.
316 00:28:50.910 ⇒ 00:28:51.590 Amber Lin: Hmm.
317 00:28:53.600 ⇒ 00:29:00.189 Miguel de Veyra: Notion service, because basically the way this works is, it’s gonna extract.
318 00:29:00.660 ⇒ 00:29:04.649 Amber Lin: For example, notion, demo users, script industries.
319 00:29:04.850 ⇒ 00:29:08.999 Miguel de Veyra: Sorry. That’s okay. There’s none here. I haven’t scraped it, but it’s gonna
320 00:29:09.870 ⇒ 00:29:13.170 Miguel de Veyra: basically come out with something like this. And then.
321 00:29:13.170 ⇒ 00:29:13.960 Amber Lin: Hmm.
322 00:29:14.010 ⇒ 00:29:23.769 Miguel de Veyra: Basically, this is the Api, and then we can process it. However, we want. So if it’s a follow up, I guess the fields that we want here are either
323 00:29:27.800 ⇒ 00:29:29.050 Miguel de Veyra: there’s no date.
324 00:29:32.752 ⇒ 00:29:37.550 Amber Lin: Maybe go to like, circle back, go to that, tab.
325 00:29:38.100 ⇒ 00:29:38.950 Miguel de Veyra: Oh, okay.
326 00:29:41.170 ⇒ 00:29:43.660 Amber Lin: Oh, there’s no! There’s no date. Oh.
327 00:29:44.040 ⇒ 00:29:47.660 Miguel de Veyra: Maybe we can. Maybe it’s a hidden ding
328 00:29:48.140 ⇒ 00:29:51.999 Miguel de Veyra: like here, we can add, oh, there you go. Date. Is there a date?
329 00:29:52.970 ⇒ 00:29:57.700 Amber Lin: Yeah, there’s probably date, but I don’t know how updated it is.
330 00:30:00.590 ⇒ 00:30:03.189 Miguel de Veyra: Yeah, cause I know in have an update yet
331 00:30:03.190 ⇒ 00:30:06.090 Miguel de Veyra: before there’s like a status resign.
332 00:30:06.230 ⇒ 00:30:10.759 Miguel de Veyra: Yeah, this is, there’s a status reason. What last contact that I think that’s the thing.
333 00:30:11.600 ⇒ 00:30:12.589 Miguel de Veyra: Oh, you’re looking for.
334 00:30:12.590 ⇒ 00:30:14.659 Miguel de Veyra: Yeah. Yeah. The last contacted.
335 00:30:14.660 ⇒ 00:30:20.170 Amber Lin: I mean, we can write down everything we need, and then we can go. Oh.
336 00:30:20.170 ⇒ 00:30:21.890 Miguel de Veyra: There you go last contact.
337 00:30:21.890 ⇒ 00:30:26.119 Amber Lin: I suggested, though it’s not, I don’t think it’s create there. It’s not there.
338 00:30:26.670 ⇒ 00:30:27.330 Miguel de Veyra: Suggesting.
339 00:30:27.330 ⇒ 00:30:28.170 Amber Lin: You know.
340 00:30:28.330 ⇒ 00:30:33.990 Amber Lin: So I mean, just write down what you think we need, and then what we need Robert to do
341 00:30:34.523 ⇒ 00:30:47.936 Amber Lin: if come to him with a plan, and he’ll be more willing to do it versus just telling him one thing, and he’s like, what is what is even this for? So once we have a plan, I’ll I’ll send it to him, and maybe he can do a bit more.
342 00:30:49.210 ⇒ 00:30:50.050 Miguel de Veyra: See.
343 00:30:53.210 ⇒ 00:30:53.850 Amber Lin: Okay.
344 00:30:53.850 ⇒ 00:30:56.829 Miguel de Veyra: Yeah, I saw, yeah, this is gonna be a bit technical.
345 00:30:56.830 ⇒ 00:30:59.829 Amber Lin: Okay, yeah, okay, great. So I’ll I’ll let you guys do.
346 00:30:59.830 ⇒ 00:31:01.780 Miguel de Veyra: Our number one, AI user.
347 00:31:03.740 ⇒ 00:31:09.470 Miguel de Veyra: It’s not with us anymore. How do I delete that to your own drawings? Okay?
348 00:31:11.360 ⇒ 00:31:16.959 Miguel de Veyra: And then I guess it’s it’s only the ones assigned with Utam or
349 00:31:17.290 ⇒ 00:31:19.730 Miguel de Veyra: Robert right, that we want to follow up with.
350 00:31:20.917 ⇒ 00:31:27.130 Amber Lin: No, I think it’ll just be everyone in the database, I think.
351 00:31:27.350 ⇒ 00:31:39.800 Amber Lin: cause we can. They’re essentially just using people’s accounts. So it doesn’t matter who it’s assigned to. I suppose we do wanna remind him to follow up with everybody right?
352 00:31:41.770 ⇒ 00:31:45.469 Miguel de Veyra: I cause. I think now, what we wanna do is we wanna find
353 00:31:46.460 ⇒ 00:31:53.279 Miguel de Veyra: last updated status reason. And then what statuses do we want to follow up.
354 00:31:54.250 ⇒ 00:31:54.770 Miguel de Veyra: Of course.
355 00:31:54.770 ⇒ 00:32:00.060 Miguel de Veyra: Yeah, cause like, I, I think, yeah, there’s a lot. So which ones do we want to follow up with
356 00:32:00.220 ⇒ 00:32:04.120 Miguel de Veyra: like, is it only this one cause. There’s a follow up.
357 00:32:05.650 ⇒ 00:32:07.880 Miguel de Veyra: Right, or I don’t know.
358 00:32:08.070 ⇒ 00:32:11.919 Amber Lin: Especially in the Circle back section, is
359 00:32:12.390 ⇒ 00:32:16.860 Amber Lin: is something that we need to follow up to.
360 00:32:17.060 ⇒ 00:32:17.700 Miguel de Veyra: Yeah.
361 00:32:17.700 ⇒ 00:32:18.180 Amber Lin: Okay, so, maybe.
362 00:32:18.180 ⇒ 00:32:20.030 Miguel de Veyra: We we start with those 2. Only.
363 00:32:20.030 ⇒ 00:32:22.030 Amber Lin: Yeah, let’s just start with.
364 00:32:22.030 ⇒ 00:32:24.080 Miguel de Veyra: And follow up.
365 00:32:24.650 ⇒ 00:32:33.639 Amber Lin: Yeah. But circle back is something that falls through the cracks a lot. Any, I think anything in the active part. Can you click on active, actually
366 00:32:34.000 ⇒ 00:32:39.839 Amber Lin: think anything in the active part is pretty good. So actually, let’s just work on Circle back. For now.
367 00:32:40.340 ⇒ 00:32:44.149 Miguel de Veyra: Circle back, and this one no need right? I mean the follow up ones.
368 00:32:44.150 ⇒ 00:32:48.779 Amber Lin: Let’s go back. And can we click on the pre-qualification research part.
369 00:32:49.010 ⇒ 00:32:49.939 Miguel de Veyra: No! This one.
370 00:32:52.310 ⇒ 00:32:53.589 Miguel de Veyra: Pre call, research, okay.
371 00:32:53.590 ⇒ 00:33:02.379 Amber Lin: Yeah, I think let’s just do the circle back, for now and then we can do the other ones later, because I think that’s the one that’s most urgent
372 00:33:02.590 ⇒ 00:33:03.120 Amber Lin: cause.
373 00:33:03.320 ⇒ 00:33:04.330 Miguel de Veyra: Bob is.
374 00:33:04.330 ⇒ 00:33:05.789 Amber Lin: What about the active part.
375 00:33:05.790 ⇒ 00:33:11.110 Miguel de Veyra: Okay, okay, let me just try something. Status is
376 00:33:11.380 ⇒ 00:33:15.569 Miguel de Veyra: follow up. How many are there? Oh, there’s only one. So there’s no pro. There’s probably no need for this.
377 00:33:17.240 ⇒ 00:33:17.930 Miguel de Veyra: Okay?
378 00:33:18.960 ⇒ 00:33:21.619 Miguel de Veyra: Because we, I think we also need this field.
379 00:33:21.730 ⇒ 00:33:23.780 Miguel de Veyra: The 1st grade that.
380 00:33:25.000 ⇒ 00:33:36.249 Amber Lin: Oh, okay, I mean, let’s just write it down, or what we need from him. Confirm with him. If this is the right thing, he wants it to do so, I mean, just say just to flesh out the ticket, that’s all.
381 00:33:36.640 ⇒ 00:33:37.540 Miguel de Veyra: Yeah, okay.
382 00:33:37.700 ⇒ 00:33:48.930 Amber Lin: Yeah. And then for the data team is still deciding on what we should do for them. First, st like, maybe the Asset Library like, that’s a pretty easy win just to propose. Sorry the prompt
383 00:33:49.060 ⇒ 00:33:50.250 Amber Lin: library.
384 00:33:51.030 ⇒ 00:33:56.939 Amber Lin: and maybe a tutorial on how to use cursor better. But that doesn’t like that doesn’t take too much technical stuff.
385 00:33:56.940 ⇒ 00:34:01.089 Miguel de Veyra: Oh, yeah, yeah, that’s fine. Which. Which is that one? Let me.
386 00:34:02.340 ⇒ 00:34:06.870 Miguel de Veyra: Follow up is, gonna be top. Let me work on this, for now.
387 00:34:07.240 ⇒ 00:34:08.710 Amber Lin: Let me just work on it for now.
388 00:34:09.040 ⇒ 00:34:11.289 Miguel de Veyra: I’ll transfer it to Casey later on.
389 00:34:11.540 ⇒ 00:34:16.239 Amber Lin: Okay. Sounds good just a quick thing on the ABC stuff.
390 00:34:16.820 ⇒ 00:34:21.549 Amber Lin: Do you want to talk about it now or later? I’m free. Later, too.
391 00:34:22.120 ⇒ 00:34:25.750 Miguel de Veyra: Wait. Sorry. Where’s the toolkit?
392 00:34:26.545 ⇒ 00:34:27.130 Amber Lin: Which?
393 00:34:27.139 ⇒ 00:34:30.219 Miguel de Veyra: The one for the prompt stuff.
394 00:34:30.429 ⇒ 00:34:31.384 Amber Lin: Oh,
395 00:34:32.340 ⇒ 00:34:33.729 Miguel de Veyra: You haven’t created it.
396 00:34:33.730 ⇒ 00:34:42.509 Amber Lin: I feel like it’s in the backlog. But just create a random ticket, so that, you know, and I’ll transfer the I’ll transfer the requirements later.
397 00:34:42.910 ⇒ 00:34:43.710 Miguel de Veyra: Okay, okay.
398 00:34:44.750 ⇒ 00:34:47.380 Miguel de Veyra: I think Utam created the prompt library, though. Right?
399 00:34:47.389 ⇒ 00:34:58.479 Amber Lin: Yes, he did. We can. We can help put in some more, or at least give a guidance on how props should be structured blah blah like simple, simple stuff.
400 00:34:58.640 ⇒ 00:35:00.410 Miguel de Veyra: Oh, this is more for the entire team.
401 00:35:00.820 ⇒ 00:35:02.010 Amber Lin: Yeah, yeah.
402 00:35:05.792 ⇒ 00:35:08.089 Miguel de Veyra: General admissions. Okay, there you go.
403 00:35:09.100 ⇒ 00:35:10.540 Amber Lin: Yeah. Great.
404 00:35:10.540 ⇒ 00:35:11.680 Miguel de Veyra: This is sales.
405 00:35:15.640 ⇒ 00:35:19.960 Miguel de Veyra: Okay, yeah, I think I’ll work on this, too, especially this one.
406 00:35:20.753 ⇒ 00:35:24.420 Miguel de Veyra: And then, yeah, we can talk. I guess. ABC stuff. Now.
407 00:35:25.170 ⇒ 00:35:29.160 Amber Lin: Yeah. So there’s 2 things.
408 00:35:29.160 ⇒ 00:35:31.150 Miguel de Veyra: Is this a new cycle? Number? Sorry.
409 00:35:31.380 ⇒ 00:35:33.339 Amber Lin: Yes, we’re on a new cycle of 2 weeks now.
410 00:35:34.310 ⇒ 00:35:48.190 Amber Lin: Yeah. I think something that I just remembered last week’s call on Friday. They were like, we want to know, because, oh, by the way, upsells is a measurement that we will earn money from
411 00:35:48.210 ⇒ 00:36:07.189 Amber Lin: right. This is very important to us, because essentially the more upsells we impact, we can earn more money. So I I just remembered because I forgot to create the ticket last Friday. And we do wanna do that. So if we if you click click on the oh, by the way, upsells 2, 2, 5,
412 00:36:07.830 ⇒ 00:36:13.179 Amber Lin: if you click on that to do cycle, the second one in the to do cycle.
413 00:36:13.180 ⇒ 00:36:14.040 Miguel de Veyra: Okay.
414 00:36:14.470 ⇒ 00:36:16.689 Amber Lin: Yeah, I’m overwhelmed by tickets. Sorry.
415 00:36:17.034 ⇒ 00:36:27.020 Amber Lin: Don’t worry. It’s a lot. So that’s just a bigger ticket if you can click on the oh, by the way, data, the last one, the 3rd one.
416 00:36:27.750 ⇒ 00:36:38.710 Amber Lin: Yeah. I just wanted to ask you guys if you think this is possible. So I wrote that if you can read it real quick, so essentially, we want to see for each conversation
417 00:36:38.710 ⇒ 00:36:58.539 Amber Lin: did we mention? Oh, by the ways, and how often, because we, the conversation might have multiple mentions. And essentially what type of oh, by the ways were mentioned so that we can tell the clients. Hey, we mentioned this category? This many times, and apparently you had more sales in this category. Therefore you should pay me
418 00:36:58.590 ⇒ 00:36:59.989 Amber Lin: like that type of thing.
419 00:37:00.420 ⇒ 00:37:06.189 Miguel de Veyra: Yeah, I’ll yeah. I’ll let any do the dashboard. But we need to give her the data.
420 00:37:06.310 ⇒ 00:37:12.900 Amber Lin: So, Casey, if you have time, I think this would be this. I don’t know how long this would take, but probably we can.
421 00:37:12.900 ⇒ 00:37:15.899 Miguel de Veyra: Yeah, I don’t think, yes.
422 00:37:16.470 ⇒ 00:37:18.230 Miguel de Veyra: Wait. What? The fuck?
423 00:37:19.320 ⇒ 00:37:24.150 Miguel de Veyra: ABC, yeah, I think honestly, Casey, I think there’s a way to easily do this.
424 00:37:24.720 ⇒ 00:37:26.470 Miguel de Veyra: we can just add it on, like
425 00:37:26.660 ⇒ 00:37:28.999 Miguel de Veyra: I would say, just add it here.
426 00:37:30.280 ⇒ 00:37:35.459 Casie Aviles: We’ll have another AI step right to analyze each response. Kind of something like that.
427 00:37:35.460 ⇒ 00:37:39.159 Miguel de Veyra: Basically just before honestly here, just add it here
428 00:37:39.370 ⇒ 00:37:42.839 Miguel de Veyra: and then, hey, does this have all, by the way or not? Yeah. They either.
429 00:37:42.840 ⇒ 00:37:43.340 Casie Aviles: Okay.
430 00:37:43.340 ⇒ 00:37:43.680 Amber Lin: Yeah.
431 00:37:43.680 ⇒ 00:37:44.360 Casie Aviles: Hello!
432 00:37:44.750 ⇒ 00:37:45.330 Amber Lin: Okay.
433 00:37:45.620 ⇒ 00:37:46.989 Casie Aviles: And yeah, I’ll have to.
434 00:37:47.240 ⇒ 00:37:50.100 Casie Aviles: We’ll want to backfill like the entire
435 00:37:50.580 ⇒ 00:37:56.909 Casie Aviles: conversation right? Like all of the messages so far. Do we want to check that as well like.
436 00:37:56.910 ⇒ 00:38:00.710 Miguel de Veyra: Oh, yeah, do we need the existing ones to have to count that data.
437 00:38:00.710 ⇒ 00:38:17.399 Amber Lin: Like, I don’t think we can just have it start this week like, it’s okay. Takes more time. I we haven’t done any too many real calls, except for the ones like from last Wednesday to Friday. So if we do this fast, it’s fine, right.
438 00:38:17.400 ⇒ 00:38:19.630 Miguel de Veyra: I guess this will probably be
439 00:38:19.760 ⇒ 00:38:22.030 Miguel de Veyra: 3 points is 3 to 5 bars right.
440 00:38:23.791 ⇒ 00:38:28.980 Amber Lin: let me check. I think 2 points is like 2, 2 to 3 h.
441 00:38:29.300 ⇒ 00:38:29.960 Amber Lin: T.
442 00:38:30.510 ⇒ 00:38:33.240 Miguel de Veyra: Wait! Let me. Can we PIN that somewhere.
443 00:38:33.470 ⇒ 00:38:37.850 Amber Lin: I know I keep having to find that it’s how we use linear.
444 00:38:38.130 ⇒ 00:38:43.639 Amber Lin: Okay, okay, 2 points is 2 to 3 h. 3 points is 4 to 5 h.
445 00:38:43.840 ⇒ 00:38:45.260 Amber Lin: half day. Task.
446 00:38:46.862 ⇒ 00:38:48.990 Casie Aviles: I think we should 22 points.
447 00:38:48.990 ⇒ 00:38:50.359 Miguel de Veyra: This could be 2 points, I think, yeah.
448 00:38:50.550 ⇒ 00:38:52.410 Amber Lin: Okay, yeah, so.
449 00:38:52.410 ⇒ 00:38:57.260 Miguel de Veyra: Oh, but we have to edit the ones in the data in Snowflake.
450 00:38:57.260 ⇒ 00:38:58.830 Casie Aviles: Flake. I’ll just have it. There.
451 00:38:58.830 ⇒ 00:39:00.250 Miguel de Veyra: I think this will be that.
452 00:39:00.620 ⇒ 00:39:01.689 Miguel de Veyra: Are you sure.
453 00:39:03.170 ⇒ 00:39:03.850 Casie Aviles: I just said like.
454 00:39:03.850 ⇒ 00:39:04.630 Miguel de Veyra: 2, 3.
455 00:39:04.810 ⇒ 00:39:05.550 Casie Aviles: Okay. Fine.
456 00:39:05.550 ⇒ 00:39:07.470 Miguel de Veyra: Yeah, let’s just do 3.
457 00:39:07.980 ⇒ 00:39:19.549 Amber Lin: Okay, let’s do 3. And then when you’re done with that, when do you think we can have it done ideally? I want to give this to Annie by like Wednesday or Thursday, because I don’t know how long she’s gonna take.
458 00:39:20.270 ⇒ 00:39:20.900 Casie Aviles: I see.
459 00:39:22.430 ⇒ 00:39:25.310 Casie Aviles: Yeah, I could work on it tomorrow.
460 00:39:25.310 ⇒ 00:39:26.859 Miguel de Veyra: I can work on this now.
461 00:39:27.950 ⇒ 00:39:32.181 Amber Lin: Do you want to work on it, Miguel? Like, are you? Or you want to do development.
462 00:39:34.110 ⇒ 00:39:40.130 Miguel de Veyra: No cause case is already working on the AI stuff internal. I think this is a bit more important.
463 00:39:40.960 ⇒ 00:39:41.680 Amber Lin: Okay.
464 00:39:43.480 ⇒ 00:39:47.490 Miguel de Veyra: But yeah, I can start something out, Casey. Just I I don’t think it’s gonna be that hard
465 00:39:47.720 ⇒ 00:39:51.180 Miguel de Veyra: like, I can just finish this one, and then we can communicate with Annie.
466 00:39:52.020 ⇒ 00:39:58.169 Amber Lin: Sounds good. Yeah, this gives us something to show it on Friday as well as they mentioned it last Friday.
467 00:39:58.170 ⇒ 00:40:00.570 Miguel de Veyra: Yeah, cause we’re not gonna be here Wednesday. So.
468 00:40:00.570 ⇒ 00:40:01.920 Amber Lin: Yeah, yeah, I remember.
469 00:40:02.163 ⇒ 00:40:04.110 Miguel de Veyra: Wanna, you know, help out where I can.
470 00:40:04.110 ⇒ 00:40:05.120 Amber Lin: Of course.
471 00:40:05.604 ⇒ 00:40:08.889 Amber Lin: So that’s good. What else? Yeah? And the agent?
472 00:40:08.890 ⇒ 00:40:14.600 Amber Lin: Bye, if you go to ticket number 2, 0, 9
473 00:40:14.890 ⇒ 00:40:18.950 Amber Lin: or 51 like those 2 in the to do part.
474 00:40:19.910 ⇒ 00:40:25.250 Amber Lin: You know, there’s 1 where is it? You might have to show self issues.
475 00:40:26.390 ⇒ 00:40:29.970 Miguel de Veyra: Oh, good, so obviously.
476 00:40:29.970 ⇒ 00:40:31.210 Amber Lin: Oh, it’s not gonna show you.
477 00:40:31.210 ⇒ 00:40:31.920 Miguel de Veyra: Sure.
478 00:40:31.920 ⇒ 00:40:41.350 Amber Lin: Oh, oh, yeah, 51. You know the connect 8 by 8 to bought data, that one we probably have to ask the data team.
479 00:40:41.560 ⇒ 00:40:44.510 Miguel de Veyra: Oh, yeah, this is the one that’s gonna Annie’s gonna do right?
480 00:40:45.556 ⇒ 00:40:48.720 Amber Lin: Yeah, I just don’t know how she’s gonna do it.
481 00:40:49.843 ⇒ 00:40:50.950 Miguel de Veyra: Don’t do.
482 00:40:53.100 ⇒ 00:40:53.680 Amber Lin: Like she.
483 00:40:53.680 ⇒ 00:40:54.420 Casie Aviles: You’ll just need to do.
484 00:40:54.420 ⇒ 00:40:56.175 Amber Lin: The same question.
485 00:40:59.890 ⇒ 00:41:00.800 Casie Aviles: Oh, wait!
486 00:41:01.910 ⇒ 00:41:04.729 Miguel de Veyra: So this is gonna be 2 dashboards right?
487 00:41:06.840 ⇒ 00:41:08.029 Miguel de Veyra: I’m not sure I’m not.
488 00:41:08.950 ⇒ 00:41:14.440 Amber Lin: What I mean is that we already have the 8 by 8 data in the dashboard. That’s done. I think the point.
489 00:41:14.440 ⇒ 00:41:14.960 Casie Aviles: That’s true.
490 00:41:14.960 ⇒ 00:41:16.330 Amber Lin: Next the
491 00:41:16.450 ⇒ 00:41:36.090 Amber Lin: our bot say, for a certain call, right? We have the performance of the bot, and we have the results in 8 by 8. I think it’s mostly like matching that. How are we gonna match that? What kind of indicators do we need to match that? But I’ll throw that question to Annie. I guess it sounds like a sequel question.
492 00:41:36.600 ⇒ 00:41:47.729 Miguel de Veyra: Yeah, but there’s like, I don’t think Casey from my I don’t know there’s not. There’s no way for us to connect, like, for example, a call that they take.
493 00:41:47.950 ⇒ 00:41:48.650 Amber Lin: Hmm.
494 00:41:48.810 ⇒ 00:41:53.420 Casie Aviles: Yeah, that I guess it’s a challenge like, how do we know that the call that they’re taking is correct?
495 00:41:53.420 ⇒ 00:41:53.780 Miguel de Veyra: Sort of.
496 00:41:53.780 ⇒ 00:41:56.740 Casie Aviles: To like the conversation with the AI.
497 00:41:57.500 ⇒ 00:42:04.840 Amber Lin: I think it’s more of okay. When who? 1st of all, who did that call? And who was asking a Google chat? Right? So we have that. And.
498 00:42:04.840 ⇒ 00:42:05.979 Casie Aviles: Oh, yeah, right? Yeah.
499 00:42:05.980 ⇒ 00:42:18.079 Amber Lin: And we have okay. When was this call taken? And did this person asked about within that timeframe? I think, cause the person can only take 2 calls at a time. So we if we have those 2, I think we’re good
500 00:42:18.390 ⇒ 00:42:19.010 Amber Lin: right.
501 00:42:19.010 ⇒ 00:42:21.080 Casie Aviles: I see. Yeah, yeah, that makes sense that.
502 00:42:21.080 ⇒ 00:42:25.880 Amber Lin: Okay, great. I’ll throw this to Annie. This is not your task. This is her sequel task.
503 00:42:25.880 ⇒ 00:42:26.810 Miguel de Veyra: Yeah, yeah.
504 00:42:28.020 ⇒ 00:42:30.779 Casie Aviles: As long as you have time data and the name.
505 00:42:32.530 ⇒ 00:42:36.120 Miguel de Veyra: Input basically input user and then ts.
506 00:42:41.000 ⇒ 00:42:43.780 Miguel de Veyra: I think that’s the best way we can match it.
507 00:42:47.440 ⇒ 00:42:51.109 Miguel de Veyra: But the thing is, Casey, it’s not really. It takes a long time to update now.
508 00:42:51.940 ⇒ 00:42:52.420 Miguel de Veyra: Thank you.
509 00:42:52.420 ⇒ 00:42:52.780 Miguel de Veyra: I’m good.
510 00:42:52.780 ⇒ 00:42:53.190 Casie Aviles: Which one.
511 00:42:53.190 ⇒ 00:42:58.129 Miguel de Veyra: To like to to get even into Snowflake, so the Timestamps won’t be the same.
512 00:42:59.870 ⇒ 00:43:01.910 Amber Lin: Oh, I mean like.
513 00:43:02.142 ⇒ 00:43:04.229 Miguel de Veyra: Guess we can use it. We can use username.
514 00:43:05.590 ⇒ 00:43:22.210 Amber Lin: What about the chat time? I know it takes a while to get into Snowflake, but this is probably something less real time, but more of a batch thing. Do you think the time like the timestamp of the chat? Bot is not accurate, like, what was your.
515 00:43:22.548 ⇒ 00:43:27.280 Miguel de Veyra: It takes like a long time to process it like, I think, for example.
516 00:43:28.660 ⇒ 00:43:38.640 Casie Aviles: Well, we do have 2 timestamps, which is the 1.st 1st timestamp is where the message comes in from the user, and then the second timestamp is when.
517 00:43:39.510 ⇒ 00:43:45.169 Miguel de Veyra: But when then, the other thing now is, for example, the on the other side, the 8 by 8.
518 00:43:45.280 ⇒ 00:43:49.520 Miguel de Veyra: The the call doesn’t really get recorded until it ends right.
519 00:43:49.680 ⇒ 00:43:50.430 Amber Lin: Yeah.
520 00:43:50.750 ⇒ 00:43:52.369 Miguel de Veyra: So it’s still gonna be a different time.
521 00:43:52.370 ⇒ 00:43:58.689 Amber Lin: We’ll have a when the call gets picked up, and when the call is hanged up right so we can confirm.
522 00:43:59.120 ⇒ 00:44:00.770 Amber Lin: confirm with a.
523 00:44:00.770 ⇒ 00:44:03.340 Miguel de Veyra: Yeah, I guess it’s gonna be like, within this timeframe.
524 00:44:04.330 ⇒ 00:44:05.499 Amber Lin: Do you agree?
525 00:44:05.500 ⇒ 00:44:11.100 Miguel de Veyra: Then, you know, because if it’s exact, I don’t think it’s gonna work. So it’s gonna probably be below plus minus 1 min.
526 00:44:13.370 ⇒ 00:44:17.239 Casie Aviles: Yeah, I guess we need to do some exploration on the 8 by 8 data.
527 00:44:17.240 ⇒ 00:44:17.930 Amber Lin: No.
528 00:44:17.930 ⇒ 00:44:18.790 Miguel de Veyra: Has to make so much.
529 00:44:19.360 ⇒ 00:44:20.989 Amber Lin: We will do that.
530 00:44:21.420 ⇒ 00:44:27.509 Miguel de Veyra: Yes, sir, cause I don’t expect the Timestamps to match.
531 00:44:28.070 ⇒ 00:44:28.770 Amber Lin: Hmm.
532 00:44:28.770 ⇒ 00:44:32.450 Miguel de Veyra: So, for example, when they pick up the call, that’s already a timestamp, right?
533 00:44:32.600 ⇒ 00:44:35.600 Miguel de Veyra: And then they’re probably gonna say their greetings and whatever.
534 00:44:36.230 ⇒ 00:44:40.460 Miguel de Veyra: And then the 1st question will probably come in like, after 30 seconds or so.
535 00:44:43.570 ⇒ 00:44:46.570 Miguel de Veyra: So it’s not exact. We can’t use it as a uid.
536 00:44:58.110 ⇒ 00:44:58.800 Miguel de Veyra: No.
537 00:44:59.500 ⇒ 00:45:03.649 Amber Lin: Okay, great. That’s that’s for her. I think we lined out some pretty
538 00:45:04.180 ⇒ 00:45:09.030 Amber Lin: like this is very explant, explanatory.
539 00:45:09.030 ⇒ 00:45:09.440 Miguel de Veyra: No worries.
540 00:45:09.990 ⇒ 00:45:13.840 Amber Lin: Exploratory. So we’re good. That’s for her.
541 00:45:14.620 ⇒ 00:45:20.840 Amber Lin: The other one, I think the last one is to figure out how to integrate the Api.
542 00:45:22.040 ⇒ 00:45:23.380 Miguel de Veyra: Which api sorry.
543 00:45:23.713 ⇒ 00:45:24.380 Amber Lin: They might.
544 00:45:24.380 ⇒ 00:45:24.950 Casie Aviles: I mean.
545 00:45:25.320 ⇒ 00:45:26.620 Miguel de Veyra: Oh!
546 00:45:26.620 ⇒ 00:45:36.809 Amber Lin: Yeah, I think I just put a spot. Let me let me do end of Friday just a spike to figure out how it’s done like. We don’t have to do it, but to have a roadmap.
547 00:45:37.030 ⇒ 00:45:40.459 Miguel de Veyra: It should data get it done like, is it this one.
548 00:45:40.460 ⇒ 00:45:44.079 Amber Lin: No, yeah. It’s number 2. 0, 9.
549 00:45:50.070 ⇒ 00:45:50.960 Miguel de Veyra: 2 owner.
550 00:45:51.290 ⇒ 00:45:53.709 Amber Lin: 2, 0, 9, yeah, 2, 0 9.
551 00:45:53.930 ⇒ 00:45:54.899 Miguel de Veyra: And to do.
552 00:45:55.550 ⇒ 00:45:57.410 Amber Lin: Oh, yeah.
553 00:45:57.410 ⇒ 00:46:00.920 Miguel de Veyra: Or 2, 1 9. No, no, that’s 2 0. 9. I don’t see a 2 0 9.
554 00:46:02.610 ⇒ 00:46:03.400 Amber Lin: Huh?
555 00:46:03.780 ⇒ 00:46:04.670 Amber Lin: Wait.
556 00:46:05.050 ⇒ 00:46:06.410 Amber Lin: Where did it go?
557 00:46:09.780 ⇒ 00:46:16.525 Amber Lin: 8 by 8. Api. If you search that? Yeah. Right under the connector. 8 by 8 data.
558 00:46:17.870 ⇒ 00:46:19.579 Miguel de Veyra: Oh, here you go 8 way.
559 00:46:19.580 ⇒ 00:46:20.220 Amber Lin: Yeah.
560 00:46:21.780 ⇒ 00:46:24.460 Amber Lin: So I just want us to have an idea of
561 00:46:24.780 ⇒ 00:46:38.300 Amber Lin: what we need to do. Any data needs any help we need from the client data team. So right now, we just have no clue. So if we explore that a little bit ideally, I can get them to meet
562 00:46:38.990 ⇒ 00:46:41.930 Amber Lin: Wednesday, because.
563 00:46:42.272 ⇒ 00:46:46.730 Miguel de Veyra: Because right now, the data from 8 by 8 is basically we’re getting.
564 00:46:46.970 ⇒ 00:46:48.270 Amber Lin: Manually, manually.
565 00:46:48.270 ⇒ 00:46:50.250 Miguel de Veyra: Yeah, okay, okay, I see.
566 00:46:50.250 ⇒ 00:46:50.940 Amber Lin: Yeah.
567 00:46:51.910 ⇒ 00:46:55.119 Amber Lin: And we don’t know how complex this need to be.
568 00:46:56.100 ⇒ 00:46:59.400 Miguel de Veyra: You can probably do it now.
569 00:47:00.430 ⇒ 00:47:07.179 Amber Lin: Oh, great! Can I put this spike as in like tomorrow? Can I have it due tomorrow?
570 00:47:07.180 ⇒ 00:47:10.329 Miguel de Veyra: I don’t think we have enough time. There’s too much on the plate already.
571 00:47:10.330 ⇒ 00:47:17.520 Amber Lin: No, I know it’s just a spike in exploration. Maybe by Wednesday. I know you guys are working Wednesday, though.
572 00:47:17.880 ⇒ 00:47:23.499 Miguel de Veyra: Wait, why can’t we? Why, they have. They probably have an enterprise agreement with that 8 by 8, right.
573 00:47:24.360 ⇒ 00:47:29.790 Miguel de Veyra: Why can’t they? Just, you know, can’t we connect with 8 by 8, using their enterprise account like, Hey.
574 00:47:30.120 ⇒ 00:47:30.590 Amber Lin: Oh!
575 00:47:30.590 ⇒ 00:47:32.200 Miguel de Veyra: Do this. Let’s probably, of course.
576 00:47:32.200 ⇒ 00:47:32.959 Miguel de Veyra: that’s the way to do it.
577 00:47:32.960 ⇒ 00:47:42.000 Amber Lin: Sure, that’s that’s essentially all I wanted to ask, like, what do you think has to be done? And what do we need to ask the client. That’s it.
578 00:47:42.000 ⇒ 00:47:48.050 Miguel de Veyra: I think, as much as we can offload the R. And D to them. That would be nice, because they probably have enterprise.
579 00:47:48.360 ⇒ 00:47:49.700 Amber Lin: Great, fantastic. Can you.
580 00:47:49.700 ⇒ 00:47:50.070 Miguel de Veyra: You just.
581 00:47:50.070 ⇒ 00:47:56.759 Amber Lin: Put that in the ticket, or how it needs to get done. And what I what I need to ask the data team, because I don’t know.
582 00:47:56.760 ⇒ 00:47:59.420 Miguel de Veyra: They have an enterprise.
583 00:48:00.360 ⇒ 00:48:06.239 Miguel de Veyra: Wait, actually, let me check 1st this 8, this 8 by 8, even have enterprise.
584 00:48:06.240 ⇒ 00:48:07.520 Amber Lin: That’s so funny.
585 00:48:10.960 ⇒ 00:48:12.279 Miguel de Veyra: That’s this one, right?
586 00:48:13.970 ⇒ 00:48:17.179 Amber Lin: Yeah. I guess
587 00:48:22.310 ⇒ 00:48:27.509 Amber Lin: we can probably talk more in a data meeting with them. They just haven’t given me.
588 00:48:27.660 ⇒ 00:48:29.640 Amber Lin: Haven’t responded to me, went.
589 00:48:29.640 ⇒ 00:48:32.539 Miguel de Veyra: Is it? Is it? 8 by 8.com? Is it this one.
590 00:48:34.776 ⇒ 00:48:43.950 Amber Lin: If you click on the link in the linear ticket and then go search from that website, it will be more accurate because I don’t.
591 00:48:43.950 ⇒ 00:48:46.569 Miguel de Veyra: Oh, I guess I think it’s this one. Yeah, yeah, it’s this one.
592 00:48:46.570 ⇒ 00:48:47.320 Amber Lin: Hmm.
593 00:48:47.320 ⇒ 00:48:50.189 Miguel de Veyra: Because eightway.com. Because if we go to pricing
594 00:48:54.750 ⇒ 00:49:02.150 Miguel de Veyra: Api, she, it could be basically they need this.
595 00:49:04.910 ⇒ 00:49:09.439 Miguel de Veyra: No, we need to know what plan they’re in, and then, if they, if their plan has Api.
596 00:49:14.790 ⇒ 00:49:20.940 Miguel de Veyra: do, they have access to 8 by 8 Api.
597 00:49:21.990 ⇒ 00:49:28.190 Miguel de Veyra: 8 by 8, and 8 by 8 and Api plan.
598 00:49:29.450 ⇒ 00:49:35.850 Miguel de Veyra: You have something like this, and then if they do, it’s just, you know. Can we connect with them.
599 00:49:37.070 ⇒ 00:49:40.450 Amber Lin: High volume SMS, voice, high volume.
600 00:49:45.740 ⇒ 00:49:46.969 Miguel de Veyra: Hi, bold. Yeah.
601 00:49:48.450 ⇒ 00:49:56.889 Miguel de Veyra: yeah, we. I guess we need to know 1st which you know which one they’re subscribed to. And then if they have basically Api.
602 00:50:04.280 ⇒ 00:50:05.200 Miguel de Veyra: Hello.
603 00:50:05.960 ⇒ 00:50:06.590 Amber Lin: M.
604 00:50:08.050 ⇒ 00:50:12.539 Miguel de Veyra: Yeah, I think that’s pretty much it for this one. Do you need me to write anything else here?
605 00:50:14.415 ⇒ 00:50:15.350 Amber Lin: It.
606 00:50:15.470 ⇒ 00:50:19.969 Amber Lin: Did you ask what we need to ask the client? Oh, you did great.
607 00:50:21.200 ⇒ 00:50:29.799 Miguel de Veyra: The event, owner reply, what plan they have, and if they are on.
608 00:50:32.700 ⇒ 00:50:38.549 Amber Lin: Sounds good I will offload this to the client as much as possible.
609 00:50:39.330 ⇒ 00:50:40.600 Miguel de Veyra: We could.
610 00:50:40.870 ⇒ 00:50:41.700 Miguel de Veyra: Good night.
611 00:50:42.150 ⇒ 00:50:45.769 Miguel de Veyra: We can add, Where’s the gun?
612 00:50:46.590 ⇒ 00:50:47.400 Miguel de Veyra: Okay.
613 00:50:48.460 ⇒ 00:50:51.799 Amber Lin: Sounds good. Great! I will move this to blocked
614 00:50:52.120 ⇒ 00:50:59.020 Amber Lin: great. So I guess the only thing for ABC today or tomorrow is to get the
615 00:51:03.010 ⇒ 00:51:03.540 Miguel de Veyra: You can see.
616 00:51:03.540 ⇒ 00:51:08.760 Amber Lin: Yeah. Yeah. Okay. Great, that’s all. Thank you guys for meeting. I don’t want to keep you too long. This was great.
617 00:51:08.760 ⇒ 00:51:11.070 Miguel de Veyra: Okay, thanks. Everyone. Have a good day. Bye. Bye.
618 00:51:11.070 ⇒ 00:51:12.230 Amber Lin: Alright. Thank you.