Meeting Title: ABC x Brainforge | Data Meeting Date: 2025-04-01 Meeting participants: Annie Yu, Uttam Kumaran, Amber Lin, Davidlopez, Brian Gonzales, Yvetteruiz
WEBVTT
1 00:00:41.130 ⇒ 00:00:43.330 DavidLopez: I’m here
2 00:00:43.650 ⇒ 00:00:44.850 YvetteRuiz: Hey! There!
3 00:00:45.020 ⇒ 00:00:45.630 DavidLopez: Hi
4 00:00:47.430 ⇒ 00:00:48.892 YvetteRuiz: How’s it going
5 00:00:49.550 ⇒ 00:00:51.949 DavidLopez: My tires changed, so all good.
6 00:00:52.500 ⇒ 00:00:54.515 YvetteRuiz: That’s good. That’s good.
7 00:00:59.300 ⇒ 00:01:00.440 Annie Yu: Hello guys.
8 00:01:01.610 ⇒ 00:01:02.950 YvetteRuiz: Hi! There!
9 00:01:02.950 ⇒ 00:01:05.560 Annie Yu: Hey? Are you all from the ABC team
10 00:01:06.880 ⇒ 00:01:08.120 YvetteRuiz: We are.
11 00:01:08.810 ⇒ 00:01:25.730 Annie Yu: I am, Annie. I just joined officially this week, so still new learning things, but so glad to be able to work with you guys and it’s so funny because I I just moved to a home. I am like a 1st time homeowner now, and I remember like
12 00:01:25.860 ⇒ 00:01:32.695 Annie Yu: going through different contractors for different projects. So when I saw your ice, I’m like this is pretty cool, like a 1. Stop shop
13 00:01:34.590 ⇒ 00:01:35.940 YvetteRuiz: Congratulations.
14 00:01:36.090 ⇒ 00:01:37.320 DavidLopez: Congratulations.
15 00:01:39.120 ⇒ 00:01:42.240 YvetteRuiz: Where are you in Austin, or
16 00:01:42.240 ⇒ 00:01:45.390 Annie Yu: No! I, Portland, Oregon.
17 00:01:45.610 ⇒ 00:01:49.554 YvetteRuiz: Portland, Oregon. Alrighty. Okay.
18 00:01:52.490 ⇒ 00:01:53.650 Amber Lin: Hello!
19 00:01:55.510 ⇒ 00:01:56.530 Brian Gonzales: If it’s
20 00:01:57.320 ⇒ 00:01:58.889 YvetteRuiz: Comes, amber
21 00:01:59.450 ⇒ 00:02:00.090 Amber Lin: Hi Evan!
22 00:02:00.090 ⇒ 00:02:01.450 YvetteRuiz: Amber.
23 00:02:01.450 ⇒ 00:02:04.680 Amber Lin: I know you got sick. How are you?
24 00:02:04.680 ⇒ 00:02:07.460 YvetteRuiz: I’m doing better. I’m actually getting.
25 00:02:07.690 ⇒ 00:02:11.659 YvetteRuiz: I’m getting tired already. Today. It’s just been kind of recovering. So I’m kind of like.
26 00:02:12.281 ⇒ 00:02:14.868 YvetteRuiz: my energy is already going down.
27 00:02:15.890 ⇒ 00:02:23.150 YvetteRuiz: Yeah, this is the team. Right here. We just met Andy. Andy just introduced herself.
28 00:02:25.410 ⇒ 00:02:26.370 YvetteRuiz: Hi, there’s
29 00:02:26.370 ⇒ 00:02:27.529 Amber Lin: Hi Brian.
30 00:02:29.710 ⇒ 00:02:30.220 Brian Gonzales: Hello!
31 00:02:30.220 ⇒ 00:02:31.630 YvetteRuiz: Are we waiting for Udem?
32 00:02:31.920 ⇒ 00:02:33.980 Amber Lin: Yeah let me ping him
33 00:02:33.980 ⇒ 00:02:34.540 YvetteRuiz: Okay.
34 00:02:34.540 ⇒ 00:02:35.620 Annie Yu: He’s here.
35 00:02:36.490 ⇒ 00:02:37.320 Annie Yu: That’s him right
36 00:02:37.320 ⇒ 00:02:37.710 Uttam Kumaran: Yes.
37 00:02:37.980 ⇒ 00:02:39.289 Amber Lin: Yeah. Thank you. Tom.
38 00:02:39.615 ⇒ 00:02:39.940 Uttam Kumaran: Hey?
39 00:02:39.940 ⇒ 00:02:41.140 Uttam Kumaran: Sorry for the delay.
40 00:02:42.780 ⇒ 00:02:43.850 YvetteRuiz: No worries.
41 00:02:44.080 ⇒ 00:02:58.680 YvetteRuiz: Alright! Well, if amber, I think. Have you met the? No, not yet. We’ll let them introduce themselves, Miss Amber, to the team, I think, Brian. And that’s about it. Yeah.
42 00:02:58.680 ⇒ 00:03:03.270 Uttam Kumaran: I met David. I think I may have met you early on in the project. Maybe the 1st meeting
43 00:03:03.270 ⇒ 00:03:04.490 DavidLopez: Exactly. Yes.
44 00:03:04.710 ⇒ 00:03:06.359 DavidLopez: Get to your janitum
45 00:03:06.860 ⇒ 00:03:07.200 Uttam Kumaran: Yeah.
46 00:03:07.200 ⇒ 00:03:07.530 YvetteRuiz: Thank you.
47 00:03:07.530 ⇒ 00:03:08.549 Uttam Kumaran: Nice to see you.
48 00:03:09.588 ⇒ 00:03:18.700 Uttam Kumaran: And then, yeah, maybe Annie and Amber, if you guys want to give intros, or unless you already have. And then, yeah, maybe. David and Brian, if you guys want to go next
49 00:03:19.220 ⇒ 00:03:31.170 Amber Lin: Yeah, Annie has gave her intro. So I’ll do mine. Hello, my name is Amber. I’m currently the project manager for our team. So our team has.
50 00:03:32.200 ⇒ 00:03:45.700 Amber Lin: Our team is composed of air engineers. So Annie is coming on to help us with the data part, so she will be our source of knowledge, and she will be the main the main person working with you guys on the data
51 00:03:50.700 ⇒ 00:03:51.933 YvetteRuiz: Sounds, great
52 00:03:54.360 ⇒ 00:03:57.370 DavidLopez: Good job. Well, I’ll go next.
53 00:03:58.040 ⇒ 00:04:16.040 DavidLopez: My name is David. I work with ABC. Obviously I’ve been working with Yvette and Brian for what seems like a hot minute. I don’t even know time flies. I can’t remember. It’s been years, but it’s been fun. I run the data team, too, Annie. So I’m right there with you, right data and I’m just.
54 00:04:16.290 ⇒ 00:04:19.569 DavidLopez: I’m here to learn all the things, too, and help out as much as I can.
55 00:04:23.230 ⇒ 00:04:31.479 Brian Gonzales: Guess I’ll go next. So I’m Brian. I’ve been with ABC. Almost coming up on 2 years now. Working with David and team.
56 00:04:31.970 ⇒ 00:04:51.810 Brian Gonzales: I’ve been in pretty much workforce management for over 5 plus years data analytics about the same so I handle a lot of their real time analytics. Anything. Kpi related any report building. So anything to do with that. If it doesn’t exist, I make it exist. So that’s basically my job title
57 00:04:55.280 ⇒ 00:04:55.960 Amber Lin: Oh, no.
58 00:04:56.674 ⇒ 00:05:03.270 Amber Lin: so I’ll let you guys set the course for this meeting, or whatever items you want to talk about, and then we’ll be here
59 00:05:05.410 ⇒ 00:05:32.589 Uttam Kumaran: Yeah, on on my side, I definitely want to get a couple of things done. So one is, me and Brian had a conversation. About the actual phone data, and and the call data. And specifically, we basically wanna, our ultimate goal is on on the Brainforge side is to make sure that our AI agent is affecting the outcomes of calls. Right? So basically, this team is tasked with gathering and
60 00:05:32.660 ⇒ 00:05:53.790 Uttam Kumaran: creating whatever data pipelines we need to accomplish that goal. Additionally, I do think that our tool which we’re using rail is actually going to really help David and Brian with some of the things they’re already doing for ABC in modeling all data locally in Excel, so definitely hopeful that
61 00:05:53.790 ⇒ 00:06:03.940 Uttam Kumaran: throughout this process they can get onboarded onto rail and then begin to use that tool for their needs as as they need to as well. So that’s sort of the
62 00:06:04.060 ⇒ 00:06:20.815 Uttam Kumaran: sort of from my perspective. We do measure several things right now. Our measurements are all constrained just to the bot and how the bot is used. But we want to expand that to then link it to the calls, and start to see how it affects those calls. Is there anything else?
63 00:06:22.080 ⇒ 00:06:23.860 Uttam Kumaran: Is there anything else that I missed?
64 00:06:25.280 ⇒ 00:06:29.100 YvetteRuiz: No, I think you covered you covered the gist of it. Udem. Yep.
65 00:06:30.040 ⇒ 00:06:31.370 Uttam Kumaran: Okay, yeah. So
66 00:06:31.370 ⇒ 00:06:43.110 Uttam Kumaran: so I think that’s that’s the main focus amber. So I think for this meeting, I definitely want to go through those items and basically start to track out how we can, how we can accomplish each of those
67 00:06:44.950 ⇒ 00:06:47.550 Amber Lin: Well, why don’t we start with?
68 00:06:48.150 ⇒ 00:06:58.819 Amber Lin: What are we measuring? So the key, Kpis, I know Yvet, you and you guys determine a few Kpis, can we know which ones we’re going to focus on
69 00:07:00.200 ⇒ 00:07:08.360 YvetteRuiz: Yes. So we talked about the oh, by the way, measurement and then we talked about 1st call resolution
70 00:07:08.690 ⇒ 00:07:09.743 YvetteRuiz: and then
71 00:07:10.840 ⇒ 00:07:19.162 YvetteRuiz: hang on. I’m sorry how many notes with me. I forgot what the last one is. Just one second, where we doing the retention piece of it, I believe
72 00:07:19.890 ⇒ 00:07:24.650 Uttam Kumaran: Yeah. And I think I thought we already have. I thought we already have some of these pieces documented
73 00:07:24.790 ⇒ 00:07:30.810 Amber Lin: Yeah, we have it documented. I think so. Starting from there. I’ll go pull it up and
74 00:07:31.050 ⇒ 00:07:36.369 Amber Lin: from there, so we can look at what kind of data we need to measure that
75 00:07:37.530 ⇒ 00:07:42.290 YvetteRuiz: And I believe it was Hc. As well, and the the call volume
76 00:07:42.730 ⇒ 00:07:46.300 Amber Lin: Yeah, okay.
77 00:07:47.100 ⇒ 00:07:54.089 Amber Lin: so from David and Brian, what do you guys think, what kind of data we have? Do we have? And
78 00:07:54.330 ⇒ 00:07:59.369 Amber Lin: how are we going to go from data to measurement? Essentially
79 00:08:02.390 ⇒ 00:08:27.999 YvetteRuiz: So we do have. Well, I I don’t wanna take it up, but we do have the. We already have the call volume, and we shared this with Udem last week when we met. So we already have last year’s call volume versus this year. Call call volume. We have it break broken down by departments. We have the aht we were experimenting with the 1st call resolution piece of it, because we’re still struggling on that. But Udem did clear up some of those things.
80 00:08:28.346 ⇒ 00:08:42.889 YvetteRuiz: We have the oh, by the way, information. So you know we’re we can share already. I know the the in the goal would be to connect directly with 8 by 8, which we can help. You guys do all that.
81 00:08:42.900 ⇒ 00:08:48.180 YvetteRuiz: So I think we have most of what you need already
82 00:08:49.110 ⇒ 00:08:49.780 Amber Lin: Great
83 00:08:50.430 ⇒ 00:08:51.900 YvetteRuiz: Yeah, she can tap in if you
84 00:08:53.020 ⇒ 00:09:14.079 Uttam Kumaran: I think short term like again. As I. As I said, I think the 2 things is like we want to be able to get all that data. So I know, I spoke with Brian, I think, basically, how can we hand over that data so that we can get it loaded and available in the dashboard. So that’s 1 thing, Amber, the second thing is getting Brian and David onboarded onto the dashboard. How to use it and how they can go explore.
85 00:09:14.250 ⇒ 00:09:34.739 Uttam Kumaran: You know, data directly in there. I think that’s a that’s another piece we want to enable. And then the 3rd item is being able to connect directly with 8 by 8. Currently, the team can just pull it manually. And I don’t want that to be a blocker, because I’m not sure how long that’s gonna take. But another item here is to make sure that we can use the Api to get data out of 8 by 8.
86 00:09:35.154 ⇒ 00:09:57.950 Uttam Kumaran: I think that I think Brian and David have all the kpis that they measure. So we I don’t think we need to go through that over the call. But I just wanna make sure that they that they know what they need to hand over. And then we can sort of maybe book a time to get that all loaded in and then basically hand over access to the dashboard so they can start exploring that data directly in there
87 00:10:00.610 ⇒ 00:10:13.650 Amber Lin: Sounds good, and I or Casey can record a quick video on how to on how to navigate a dashboard, and we’ll get it to you as soon as possible. So you can get a sense of it, and
88 00:10:13.900 ⇒ 00:10:17.870 Amber Lin: in the future we can also book a call to walk over it together
89 00:10:19.480 ⇒ 00:10:26.390 YvetteRuiz: I did share with Brian already the the tutorial that you had originally shared with us. Amber
90 00:10:26.770 ⇒ 00:10:27.370 Amber Lin: Great
91 00:10:27.370 ⇒ 00:10:30.203 YvetteRuiz: I don’t know if he’s had a moment to check it out
92 00:10:31.870 ⇒ 00:10:34.390 Brian Gonzales: Yes, I’ve checked it out so I’m good on that amber
93 00:10:35.270 ⇒ 00:10:42.279 Amber Lin: And we made a few updates since then, but pretty much the structure of it is the same.
94 00:10:42.935 ⇒ 00:10:47.290 Amber Lin: I assume you guys have been, are not very familiar with real right
95 00:10:50.130 ⇒ 00:10:51.989 Brian Gonzales: That would be correct in your assumption
96 00:10:52.330 ⇒ 00:10:55.669 Amber Lin: Okay, okay, so we will.
97 00:10:56.630 ⇒ 00:10:58.310 Amber Lin: Let’s see,
98 00:10:59.820 ⇒ 00:11:09.330 Amber Lin: would you guys need help in learning how to use that? Or would the documentation on real. Be good for now
99 00:11:10.300 ⇒ 00:11:11.814 Brian Gonzales: I think honestly.
100 00:11:12.320 ⇒ 00:11:14.019 Uttam Kumaran: Yeah, go ahead. Got it? Go right now.
101 00:11:14.020 ⇒ 00:11:20.970 Brian Gonzales: No, I was just gonna say I think I could run through it and get like as far as like, cause I’m very concrete when it comes to learning stuff like that.
102 00:11:21.687 ⇒ 00:11:48.969 Brian Gonzales: So I can jump in, get what I can, and then kind of notate where I have my like. I can have. David and I kind of like find whatever roadblocks we come into, and then we can set up like an like. I can send you an email. And this is just an idea like, I can send you an email of, here’s some points that we’re running into and then that way, we can kind of focus the training a bit on helping us navigate that a little bit better. So I don’t know how that sounds to everybody.
103 00:11:49.530 ⇒ 00:11:55.839 Amber Lin: Yeah. So you guys will learn the 1st parts and then we’ll address specific blockers instead of making it a tutorial
104 00:11:57.890 ⇒ 00:11:59.060 Brian Gonzales: Yep, exactly.
105 00:11:59.060 ⇒ 00:12:01.770 Amber Lin: Yeah, gutam, you had something to say earlier
106 00:12:01.770 ⇒ 00:12:20.499 Uttam Kumaran: No, that’s perfect, I think. Yes, I I basically my goal. Is that anything we do data wise? It can get run through David and Brian as well. So I just wanna make sure that we also talk about like, you know, we have our Friday meetings. I know we have our daily stand ups. We may need some other time or
107 00:12:20.630 ⇒ 00:12:34.959 Uttam Kumaran: have our team connect directly with the data team on what insights we’re seeing and how to basically get that over to them. I think the Friday meeting we have a lot of people on, so it’s probably best we just stick to like high level Kpis. But I definitely want
108 00:12:35.000 ⇒ 00:12:51.222 Uttam Kumaran: us to have time with this crew to go in depth and basically start to ask some specific questions about retention, about 1st call resolution and sort of work backwards for there. So I just wanna make sure that that can all you know get booked. And
109 00:12:51.980 ⇒ 00:12:52.330 Amber Lin: Okay.
110 00:12:52.330 ⇒ 00:12:53.250 Uttam Kumaran: Coordinated.
111 00:12:55.380 ⇒ 00:13:05.420 Amber Lin: Are you? How available are you guys in terms of meetings? Because I’m thinking that well, we at least need one weekly meeting, and then probably we can have
112 00:13:05.700 ⇒ 00:13:10.439 Amber Lin: maybe one or 2 other stand ups to check in the progress. How does that sound
113 00:13:15.130 ⇒ 00:13:16.079 Brian Gonzales: You wanna take that? One day
114 00:13:16.080 ⇒ 00:13:16.926 YvetteRuiz: It sucks.
115 00:13:17.350 ⇒ 00:13:19.330 DavidLopez: Yeah, that works for me definitely.
116 00:13:20.769 ⇒ 00:13:30.990 Amber Lin: I will coordinate with you guys on the time availabilities. Then is this time a good time, or are you guys more free in the mornings, and what would that look like
117 00:13:33.010 ⇒ 00:13:42.280 DavidLopez: I would prefer anything the last half the week, like Wednesday, Thursday, Friday, as Mondays and Tuesdays are generally pretty busy around these parts.
118 00:13:43.380 ⇒ 00:13:47.549 Amber Lin: Oh, so sorry you said Wednesday, Thursday. Fridays are really really busy
119 00:13:47.840 ⇒ 00:13:52.760 DavidLopez: No, no, those are the dates that I’ll prefer, as Mondays and Tuesdays are pretty busy
120 00:13:52.760 ⇒ 00:13:54.300 Amber Lin: I see, too.
121 00:13:55.480 ⇒ 00:14:00.770 Amber Lin: Think, okay. Sounds good. Brian. Is that the same for you as well
122 00:14:01.190 ⇒ 00:14:03.579 Brian Gonzales: Oh, yeah, whatever is good for him is good for me. So
123 00:14:03.580 ⇒ 00:14:05.890 Amber Lin: Okay, okay, good.
124 00:14:06.250 ⇒ 00:14:18.109 Amber Lin: And, Yvette, we can always. We can always email you afterwards. Or I know you have a note taker that will take notes for you as well, so we’ll keep you posted on all these meetings, but you probably don’t have to be present at all of them.
125 00:14:18.690 ⇒ 00:14:19.800 YvetteRuiz: Sounds good
126 00:14:19.800 ⇒ 00:14:20.330 Amber Lin: Hmm.
127 00:14:24.500 ⇒ 00:14:35.340 Brian Gonzales: Just to add amber. We also, I’m also in the slack channel. So if I if I have, like, probably smaller stuff that can be addressed quickly. I can go in there and ask in there. Correct
128 00:14:35.590 ⇒ 00:14:39.819 Amber Lin: Yeah, yeah, that will be awesome, because email exchanges always take more time.
129 00:14:42.005 ⇒ 00:14:46.610 Amber Lin: David, are you in the slack? Or would you want to be in the slack channel, as well
130 00:14:46.610 ⇒ 00:14:48.570 DavidLopez: I am not currently but sure.
131 00:14:48.680 ⇒ 00:14:50.269 DavidLopez: Get some insight in there
132 00:14:52.290 ⇒ 00:15:01.050 Amber Lin: Okay, nice. So I think we tackled the meetings.
133 00:15:01.420 ⇒ 00:15:03.469 Amber Lin: Let’s see what other things.
134 00:15:03.940 ⇒ 00:15:11.029 Amber Lin: Yeah, why don’t we? We also talked about it. Why don’t we talk about how we’re gonna hand over the data?
135 00:15:11.710 ⇒ 00:15:21.819 Amber Lin: So I think I will need you guys to give me guidance, or we don’t to give me guidance because I am not that familiar with all the technical aspects of how we’re going to migrate that
136 00:15:23.010 ⇒ 00:15:24.779 DavidLopez: No worries, and this is from.
137 00:15:25.080 ⇒ 00:15:25.880 DavidLopez: If I
138 00:15:25.880 ⇒ 00:15:26.420 Uttam Kumaran: Go ahead.
139 00:15:26.680 ⇒ 00:15:40.450 Uttam Kumaran: Yes, correct. So I think, Brian, I think we’re basically Brian showed us a little bit of like the raw call logs. So I think, basically, we wanna try to work backwards from the metric. Like, if we were to try to basically link what calls
140 00:15:40.610 ⇒ 00:15:59.370 Uttam Kumaran: like, basically, our 1st goal is just to understand. Can we link a call to a given chat log and basically work backwards from there so ideally what we have on our side. And any. I think this is what you’ll see in the data. But correct me if I’m wrong. We have. We have the chat name we know when the chat happened. We also have the contents.
141 00:15:59.775 ⇒ 00:16:23.910 Uttam Kumaran: So we’d we’d look to use all that to basically join to identify what call was happening when the chat was happening. So ideally, if we’re able to at least start with that, and we can get information on who made the call? Like, who was the call to and then, even if you guys have the transcript or other items that would be helpful, we’ll take that and then basically find that we’ll find the
142 00:16:24.200 ⇒ 00:16:26.880 Uttam Kumaran: how to do the appropriate join to our chat data
143 00:16:28.650 ⇒ 00:16:36.900 DavidLopez: Yes. So what I’m hearing is basically linking the phone call time person, like Agent Leaky.
144 00:16:36.900 ⇒ 00:16:39.319 DavidLopez: See? When chat occurred, kind of bring it together. Okay.
145 00:16:40.020 ⇒ 00:16:40.860 Uttam Kumaran: Correct.
146 00:16:40.860 ⇒ 00:16:41.470 DavidLopez: Yeah.
147 00:16:41.620 ⇒ 00:16:49.250 DavidLopez: So what I can do, too, is we can build kind of like a template. And how to do that, because I’m assuming y’all are going to be using apis
148 00:16:49.460 ⇒ 00:16:50.529 DavidLopez: for 8 by 8
149 00:16:51.400 ⇒ 00:17:02.000 Uttam Kumaran: Yeah, I mean, I think you know, if we were to just try to make some progress even like this week, I think we probably just request a historical log, or maybe just at least starting in the last
150 00:17:02.290 ⇒ 00:17:10.319 Uttam Kumaran: month or so, and then we’ll in parallel begin to build the build the Api integration
151 00:17:10.810 ⇒ 00:17:14.399 DavidLopez: Okay, we can work on getting that for you, for sure.
152 00:17:16.270 ⇒ 00:17:19.509 Uttam Kumaran: I think, Amber. That’s just I think we wanna just task that out like the
153 00:17:19.510 ⇒ 00:17:19.880 Amber Lin: Yeah.
154 00:17:19.880 ⇒ 00:17:26.260 Uttam Kumaran: Api integration and then task out the basically getting and loading the historical logs
155 00:17:26.410 ⇒ 00:17:30.819 Uttam Kumaran: and then joining the data and then making it available in the dashboard, and those are the probably the key steps. Here
156 00:17:35.910 ⇒ 00:17:40.209 Amber Lin: Yeah. So for the historical logs, what
157 00:17:40.700 ⇒ 00:17:53.720 Amber Lin: I don’t know. If this is important, like, what? What format is it in like where we where we do. We already have it, and then we already have it. And what format is it going to be? How is it going to be sent over
158 00:17:55.030 ⇒ 00:18:00.390 DavidLopez: So it 8 by will allow us to do 2 formats, either a Csv. Or an Excel file.
159 00:18:00.510 ⇒ 00:18:03.439 DavidLopez: and either way we could just download and send that over
160 00:18:03.440 ⇒ 00:18:06.129 Amber Lin: Oh, okay, sounds good. That’s great.
161 00:18:06.130 ⇒ 00:18:07.489 Uttam Kumaran: Yeah. Csv is fine.
162 00:18:07.880 ⇒ 00:18:08.450 Amber Lin: Okay.
163 00:18:10.050 ⇒ 00:18:13.910 Uttam Kumaran: And if it’s if it’s like small enough, you can just send it directly in slack.
164 00:18:14.120 ⇒ 00:18:19.029 Uttam Kumaran: That’s fine. If it’s too big, then we’ll give you. We can give you a Google drive to upload it
165 00:18:20.360 ⇒ 00:18:24.260 DavidLopez: Okay, we could do that. We’ll check it out because you said for the past month, yeah.
166 00:18:25.180 ⇒ 00:18:27.100 Uttam Kumaran: Yeah, I think that’s fine.
167 00:18:28.390 ⇒ 00:18:28.940 DavidLopez: Wait!
168 00:18:29.080 ⇒ 00:18:44.820 Uttam Kumaran: Ideally, we just wanna be able to see that we wanna I I think this week we’re we’re gonna start to roll out to 5 Csr. So we may not. We may not have active calls to link it to, but starting this week, I think after this week, we should, we should be able to start to see that
169 00:18:47.860 ⇒ 00:18:49.550 DavidLopez: Love it. Okay, we can definitely do that
170 00:18:50.720 ⇒ 00:18:51.520 Uttam Kumaran: Okay, cool.
171 00:18:51.720 ⇒ 00:19:10.239 Uttam Kumaran: And then I think, amber. Probably this is more task for for Annie, which is actually starting to build towards those core kpis. I think this is where probably I know, Brian, you you already shared some of those metrics, but we can basically help you, or or at least
172 00:19:10.627 ⇒ 00:19:35.339 Uttam Kumaran: side by side. Show you how we’re gonna build those metrics directly in real that way you can make modifications or changes as as you need to. But again, you were looking at things like, you know, time on call average whole time calls per agent. All of that I basically want to try to replicate. So then you can also spot, check and make sure it matches your numbers as well. So amber. I think that’s probably all stuff
173 00:19:35.874 ⇒ 00:19:41.950 Uttam Kumaran: for Andy to take on. I guess, Annie, any questions about anything so far
174 00:19:43.060 ⇒ 00:19:44.570 Annie Yu: No.
175 00:19:45.220 ⇒ 00:19:57.980 Annie Yu: no problems on the kind of building a mock up in real. I do have questions for data tables, though. I still am kind of confused where I would go to get those data
176 00:20:00.500 ⇒ 00:20:03.199 Uttam Kumaran: Like the call logs or the the chat logs
177 00:20:05.210 ⇒ 00:20:06.240 Annie Yu: Both
178 00:20:07.530 ⇒ 00:20:10.819 Uttam Kumaran: So we just discussed like the call logs. We still need to get from
179 00:20:11.010 ⇒ 00:20:11.770 Annie Yu: The scene.
180 00:20:11.770 ⇒ 00:20:13.619 Annie Yu: Yes. Is that a Cs
181 00:20:13.620 ⇒ 00:20:14.069 Uttam Kumaran: We don’t
182 00:20:14.554 ⇒ 00:20:15.040 Annie Yu: File.
183 00:20:15.040 ⇒ 00:20:21.899 Uttam Kumaran: Yeah, that’s the Csv file. So we don’t have that yet. And then the chat logs are are already. You should see that in Snowflake already
184 00:20:21.900 ⇒ 00:20:22.260 Annie Yu: Okay.
185 00:20:24.060 ⇒ 00:20:24.760 Uttam Kumaran: Yeah.
186 00:20:36.890 ⇒ 00:20:44.209 Amber Lin: Let’s move to go over what we’re gonna do for this week. I think 1st of all, to establish that meeting cadence
187 00:20:44.330 ⇒ 00:20:51.400 Amber Lin: and to so get the data for the last month, and
188 00:20:51.870 ⇒ 00:21:05.570 Amber Lin: for you guys to explore Braille and explore the dashboards, and for us to see how we can build towards those kpis and make sure that it matches the numbers you have, and
189 00:21:06.740 ⇒ 00:21:19.470 Amber Lin: we’ll be thinking about 2 of how to match the call logs to a given chat log, and also to in parallel, to think about how we’re going to build that Api for 8 by 8
190 00:21:22.630 ⇒ 00:21:22.990 YvetteRuiz: Okay.
191 00:21:22.990 ⇒ 00:21:24.960 Amber Lin: Anything else that I missed
192 00:21:27.440 ⇒ 00:21:30.104 YvetteRuiz: No, I think that’s the start.
193 00:21:34.370 ⇒ 00:21:36.470 YvetteRuiz: Okay. So.
194 00:21:37.120 ⇒ 00:21:44.929 YvetteRuiz: Brian, you’re gonna send that information over, or did you need me to send again the Kpis we discussed last week?
195 00:21:45.177 ⇒ 00:21:47.899 Amber Lin: We have it so I have what you sent over so
196 00:21:47.900 ⇒ 00:21:48.660 YvetteRuiz: Okay.
197 00:21:51.240 ⇒ 00:21:54.110 Brian Gonzales: And I’ll take care of the Csv data. Me and David
198 00:21:54.110 ⇒ 00:21:55.140 Uttam Kumaran: Cool. And then.
199 00:21:55.260 ⇒ 00:21:59.829 Uttam Kumaran: Yvette, do you have a contact at 8 by 8? Or maybe we can just note down
200 00:22:00.100 ⇒ 00:22:04.170 Uttam Kumaran: how we can start on getting the Api access, and things like that
201 00:22:04.660 ⇒ 00:22:05.095 YvetteRuiz: Yep.
202 00:22:06.490 ⇒ 00:22:07.080 Uttam Kumaran: Okay. Cool.
203 00:22:07.080 ⇒ 00:22:07.480 YvetteRuiz: Like I
204 00:22:07.480 ⇒ 00:22:08.240 Uttam Kumaran: With Ambroll.
205 00:22:09.200 ⇒ 00:22:10.680 Uttam Kumaran: Yeah, that’s perfect.
206 00:22:10.890 ⇒ 00:22:13.499 Uttam Kumaran: Yeah, if you can send that over, that’d be perfect.
207 00:22:14.340 ⇒ 00:22:15.260 YvetteRuiz: Sounds good.
208 00:22:18.750 ⇒ 00:22:21.120 YvetteRuiz: I will send that over here shortly.
209 00:22:23.260 ⇒ 00:22:26.259 Amber Lin: I think that’s all that’s all for today. Right?
210 00:22:27.460 ⇒ 00:22:34.189 YvetteRuiz: Okay, well, it was great to meet you, Annie. Congratulations again on your new home.
211 00:22:34.610 ⇒ 00:22:36.290 YvetteRuiz: Thank you so much.
212 00:22:36.710 ⇒ 00:22:38.748 YvetteRuiz: Yeah. Look forward to working with you.
213 00:22:40.840 ⇒ 00:22:48.119 Amber Lin: And lovely to meet you, David and Brian. We’ll be meeting pretty frequently down the road
214 00:22:48.730 ⇒ 00:22:54.520 DavidLopez: Awesome. Well, it’s nice to meet you, too, and just so, you all know, I dropped in a site for 8 by 8 Apis.
215 00:22:54.520 ⇒ 00:22:58.000 DavidLopez: Oh, there as well, so it could be a good starting point for y’all
216 00:22:58.490 ⇒ 00:22:59.180 Amber Lin: Okay.
217 00:23:00.100 ⇒ 00:23:07.214 YvetteRuiz: Annie. I’m I mean amber. I’m gonna forward you the color palette here in a minute. Thank you so much for all the updates. I’m just getting caught up on all your emails.
218 00:23:07.430 ⇒ 00:23:10.779 YvetteRuiz: Yeah, of course, it’s just so that you have a place to read them.
219 00:23:11.230 ⇒ 00:23:14.430 YvetteRuiz: Thank you so much. Yeah, I’ll forward it here in just a minute.
220 00:23:14.550 ⇒ 00:23:18.009 YvetteRuiz: Okay, thank you. Guys. Alrighty. Bye-bye.