Meeting Title: Brainforge x Andy Feedback Sync Date: 2026-05-05 Meeting participants: read.ai meeting notes, Pranav, YvetteRuiz
WEBVTT
1 00:03:48.990 ⇒ 00:03:53.089 YvetteRuiz: Mmm… No, no, cancel.
2 00:03:56.890 ⇒ 00:03:57.770 Pranav: Evette?
3 00:03:57.770 ⇒ 00:04:00.790 YvetteRuiz: this dead time. Hey there, how are you?
4 00:04:00.940 ⇒ 00:04:02.309 Pranav: Good, I’m good, how are you doing?
5 00:04:02.310 ⇒ 00:04:06.030 YvetteRuiz: I’m doing alright, just turning around like a crazy person.
6 00:04:07.980 ⇒ 00:04:09.880 Pranav: That’s just how it goes today, you know?
7 00:04:10.050 ⇒ 00:04:11.740 YvetteRuiz: Yeah…
8 00:04:11.740 ⇒ 00:04:12.840 Pranav: Avoid it.
9 00:04:12.840 ⇒ 00:04:15.290 YvetteRuiz: You feeling better?
10 00:04:16.029 ⇒ 00:04:20.989 Pranav: Yeah, it’s just that time of the year for me, like, allergies just are kicking my butt.
11 00:04:21.170 ⇒ 00:04:22.060 YvetteRuiz: Yeah.
12 00:04:22.790 ⇒ 00:04:23.649 YvetteRuiz: Thank goodness, well.
13 00:04:23.650 ⇒ 00:04:24.330 Pranav: Yeah.
14 00:04:25.080 ⇒ 00:04:27.190 YvetteRuiz: Glad you’re feeling a little bit better.
15 00:04:27.190 ⇒ 00:04:31.489 Pranav: Yeah, I’m definitely, you know, I’m good, you know, I’m good.
16 00:04:31.860 ⇒ 00:04:32.959 Pranav: Appreciate you asking, though.
17 00:04:34.340 ⇒ 00:04:35.500 YvetteRuiz: Yeah, so…
18 00:04:36.210 ⇒ 00:04:43.910 YvetteRuiz: Thank you for scheduling the meeting, and you know, I’m sure you probably already read through the survey.
19 00:04:44.540 ⇒ 00:04:45.490 Pranav: Yep, yep.
20 00:04:45.490 ⇒ 00:04:49.670 YvetteRuiz: You did? Okay, alright. What was your take on… the feed.
21 00:04:49.670 ⇒ 00:04:55.229 Pranav: Yeah, so, in terms of the metrics themselves, I…
22 00:04:55.780 ⇒ 00:05:04.170 Pranav: I’m a little bit surprised with, like, and I mean, I’ve heard it from you guys, too, as well, but just in terms of the amount of…
23 00:05:04.560 ⇒ 00:05:15.279 Pranav: you know, I guess, people feel like that they’re using Andy just on… just because of, you know, because they’re being asked to use it. That’s a little bit concerning to me, you know?
24 00:05:15.280 ⇒ 00:05:15.840 YvetteRuiz: Yes.
25 00:05:15.840 ⇒ 00:05:19.390 Pranav: Because in seasons like this, where…
26 00:05:19.930 ⇒ 00:05:27.379 Pranav: you know, things are very busy. They shouldn’t just be thinking of, like, okay, we’re using Andy to help support it so it gets better at one point.
27 00:05:27.490 ⇒ 00:05:32.859 Pranav: that is not where I want to be at all. And so…
28 00:05:33.010 ⇒ 00:05:43.999 Pranav: you know, I joined kind of, like, the project a couple months ago, and I was, just kind of helping with, like, how things are going currently, and I just kind of want to move this forward. You know, I think…
29 00:05:44.290 ⇒ 00:05:57.440 Pranav: looking back, right, with, trying to go… and I think what we’re doing now, too, is we’re trying to not take on the whole beast at the same time, which is, like, for specifically transcripts, you know, we don’t want to kind of take on…
30 00:05:57.570 ⇒ 00:06:15.629 Pranav: every single department at the same exact time and pull in every single theme, because we know that’s going to be an effort that takes months and months. So we want to… how I’ve been thinking about it, and I think you’ve also been thinking the same way as me, is, okay, let’s just focus on specific measures that are the most concerning to us right now.
31 00:06:15.630 ⇒ 00:06:27.970 Pranav: And I think we can implement a similar thing with Andy as well. You know, we are currently supporting, you know, however many departments, you know, it’s like 5 to 7, based on how you want to put it up.
32 00:06:28.180 ⇒ 00:06:33.929 Pranav: Let’s focus on a few of those departments specifically.
33 00:06:34.130 ⇒ 00:06:38.669 Pranav: And let’s really try to, like, nail those ones, is how I’m thinking about it. So…
34 00:06:39.450 ⇒ 00:06:55.639 Pranav: that’s just kind of, like, an overarching picture of, like, how, like, my feed… like, my take on the feedback. You know, in terms of, like, the granular, there weren’t, I didn’t look in-depth enough to know, like, okay, these are the specific feedbacks that, like, stood out to me.
35 00:06:55.640 ⇒ 00:07:05.330 Pranav: I’ll do another pass on that as well. I focused mostly before this call to outline a few of the different projects that I feel like I want to work on.
36 00:07:05.330 ⇒ 00:07:18.919 Pranav: With you, based on the things that you brought up with me, and based on what I think is going to, increase accuracy for, for the trainers and for the CSRs. So, yeah.
37 00:07:19.110 ⇒ 00:07:23.720 Pranav: Yeah, I mean, let me know if you want to go into further depth about just, like, that feedback, but…
38 00:07:23.720 ⇒ 00:07:33.939 YvetteRuiz: Yeah, no, I would just kind of… I wanted just to kind of get your take on the feedback, you know, because one, like I shared with Steven.
39 00:07:34.130 ⇒ 00:07:44.270 YvetteRuiz: Yes, there’s no question there was some negative feedback, right? And it was… there’s validation to some of that, right? And I welcome all feedback, right? Because I know
40 00:07:45.140 ⇒ 00:07:47.300 YvetteRuiz: I know enough to go in there and kind of
41 00:07:47.300 ⇒ 00:08:05.339 YvetteRuiz: Alright, that one’s described… you know what I mean? Like, I know, like, the areas to hone in, but I value all the feedback, right? And, you know, the areas that we’re seeing are around accuracy, consistency, and the overall user, you know, the usability, right? To your point, you said it, and I covered this in my manager’s meeting this morning, where people feel like
42 00:08:05.430 ⇒ 00:08:07.480 YvetteRuiz: They’re forced, or they have to, and…
43 00:08:07.570 ⇒ 00:08:26.540 YvetteRuiz: like, I told them, I said, we’ve gotta… I don’t know where they’re leading with that, like, why are they… why would somebody provide that feedback? I could see that maybe in the beginning, when we were first rolling that out. Our intention was, hey, use Andy, use Andy, because we were trying to get them to give us the feedback, but the whole…
44 00:08:26.950 ⇒ 00:08:37.290 YvetteRuiz: the whole, way and the way we pitched it, I mean, I can go through emails and post and everything that we’ve talked about, and then you guys should be referring… I mean, relating the same information is…
45 00:08:37.510 ⇒ 00:08:47.890 YvetteRuiz: This is… this is a tool for us, right? This is having a quick way to get your answers versus the spreadsheets and going through all those type of things, so…
46 00:08:47.930 ⇒ 00:09:01.589 YvetteRuiz: That one, to your point, it kind of took me a little bit back, but some of it is more of, okay, if we’re talking to our people on a regular basis, and we are going through this and listening to the requests.
47 00:09:01.660 ⇒ 00:09:16.189 YvetteRuiz: I don’t think that we would be right here, and so that’s kind of… that’s a gap that I want to bridge with my leaders, right? Because it starts with us and the way we’re presenting it, the feedback that we’re getting, because… and, you know, obviously everybody looked at me like, that’s not true, and I’m like.
48 00:09:16.680 ⇒ 00:09:36.130 YvetteRuiz: That’s perception, but you know what I mean? If you’re not having these conversations, again, you guys identified y’all’s trainers, right? So your trainers are the people that are bridging that gap with them, but if you’re not taking lead, or you’re not asking or saying full lead, your people aren’t getting the same message, and so we’ve got to make sure that we’re all in full alignment, because
49 00:09:36.130 ⇒ 00:09:54.800 YvetteRuiz: I welcome all kinds of feedback, like I said earlier, you know what I mean? The negative error, that’s how we learn, that’s how we do that, but if we know that there’s things that are kind of like, wow, how are they still stuck on this, when… you know, so I shared some of that with them, so again, my point was I wanted to get your feedback on it, because like I shared with Steven, I said.
50 00:09:55.850 ⇒ 00:09:57.439 YvetteRuiz: We’re on the right track.
51 00:09:57.990 ⇒ 00:10:00.170 YvetteRuiz: There’s no question, right? Like.
52 00:10:00.490 ⇒ 00:10:20.249 YvetteRuiz: this was, you know, this wasn’t a speed race or anything like that. We know that these things take time to develop. We’ve made good progress, right? We know that this is the future, right? Now it’s kind of really, you know, we… and we kind of pulled the reins back, just like you’re saying, okay, let’s focus on this, because when you’re focusing on this.
53 00:10:20.250 ⇒ 00:10:25.660 YvetteRuiz: We’re gonna be all over the map, so we got some good control, we made some good progress, but now we’re here.
54 00:10:25.900 ⇒ 00:10:28.169 YvetteRuiz: Right? We got that feedback.
55 00:10:28.400 ⇒ 00:10:32.060 YvetteRuiz: The reality is, of it is… you know.
56 00:10:32.650 ⇒ 00:10:38.010 YvetteRuiz: They are engaged, because they wouldn’t provide that level of feedback to this if they weren’t.
57 00:10:38.010 ⇒ 00:10:41.470 Pranav: Yeah, and I think, to your point too, I think,
58 00:10:41.770 ⇒ 00:10:46.529 Pranav: getting support is gonna be huge. You know, we… we did…
59 00:10:46.710 ⇒ 00:10:54.809 Pranav: We are obviously showing that there is some value, you know, people are reporting, you know, what does it say? Let me pull it up again.
60 00:10:55.830 ⇒ 00:11:03.419 Pranav: It’s, saying… people are saying 50%, or at least being that it’s somewhat easier, and then there’s more saying that it’s much easier, right? So it’s over…
61 00:11:03.450 ⇒ 00:11:09.210 Pranav: Over 55% of people are saying that, right? So, that is showing that it is providing value.
62 00:11:10.300 ⇒ 00:11:27.650 Pranav: Now, I think, you know, the gap of the other 45% isn’t necessarily just on improving Andy. I think Andy could actually be completely stagnant, and we could show that it is making their workflow much easier, or somewhat easier, just by giving them some more support. And so…
63 00:11:27.650 ⇒ 00:11:34.440 Pranav: that’s where, I’m totally on the same page as you, is, like, what I’m hearing when you say that is, how do we improve
64 00:11:34.440 ⇒ 00:11:41.949 Pranav: some of the triage workflow. So, you know, we’re getting feedback in various different ways, whether it be thumbs up or thumbs down.
65 00:11:42.140 ⇒ 00:11:49.740 Pranav: Whether it be in my conversations with trainers, and they’ll bring up things that aren’t encapsulated within those, linear tickets.
66 00:11:50.120 ⇒ 00:12:06.449 Pranav: So, those are certain things that I’ve thought about, built kind of, like, just kind of projects along those roads, because I think, you know, that is actually what’s going to increase anti-adoption more, not just necessarily increasing accuracy. You know, so…
67 00:12:07.070 ⇒ 00:12:16.269 Pranav: That is something that I’ve thought about, about working in, starting, like, in, the next couple weeks. I think, that could be super good.
68 00:12:16.540 ⇒ 00:12:25.309 YvetteRuiz: And I really like your… your… your focus, right? Focus on a couple of… and I’m down, like, okay, let’s take one. Let’s take…
69 00:12:25.520 ⇒ 00:12:31.290 YvetteRuiz: dispatch, or a home improvement, wherever we want to start, okay? Let’s start there, right?
70 00:12:33.000 ⇒ 00:12:57.850 YvetteRuiz: do we… do we hold back on… I mean, no, we’re not going to hold back on the others, they’re going to continue doing it, they’re going to give our feedback, but if… if you and I say, okay, we’re going to commit to, okay, let’s drill down, let’s really get in here, and let’s really get that message out there, I’m going to focus on this department, and every… we’re going to get the data. You know, what specifically are we hearing, right? Going back and sharing those updates, and… and so forth, and really start drilling down.
71 00:12:57.850 ⇒ 00:13:07.620 YvetteRuiz: I feel like we’re gonna start making some impact, and we’re really gonna start getting down to, okay, where is the true issue, right? And then, the data’s gonna be everything.
72 00:13:07.620 ⇒ 00:13:16.359 YvetteRuiz: Because then the data’s gonna be able to tell me, you know what I mean? Because I think, not in this survey right here, but I think it was something voiced by another leader.
73 00:13:17.060 ⇒ 00:13:26.640 YvetteRuiz: and again, how conversations go, you know how stuff just kind of just gets twisted or whatever. But, you know, there was a call that had came in.
74 00:13:26.640 ⇒ 00:13:38.520 YvetteRuiz: And… the theory, or the assumption behind the way that call was, it was a bad experience, but the assumption was that they were using Andy to get the answer, and they weren’t, and that’s why it took so long, and I’m like.
75 00:13:39.610 ⇒ 00:13:56.659 YvetteRuiz: That’s an assumption, you don’t know that, you know what I mean? I can only go based off of… but this is where data’s gonna matter, you know what I mean? What we’ve talked about before, how do we know that they’re utilizing it when they have these longer phone calls, or quick… I don’t know that.
76 00:13:56.660 ⇒ 00:14:12.600 Pranav: Yeah, and to your point, too, like, in the last, and I completely agree with you. I think data is actually going to be a much better indicator than just, one-off kind of anecdotes that we get from, you know, trainers or CSRs. And I’ve noticed that as well, like.
77 00:14:12.960 ⇒ 00:14:14.500 Pranav: They’re…
78 00:14:15.690 ⇒ 00:14:27.909 Pranav: it’s easy, kind of, to, it’s hard, actually, I should say, for understanding, like, where the bottleneck is, in terms of, like, okay, where is Andy not,
79 00:14:28.070 ⇒ 00:14:30.400 Pranav: Not providing the right,
80 00:14:30.670 ⇒ 00:14:48.369 Pranav: I guess not bridging the gap for, like, where they’re trying to be. And so, I think what we need to do, and I’ve had those conversations for the past, like, 3 or 4 weeks, right? Just, like, specifically with the trainers. And so I think what we need to do is exactly what you’re saying, is, like, let’s focus more on what is the data telling us.
81 00:14:49.160 ⇒ 00:15:00.910 Pranav: Because I think we have more than enough information about, okay, what are the trainers, what are the CSRs telling us? Now let’s look into the data itself, and let’s see if it matches, you know? Like, I think in a lot of situations, it’s going to match.
82 00:15:00.910 ⇒ 00:15:12.510 Pranav: But I think there are certain situations where, you know, the root cause isn’t necessarily what the trainers are seeing, or what the CSRs are seeing as the potential issue, so…
83 00:15:12.830 ⇒ 00:15:31.240 YvetteRuiz: Yeah, because even to your point, I don’t know if you saw my last email when I finally was able to… because, like I told you yesterday, I hadn’t had a chance to go through the front stuff, the data that was going through the front, and if I was interpreting that right, there’s kind of like, okay, wait a minute, that’s not aligning with some of this. Yes, yes and no, but…
84 00:15:31.300 ⇒ 00:15:42.769 YvetteRuiz: the data is showing us a lot of thumbs up, not a whole lot of thumb… you know what I mean? So, there’s gaps there, and the more that we can get clarity on that is gonna be where we’re gonna get the win-win, I feel.
85 00:15:42.930 ⇒ 00:15:54.399 Pranav: Yeah, yeah, and I was just working with, Casey before this, too, to… you know, we’re seeing a lot of thumbs up, a lot of thumbs down, but what we’re seeing the most of is just, like, no responses, and so…
86 00:15:54.830 ⇒ 00:16:05.740 Pranav: most cases, when people don’t give a response, it’s because it was not a problematic response, you know? And I think that’s what we’re noticing, too. Like, people are letting us know, because people are very, you know.
87 00:16:06.630 ⇒ 00:16:16.210 Pranav: passionate about using Andy right now, that, they’re going to give the negative feedback if it gave them the wrong answer. Yeah. And so, I think
88 00:16:16.210 ⇒ 00:16:28.409 Pranav: you know, the thumbs up are gonna increase even more, because what we’re planning on doing is we’re gonna have an end-of-week, report, Casey’s working on that right now, to just say, hey, on per department, these are the things that didn’t get feedback.
89 00:16:28.410 ⇒ 00:16:39.159 Pranav: sending it to the trainers for them to just say, hey, is this a thumbs up or a thumbs down? And if it’s a thumbs down, then that will trigger another, triage ticket. And so…
90 00:16:39.520 ⇒ 00:16:57.499 Pranav: kind of bringing me into, like, what I was saying before, which is, like, okay, how do we automate a lot more of these triage tickets? Because one thing that I saw on your message, and this is something that me and Janiece had been talking about for the last week, is there are a hundred tickets that are still in that new state. And so.
91 00:16:57.500 ⇒ 00:17:12.749 Pranav: that’s a lot on Janice’s plate, you know, and this is busy season two, for her to go out and assign it to everybody is difficult. And this is also a process that is, you know, it’s very much so, like, okay, Janiece just needs to read the ticket, and it’s very clear who to assign it to.
92 00:17:12.750 ⇒ 00:17:28.340 Pranav: And to an AI system, and to me, too, like, if I read it, like, I know this, you know, certain thing is gonna go to Ashley, a certain thing is gonna go to, you know, whoever, on whatever department, right? So, we’re gonna build automations for this triage system as well, because.
93 00:17:28.349 ⇒ 00:17:37.909 YvetteRuiz: So then it identifies based on… I’m sorry, I don’t mean to cut you up, but I just want to make sure. It identifies, like, if it’s this, it goes here. Like, anything dispatch.
94 00:17:38.319 ⇒ 00:17:39.199 YvetteRuiz: You guys could.
95 00:17:39.200 ⇒ 00:17:39.860 Pranav: Yes.
96 00:17:39.860 ⇒ 00:17:40.649 YvetteRuiz: Tag it like that?
97 00:17:40.650 ⇒ 00:17:56.490 Pranav: Yeah, so I feel very confident that that is an automation that we could build that would be very accurate. Okay. So, basically, nothing is going to get stuck in that new pipeline anymore, because that’s too much for Janice to go through every single day, and things pile up very quickly.
98 00:17:56.490 ⇒ 00:18:02.809 YvetteRuiz: for clearing that up, because that’s where I was stuck. It’s like, okay, where is that holdup with the tickets? Yeah. Because that’s what I have in my list.
99 00:18:03.000 ⇒ 00:18:20.069 Pranav: So, I think it’s, it just kind of all falls to Janiece’s plate. I think she has a little bit too much ownership on how the triage system moves, and it’s not really… it’s not really scalable for what we’re trying to build, right? Which is going to be a system that…
100 00:18:20.070 ⇒ 00:18:23.249 Pranav: Has maybe 10x the usage as it has right now.
101 00:18:23.250 ⇒ 00:18:30.769 Pranav: you know, that could be 10x the amount of feedback. That’s gonna be a huge bottleneck for us if that’s… if there’s a ton of tickets just kind of stuck in linear.
102 00:18:30.770 ⇒ 00:18:32.120 YvetteRuiz: Yeah, no, that makes sense.
103 00:18:32.300 ⇒ 00:18:33.170 Pranav: Yeah, so…
104 00:18:33.170 ⇒ 00:18:38.870 YvetteRuiz: We’re not acting on them quick enough, we’re not updating it, people aren’t getting updated, so totally get it, yeah.
105 00:18:39.110 ⇒ 00:18:52.159 Pranav: So, and you let me know if you still want to keep on talking about the survey as well, because I’m happy to, but at some point, too, I want to talk to you a little bit about how we want to have the conversation with, Steven. Yeah.
106 00:18:52.370 ⇒ 00:19:05.739 Pranav: I… I think these problems that you’re bringing up are the exact problems that I was thinking about. You also talked about, like, the CSR scorecard, how we can get, like, you know, more real-time information, as well.
107 00:19:06.060 ⇒ 00:19:08.969 Pranav: And then also just, like, the specific, you know.
108 00:19:09.170 ⇒ 00:19:19.390 Pranav: Department-based insights, but even more granular to specific, buckets, like cancellations, or even, you know, specific services, like, you know.
109 00:19:19.560 ⇒ 00:19:26.260 Pranav: Like, lawn, or, you know, like, pet, like, specific things within pests, so…
110 00:19:26.370 ⇒ 00:19:28.530 Pranav: I think those are, like…
111 00:19:29.330 ⇒ 00:19:39.729 Pranav: four things, essentially, that we can say, hey, these are things that are going to, like, directly increase usage, and make our CSRs more…
112 00:19:39.930 ⇒ 00:19:42.150 Pranav: more productive. Yep.
113 00:19:43.230 ⇒ 00:19:48.620 Pranav: Is that kind of how you want to, like, have the conversation with Steven? Does that make, like, the most sense for when we have that conversation?
114 00:19:48.620 ⇒ 00:19:54.029 YvetteRuiz: Yeah, I mean, I think, you know, from Steven’s perspective, you know, again.
115 00:19:54.810 ⇒ 00:20:17.700 YvetteRuiz: the reason we did the survey in the first place, right, is to gauge, again, when other leaders hear X amount. We’re in San Antonio, they’re in Austin, so there’s times where things are… again, you don’t know how things go up the pipeline. Obviously, myself, I’m in charge of the project. I work with Steven on this. I know what we’re working on, right? I know who are the point people for all this.
116 00:20:17.920 ⇒ 00:20:23.719 YvetteRuiz: But I also have to have updates and what is the return of investment, because I’m…
117 00:20:23.880 ⇒ 00:20:27.260 YvetteRuiz: I’m the one speaking to it to Matt and Bobby and so forth.
118 00:20:27.260 ⇒ 00:20:27.580 Pranav: Totally.
119 00:20:27.580 ⇒ 00:20:41.970 YvetteRuiz: from mine and Steven’s perspective, we feel we’re on track, we’ve still, you know what I mean? We got ourselves back on track when you started taking over the program and so forth, but when that kind of escalated, that’s kind of what triggered. I’m like, okay, well, pause. I’m gonna do a survey, I’m gonna get
120 00:20:41.970 ⇒ 00:20:53.399 YvetteRuiz: true feedback. That’s where this survey came through, right? So, from Steven’s perspective, we are on the right path. The thing is, is now, okay, now that we know, we know that, okay.
121 00:20:53.930 ⇒ 00:21:06.909 YvetteRuiz: yeah, we got the… like I said earlier, we got the negative feedback, yes, that’s all good and everything. Now it’s kind of like, okay, where do we go from here, right? And we know that data’s gonna be the next… it has to be, right? And so, yes, these things are going to be things
122 00:21:07.620 ⇒ 00:21:26.000 YvetteRuiz: But at the same time, we want to make sure that, okay, when we speak accuracy, when we speak speed, all that stuff, I want to be able to speak to that, okay? Why are they saying that? And so, as long as we have a plan, you know what I mean, to make sure that… and that’s why I liked when you started, okay, we’ll start with specific areas, that’s fine, we can do that, but I just…
123 00:21:27.030 ⇒ 00:21:36.160 YvetteRuiz: I want to make sure that we address these things, because if we don’t address these things, they’re not going to be prone to use the tool the way it needs to be used. Does that make sense?
124 00:21:36.640 ⇒ 00:21:42.870 Pranav: Yeah, totally. And, you know, certain things, like, considered speed, right? Like…
125 00:21:43.620 ⇒ 00:21:58.710 Pranav: I think maybe certain people have had bad experiences with speed in the past, which has then, you know, influenced what they probably said in the survey, because we have the exact measurements of how much each, how long each, response is taking.
126 00:21:58.710 ⇒ 00:22:07.329 Pranav: Right? And we’ve shown, like, the P80, P90, P95 times, all being under 5 seconds. And so…
127 00:22:07.400 ⇒ 00:22:12.310 Pranav: I feel really confident that, you know, speed is not as much of an issue as it may have been in the past.
128 00:22:12.310 ⇒ 00:22:22.950 YvetteRuiz: I do, because, I mean, I was testing Andy earlier, because I was looking at some, you know, questions, just as far as, like, just kind of some of the… when I was looking at some of the feedback, like.
129 00:22:23.680 ⇒ 00:22:32.470 YvetteRuiz: some of the information is, like, too long, and I know why it’s too long, because it’s pulling it from the training, but at some point, do we want to shorten that?
130 00:22:33.060 ⇒ 00:22:35.230 YvetteRuiz: And so, the other…
131 00:22:35.510 ⇒ 00:22:48.369 YvetteRuiz: So anyhow, I don’t want to get off track on this piece of it. So, yes, I still want to… I don’t want to stay too focused on the survey, but I do want to make sure that we’re addressing the bigger concerns on that. But yes, to your point, I do want to talk about
132 00:22:48.650 ⇒ 00:23:05.430 YvetteRuiz: the scorecard, how do we start digging deeper into the cancellation piece of it? Because those are going to be tools that are going to be necessities for them to use, specifically that we’re now rolling out this whole focus on the cancellation piece of it.
133 00:23:06.060 ⇒ 00:23:17.469 Pranav: Totally, yeah, and we can talk about if we want to combine those two efforts into one, because, you know, cancellations, we can have it as a per CSR type of report.
134 00:23:18.350 ⇒ 00:23:35.519 Pranav: However, how I was thinking about cancellations and how I was thinking about the CSR scorecard is that they’re a little bit separate. So cancellations, you know, are going to be based on, you know, specific departments that we want to first analyze, and then group them into certain buckets for why did that cancellation take place.
135 00:23:35.520 ⇒ 00:23:43.539 Pranav: You know, we talked about how the billing team, like, puts certain tagging on each transcript based on what they say was the reason, but…
136 00:23:43.570 ⇒ 00:23:56.130 Pranav: sometimes it’s not as accurate as it could be, and it’s not based off of their understanding of the transcript itself. And so, what we’ve built already with, like, a POC is that we’ve,
137 00:23:56.130 ⇒ 00:24:06.520 Pranav: we’re able to pull in transcripts on a per… I think they’re on a per queue basis, so… Okay. In 8x8, there’s, like, certain queues, right? Yes.
138 00:24:06.520 ⇒ 00:24:17.799 Pranav: And so, based on a certain queue, we’re able to pull in those transcripts, and then we then put it into our AI system to then assess, is this a cancellation transcript or not a cancellation transcript? Okay.
139 00:24:17.800 ⇒ 00:24:26.220 Pranav: further on, if it is a cancellation transcript, how do we further identify it? And so then, is it a moving, cancellation? Which,
140 00:24:26.400 ⇒ 00:24:45.799 Pranav: is a predefined bucket that you could provide to us. So, you know, move the one that you said is predefined. If there’s any other predefined ones, you know, like, because, let’s say, for example, the service is completed, no longer needed, that’s another predefined bucket that we can toss certain calls into. But then we can also have AI-generated buckets. So, maybe.
141 00:24:45.800 ⇒ 00:24:46.130 YvetteRuiz: Okay.
142 00:24:46.130 ⇒ 00:24:52.840 Pranav: that you and I haven’t thought of, the AI will be able to group certain things into different categories of, hey, this is why they canceled.
143 00:24:53.130 ⇒ 00:24:53.710 YvetteRuiz: Yeah.
144 00:24:53.710 ⇒ 00:24:57.360 Pranav: I think that would be super interesting too, right? So we’re not thinking retroactively.
145 00:24:57.790 ⇒ 00:25:17.080 YvetteRuiz: Absolutely, I agree with that, for sure. And so you… so when you’re saying you… you guys have already… that’s already something that’s already been kind of built, because that’s great. I know we… we talked about maybe starting with the queue, or a smaller queue, whether it be the home improvement, because you know how in home improvement, they have the queue, but in there, they have
146 00:25:17.080 ⇒ 00:25:22.719 YvetteRuiz: they’re 3 different trades, or whatever, not like tests. PESS is just one big bucket. One big cube.
147 00:25:22.720 ⇒ 00:25:34.359 Pranav: Yeah, so that is something I want to talk to you about, because we’ve, I think we’ve done all the validation that this is going to work at scale when we push it into production.
148 00:25:34.360 ⇒ 00:25:42.970 Pranav: what I’ve been working with Sam and Casey on is, okay, how can we just first prove that we’re getting the correct insights from this process?
149 00:25:42.970 ⇒ 00:26:00.489 Pranav: And so I said, hey, Sam, just, like, pull in this specific, like, Windows queue, and then pull in all the transcripts from there, and then, assess whether each of these transcripts are canceled or non-canceled. And then within the canceled ones, figure out which ones are moving, and then for all the ones that aren’t moving.
150 00:26:00.490 ⇒ 00:26:14.160 Pranav: then create the AI-generated buckets. So, now it’s just really a matter of, hey, Yvette, you tell me which queue to focus on, which, bucket within the queue to, like, further focus on, and then, yeah, the system is.
151 00:26:14.160 ⇒ 00:26:14.660 YvetteRuiz: Okay.
152 00:26:14.660 ⇒ 00:26:17.680 Pranav: We’ll need, like, some time to put into production.
153 00:26:17.920 ⇒ 00:26:23.510 YvetteRuiz: Let me, I was gonna give you, kind of, our predefined cancels.
154 00:26:24.100 ⇒ 00:26:25.330 YvetteRuiz: reasons.
155 00:26:27.710 ⇒ 00:26:33.300 YvetteRuiz: And then everything else could… Yeah, where is…
156 00:26:39.680 ⇒ 00:26:44.090 YvetteRuiz: I’m gonna share my screen real quick, and you tell me how you would prefer me to send this to you.
157 00:26:47.060 ⇒ 00:26:48.219 Pranav: Definitely, yeah.
158 00:26:48.580 ⇒ 00:26:49.839 YvetteRuiz: Can you see my screen?
159 00:26:50.520 ⇒ 00:26:51.680 Pranav: It’s still loading from yellow.
160 00:26:51.680 ⇒ 00:26:53.600 YvetteRuiz: Oh, there it is. Is it there?
161 00:26:53.600 ⇒ 00:26:55.639 Pranav: Yeah, yeah, so these are all the buckets, that’s great.
162 00:26:55.640 ⇒ 00:27:15.110 YvetteRuiz: Yeah, so these are all the buckets that we have to choose from, although I would love to condense all this. Once we start getting more data from the transcripts, I think we’ll be able to get a better idea of, like, okay, what… you know what I mean? Like, if AI or whatever, you know, if they create their own reasoning.
163 00:27:15.500 ⇒ 00:27:17.070 YvetteRuiz: It’ll be interesting to kind of…
164 00:27:17.410 ⇒ 00:27:30.080 Pranav: what would be great here is if you can just screenshot this view, and then just send it to me. And then internally, what we’ll do is, just create predefined buckets for all of this stuff, because I think that is very, you know…
165 00:27:30.730 ⇒ 00:27:40.110 Pranav: this is probably too many buckets, which is good, because from there, we’ll see, okay, there’s nothing falling into these other buckets, let’s just delete them. And then…
166 00:27:40.400 ⇒ 00:27:46.850 Pranav: You know, we’ll further refine this in the future, but just to kind of get things going, let’s just use these buckets as the default.
167 00:27:48.910 ⇒ 00:27:53.590 YvetteRuiz: Are you okay that I chatted it to you in front? Okay, I just did that.
168 00:27:53.590 ⇒ 00:27:55.289 Pranav: Perfect. I appreciate that.
169 00:27:57.860 ⇒ 00:27:58.780 Pranav: Cool, cool.
170 00:27:59.040 ⇒ 00:28:08.729 Pranav: All right, I also want to talk about the CSR scorecard, because I know that’s something that, you know, you just mentioned, and we’ve been talking about for the last couple weeks.
171 00:28:09.750 ⇒ 00:28:16.169 Pranav: how I see that working, and I didn’t get to talk to you about this in the last couple weeks, because, you know, we just couldn’t sync up.
172 00:28:16.510 ⇒ 00:28:34.240 Pranav: we… a couple weeks ago, when you and I, and I think Utam was on that call too, we talked about just, like, all of the siloed data, right? Within 8x8, all the transcripts exist there, you know, ANDI usage exists in real… in real, and in our systems.
173 00:28:34.240 ⇒ 00:28:38.930 Pranav: Then there’s Evolve, that is just, you know, the… the CRM.
174 00:28:39.450 ⇒ 00:28:53.879 Pranav: how can we take in all of this information so then you can have, like, an interface to ask specific questions, and it not be based in just, hey, this is what billing said, or this is what a CSR said? How can it be based off of actual transcript data?
175 00:28:53.880 ⇒ 00:29:10.039 Pranav: And so what I’m really envisioning is kind of just like a dashboard for you that is connected to all of these different places, and you can ask specific questions about, hey, what was the reason for, you know, the 1.5 million in cancellations last month?
176 00:29:10.220 ⇒ 00:29:14.739 Pranav: And then this will give you a full breakdown of exactly
177 00:29:14.750 ⇒ 00:29:28.500 Pranav: where that happened, for what reasons were the cancellations, what departments saw the most cancellations, and this will be, like, a perfect report for you to just get ad hoc. You don’t need to ask your data team.
178 00:29:28.500 ⇒ 00:29:35.969 Pranav: You know, and then get a response, like, later on. You’ll be able to, like, click into specific things to understand, like, okay, there’s…
179 00:29:35.970 ⇒ 00:29:50.879 Pranav: this is, specifically for one department. Now let me, like, ask a follow-up question of, like, okay, why did this happen specifically in this department? Okay. You’ll be able to just, like, you know, you mentioned how, like, and I’m feeling the same way, too, that there’s all of this data, we’re not…
180 00:29:50.880 ⇒ 00:30:05.370 Pranav: acting on this data, or we’re not able to ask questions about this data, that’s what this thing is gonna be, this is going to help us with. And, what’s also great about this is that it’s going to be a stepping-off point for our next thing, which is the CSR scorecard.
181 00:30:05.370 ⇒ 00:30:11.999 Pranav: Because, essentially, what the scorecard is, is that it’s supposed to pull in information from all these different systems and evaluate
182 00:30:12.000 ⇒ 00:30:31.889 Pranav: each of the CSRs based on all of the metrics that you provided. I know, hold time was a reason, you know, and hold time is just a quantitative measure, but why was hold time so high, or, you know, maybe even so low? Why did they do so well, right? And so that is also all going to be based off of 8x8, the transcripts, right?
183 00:30:32.120 ⇒ 00:30:33.610 YvetteRuiz: Yeah, yeah, okay.
184 00:30:33.970 ⇒ 00:30:37.689 Pranav: what we want to tie this into is, like, hey, how has Andy Usage
185 00:30:38.090 ⇒ 00:30:45.249 Pranav: trended towards being a beneficial tool for each of these CSRs. So that’ll be part of the scorecard as well.
186 00:30:46.040 ⇒ 00:31:08.530 YvetteRuiz: So, walk me through that for now, because you got me very intrigued now. So, you know, and I’m following you here, right? Because we’re basing this off of transcripts, right? And that’s kind of… so, if we have the scorecard data, right? So, let’s say, for instance, we’ll just use the holds, for example, right? Last week, Pest had 9 hours worth the whole time.
187 00:31:08.950 ⇒ 00:31:14.789 YvetteRuiz: I just use that. I mean, we can get granular, we can go… I’m just asking the question, can we go…
188 00:31:14.990 ⇒ 00:31:18.190 YvetteRuiz: Q…
189 00:31:18.640 ⇒ 00:31:36.849 YvetteRuiz: we can dive in deep to the queue, could we dive into the CSR level? How would that work? Depending on where it’s… so I’m just trying to… I want to make sure that I understand, like, where would… where would… where is that data going to be pulling from? The transcripts, from the 8x… I’m just… I’m envisioning that, so can you help me understand that? Yeah, totally.
190 00:31:36.850 ⇒ 00:31:53.149 Pranav: Absolutely. So to answer, like, that initial question of, like, hey, am I going to be able to get as granular as, like, the specific queue, and then specific, topics within that queue, and then even to the CSR themselves? Yes, that’s the idea. We’ll be able to do that.
191 00:31:53.340 ⇒ 00:32:05.110 Pranav: what does that look like from our end, is that a lot of this organization is already taken care of by 8x8, right? Because there’s already the queue system, there’s already the department system, already all the CSRs are…
192 00:32:05.110 ⇒ 00:32:16.050 Pranav: Being grouped by all their transcripts. And so what we’ll do is we’ll just mimic that exact same format on our end, and then if we notice that we need to make certain enhancements, or maybe…
193 00:32:16.050 ⇒ 00:32:27.449 Pranav: maybe things are a little bit too fragmented in 8x8, then we’ll do some condensing there, but to start off, I don’t think there’ll be any issues with just following suit with how 8x8 organizes all the transcripts.
194 00:32:27.860 ⇒ 00:32:28.390 YvetteRuiz: Yeah.
195 00:32:28.640 ⇒ 00:32:30.749 Pranav: So, yeah, that’s where we would begin.
196 00:32:31.190 ⇒ 00:32:33.290 YvetteRuiz: Okay, alright. So…
197 00:32:33.680 ⇒ 00:32:43.510 YvetteRuiz: how do we start pooling that? What does that… what does that look like for us? Like, I don’t know if that’s… is that something that is within what we’re working with? Is that another proposed cost? What is… what…
198 00:32:44.030 ⇒ 00:32:53.500 Pranav: Yeah, so, I mean, that is going to be a longer endeavor, right? Like, so, that’s not something we’ll just, like, flip on in two weeks.
199 00:32:53.500 ⇒ 00:33:05.520 Pranav: However, I think this is the right time to kind of, like, have these conversations, you know, and then we’ll… we’ll talk about, like, okay, what is… what are the future projects that we want to, you know, partner on? And so…
200 00:33:05.630 ⇒ 00:33:20.849 Pranav: what I’ll do is, like, I first wanted to… I’m happy I’m, like, able to kind of talk to you about these things, because I… I know that this will be something of interest to you, so what I’ll do now is, I’ll further refine the exact, like, okay, this is the scope of these projects.
201 00:33:20.850 ⇒ 00:33:29.380 Pranav: And then make sure that, you know, this is something that’s interesting to you. I want your buy-in, first and foremost, to, like, say that, hey, this is going to be useful for me.
202 00:33:29.480 ⇒ 00:33:40.350 Pranav: And then I want you to be critical, too, to say, like, hey, these aren’t as useful, because I’m going to give you a ton of options for what you think is going to be the best things to work on right now. Okay.
203 00:33:40.450 ⇒ 00:33:51.810 Pranav: Yeah, so… yeah, so it sounds like, you know, that sounds super interesting to you. I’ll get started on just, like, really defining what that solution looks like.
204 00:33:51.980 ⇒ 00:34:09.590 YvetteRuiz: Yeah, no, for sure. I mean, especially if it’s… it’s a click of a button where you can search. I mean, obviously, like, you guys… that’s why I wanted just confirmation, okay, how would that search it? And you’re right, I mean, you guys have the access to 8x8, and of course, just tying those metrics, those transcripts, it makes sense. I just didn’t… wasn’t sure, like.
205 00:34:09.590 ⇒ 00:34:16.200 YvetteRuiz: how granular, how that data’s gonna be pulled, and I just wanted a verification of that in. But yeah, no, for sure.
206 00:34:16.560 ⇒ 00:34:17.679 Pranav: Cool, cool.
207 00:34:18.550 ⇒ 00:34:21.729 Pranav: Sounds good to me. So…
208 00:34:21.960 ⇒ 00:34:26.760 Pranav: Yeah, I think the next conversation that we want to have, too, is with Steven.
209 00:34:27.810 ⇒ 00:34:41.760 Pranav: Now, with Steven, I haven’t had a chance to really talk much with Steven. I think maybe he’s joined a couple of our calls, but is there any, like, prep that you want to have for that specific conversation? Maybe a first.
210 00:34:41.760 ⇒ 00:34:46.609 YvetteRuiz: So, I think for Steven, I think for us, we just… we really just want clarity, like, right now.
211 00:34:46.610 ⇒ 00:35:02.639 YvetteRuiz: I… and for me, too, because, of course, I’m just kind of going back and forth with Andy, obviously, making sure that we’ve got it, we’re working on the right things to fix it, to build the trust, to do all that, and of course, then we have the transcripts that we want to talk… we were… we were talking about, but…
212 00:35:03.110 ⇒ 00:35:07.279 YvetteRuiz: We just want to know, like, what are we actively on?
213 00:35:07.680 ⇒ 00:35:26.000 YvetteRuiz: I don’t even know where we’re at with our agreement with Brain Forge, to be honest. Like, I just want… I need to know, like, okay, what is it that we’re paying for today? Just reconfirm… reconfirm, probably for me and for Steven, what is it today that we are paying for, that we are working on, and then what are these projects looking like?
214 00:35:26.830 ⇒ 00:35:27.150 Pranav: Okay.
215 00:35:27.150 ⇒ 00:35:31.310 YvetteRuiz: or what? Like, I think we need clarity on that, Bernab.
216 00:35:31.450 ⇒ 00:35:49.930 Pranav: Gotcha, yeah, yeah. So, quick clarity on just kind of how our contract is currently scoped in as, is that purely based on usage. So, it’s like, if we give you guys… if there’s, like, zero usage to Andy, then, you know, it’s the bottom tier of, like, how much, we get paid on a monthly basis.
217 00:35:49.930 ⇒ 00:35:53.369 Pranav: And so, I really like that type of…
218 00:35:53.820 ⇒ 00:36:11.060 Pranav: kind of tiered mechanism, actually, because it’s like, I can see how passionate you and Janiece are about getting Andy usage up, and it really requires both me and you guys to, like, focus on the same goal. And so that’s… that’s what our current engagement looks like. I think,
219 00:36:11.670 ⇒ 00:36:30.590 Pranav: some of the problems with that current engagement is that it makes us think on a more week-to-week basis instead of a month-to-month basis. Okay. So something like the CSR scorecard, that is something that, you know, I just mentioned that it’s pulling in all these different sources of data. It’s not something that we’re just going to be able to turn on in two weeks. It’s going to be something.
220 00:36:30.590 ⇒ 00:36:30.950 YvetteRuiz: Thank you.
221 00:36:30.950 ⇒ 00:36:45.670 Pranav: that will take at least a month for us to develop on. Maybe not just a month to develop on, but in terms of, like, actually turning it on and having it fully utilized by you and Janiece, it may take a month. And so…
222 00:36:45.770 ⇒ 00:37:01.819 Pranav: I would like to have two different streams, potentially, and, you know, I’ll do some talking, too. It has to be a right arrangement for both us at Brainforge, and then for you guys, too, that, you know, sounds good. I think right now, it’s a really unique contract, right? And I think it’s, it has a lot of benefits.
223 00:37:02.090 ⇒ 00:37:06.270 Pranav: I think we should continue forward with a similar type of structure.
224 00:37:06.270 ⇒ 00:37:16.810 YvetteRuiz: Okay, yeah, and I think that would be good to bring Steven in, is let’s talk about, again, what do we… let’s just reconfirm, let’s get clarity again on what we have today.
225 00:37:17.280 ⇒ 00:37:22.960 YvetteRuiz: you know, what our plan is, what our plan, what’s covered in our plan, right? And…
226 00:37:23.340 ⇒ 00:37:35.580 YvetteRuiz: what’s… what are the tran… are the transcripts part of that? Is that another… I’m… I just… that’s the other thing that Steven and I need clarity on, because, you know, I know we want… you have… we’re working on that.
227 00:37:35.600 ⇒ 00:37:45.750 YvetteRuiz: even with the pop screen with Tim’s part of it, so I just… I just… we really just need a breakdown on this, and then once we know that, then we can really start making decisions on where we need to head.
228 00:37:45.750 ⇒ 00:37:48.700 Pranav: Yeah, that makes sense. So,
229 00:37:49.530 ⇒ 00:38:06.840 Pranav: maybe what we’ll do is, let’s have another conversation tomorrow, like a 30-minute call where I will, you know… because with our current contract, we’re definitely going a little bit beyond what we initially scoped, right? I think transcripts is thought of as, like, let’s create a new,
230 00:38:07.250 ⇒ 00:38:16.360 Pranav: let’s create a new kind of work stream on that front. However, you know, how we’ve kind of defined the contract, too, which is just based on usage, it’s like.
231 00:38:16.360 ⇒ 00:38:30.740 Pranav: transcripts are definitely going to… doing analysis on transcripts is going to increase usage, right? And so, it definitely falls under suit there as well. So, it’s really based on, you know, we know how much things are going to… how long things are going to take.
232 00:38:30.740 ⇒ 00:38:43.769 Pranav: Internally, and so we just want to make sure, like, everything makes sense in terms of, like, how we’re structuring the contract. And I think in the future one, that we’ll sign, because I think this one ends at the end of this month.
233 00:38:43.770 ⇒ 00:38:45.149 YvetteRuiz: That’s what I was… yeah.
234 00:38:45.150 ⇒ 00:38:45.690 Pranav: Yeah.
235 00:38:45.690 ⇒ 00:38:48.790 YvetteRuiz: that Utem had mentioned when we met last time.
236 00:38:48.790 ⇒ 00:39:03.540 Pranav: Yeah, yeah, so this one ends at the end of this month, so I think this is the perfect time to talk about, hey, this one ends at this month, this is kind of, like, what we… this is how we see, like, a partnership going forward. Yep. And so, yeah, let’s have another 30-minute call tomorrow, and then…
237 00:39:03.540 ⇒ 00:39:03.910 YvetteRuiz: Okay.
238 00:39:03.910 ⇒ 00:39:14.959 Pranav: Make sure that… because it sounds like, you know, you want to feel more clear about, like, okay, what does the structure look like going forward? Does that sound good to you? And then we can bring Steven in to, like, get another cosign on that.
239 00:39:14.960 ⇒ 00:39:34.229 YvetteRuiz: Yeah, no, for sure, because that’s… that is definitely one thing that I know that they’ve been wanting the clarity on, because I know we’ve been talking about transcripts for a minute, and I know that we were trying to get Andy going, and then the transcript piece of it, which, you know, I know we’re… we’re still working through that, but is that… when are we going to get that? Is that part of what we’re… we’re… we’re…
240 00:39:34.230 ⇒ 00:39:36.470 YvetteRuiz: Currently, in our agreement.
241 00:39:36.470 ⇒ 00:39:42.399 YvetteRuiz: And then, of course, now, what does that mean with when we add scorecards, the board, what… I don’t know.
242 00:39:43.180 ⇒ 00:39:50.970 Pranav: Yeah. Yeah, so… with this next, like, scope of work that we create for you guys, like, that will all be clearly defined, like.
243 00:39:50.970 ⇒ 00:39:51.540 YvetteRuiz: Okay.
244 00:39:51.540 ⇒ 00:40:03.140 Pranav: Within this contract, we will have these things completed, and then we’ll clearly define, like, hey, these are things, you know, that we’ve kind of talked about that seem interesting, but they’re not in this scope of work.
245 00:40:03.140 ⇒ 00:40:17.179 YvetteRuiz: Okay, perfect. That’ll be excellent. And then, of course, when we’re talking to them, what… and I will know, I mean, that’s where we could talk to, okay, what benefits are those going to bring us that enhance any… all those things that tie together?
246 00:40:17.470 ⇒ 00:40:33.739 Pranav: Exactly, yeah. And so, I’ll draft up all of that stuff, I’ll have that ready for you tomorrow for our 30-minute conversation, and then I think it would be great if we could have, like, some time, even if you want to schedule it right after this call with Steven at some point, like, on Thursday or Friday.
247 00:40:33.740 ⇒ 00:40:36.039 YvetteRuiz: I have to be Friday, because he’s out.
248 00:40:36.080 ⇒ 00:40:54.539 YvetteRuiz: Steven just… the reason Steven hasn’t been, a lot in the meetings is he… he recently got a promotion on him before we told you, but he recently got a promotion, so now he’s not just branch manager of San Antonio, he’s branch manager over all our Corpus, College Station, RGB, our other locations, so he’s… his schedule’s changed a lot.
249 00:40:54.700 ⇒ 00:41:05.340 Pranav: Gotcha, okay, okay, that sounds good. Well, yeah, let’s… if Friday works best for him, then let’s do that. If it’s, we have to push to Monday, too, no big deal, but let’s just try to get that on the calendar as soon as possible.
250 00:41:05.610 ⇒ 00:41:09.319 YvetteRuiz: Okay, and will you send an invite for us for tomorrow at 30 minutes, or… Yep.
251 00:41:09.320 ⇒ 00:41:10.199 Pranav: I’ll do that.
252 00:41:10.200 ⇒ 00:41:20.729 YvetteRuiz: Go ahead and do that, and then I’ll reach out to Steven, today, just to kind of plan to see what day would be available, what would be best for him, either Friday or Monday, and then I’ll let you know tomorrow so we can get it all scheduled.
253 00:41:21.120 ⇒ 00:41:22.849 Pranav: Perfect. Sounds good, Yvette.
254 00:41:23.050 ⇒ 00:41:29.869 YvetteRuiz: Alright, well, I appreciate your time, and talking through all this with me. I really do appreciate that.
255 00:41:29.870 ⇒ 00:41:41.010 Pranav: Cool, yeah, and I’m so happy to have these one-on-ones more frequently, so feel free to ping me on Slack, too, if you ever just want to, like, you know, you hop out of a meeting with somebody else, and you’re like, hey, I need more clarity on this, like, we can hop in a huddle, too. That works.
256 00:41:41.010 ⇒ 00:41:41.620 YvetteRuiz: Okay.
257 00:41:41.880 ⇒ 00:41:45.440 YvetteRuiz: Sounds good. Alrighty, thank you so much, Bernav. I hope you have a good rest of your day.
258 00:41:45.590 ⇒ 00:41:46.280 Pranav: You too.
259 00:41:46.280 ⇒ 00:41:47.250 YvetteRuiz: Okay, bye.