Meeting Title: ABC x Brainforge KPI and SOP Sync Date: 2026-04-21 Meeting participants: JanieceGarcia, read.ai meeting notes, YvetteRuiz, Pranav, Uttam Kumaran
WEBVTT
1 00:00:43.030 ⇒ 00:00:45.009 YvetteRuiz: Doop-doop, dip, dupe!
2 00:00:46.860 ⇒ 00:00:49.629 YvetteRuiz: I’m trying to understand… we received a tech…
3 00:00:49.890 ⇒ 00:00:54.689 YvetteRuiz: technical complaint regarding Diana. To clarify, please provide the customer’s
4 00:00:55.670 ⇒ 00:01:01.589 YvetteRuiz: Last name. The ETA and dispatch call, as this is standard process.
5 00:01:02.390 ⇒ 00:01:03.950 YvetteRuiz: I don’t understand that.
6 00:01:08.060 ⇒ 00:01:09.269 JanieceGarcia: Very funny.
7 00:01:12.140 ⇒ 00:01:14.599 YvetteRuiz: Did you read that complaint that she sent on Diana?
8 00:01:14.880 ⇒ 00:01:15.530 JanieceGarcia: I did.
9 00:01:15.530 ⇒ 00:01:17.700 YvetteRuiz: I’m not following, though.
10 00:01:17.980 ⇒ 00:01:19.450 JanieceGarcia: So, what I’m putting…
11 00:01:19.450 ⇒ 00:01:20.570 YvetteRuiz: She knew wrong.
12 00:01:21.550 ⇒ 00:01:27.910 JanieceGarcia: If you… my whole thing, one, it’s not that she was asking for the account number, if you can’t find it.
13 00:01:28.040 ⇒ 00:01:32.510 JanieceGarcia: okay, looking on his schedule, looking at just Trevino, right?
14 00:01:32.990 ⇒ 00:01:40.850 JanieceGarcia: But… and if you look at the two, she’s asking one for one tech the account number, but the other tech, she’s not.
15 00:01:41.200 ⇒ 00:01:53.479 JanieceGarcia: So she’s not treating the texts the same. I get that. But my problem with that was I’m not daytime dispatch, I’m not Tara or Cass, I’m working on multiple things and answering all the calls.
16 00:01:56.070 ⇒ 00:02:07.110 YvetteRuiz: I don’t like that response, but I guess I’m still not following where she’s adding, like, the question, because I’m like, Robert Tomez, that first one, Trevino 20, like…
17 00:02:07.320 ⇒ 00:02:08.899 YvetteRuiz: How am I supposed to know that?
18 00:02:09.840 ⇒ 00:02:14.410 JanieceGarcia: Right, yeah. I would think we need an account number, and are you saying 20-minute ETA?
19 00:02:14.600 ⇒ 00:02:27.569 YvetteRuiz: Yeah, like, I don’t know that. I don’t know, and maybe she’s… Yeah, they’re trained to learn, like, know that lingo. I don’t know, and so I don’t see any… I mean, the bottom part, yeah, but, you know, she shouldn’t have responded that way, but at the same time, like.
20 00:02:28.170 ⇒ 00:02:39.179 YvetteRuiz: like, he, you know, she tried to ask for an account number, please, or are you there? Please send the account number, please always send an account number with ETA,
21 00:02:40.440 ⇒ 00:02:41.319 YvetteRuiz: Right, he was…
22 00:02:41.320 ⇒ 00:02:42.329 JanieceGarcia: Very vague.
23 00:02:42.510 ⇒ 00:02:45.199 YvetteRuiz: Yeah. Hi, Pranav! And so, I don’.
24 00:02:45.200 ⇒ 00:02:45.779 Pranav: There we go.
25 00:02:45.780 ⇒ 00:02:47.600 YvetteRuiz: The other one at the bottom.
26 00:02:47.810 ⇒ 00:02:48.750 JanieceGarcia: -
27 00:02:48.750 ⇒ 00:02:50.299 YvetteRuiz: How are you doing?
28 00:02:50.700 ⇒ 00:02:53.450 Pranav: I am good, I’m good. It’s been a busy weekend.
29 00:02:53.930 ⇒ 00:02:55.550 YvetteRuiz: Has it?
30 00:02:55.550 ⇒ 00:03:04.279 Pranav: Yeah, yeah, and then the weather, it was okay the last couple days, because it was nice and cool, but now it’s pouring, so… not the best.
31 00:03:04.280 ⇒ 00:03:05.709 YvetteRuiz: Gosh, I know.
32 00:03:06.200 ⇒ 00:03:09.809 YvetteRuiz: We… yesterday, we had, like, non-stop rain in San Antonio.
33 00:03:10.770 ⇒ 00:03:11.440 Pranav: Really?
34 00:03:11.710 ⇒ 00:03:16.659 YvetteRuiz: Yeah, like, non-stop. We were like, how are we gonna get home?
35 00:03:16.660 ⇒ 00:03:18.629 JanieceGarcia: Yeah, you bet, and I swam home.
36 00:03:18.970 ⇒ 00:03:30.290 YvetteRuiz: Oh, wow. Yeah. I know, and then I was like, I should have emailed you guys last night, but I was like, well, maybe it would… you know what I mean? Like, it would be better, but then this morning, I was like.
37 00:03:30.580 ⇒ 00:03:31.980 YvetteRuiz: I don’t know.
38 00:03:32.780 ⇒ 00:03:41.910 Pranav: No problem. I think next time it’ll be better, too. We can plan something, we can plan it so, like, it works for, like, longer in the day as well, so… I think…
39 00:03:42.210 ⇒ 00:03:43.849 Pranav: I’ll be back soon enough, you know?
40 00:03:44.690 ⇒ 00:03:45.349 Pranav: I’m not…
41 00:03:45.350 ⇒ 00:03:52.510 YvetteRuiz: Thank you for the gift, that was sweet, although I won’t be there to get it, but thank you!
42 00:03:52.660 ⇒ 00:03:55.040 Pranav: I’m sure… I’m sure the office will appreciate it, you know?
43 00:03:55.040 ⇒ 00:04:02.199 YvetteRuiz: Yeah, I was gonna, I was gonna chat my leaders and say, hey, this is coming, y’all enjoy. Yeah. Send me a.
44 00:04:02.200 ⇒ 00:04:02.560 Pranav: Absolutely.
45 00:04:03.300 ⇒ 00:04:05.799 Pranav: Yeah, exactly, I was gonna say, have them send a picture, that’d be fun.
46 00:04:06.810 ⇒ 00:04:10.200 JanieceGarcia: Well, I can bring… did y’all say flowers, Prunov?
47 00:04:10.260 ⇒ 00:04:11.460 Pranav: Are there? Yes.
48 00:04:11.460 ⇒ 00:04:15.999 JanieceGarcia: So, I can bring her flowers back Thursday, because I’m still going… I’ll go Thursday.
49 00:04:16.890 ⇒ 00:04:21.830 JanieceGarcia: I have to go Thursday for meetings, but, I’ll bring the flowers back.
50 00:04:22.440 ⇒ 00:04:22.980 JanieceGarcia: For her.
51 00:04:23.220 ⇒ 00:04:23.890 Pranav: That’s awesome.
52 00:04:23.890 ⇒ 00:04:25.370 YvetteRuiz: Thank you.
53 00:04:25.370 ⇒ 00:04:26.890 JanieceGarcia: I’m trying to keep them alive, I don’t have.
54 00:04:26.890 ⇒ 00:04:31.540 YvetteRuiz: Hi, Utah!
55 00:04:31.540 ⇒ 00:04:33.560 Uttam Kumaran: Hi! How’s everyone doing?
56 00:04:33.560 ⇒ 00:04:36.549 YvetteRuiz: Good! Haven’t seen you in a minute, and…
57 00:04:36.550 ⇒ 00:04:49.940 Uttam Kumaran: I know, we were excited, but it’s kind of crazy weather, like, this year, so I totally understand. Even yesterday, we were driving back, and it was kind of, like, pretty dangerous out, so… I’m sure there’s a lot of accidents this morning.
58 00:04:50.730 ⇒ 00:04:56.599 YvetteRuiz: We had a lot of flooding areas for Sherrosheim Pernav. I mean, yesterday, we got nothing but non-stop rain yesterday.
59 00:04:56.600 ⇒ 00:04:57.520 Uttam Kumaran: Yeah, in San Antonio.
60 00:04:57.520 ⇒ 00:05:03.180 YvetteRuiz: Yeah, so… and then this morning, there was a lot of areas where it was… it was crazy flooding.
61 00:05:03.180 ⇒ 00:05:05.279 JanieceGarcia: It was bad. Wow.
62 00:05:05.610 ⇒ 00:05:09.939 Uttam Kumaran: Well, yeah, I hope you get to see the flowers sometime this week, with some of the cookies.
63 00:05:09.940 ⇒ 00:05:10.779 YvetteRuiz: I was like…
64 00:05:10.780 ⇒ 00:05:13.139 Uttam Kumaran: Some folks should eat some of the cookies today. We bought a…
65 00:05:13.140 ⇒ 00:05:19.119 YvetteRuiz: Oh, yeah, I was gonna tell… I was telling Pranav, I said, I’ll tell my team there, they can’t… Janiece said, I’ll bring back your flowers. I was like, oh, send me.
66 00:05:19.120 ⇒ 00:05:23.109 Uttam Kumaran: Keep a couple cookies to yourself, you know?
67 00:05:23.110 ⇒ 00:05:24.830 JanieceGarcia: He’s gone by the time.
68 00:05:24.830 ⇒ 00:05:31.669 Uttam Kumaran: That’s what I thought, I was like, oh my gosh, I don’t feel bad because it’s for everybody, but I’m like, leave her one cookie.
69 00:05:34.330 ⇒ 00:05:37.519 YvetteRuiz: Oh, my goodness, that’s funny.
70 00:05:37.740 ⇒ 00:05:39.269 YvetteRuiz: And alright, so what…
71 00:05:39.270 ⇒ 00:05:42.830 Pranav: the rest of the week, right? Today is your last day for the week?
72 00:05:42.830 ⇒ 00:05:45.810 YvetteRuiz: Yeah, today’s my last day, then I’ll be out the rest of the week.
73 00:05:45.810 ⇒ 00:05:48.030 Pranav: Okay, gotcha.
74 00:05:48.420 ⇒ 00:05:57.220 Pranav: Yeah, so, KPI meeting, we kind of talked about a little bit. We can talk a little bit about that if you have, anything you wanted to discuss there.
75 00:05:58.790 ⇒ 00:06:00.210 YvetteRuiz: On the KPI stuff?
76 00:06:00.210 ⇒ 00:06:01.710 Pranav: On the KPI stuff, yeah.
77 00:06:02.390 ⇒ 00:06:04.709 YvetteRuiz: Yeah, so, I mean, I think…
78 00:06:04.950 ⇒ 00:06:13.950 YvetteRuiz: I sent you over just kind of my email and kind of what I’m trying to really start gathering, that type of data.
79 00:06:13.950 ⇒ 00:06:29.690 YvetteRuiz: And so, I’m just anxious to really start getting that going whenever, you know, we can start working on building that part of it, for sure. Because, like, we talked again on Friday, it would be really good to go back to the trainers and go back through, you know, it’s like we talked about, like, the…
80 00:06:29.690 ⇒ 00:06:44.000 YvetteRuiz: the questions that they’re asking, you know what I mean? Like, okay, how many of those are thumbs up? How many of those were thumbs down? And what percentage of that is on us, you know what I mean? That we don’t have the correct information.
81 00:06:44.000 ⇒ 00:06:53.989 YvetteRuiz: And then, of course, where’s the other delay? Because I think having big visuals, I mean, the actual visual on that is really going to help them, kind of, okay, what do we need to clean up?
82 00:06:54.310 ⇒ 00:07:10.249 Pranav: Right, and I think what we started with these triage tickets is, that daily memo that’s gonna come out, and we can just continue to expand on that. Already right now, as we’re capturing the daily thumbs up and thumbs down.
83 00:07:10.280 ⇒ 00:07:16.599 Pranav: But what we can also expand that to be is, where is it getting caught in the different stages of the triage ticket? So…
84 00:07:16.600 ⇒ 00:07:33.409 Pranav: I think that’s what we decided was gonna be more accurate than just thumbs up and thumbs down, because, you know, certain things that go through triage, they are on us, sometimes they’re on zip code updates to be made, and sometimes they’re actually not on Andy, they’re actually on incorrect prompting.
85 00:07:33.490 ⇒ 00:07:51.790 Pranav: And so, yeah, Casey’s actually working on that effort right now to just continuously update this. We’ll have an update for you. I’ll forward, actually, some of these internal messages that we’re using to just test out these, daily memos into the external channel, so you can take a look and then give a little bit of feedback there.
86 00:07:51.880 ⇒ 00:07:52.920 Pranav: Okay.
87 00:07:53.110 ⇒ 00:07:58.700 Pranav: So… Yeah, I think we’re… I think we’re aligned on that. I did… I did see that email,
88 00:07:58.820 ⇒ 00:07:59.950 Pranav: And…
89 00:08:00.400 ⇒ 00:08:09.610 Pranav: Yeah, so… one other thing as well that we talked about last week was just, like, the individual SOPs for how to use Andy.
90 00:08:09.610 ⇒ 00:08:11.019 YvetteRuiz: Right? Yes. And that’s…
91 00:08:11.020 ⇒ 00:08:17.560 Pranav: And I think right now, department to department, there is some… there is some fluctuation in terms of…
92 00:08:17.810 ⇒ 00:08:29.459 Pranav: which one… which departments are asking more, like, maybe zip code questions versus central docked questions? And, we can give more, like, tailored procedures about how they can use ANDI.
93 00:08:29.460 ⇒ 00:08:43.670 Pranav: And then that’ll all be based off of, too, what we’re noticing with the current usage, right? I think it was really eye-opening last week, Janiece, when we had that meeting with, with, like, the… with dispatch and Mechanical.
94 00:08:43.679 ⇒ 00:08:44.859 JanieceGarcia: mechanical.
95 00:08:44.860 ⇒ 00:08:49.710 Pranav: Yeah, exactly. Because we noticed that there were some prompts in there that were just…
96 00:08:50.310 ⇒ 00:08:54.879 Pranav: they’re not going to be enough context for Andy to, like, retrieve the information.
97 00:08:54.880 ⇒ 00:08:55.890 YvetteRuiz: Right, yep.
98 00:08:56.290 ⇒ 00:09:05.570 Pranav: Yeah, so if we can, you know, write that out, like, explicitly in the… in the procedure document, I think…
99 00:09:05.930 ⇒ 00:09:14.420 Pranav: Then, if it happens again, you’ll be able to reference, hey, this is, like, the procedure you guys need to go by, and it’ll just help with training everybody.
100 00:09:14.640 ⇒ 00:09:22.700 YvetteRuiz: Yeah, and that’s what I was gonna say. I think… I think we gotta dial that a little bit back, and this is the conversation I’ve been having with Janiece, is we just kind of went into it.
101 00:09:22.700 ⇒ 00:09:35.900 YvetteRuiz: Because we just started building, and we wanted all hands in there, and everyone’s giving us feedback and everything. Right. But now that we’ve gotten to where we’re at today, right, we’ve gotten all the platforms laid out, we’ve gotten their speed, accuracy has, you know.
102 00:09:35.900 ⇒ 00:09:40.779 YvetteRuiz: we’ve ramped it up, it’s really working. Now we really gotta take it back and say, okay.
103 00:09:40.780 ⇒ 00:10:05.759 YvetteRuiz: now we’re gonna train you, because no one’s really gotten, you know what I mean? Like, yes, they’ve gotten… they know how to go use Andy, they… but no one’s actually… if we’re gonna build SLPs, like, I need you to understand, why is Andy here? Like we talked about, why is Andy here? Why is it important that we use it? What’s the goal behind Andy? And so, when we built those SLPs, I do feel like the trainers, it would be a time for you guys to schedule a meeting with the trainers, just like we do any other Lunch and Learn, or any training, and we would go through those.
104 00:10:05.760 ⇒ 00:10:18.039 YvetteRuiz: then that way they know exactly, and they’re asking those questions, because what… I think that’s more proactive versus, okay, if someone’s not doing it right, we’re going to address them. We just train them right then and then. That’s just kind of the way I’m thinking.
105 00:10:18.560 ⇒ 00:10:38.529 JanieceGarcia: We’ve actually talked about that, Yvette, and I did talk to Pranav about that. I don’t know if it was last week, but we, we had a side chat about it as well, and doing… whether it was, like, a lunch and learn, or doing, like, little breakout sessions to actually go over the SOPs with them, and then train them on that.
106 00:10:38.530 ⇒ 00:10:47.170 JanieceGarcia: So we did… that is definitely in discussion, so once we get all of those done and set up, and then you approve them, then I’ll schedule that with data.
107 00:10:47.660 ⇒ 00:10:59.730 YvetteRuiz: That’s perfect, because even, like, we talked about the new hires, too, right? Yes. Like, what is… how… we already have our new hire training platform, right? That’s 7 to… what is it? 5 to 7 weeks, right?
108 00:10:59.730 ⇒ 00:11:00.270 JanieceGarcia: I mean…
109 00:11:00.270 ⇒ 00:11:25.219 YvetteRuiz: And Uta, if you remember, that was one of the original things that we said, okay, we want to kind of break… lower that time, right? And so, how are we incorporating that into new hire training today? And I think Tara can give you some really good feedback on that, because that… she was the first person I challenged. I said, okay, you’re bringing your person on, I want to know what the difference is, the ramp-up time, now that you… you’re
110 00:11:25.220 ⇒ 00:11:34.069 YvetteRuiz: very confident in Andy, how Chelsea’s using it, and so she’s kind of really taken that and ran with that.
111 00:11:34.210 ⇒ 00:11:37.350 YvetteRuiz: It’s how… and so, when we hire someone.
112 00:11:37.620 ⇒ 00:11:45.659 YvetteRuiz: what does that look like from day one, introducing Andy into, to the new hires, and how they’re going to be dependent on it?
113 00:11:45.870 ⇒ 00:11:55.399 Pranav: Yeah, how often are you guys having new hires? Like, is there someone coming down the line right now? Because it would be a good case study, really, to just assess.
114 00:11:55.400 ⇒ 00:12:01.760 Uttam Kumaran: Yeah, do you guys pace… do you guys pace it out? Like, you know, we tried to… I mean, we’re trying to have people come in and just, like.
115 00:12:02.250 ⇒ 00:12:06.760 Uttam Kumaran: End of the month, or, like, a certain day to do all the training and onboarding.
116 00:12:07.450 ⇒ 00:12:09.300 YvetteRuiz: Yes, so…
117 00:12:09.620 ⇒ 00:12:17.919 YvetteRuiz: how often is… we always look at forecasts, right? Okay, are we gonna need head count? So, like, we were… last year, we were looking into 2026 forecasts. We already knew.
118 00:12:17.940 ⇒ 00:12:40.089 YvetteRuiz: you know, how many people we needed. We really didn’t need anyone going in, but we have, at risk, and when we have the turnover, that’s when we got to, and right now, that’s kind of what we’re dealing with right now. Not really turnover, but people that are going into other open positions, so we have a couple positions that are being replaced today. But to answer your question, yeah, we have certain days that they come in. What is it? We have them start on…
119 00:12:40.490 ⇒ 00:12:47.770 JanieceGarcia: We would like them to start on either the 1st or, no, the second or the fourth week.
120 00:12:47.880 ⇒ 00:12:51.460 JanieceGarcia: Because we have basic week setting…
121 00:12:51.670 ⇒ 00:12:56.879 JanieceGarcia: Around those same times, but then we also have to think about our orientation, because our orientation as a whole
122 00:12:56.940 ⇒ 00:13:12.280 JanieceGarcia: for ABC is always the third Wednesday of the month, so we try to make it to where that very first week that they come in, they’re with the manager, they’re getting to know their manager, their team, who their trainer is, their schedules, all the…
123 00:13:12.490 ⇒ 00:13:32.250 JanieceGarcia: fun stuff, but HR stuff too, you know, all the policies. And then the next week, they’re actually with me in Basic Week. So, with that, I do have one that will be in Basic Week next week, and then I myself, and I know there’s another department, commercial is hiring.
124 00:13:32.680 ⇒ 00:13:35.009 JanieceGarcia: I don’t know if anybody else is… oh, Shannon’s hiding.
125 00:13:35.010 ⇒ 00:13:53.960 YvetteRuiz: Yes, we have, we have several. We have several. We have Shannon’s hiring, commercials hiring, mechanical’s hiring, so we have three people, and Dispatch is hiring, actually. All four of them are hiring. So we will have a group, we’ll aim to hire them all during that period, so then that way we get them to start basic week, and Basic Week is pretty much covering
126 00:13:54.000 ⇒ 00:13:55.790 YvetteRuiz: Everything that’s universal.
127 00:13:55.790 ⇒ 00:14:19.429 YvetteRuiz: You know what I mean? Before they go into their trade specifics. So, I think in basic week, just like we’ve done with everything else, we would have a day that would be focused on Andy, and maybe it’s not a day, maybe it’s half a day that’s focused on Andy to go in there and introduce, what is Andy? How does it work, what questions are we asking, all that. Just like, you know, we have, like, 8x8. Everyone uses the 8x8 system, so they gotta go through the same training, right?
128 00:14:19.430 ⇒ 00:14:25.510 YvetteRuiz: Oh, by the way, everybody’s gotta know, what is, oh, by the way, how do we put more leads in? So I would think Andy would be part of that.
129 00:14:25.930 ⇒ 00:14:26.920 YvetteRuiz: basic week training.
130 00:14:27.970 ⇒ 00:14:29.809 YvetteRuiz: Because then they would get the…
131 00:14:30.480 ⇒ 00:14:33.499 YvetteRuiz: The best OP, or, you know, all the how-tos.
132 00:14:33.980 ⇒ 00:14:46.550 Pranav: Yeah, totally. When is it that they will, like, kind of get started on, you know, taking calls and, like, really getting into, like, their normal day-to-day? I’m asking this just so, like.
133 00:14:46.750 ⇒ 00:15:01.959 Pranav: maybe if some… Janice, you just mentioned someone will be starting next week. I think it would make sense for us to maybe just, like, pay closer attention to their usage, potentially their transcripts that are coming in. Okay. Just so we can assess, like, hey.
134 00:15:02.540 ⇒ 00:15:16.549 Pranav: where are their… where are the gaps currently? Because at that point, we will already, have the SOP sent out to them. They should have been able to review it by that point. But what we’ll be able to see is, okay, where are the… where are still the gaps?
135 00:15:16.600 ⇒ 00:15:33.940 Pranav: right now, with all of the CSRs and the trainers, like, we’ve kind of… they’ve been slowly, like, learning about Andy, right? But this is kind of a net new person, so it’d be interesting to see, like, how can we just, like, give them every… all the context of Andy as, like, as quickly as possible.
136 00:15:34.070 ⇒ 00:15:40.370 Pranav: Yeah, so, yeah, I guess basically my question is just, like, how… at what point, is it, like, week 2, week 3, that they would start taking calls?
137 00:15:40.990 ⇒ 00:15:42.700 YvetteRuiz: Normally 4. Let me share this with you.
138 00:15:43.030 ⇒ 00:15:43.680 YvetteRuiz: But…
139 00:15:46.390 ⇒ 00:15:51.200 JanieceGarcia: And that’s inbound calls, because usually outbound, I’ve seen them pretty quick.
140 00:15:51.200 ⇒ 00:15:52.509 YvetteRuiz: Can y’all see my screen?
141 00:15:52.510 ⇒ 00:15:53.349 JanieceGarcia: Yes, sir. Yup.
142 00:15:53.350 ⇒ 00:15:55.520 YvetteRuiz: I don’t know what y’all are seeing, what are y’all seeing?
143 00:15:56.580 ⇒ 00:15:57.110 JanieceGarcia: here.
144 00:15:57.430 ⇒ 00:15:59.810 JanieceGarcia: other training, schedule.
145 00:16:03.410 ⇒ 00:16:07.390 YvetteRuiz: Yeah, so… Week 2…
146 00:16:07.850 ⇒ 00:16:20.140 YvetteRuiz: Okay, so week 3 is normally hands-on, outbound, and monitoring, so week 3 is when they’re already starting to take phone calls, and they’re getting into… or is… are they still shadowing at this point, Janiece?
147 00:16:20.560 ⇒ 00:16:20.940 JanieceGarcia: They’re just.
148 00:16:20.940 ⇒ 00:16:21.369 YvetteRuiz: No, no.
149 00:16:21.370 ⇒ 00:16:22.040 JanieceGarcia: Outbound.
150 00:16:22.040 ⇒ 00:16:27.159 YvetteRuiz: It is 4, I’m sorry, so at week 4, that’s when they’re put in at a 20%
151 00:16:27.220 ⇒ 00:16:42.980 YvetteRuiz: skill set to take inbound calls, so we don’t put them, like, at the highest level, we put them little by little into the queues, so then that way we can start introducing them to the calls that are coming in. So, maybe during this point, and again, I’m hoping to shave off more time.
152 00:16:42.980 ⇒ 00:16:57.219 YvetteRuiz: Than it going through, like, this whole 7-week process, because then it’s gonna go… week 4 is gonna be 20, week 5 is gonna be at a 50 scale, and then the last week is gonna be at the highest level, when they’re gonna kind of be left alone.
153 00:16:57.930 ⇒ 00:17:04.300 Pranav: Gotcha. Are you having, like, daily check-ins with them to see how they’re doing, assessing performance, or…
154 00:17:04.560 ⇒ 00:17:18.279 YvetteRuiz: It’s weekly? Okay. Yeah, it’s weekly. Their manager should be checking in every week, getting, how everything’s going, getting the feedback from there. And that’s what Tara’s done with Chelsea. She was the first one that went in there, so…
155 00:17:18.589 ⇒ 00:17:26.410 YvetteRuiz: during week 2, she was sitting along with her and seeing how she was navigating asking Andy questions and utilizing him.
156 00:17:27.380 ⇒ 00:17:39.019 Pranav: That’s good to know. I wonder, Janice, do you think that there’s a specific department that would make sense for me to maybe join in on those? If possible, join in on those weekly check-ins?
157 00:17:40.810 ⇒ 00:17:41.879 JanieceGarcia: I mean, I think…
158 00:17:41.880 ⇒ 00:17:42.470 Pranav: Yeah.
159 00:17:42.740 ⇒ 00:17:59.100 JanieceGarcia: Tara would be a good one. Tara’s very, very, very involved, and she’s very involved with Andy. She’s always been. Perfect. But if anything, I mean, Eva, I don’t know if you think of anybody different, but I think Tara would be perfect for that.
160 00:17:59.320 ⇒ 00:18:00.430 JanieceGarcia: True.
161 00:18:00.430 ⇒ 00:18:04.499 YvetteRuiz: I agree. Tara, the mechanical group is always our…
162 00:18:04.800 ⇒ 00:18:20.140 YvetteRuiz: people that we go test with, just because they’re so disciplined, you know what I mean? Like, they go through it, and they really do provide a lot of good feedback. I mean, everything that we’ve kicked off, we’ve started with them, because she gets her team going. And so if we can start off with her, it’d be great.
163 00:18:20.750 ⇒ 00:18:26.040 Pranav: Awesome. Okay, I’ll, should I reach out to Tara, then? I can, I can set that up,
164 00:18:26.410 ⇒ 00:18:32.750 Pranav: So, in the check-ins, they’ll start on week 4? Or will they stack… will they, start week 1?
165 00:18:32.750 ⇒ 00:18:34.849 JanieceGarcia: She’s probably already started her chickens.
166 00:18:34.850 ⇒ 00:18:39.570 YvetteRuiz: They should have a… when they’re new hires, it should be every week. Every week that they’re in training, they should be.
167 00:18:39.570 ⇒ 00:18:39.900 Pranav: Okay.
168 00:18:39.900 ⇒ 00:18:41.150 YvetteRuiz: with them weekly.
169 00:18:41.150 ⇒ 00:18:46.099 Pranav: Okay, that’s great. I’ll check in with Tara, then, and see if I can join in on some of that.
170 00:18:47.040 ⇒ 00:18:47.560 YvetteRuiz: Yeah.
171 00:18:47.560 ⇒ 00:18:56.770 Pranav: Yeah. Okay, cool. And then, yeah, Yvette, probably we’ll loop you in into the SOP once you’re back, but Janiece, if we can,
172 00:18:56.800 ⇒ 00:19:13.899 Pranav: if it makes sense to have some of those meetings with, the department trainers this week, that could be… that could be good. We’ll have those SOPs out very shortly, and so we can even start as of this week, to start, you know, just sending those out to the individual departments.
173 00:19:14.220 ⇒ 00:19:32.900 JanieceGarcia: Well, once you… once you do get them out, can you send them to me so I can understand myself, and then we can go ahead and schedule those? Because that’s what I want to make sure, because I want to do, like, a group session, not just send something out to them, and then be like, what is this? So that’s when we’ll do, like, the breakout sessions that we were talking about.
174 00:19:33.100 ⇒ 00:19:39.819 Pranav: Yeah, and I think we could even have those breakout sessions with me, too. So, like, I can join in. Okay, perfect, perfect.
175 00:19:39.820 ⇒ 00:19:41.990 JanieceGarcia: Absolutely. Let me… I’ll set up some time.
176 00:19:41.990 ⇒ 00:19:46.749 Pranav: You… okay, perfect, perfect. I’ll set up some time with you tomorrow, Janiece.
177 00:19:47.000 ⇒ 00:19:50.870 JanieceGarcia: Okay, what time tomorrow? Because I do have two interviews tomorrow.
178 00:19:51.290 ⇒ 00:19:55.059 Pranav: Okay, my day’s pretty wide open, what time works for you?
179 00:19:55.800 ⇒ 00:20:00.990 JanieceGarcia: I can… I can do anywhere from 8 to 10.
180 00:20:01.160 ⇒ 00:20:06.889 JanieceGarcia: And then I can do… 10.30, 11…
181 00:20:11.330 ⇒ 00:20:15.470 JanieceGarcia: Where’s my… oh, there it is. Nope, can’t do 11. Sorry, take that one back.
182 00:20:15.980 ⇒ 00:20:26.040 JanieceGarcia: And then I can do 12 to 1.30, because my next interview is at 1.30, and then I can switch around, for any time after 2.30.
183 00:20:27.150 ⇒ 00:20:29.109 Pranav: I think 1.30 would be perfect.
184 00:20:29.340 ⇒ 00:20:29.890 JanieceGarcia: Okay.
185 00:20:30.420 ⇒ 00:20:31.770 Pranav: Yeah, let’s do that.
186 00:20:31.930 ⇒ 00:20:32.850 Pranav: Okay, cool.
187 00:20:37.840 ⇒ 00:20:42.389 YvetteRuiz: So, Pranav, going back to the scorecard piece of it, so…
188 00:20:43.170 ⇒ 00:20:51.909 YvetteRuiz: And on the average handle time, I know I kind of shared, you know, a score… the scorecard, kind of our metrics, from our agents.
189 00:20:52.440 ⇒ 00:21:07.439 YvetteRuiz: do we want to start off with the new… well, because the new hire is going to be a two-fold, because then you’re… I mean, well, you’re going to get it from everyone, actually, but new hire, you’re going to get the ramp-up time. Obviously, that’s kind of going to be a goal, like, we have seven weeks, is that going to… did Andy help shave some of that?
190 00:21:08.360 ⇒ 00:21:15.350 YvetteRuiz: And we’ll… we’ll start testing that once we start these couple new hires. But the AHT piece of it.
191 00:21:16.350 ⇒ 00:21:20.050 YvetteRuiz: What is… how do we want to start really looking at that?
192 00:21:21.030 ⇒ 00:21:21.570 Pranav: Yeah.
193 00:21:21.790 ⇒ 00:21:25.680 Pranav: So, all that information right now is in 8x8, right?
194 00:21:25.680 ⇒ 00:21:38.459 YvetteRuiz: Correct. Yeah. Yeah. We could share… we could actually just… I can have David share all that data for you, so you guys aren’t fishing for it. So, like, I could give you… I think I shared… I showed you our scorecard.
195 00:21:38.660 ⇒ 00:21:44.799 YvetteRuiz: I’m just trying to figure out which one’s the best one to share with you overall.
196 00:21:45.010 ⇒ 00:21:57.100 YvetteRuiz: And that’s what I was working with David the last time I spoke with you, because I’m… I want to just give you a snapshot of the key… key KPIs that are going to kind of work hand-in-hand with Andy, and I was thinking, like, okay, do we just do…
197 00:21:57.100 ⇒ 00:22:10.170 YvetteRuiz: all agents and their average handle time, or do we give you the average handle time per the division? So, like, what’s the average handle time for the mechanical department, or do we want to get more granular and look at them per agent?
198 00:22:10.170 ⇒ 00:22:18.200 Pranav: I think we go per agent, just because we’ll just get more data that way, and then we can kind of develop the per-department insights, and then.
199 00:22:18.800 ⇒ 00:22:22.199 Pranav: Even a little bit deeper, like, per trainer.
200 00:22:22.540 ⇒ 00:22:35.730 Pranav: And so, I guess I have two questions, because what we can do with this, which I think will be super beneficial, is on a week-to-week basis, we can show insights, we can show week-over-week trends,
201 00:22:36.100 ⇒ 00:22:44.139 Pranav: that’s a little bit of a bigger effort that I want to talk to you about with… maybe we do that after the department-based insights?
202 00:22:44.140 ⇒ 00:22:44.540 YvetteRuiz: Okay.
203 00:22:44.540 ⇒ 00:22:47.469 Pranav: So we pull in the transcripts, and then, you know, after
204 00:22:47.850 ⇒ 00:22:53.039 Pranav: After we get those weekly reports sent out and automated, then we can maybe work on that.
205 00:22:54.180 ⇒ 00:22:57.370 Pranav: also… and I think maybe what we do…
206 00:22:57.690 ⇒ 00:23:11.399 Pranav: in parallel, is we can do, like, a one-time… a one-time analysis of, all this data, and then we can… what that’ll help us do, too, is just, like, as soon as we finish the department-based insights, we have an easy…
207 00:23:11.400 ⇒ 00:23:18.370 Pranav: head start on doing the weekly report as well for the scorecard. How does that sound, in terms of, like…
208 00:23:18.370 ⇒ 00:23:20.829 YvetteRuiz: I know, I think that that’d be great.
209 00:23:21.000 ⇒ 00:23:24.630 YvetteRuiz: Yeah. That would be great. We… I did a breakdown
210 00:23:24.940 ⇒ 00:23:42.699 YvetteRuiz: because I’m starting to share week to week, now that we’re picking up, I was sharing week to week, differences, okay, how was last week versus this week, right? Call volume, all that, and so we struggled in our pest department, hitting our SLA,
211 00:23:42.930 ⇒ 00:23:46.580 YvetteRuiz: levels, last week, and…
212 00:23:46.580 ⇒ 00:24:04.959 YvetteRuiz: the question was, okay, the previous week I had higher call volume, this week I had not that much higher call volume, but we tanked, okay? And so, doing the exercise, going through, and again, I look at the data regularly, so I already knew what kind of the areas were, but this is where I challenged my teams to go in there and say, okay.
213 00:24:05.190 ⇒ 00:24:18.509 YvetteRuiz: what is going on with that? And, you know, of course, someone will say, well, we have attendance. I’m like, well, your attendance wasn’t that. And so, when you really started drilling down on it, if we would have had our AHT, I mean, you have agents that are taking
214 00:24:18.600 ⇒ 00:24:32.060 YvetteRuiz: high-volume calls, and then you have some that are stuck at 50 calls, right? Had I had my entire staff at this level up here, and so when you start looking and you start drilling down at the lower ones that, you know, you have that big gap.
215 00:24:32.190 ⇒ 00:24:32.760 Pranav: Yeah.
216 00:24:32.760 ⇒ 00:24:38.639 YvetteRuiz: It’s their AHT, it’s their hold times that they’re not doing, and just that… those… that much…
217 00:24:38.640 ⇒ 00:24:52.880 YvetteRuiz: It’s like, what are they doing during that time? And so when I go to data, they’re like, they’re in customer issues. I’m like, okay, they’re struggling with something, and I need to find out what are they struggling with, and I… because I feel like that’s where this big piece is going to connect us.
218 00:24:52.880 ⇒ 00:24:56.839 Pranav: Yeah, so that’s actually really interesting. So, it sounds like on…
219 00:24:57.050 ⇒ 00:25:03.349 Pranav: You kind of go into the data, like, on demand, every once in a while, not on some, like, scheduled interval, per se.
220 00:25:03.960 ⇒ 00:25:20.650 Pranav: There’s some really good use cases for a chat interface for just you to query over this data, to just get the insight immediately. It’s a lot lower effort than having an end-to-end automated approach of, hey, we get a report every week.
221 00:25:20.750 ⇒ 00:25:28.450 Pranav: And it sounds like also sometimes you want to not have to wait until the end of the week to just get the report and the insights.
222 00:25:28.820 ⇒ 00:25:33.100 Pranav: And maybe you want more specific information, too, so… I f-
223 00:25:33.330 ⇒ 00:25:43.979 Pranav: This is really good to know. Actually, Yvette, if you don’t mind, I know we don’t have a ton more time. Do you kind of want to show me where you’re doing this, like, data, like, deep dive?
224 00:25:44.110 ⇒ 00:25:55.489 Pranav: if you don’t mind, like, if you have it ready right now, or maybe we can connect at a later time, but, I just kind of want to see, like, what is the interface that you’re looking to see this data, what does the…
225 00:25:55.490 ⇒ 00:26:02.499 YvetteRuiz: drilled down a couple of things, a couple of, layers. Okay. So let me just show you.
226 00:26:03.510 ⇒ 00:26:04.750 YvetteRuiz: Contact.
227 00:26:05.770 ⇒ 00:26:08.150 Pranav: Is it Google Sheet, or is it 8x8?
228 00:26:08.490 ⇒ 00:26:11.740 YvetteRuiz: It’s through the Google… everything that the data team puts together for me.
229 00:26:12.580 ⇒ 00:26:13.870 Pranav: Okay, okay.
230 00:26:18.300 ⇒ 00:26:21.189 YvetteRuiz: Oh, did I not send this one? Oh, here it is.
231 00:26:22.860 ⇒ 00:26:25.349 YvetteRuiz: I’m gonna share this with you really quick.
232 00:26:25.960 ⇒ 00:26:26.690 Pranav: Perfect.
233 00:26:29.750 ⇒ 00:26:35.310 Pranav: And does the data team, do they update this regularly, or this… you just ask them, or they get back to you?
234 00:26:35.690 ⇒ 00:26:39.019 YvetteRuiz: So there’s… there’s… oh gosh, did I not share my screen?
235 00:26:39.930 ⇒ 00:26:40.550 JanieceGarcia: Thank you.
236 00:26:40.760 ⇒ 00:26:41.520 Pranav: Naya.
237 00:26:41.850 ⇒ 00:26:43.760 YvetteRuiz: Okay, hang on. Dang it.
238 00:26:44.010 ⇒ 00:26:45.140 YvetteRuiz: Understood.
239 00:26:45.360 ⇒ 00:26:47.559 YvetteRuiz: Nowhere is it?
240 00:26:57.990 ⇒ 00:27:00.429 YvetteRuiz: Okay, can you see my screen?
241 00:27:01.080 ⇒ 00:27:04.009 Pranav: It’s loading for me,
242 00:27:04.750 ⇒ 00:27:06.960 Pranav: I think it’s about to share. Yep, there it is.
243 00:27:08.250 ⇒ 00:27:18.280 YvetteRuiz: Alrighty, so… weekly, I get… this report from them, and it shows me the SLA,
244 00:27:18.410 ⇒ 00:27:27.899 YvetteRuiz: For the entire week per department. So, obviously, everything is… in red is every time we did not hit our service level. There’s a lot of red on my board right there, so I’m kind of…
245 00:27:28.030 ⇒ 00:27:42.800 YvetteRuiz: Especially when I have numbers like 69, you know what I mean? Like, how did we get that low, when our service goal is 85? And so, then I, you know, we… I go and I look at, okay, how many calls came in that day?
246 00:27:43.260 ⇒ 00:27:48.919 YvetteRuiz: Every day. And then I look at the AHT, the average handle time for the… for that.
247 00:27:49.170 ⇒ 00:28:00.150 YvetteRuiz: for them for that day, for that month, I mean, for that day, I’m sorry. And then I look at attendance. Well, this is kind of… he did a layer. So, when I start.
248 00:28:00.280 ⇒ 00:28:25.229 YvetteRuiz: looking at that, when I… when I get this overview right here, then I go in there and I peel another layer down, saying, okay, how much staff did I have this time? Give me the… give me the agents, all of them by one by one, and tell me how many calls did they answer each of them, what was their average handle time, how much hold time did they got? So I go to another layer to kind of connect all those dots, and that’s what I work with the data team on. And once we do that whole analysis, and it comes down, and it really
249 00:28:25.230 ⇒ 00:28:30.679 YvetteRuiz: just shows me, okay? I had, during my, my, my, my,
250 00:28:30.680 ⇒ 00:28:39.160 YvetteRuiz: my most impact call volume window, so, like, let’s say between 9 and 10, the majority of my calls came in. I had 5 agents that were offline.
251 00:28:39.370 ⇒ 00:28:53.419 YvetteRuiz: and they were in a status saying customer issue. And so that’s where I can kind of really hone down in and say, okay, had I not had all those people offline doing I don’t know what, I would have not… I would have never gotten to this place right here, right?
252 00:28:53.420 ⇒ 00:28:53.780 Pranav: Right.
253 00:28:53.780 ⇒ 00:28:56.370 YvetteRuiz: Then I go in there and I look at another layer, too, saying.
254 00:28:56.490 ⇒ 00:29:13.939 YvetteRuiz: how many calls did I actually… were actually abandoned? Because that’s a bigger flag for me. If I’m… there’s… it’s one thing if customers are okay and they’re holding, it’s not a great experience, but if they’re dropping because they don’t want to hold, then I have another layer that I’m concerned with. You know, in this instance, we don’t really have an abandoned problem.
255 00:29:13.940 ⇒ 00:29:24.489 YvetteRuiz: I think our callback feature helps a lot, so customers have the availability, like, you can… if you want to… if you don’t want to continue holding, push 1, and we’ll call you back as soon as we’re available.
256 00:29:24.550 ⇒ 00:29:31.090 YvetteRuiz: I digress there. Anyhow, but this is how I start just kind of peeling the onion layer back, to get the information.
257 00:29:31.090 ⇒ 00:29:43.440 Pranav: And so, you’ll… it sounds like you’ll get on a call with the data team, maybe they’re in Excel or Google Sheet with a bunch of data. You’ll tell them, okay, these are the certain metrics that I want to be looking at on a week-to-week basis.
258 00:29:43.640 ⇒ 00:29:49.669 Pranav: Broken down by these departments, for example, and then they kind of assemble it in real time for you.
259 00:29:49.990 ⇒ 00:29:57.919 YvetteRuiz: That’s correct, yeah. So, like, they would already know, like, Brian would… well, Brian would already know, and they’re just down the hall from me, so I just yell at them.
260 00:29:57.920 ⇒ 00:30:15.250 YvetteRuiz: I’m just joking. They’ll come back, and they’ll, like, know, okay, Beth needs a deep dive on this day right here, because she’s going to want to know, why did we tank, right? And they’ll normally lay all that stuff out for me, so they’ll go in there. That’s exactly what I ended up finding, is like, okay, but during this time, we were hot, it wasn’t your attendance.
261 00:30:15.380 ⇒ 00:30:20.600 YvetteRuiz: It was all your people that were offline, or look, This person struggled with
262 00:30:20.700 ⇒ 00:30:26.949 YvetteRuiz: whole time, they had X amount of whole time, things like that, so… but that’s generally how I get it all.
263 00:30:27.340 ⇒ 00:30:30.760 Pranav: Okay, yeah, I think some of this would be…
264 00:30:31.030 ⇒ 00:30:39.549 Pranav: nice to get in real time as well, or closer to real time. It seems like you see… you’re probably noticing a fire, and then…
265 00:30:39.880 ⇒ 00:30:48.529 Pranav: Retroactively, you have to assemble these dashboards to see, okay, diagnosing exactly what went wrong on a certain day, right?
266 00:30:49.060 ⇒ 00:31:08.289 Pranav: Yeah, okay, that’s really interesting to hear. I didn’t know that was exactly, like, your process on… because I think there’s some really good tools that we can build there for you to have, like, real-time alerting, or even just an interface where you can assemble this data just on your own, on the fly, whenever you want.
267 00:31:08.420 ⇒ 00:31:11.769 Pranav: Okay, yeah, let me, let me also do some thinking on that as well.
268 00:31:11.770 ⇒ 00:31:12.760 YvetteRuiz: we…
269 00:31:13.140 ⇒ 00:31:28.159 YvetteRuiz: We were short a person with the data people, because we did have a real-time analyst… a real-time analyst person that was keeping what… he’s the eye in the sky that looks at this on a regular, so we’re not tanking at that point. Obviously, he was not here.
270 00:31:28.160 ⇒ 00:31:29.869 Uttam Kumaran: And so we’ve been operating blindly.
271 00:31:30.290 ⇒ 00:31:36.749 Uttam Kumaran: How often are… were you previously, like, preparing some of those? Is that a weekly basis, or was that a monthly view?
272 00:31:37.070 ⇒ 00:31:38.260 YvetteRuiz: On what? I’m sorry.
273 00:31:38.260 ⇒ 00:31:40.399 Uttam Kumaran: The report that you just shared.
274 00:31:40.400 ⇒ 00:31:41.469 YvetteRuiz: That was weekly.
275 00:31:41.470 ⇒ 00:31:41.850 Uttam Kumaran: Okay.
276 00:31:42.140 ⇒ 00:31:58.630 YvetteRuiz: So, they give us the data every day, so data produces data every day for us for the previous day. Obviously, that’s still reactive, right? You’re not being proactive at that point, because you’re getting it from the next day. But when we have the real time, at least at that point.
277 00:31:58.630 ⇒ 00:32:03.139 YvetteRuiz: That person’s looking at it real time and saying, okay, hey, you have 5 agents off phone.
278 00:32:03.310 ⇒ 00:32:07.609 YvetteRuiz: somebody needs to do something, right? But it’s still not gonna tell me
279 00:32:07.850 ⇒ 00:32:17.429 YvetteRuiz: what are those agents on the… what are they… what’s the reason that they’re stepping away? And this is where I kind of hope that Andy would help me, or the transcripts would help me, like.
280 00:32:17.430 ⇒ 00:32:31.640 YvetteRuiz: What specifically are they being challenged with? Because, you know, if I have an agent that consistently jumps into customer issues, and when I say that, that’s the status that they put themselves on. It’s like, what specific customer issues are they.
281 00:32:32.070 ⇒ 00:32:32.410 JanieceGarcia: Happy.
282 00:32:32.410 ⇒ 00:32:41.969 YvetteRuiz: having problems with, right? Like, they can… that they cannot answer, because that’s gonna help us a whole lot, like, we’re… we need to train. And I know right now, because I’ve actually done sit-alongs.
283 00:32:42.410 ⇒ 00:32:53.519 YvetteRuiz: One of our biggest challenges, and it’s not everything, but one of our biggest challenges is our Evolve software. When we schedule, I mean, you guys are very familiar with ABC and the lines of business that we schedule, right? We have…
284 00:32:54.530 ⇒ 00:33:07.360 YvetteRuiz: you know, tons of technicians, but our software is not easy to go in there and say, okay, I need to reschedule this person. I’m looking at… I have to jump through hoops. That’s kind of why Andy kind of helped us with the zip code thing.
285 00:33:07.690 ⇒ 00:33:19.379 YvetteRuiz: But if we could have a better utility with the scheduling portion of it, you know, that gives us the answer on the fly, it’s gonna shave that really, really quick. So, I’ve already identified, and we’re working on some of those solutions, but
286 00:33:19.380 ⇒ 00:33:29.779 YvetteRuiz: that’s not entirely everything that they, you know, that they’re having challenges with. It’s more of knowledge as well. It’s more of them being able to answer the questions as well.
287 00:33:30.300 ⇒ 00:33:45.159 Pranav: Okay. Yeah, no, that’s very… that’s very good to know. And with that report as well, it’s not getting the context, like you said, of transcripts. It’s just getting statuses that, the reps are putting them on, that are putting themselves on. Yep.
288 00:33:45.260 ⇒ 00:34:00.280 Pranav: However, like, in that report, for example, if you could double-click onto that 69% and see, okay, what was… what were our current staff up to? What was taking their time? I think that would be even more insight.
289 00:34:00.400 ⇒ 00:34:06.760 Pranav: Okay, and then you said Brian is the one? I don’t think I’ve been able to meet Brian yet, so I can… Yeah, great.
290 00:34:06.760 ⇒ 00:34:09.130 Uttam Kumaran: Yeah, Brian and David, probably, yeah.
291 00:34:09.520 ⇒ 00:34:10.050 JanieceGarcia: Okay.
292 00:34:10.920 ⇒ 00:34:16.050 Pranav: Okay, that sounds great. I know we’re a little bit over time, but anything else?
293 00:34:18.040 ⇒ 00:34:19.399 YvetteRuiz: I think that was it.
294 00:34:19.710 ⇒ 00:34:20.100 Pranav: Okay.
295 00:34:20.100 ⇒ 00:34:20.810 YvetteRuiz: For now?
296 00:34:20.960 ⇒ 00:34:25.379 YvetteRuiz: Cool, cool. So when are you… summer, you’ll be back? You’ll be moving to Austin officially?
297 00:34:25.610 ⇒ 00:34:31.619 Pranav: Actually, yeah, so, yesterday I put down a deposit on a… on a place in Austin, so…
298 00:34:31.620 ⇒ 00:34:35.420 Uttam Kumaran: It’s actually the apartment I used to live at, which is, like, very, like…
299 00:34:35.429 ⇒ 00:34:36.389 YvetteRuiz: Oh, cool!
300 00:34:36.389 ⇒ 00:34:45.109 Uttam Kumaran: Very funny. Same building, because I have some friends there, and I was like, you should look at that building, it’s pretty nice, and yeah.
301 00:34:45.360 ⇒ 00:34:47.579 JanieceGarcia: Downtown, or what area?
302 00:34:47.580 ⇒ 00:34:50.870 Uttam Kumaran: Yeah, it’s off of, like, 11th and 35.
303 00:34:51.170 ⇒ 00:34:51.809 JanieceGarcia: So, like, you’re.
304 00:34:51.810 ⇒ 00:34:53.400 Uttam Kumaran: Franklin’s Barbecue.
305 00:34:53.610 ⇒ 00:34:54.100 JanieceGarcia: Yay!
306 00:34:54.100 ⇒ 00:34:54.860 Uttam Kumaran: in the area.
307 00:34:55.469 ⇒ 00:34:57.200 JanieceGarcia: Nice area!
308 00:34:57.200 ⇒ 00:35:00.210 YvetteRuiz: I’m not familiar with Austin, so I have no idea where to talk about.
309 00:35:01.580 ⇒ 00:35:03.719 Pranav: Maybe we do something at Franklin’s Barbecue, though.
310 00:35:03.720 ⇒ 00:35:04.180 Uttam Kumaran: Yeah.
311 00:35:04.180 ⇒ 00:35:06.519 YvetteRuiz: There you go, yeah, there you go.
312 00:35:06.520 ⇒ 00:35:09.640 JanieceGarcia: Can we get in at Franklin’s?
313 00:35:09.640 ⇒ 00:35:14.759 Uttam Kumaran: I’ll go wait in line at 6 AM, and then y’all can meet me, whatever.
314 00:35:14.870 ⇒ 00:35:18.149 Uttam Kumaran: Y’all could meet me at 9am for breakfast barbecue.
315 00:35:18.150 ⇒ 00:35:19.460 YvetteRuiz: Breakfast barbecue.
316 00:35:19.460 ⇒ 00:35:20.090 Uttam Kumaran: Excellent.
317 00:35:20.090 ⇒ 00:35:23.020 YvetteRuiz: They’re gonna be worth it if it’s, like, that poppin’.
318 00:35:23.020 ⇒ 00:35:23.640 Uttam Kumaran: Yeah.
319 00:35:24.500 ⇒ 00:35:25.700 YvetteRuiz: It’s cool!
320 00:35:25.700 ⇒ 00:35:26.560 JanieceGarcia: Yep.
321 00:35:26.560 ⇒ 00:35:31.720 YvetteRuiz: Well, Utam Pranav has done a phenomenal… oh, sorry, Janice, I didn’t mean to.
322 00:35:31.720 ⇒ 00:35:40.140 JanieceGarcia: No, no, no, you’re okay. I can work with Pranav a little bit later, but I want to understand the tickets more, because I want to make sure that I’m not messing anything up in Lanier.
323 00:35:40.450 ⇒ 00:35:41.659 JanieceGarcia: That’s the mind.
324 00:35:41.660 ⇒ 00:36:00.679 Uttam Kumaran: Casey just sent… I think Casey just sent some stuff. I mean, we now have a process daily that sends us, like, a note of what all the new tickets added, and we’re quickly fixing all those, so I think Casey must have just sent some messages today, so I think all the context is in the channel, but please just, like.
325 00:36:01.110 ⇒ 00:36:04.319 Uttam Kumaran: send any questions, but honestly, I would…
326 00:36:04.480 ⇒ 00:36:13.980 Uttam Kumaran: if you’re breaking something, it’s most likely all on us to, like, figure it out. I don’t think you should have, like, too much risk in whatever’s going on there, right, Pran?
327 00:36:14.380 ⇒ 00:36:28.619 Pranav: Yeah, I agree, and I think in Casey’s last message, I just, took a peek at it again. He’s… I think what will be super helpful is that link that he sent to the linear view. That will be a good place for you to kind of live and see, okay, what tickets are on my plate?
328 00:36:28.620 ⇒ 00:36:41.499 Pranav: And the other tickets are really, you know, on other individuals, maybe at Brainforge, maybe, specific trainers, or just for the automation. So, those are… they honestly… this will reduce the clutter a lot for you.
329 00:36:42.090 ⇒ 00:36:49.250 JanieceGarcia: Okay, I just want to make sure that I am reading it right, and it seems like I am, but I will… I’ll… I’m gonna dive in a little bit more. I’m gonna have some more time today, so…
330 00:36:49.490 ⇒ 00:36:51.140 Pranav: Perfect. Perfect, perfect.
331 00:36:52.020 ⇒ 00:37:00.099 YvetteRuiz: Yes, but just Utam, again, Pranav has been amazing. He’s, you know, he’s just really a breath of fresh air. He’s easy to work with.
332 00:37:00.100 ⇒ 00:37:00.650 Uttam Kumaran: Of course.
333 00:37:00.650 ⇒ 00:37:06.979 YvetteRuiz: A lot of… he’s moved the needle on a lot of things, so super appreciative of him, so thank you.
334 00:37:06.980 ⇒ 00:37:31.950 Uttam Kumaran: Yeah, I’m still looking forward to… I mean, I’m sure Pranav will actually be back in Austin sooner than June for some stuff, so we should still try to do something in person, I think, together. And, you know, I was mentioning to Pranav that we’d love to… now that I feel like the next, like, month or so are pretty aligned, our existing contract just goes until end of May, so I want to just hear about, like, okay, how can we continue to just, like, exceed expectations?
335 00:37:32.090 ⇒ 00:37:50.230 Uttam Kumaran: align on if we feel good about, you know, our scope. You know, a couple things that I was talking to Pranav about is, one, you know, we wanted to go deeper on transcripts. Second, we also talked about the voice work that I think we actually can totally enable. I know we just… we’re continuing to do more on
336 00:37:50.230 ⇒ 00:37:53.910 Uttam Kumaran: training and things like that, but I was talking to him about, like, how do we…
337 00:37:54.060 ⇒ 00:38:06.840 Uttam Kumaran: it’s kind of split into two ways, like, one, continuing to work, and then thinking about these ways to, like, just continue to push the needle on what’s possible. Because some of the stuff you guys are doing is actually still very cutting edge.
338 00:38:06.840 ⇒ 00:38:21.489 Uttam Kumaran: even though, like, you know, we’ve been working on it for so long, we still talk to so many customers that are nowhere close. And so, a couple things, I was like, hey, can Andy start getting used across the business, right? And then, so my question for you, Yvette, is that, like.
339 00:38:21.610 ⇒ 00:38:40.269 Uttam Kumaran: Is that something you own? So, I don’t want to put more on your plate, but also, it’s a credit, awesome opportunity, because still, I think, you know, in our work, talking to, Bobby, Bo, I think your team is still the most, like, AI-forward team. So, I think there’s a great opportunity
340 00:38:40.270 ⇒ 00:38:44.390 Uttam Kumaran: You know, for you and your team to continue to, like, push that on the rest of your organization.
341 00:38:44.480 ⇒ 00:38:52.470 Uttam Kumaran: But of course, like, that adds a lot more scope, you know, so… yeah, I feel like having conversations about that would be… would be helpful.
342 00:38:52.700 ⇒ 00:39:08.129 YvetteRuiz: Yeah, no, no, for sure, and that is, I know that that’s the direction we want to go, it’s just we’re working on so many different things, and I think I was mentioning this to Pranav the other day, is, you know, I work with you guys, I’m also working with
343 00:39:08.290 ⇒ 00:39:11.740 YvetteRuiz: we’re talking a dream. I think… I mean, you may have had some conversations.
344 00:39:11.740 ⇒ 00:39:14.740 Uttam Kumaran: Yes, yes, yes, let’s talk to Dream a bunch. Yeah.
345 00:39:14.740 ⇒ 00:39:29.699 YvetteRuiz: So, we’re looking at possibly moving over to them versus Evolve, right? Because, you know, they are a lot more into the AI, all this stuff, so I’m working with them, but then I’m also working with Evoca, who does
346 00:39:29.770 ⇒ 00:39:38.719 YvetteRuiz: all our… they have our bots for after hour and our dispatching piece of it, and it’s like, all these things I know that we can bring together.
347 00:39:38.720 ⇒ 00:39:57.939 YvetteRuiz: It’s just a matter of, okay, we’re, you know, kind of what’s the end goal, and stuff like that. And so, you mentioned voice, and that’s another huge thing. I mean, I was actually just talking to Matt the other day, I mean, if they can listen to the phone calls and kind of just give you, okay, here’s what they’re asking, or Andy connects and says, boom, here’s
348 00:39:58.440 ⇒ 00:40:03.090 YvetteRuiz: what you’re looking for, here’s the… I mean, that’s next-level things, you know what I mean?
349 00:40:03.090 ⇒ 00:40:03.420 Uttam Kumaran: Yes.
350 00:40:03.420 ⇒ 00:40:05.999 YvetteRuiz: I even, you know, talked to Matt, you know, I said.
351 00:40:06.000 ⇒ 00:40:23.979 YvetteRuiz: again, it would be so awesome if, in Evolve, you know, we have all these notes, right, in accounts, and for me to have to search each and every account, I mean, that’s brutal. If we just had, like, an AI or something that would summarize those notes, and just… I mean, there’s just so many different things.
352 00:40:23.980 ⇒ 00:40:24.400 Uttam Kumaran: Yeah.
353 00:40:24.400 ⇒ 00:40:25.750 YvetteRuiz: and start working on.
354 00:40:25.750 ⇒ 00:40:26.710 Uttam Kumaran: Okay, okay.
355 00:40:27.060 ⇒ 00:40:27.630 YvetteRuiz: Yep.
356 00:40:27.630 ⇒ 00:40:28.620 Uttam Kumaran: Yep. Great.
357 00:40:29.170 ⇒ 00:40:34.699 YvetteRuiz: But yeah, we can definitely continue the conversation, but yes, I’m looking forward to us meeting in person for sure.
358 00:40:34.700 ⇒ 00:40:35.040 Uttam Kumaran: Okay.
359 00:40:35.470 ⇒ 00:40:36.590 Pranav: Cool. Me too.
360 00:40:36.590 ⇒ 00:40:37.260 Uttam Kumaran: Perfect.
361 00:40:37.670 ⇒ 00:40:38.960 YvetteRuiz: Alrighty, guys!
362 00:40:39.750 ⇒ 00:40:40.280 Uttam Kumaran: Yeah, everyone.
363 00:40:40.320 ⇒ 00:40:44.349 Pranav: Have a good, long weekend, and yeah, Janiece, we’ll talk tomorrow.
364 00:40:44.350 ⇒ 00:40:46.470 JanieceGarcia: Yep, sounds good. Bye, safe travels.
365 00:40:46.470 ⇒ 00:40:46.960 YvetteRuiz: Bye, guys!
366 00:40:46.960 ⇒ 00:40:47.430 Uttam Kumaran: Hi.
367 00:40:47.980 ⇒ 00:40:48.660 Pranav: Yeah.