Meeting Title: ABC | Weekly KPI Review Date: 2026-02-09 Meeting participants: YvetteRuiz, JanieceGarcia, read.ai meeting notes, Yvette’s Notetaker (Otter.ai), Amber Lin
WEBVTT
1 00:02:37.600 ⇒ 00:02:38.940 JanieceGarcia: Hey, vet!
2 00:02:47.720 ⇒ 00:02:55.989 YvetteRuiz: My team is running and waiting to help replace Simone A. Sorry, sorry, sorry, sorry.
3 00:02:56.770 ⇒ 00:02:58.250 YvetteRuiz: That is so funny.
4 00:02:59.070 ⇒ 00:03:00.990 JanieceGarcia: Huh? She is funny.
5 00:03:03.030 ⇒ 00:03:06.199 YvetteRuiz: God, I am sorry.
6 00:03:30.230 ⇒ 00:03:34.730 YvetteRuiz: Emma Luna, who is that? Is back starting today. Who is that?
7 00:03:35.820 ⇒ 00:03:37.020 JanieceGarcia: Emma Luna?
8 00:03:37.930 ⇒ 00:03:46.420 YvetteRuiz: Emma Luna. Emma is back, and we began putting landscape leads in Austin area on her after this week.
9 00:03:51.200 ⇒ 00:03:54.030 YvetteRuiz: This is where I feel like things get a little bit messy.
10 00:03:54.340 ⇒ 00:03:58.240 YvetteRuiz: I’m a Luna. That’s… she’s a landscape person.
11 00:03:59.630 ⇒ 00:04:02.079 YvetteRuiz: Patricia’s just looping us in to make sure that.
12 00:04:02.380 ⇒ 00:04:03.039 JanieceGarcia: We know.
13 00:04:03.040 ⇒ 00:04:12.329 YvetteRuiz: she’s back on the inspector guidelines, but I think that means… I don’t know what’s been sent to contract, like… not contract into, but, like, the exercise we just did with Julie, like.
14 00:04:12.810 ⇒ 00:04:15.800 YvetteRuiz: What is… how is she currently set up? Where was she?
15 00:04:16.440 ⇒ 00:04:20.389 YvetteRuiz: She was back, back from where?
16 00:04:20.390 ⇒ 00:04:24.579 JanieceGarcia: Yeah, I didn’t know if she left unless that was a long time ago.
17 00:04:25.990 ⇒ 00:04:28.370 JanieceGarcia: Pulling up my inspector sheet…
18 00:04:52.980 ⇒ 00:04:54.080 Amber Lin: Bye!
19 00:04:54.880 ⇒ 00:04:57.120 JanieceGarcia: Hi, good morning. Hi, good morning.
20 00:04:57.320 ⇒ 00:04:59.050 Amber Lin: Good morning, how are you?
21 00:05:00.520 ⇒ 00:05:02.010 YvetteRuiz: Good, and yourself?
22 00:05:02.240 ⇒ 00:05:06.689 YvetteRuiz: Yeah, good. Had a good, restful weekend. Good!
23 00:05:06.690 ⇒ 00:05:09.199 Amber Lin: watched the… A little bit.
24 00:05:09.530 ⇒ 00:05:11.659 Amber Lin: Best Bowl Super Bowl, yes.
25 00:05:13.540 ⇒ 00:05:16.200 YvetteRuiz: Was the team you were rooting for? Did they win?
26 00:05:16.200 ⇒ 00:05:26.470 Amber Lin: No, no, I just went to… I just went to meet the… see the people there, so it was a… it was some friends that I haven’t seen in a while, so it was nice.
27 00:05:26.610 ⇒ 00:05:31.250 YvetteRuiz: Okay, it’s more for the socializing, not the game.
28 00:05:31.250 ⇒ 00:05:32.130 Amber Lin: What about you?
29 00:05:32.650 ⇒ 00:05:35.590 YvetteRuiz: Yeah, same thing, hung out,
30 00:05:36.090 ⇒ 00:05:44.109 YvetteRuiz: at my sister’s yesterday, we, watched the game there. I wasn’t… I normally try to read the stories on the players,
31 00:05:44.110 ⇒ 00:05:44.470 Amber Lin: Yay!
32 00:05:44.470 ⇒ 00:05:49.350 YvetteRuiz: You know, just to kind of see, you know, who’s… I’m a big fan of the underdog, so…
33 00:05:49.350 ⇒ 00:05:49.860 Amber Lin: Mmm.
34 00:05:49.860 ⇒ 00:05:58.009 YvetteRuiz: That’s who I normally root on for, so I was super, like, proud of, excited for the Seahawks. I think the quarterback had a good story.
35 00:05:58.440 ⇒ 00:05:59.030 Amber Lin: Awesome.
36 00:06:00.260 ⇒ 00:06:04.270 Amber Lin: Alright, let me go through the stuff I have.
37 00:06:04.660 ⇒ 00:06:06.930 Amber Lin: I think,
38 00:06:07.550 ⇒ 00:06:16.430 Amber Lin: So, the ZIP one, the Zipco stuff is ongoing. We were able to add the mechanical tech levels last week, so that’s really good.
39 00:06:16.430 ⇒ 00:06:33.439 Amber Lin: And I know Casey’s… I’ve been asking Casey, hey, can you go through the lawn stuff to make sure that we’re not missing anyone there, and if we’re missing, add them, because I think that’s the area that we’ve seen the most. Hey, Andy doesn’t know who this is.
40 00:06:33.770 ⇒ 00:06:48.479 Amber Lin: So, I think this week we’re gonna tackle that, and then if we have some time, we’re gonna look at home improvement and do the matching there. So that’s the first thing on the zip codes. The second thing on the…
41 00:06:48.900 ⇒ 00:06:54.820 Amber Lin: The central dog… and the automation stuff. So…
42 00:06:55.920 ⇒ 00:06:58.999 Amber Lin: I know the team has already started to…
43 00:06:59.010 ⇒ 00:07:18.549 Amber Lin: yes, last week we pulled all the questions asked, and we were able to categorize them into, oh, these are about these questions, that’s about this question, so we’re gonna meet, probably today or tomorrow internally to go through that, and then, I know Mustafa’s working on
44 00:07:18.650 ⇒ 00:07:34.020 Amber Lin: checking the documents to see if there’s duplicates or conflicting information. So that’s going to be our first step, is to know where to start first and know where the problems are. And once we have that, we can then pass it through
45 00:07:34.020 ⇒ 00:07:40.710 Amber Lin: the AI or the automations to say, hey, this is the improvements we want. So I think at this step.
46 00:07:40.720 ⇒ 00:07:53.899 Amber Lin: No input needed, but then as soon as we go through, as once we have the results of, I think this section needs work, that section needs work, I think we’ll need input from your guys of
47 00:07:53.900 ⇒ 00:08:03.749 Amber Lin: What’s optimal, and what needs to change, or what stuff is missing, and then we’ll need the trainers to come in and say, hey, this is… this is what’s actually true.
48 00:08:05.290 ⇒ 00:08:05.990 Amber Lin: Yeah.
49 00:08:05.990 ⇒ 00:08:06.550 YvetteRuiz: Okay.
50 00:08:06.850 ⇒ 00:08:08.739 Amber Lin: Let me see…
51 00:08:10.050 ⇒ 00:08:25.199 Amber Lin: I think on the transcript size, we’ve been able to get the transcripts, but Sam is, one, confirming with Tim on, what the rate limit is, so we don’t, like, charge… do something, and then they…
52 00:08:25.500 ⇒ 00:08:29.629 Amber Lin: Gets a big bill that gets sent to you guys, or want to confirm first.
53 00:08:29.650 ⇒ 00:08:45.960 Amber Lin: And then we’re working on matching that record with Andy. We were doing some of that before, but I think, Sam has to pick it back up and match it up with Andy, but so far, I’m gonna check how much he has in there, and if he has it
54 00:08:46.000 ⇒ 00:08:48.640 Amber Lin: I think we can start doing…
55 00:08:48.960 ⇒ 00:08:56.099 Amber Lin: a little bit of analysis. Is there any particular questions you would like to look into first for the transcripts?
56 00:08:58.220 ⇒ 00:09:02.549 YvetteRuiz: So, just so I have a full understanding,
57 00:09:03.350 ⇒ 00:09:15.599 YvetteRuiz: we’re not quite yet pulling them. We’re waiting for Tim to get you the rate limit first, before we start doing that, or yes, we’re starting to do it, or… I guess that’s why I’m a little bit confused.
58 00:09:15.680 ⇒ 00:09:21.759 Amber Lin: I see. I believe we’re able to get the transcript data in our database.
59 00:09:21.760 ⇒ 00:09:24.429 YvetteRuiz: I think that’s what he’s…
60 00:09:25.950 ⇒ 00:09:35.779 Amber Lin: But I don’t know if it’s in the database yet. I think we have the ability to, but I don’t know if we have pulled all of them, because some of them might have credit card information.
61 00:09:36.350 ⇒ 00:09:38.560 YvetteRuiz: Right, right, right. Okay.
62 00:09:39.790 ⇒ 00:09:40.810 YvetteRuiz: Okay.
63 00:09:41.010 ⇒ 00:09:44.880 YvetteRuiz: Hang on, maybe, maybe just, I want to… I wanna make sure I get completely clear.
64 00:09:44.880 ⇒ 00:09:50.140 Amber Lin: I know, like, we can do it, I don’t… we don’t have it in there yet.
65 00:09:50.140 ⇒ 00:09:51.030 YvetteRuiz: Okay.
66 00:09:51.030 ⇒ 00:09:53.270 Amber Lin: So I think that’s where we’re at.
67 00:09:54.890 ⇒ 00:10:00.780 YvetteRuiz: Okay, so when you guys are reviewing the transcripts, I guess I just need…
68 00:10:00.780 ⇒ 00:10:20.760 YvetteRuiz: because it’s been a minute. I wasn’t on the Thursday meeting, I was trying to get clarity on what was being asked on the questions. Utam responded, saying we just… again, I… that doesn’t kind of align with what you’re saying right here, so I’m just trying to, from start… I’m just trying to make sure that I completely understand. So, are we saying that…
69 00:10:20.810 ⇒ 00:10:27.780 YvetteRuiz: Okay, we are integrated with 8x8, we can pull all the transcripts today.
70 00:10:28.750 ⇒ 00:10:41.700 YvetteRuiz: Are we going… is Brainforge going to be reviewing all those transcripts that are coming through us and giving us a breakdown, or only certain? What… what is… what are we doing with the transcripts? I mean, that’s kind of where I’m kind of…
71 00:10:42.220 ⇒ 00:10:44.070 YvetteRuiz: maybe I’m confused on what.
72 00:10:44.070 ⇒ 00:11:02.709 Amber Lin: I see, I see. I guess that’s more, what we do with the transcript is more of a question for you guys of what do you want to do with the transcripts? We are able to access it, we’re able to… and then once we have it, we’ll able… we’ll be able to run an analysis on it, but then, I think our question was.
73 00:11:02.710 ⇒ 00:11:19.390 Amber Lin: what do you think… where should we apply it first? Because I… we did it because, remember, you were very, very, excited about having the transcript, so… Yes. Now that we have more time, we’re… we’re just picking that back up and wanting to give us another dimension, too.
74 00:11:19.700 ⇒ 00:11:29.670 YvetteRuiz: Okay, gotcha, gotcha. And I… that’s… so that’s kind of where I wanted the clarity, because there was… there’s been a different com… there’s been different conversations. One was like, okay.
75 00:11:29.670 ⇒ 00:11:40.129 YvetteRuiz: call source, because you guys know that we use CallSource. Call Source does all our gradings. They don’t do our coachings, they do our gradings, right? We go in there, and CallSource pretty much
76 00:11:40.300 ⇒ 00:12:00.930 YvetteRuiz: uses AI to get the transcripts and grades based off of that. We have some gaps, yes, we know we do. We’ve identified, you know, hey, maybe this isn’t the most efficient way, and I know when Utem came, he was all like, hey, we can get better at doing those things and providing you stuff, like tools, where you can better coach and stuff like that, so…
77 00:12:01.610 ⇒ 00:12:20.359 YvetteRuiz: I mean, if that’s something that you guys can provide, yes, that’s the direction that I want to go, you know what I mean? Like, hey, yeah, pull the transcripts that are coming through, let’s see where are we missing the gaps, right? Like, what are areas where Andy can help us, and where is it where this person could probably use coaching? Hey, we’re losing this much customers because
78 00:12:20.480 ⇒ 00:12:25.369 YvetteRuiz: the CSRs don’t know the answer to it, or those type of things, so yeah, I mean.
79 00:12:25.510 ⇒ 00:12:34.799 YvetteRuiz: if the question is for me, that’s how I would like to start getting more information. Hey, is this something that we can possibly use, start using to replace
80 00:12:34.880 ⇒ 00:12:36.200 YvetteRuiz: call source.
81 00:12:36.200 ⇒ 00:12:46.440 YvetteRuiz: Because at this instance, I feel like you guys would be able, not really necessarily do the grading, but at least you’re going to be able to say, hey, we’re looking at all your transcripts, and based off of this.
82 00:12:46.440 ⇒ 00:13:04.899 YvetteRuiz: you get this many phone calls that are for new scheduling customers, and we’re booking X amount, right? So, you have a gap where you have a… I mean, not a gap, but you have the… what’s the word that I’m looking for? You have an opportunity to start saving customers if you start working on this part of it, right, over here.
83 00:13:04.900 ⇒ 00:13:05.310 Amber Lin: Okay.
84 00:13:05.310 ⇒ 00:13:13.700 YvetteRuiz: hey, your agents, and I know this for a fact, because I’ve been, again, once again, in the weeds with the whole,
85 00:13:14.160 ⇒ 00:13:20.000 YvetteRuiz: the customer service experience with the recordings, I know that we sound very…
86 00:13:20.900 ⇒ 00:13:25.440 YvetteRuiz: we’re checking the box type thing, Amber. So, pretty much when a customer calls in.
87 00:13:25.710 ⇒ 00:13:43.170 YvetteRuiz: I’m just going in there saying, okay, can I have your name? Can I have your number? Can I verify this? And yes, all those verifications are important, but the key important thing, and we’ve tried very hard to do this, is I need to build the relationship. I need to build the rapport with the customer, right? So.
88 00:13:43.170 ⇒ 00:13:57.620 YvetteRuiz: That needs to come first, you know what I mean? Yes, I understand, you’re calling the right place. How can I help you? And stuff like that. So, that is where I would really start wanting to get this transcript, this data on all that. Does that kind of answer the question?
89 00:13:57.620 ⇒ 00:14:11.869 Amber Lin: Yeah, gosh, so I can summarize, I think, in three parts. Like, one is, coaching opportunities of, where are people missing the gaps? Like, I guess this is more informational. What do people not know?
90 00:14:11.870 ⇒ 00:14:14.640 YvetteRuiz: And the second part is…
91 00:14:15.940 ⇒ 00:14:35.780 Amber Lin: categorization of questions, and the closing rates, and seeing where we’re losing, so that’s more of a overall numbers overview of, like, how we’re doing for each category. And then the third part you just said is, more on the qualitative side of how are people building relationships, how are people,
92 00:14:35.790 ⇒ 00:14:42.180 Amber Lin: carrying themselves and representing the brand, so I think that’s the… that’s the three areas that I just heard.
93 00:14:43.200 ⇒ 00:15:06.629 YvetteRuiz: Yeah, and if I could, and again, I don’t want to go too big too soon, but, you know, I think there’s a lot of things that we can do with the transcripts, right? And, you know, man, it would be very cool to see, okay, how many of those calls that come through to us are billing-related, right? That could be automated, you know what I mean? Like, someone who just wants to take a payment, you know what I mean? How many of those are cancellations that are coming through?
94 00:15:06.630 ⇒ 00:15:13.369 YvetteRuiz: You know, that really give you the type of phone calls that are coming through, because I can just kind of tell you today
95 00:15:13.510 ⇒ 00:15:15.730 YvetteRuiz: Specifically for…
96 00:15:15.920 ⇒ 00:15:30.910 YvetteRuiz: water quality and electric, I have one of our data person that has to scrub each and every one of those phone calls to tell me the type of call that it’s coming through. And that’s taking tons of time, where I know that, man, if we can just do this based off the transcripts.
97 00:15:31.220 ⇒ 00:15:31.760 YvetteRuiz: I can…
98 00:15:32.110 ⇒ 00:15:43.949 YvetteRuiz: can save him tons of time. And that’s not even scratching the surface, but it’s given us good information to tell us, hey, you guys had this many leads that came in for water quality. You were only able to book
99 00:15:44.770 ⇒ 00:15:52.689 YvetteRuiz: 60% of them, because you guys are booking out way, way too far and stuff like that, so those type of things are super helpful for us.
100 00:15:52.690 ⇒ 00:16:00.019 Amber Lin: Okay, yeah, awesome. So, I’m just gonna ask Sam, and then probably Tim on how the progress is. I think…
101 00:16:00.020 ⇒ 00:16:00.390 YvetteRuiz: Yes.
102 00:16:00.390 ⇒ 00:16:15.599 Amber Lin: where we’re at is we’re ready to go once he confirms, and then we’ll know what we cannot touch, and then we’ll pull the rest. Okay, perfect. And then I’m gonna tell him, hey, these are the stuff that we’re interested in, do you think is possible? So…
103 00:16:15.600 ⇒ 00:16:22.350 YvetteRuiz: Sounds good, sounds good. Okay, well, thank you for walking through that with me, because I just needed more clarity as far as, like, what does that mean?
104 00:16:22.350 ⇒ 00:16:29.249 Amber Lin: Yeah, yeah, of course. That… I know we have a meeting with the trainers on…
105 00:16:29.720 ⇒ 00:16:40.330 Amber Lin: On Friday. Yeah, probably won’t take as long, but… but do you think… I still think it would be nice to meet, and I can just drop in for, say.
106 00:16:40.330 ⇒ 00:16:57.790 Amber Lin: 30 minutes or so. It’s also nice if I can, even if we don’t talk about the central dock automations, I want to know, like, what problems they’re seeing, because it’s hard to just have everybody in the room. Yeah. If they’re free, because they had a lot of questions last time. I would love to
107 00:16:57.790 ⇒ 00:17:09.149 Amber Lin: I think they have more questions, but I… I don’t think I’m… I’m there with them all the time. So I want to answer questions they have, and then hopefully we have the
108 00:17:09.230 ⇒ 00:17:13.810 Amber Lin: central DOP analysis results, so I can tell them, hey, this is what we found.
109 00:17:14.260 ⇒ 00:17:20.060 Amber Lin: I need these information from you, so, I’ll check with the team on, how that is.
110 00:17:20.740 ⇒ 00:17:45.599 YvetteRuiz: Yeah, no, definitely, Amber. I think it… I would appreciate you being part of that one. You know, you start building the relationships with them, and you’re able to get insight, for sure. And then, excellent, if you have the results by then, that’ll even be even better. So then that way they already know what their action items are, but I’m glad that we’re going this direction, where you guys are going in there and reviewing all that, and kind of telling us where do we need to start focusing on cleaning up, because that was kind of
111 00:17:45.600 ⇒ 00:17:48.539 YvetteRuiz: Where everybody was kind of all over the map and stuff.
112 00:17:48.540 ⇒ 00:18:05.199 Amber Lin: I know, I know, and I… when I had the meeting with… with you guys, like, I wasn’t even sure what we’re able to do in automations. I just had the meeting, I thought about all the stuff we had to do, and I was like, oh my god, Utem, I don’t… I don’t know if I can do this in the amount of time we have.
113 00:18:05.620 ⇒ 00:18:18.850 Amber Lin: It’s like, actually, let me grab my, like, architect and, like, more senior AI folks, and I think that’s the new stuff we’re now able to do with, like, advancements in AI, etc.
114 00:18:18.850 ⇒ 00:18:27.750 YvetteRuiz: Yeah, no, for sure. I mean, I love what you emailed. I mean, that’s everything that I’ve been trying to throw in myself, you know, to kind of narrow stuff down, but…
115 00:18:27.790 ⇒ 00:18:29.570 JanieceGarcia: You know, without kind of…
116 00:18:29.760 ⇒ 00:18:37.359 YvetteRuiz: having a starting point, you know what I mean, and bringing everybody together, knowing, okay, hey, this is what we need to work on.
117 00:18:37.990 ⇒ 00:18:43.610 YvetteRuiz: Specifically, yeah. So, but, I mean, all that, what you shared, just that breakdown right there, that was very interesting data.
118 00:18:43.770 ⇒ 00:18:44.150 Amber Lin: Yeah.
119 00:18:44.150 ⇒ 00:18:45.730 YvetteRuiz: Send out on Friday.
120 00:18:45.730 ⇒ 00:18:47.609 Amber Lin: Awesome. So…
121 00:18:47.810 ⇒ 00:18:56.179 Amber Lin: That’s all the… all the stuff on my side, so the zip, central talk, and transcript. Anything on… on your side that you wanted to ask about?
122 00:18:56.840 ⇒ 00:19:16.120 YvetteRuiz: I just… again, I think you answered mine, which is the transcript piece of it. That’s what I wanted. I mean, everything else on, like, the… the training and all the documents was pretty straightforward based off your email, so I was just waiting for the analysis, all that to come through. My big thing was the transcript, because I am very… I’ve been very excited about the transcripts, I just…
123 00:19:16.250 ⇒ 00:19:19.459 YvetteRuiz: it pause, and I’m just trying to kind of get…
124 00:19:19.700 ⇒ 00:19:25.410 YvetteRuiz: Realigned with what we want, what are we… what can we do with what you guys are doing
125 00:19:25.750 ⇒ 00:19:29.969 YvetteRuiz: And the big… and the big things are everything that I just listed.
126 00:19:30.690 ⇒ 00:19:36.379 Amber Lin: Awesome. Alright, so if that’s everything, I think I don’t have anything else.
127 00:19:36.590 ⇒ 00:19:42.100 Amber Lin: I’ll keep in touch with you guys on email or on… on Google.
128 00:19:42.880 ⇒ 00:19:43.470 YvetteRuiz: Sounds good!
129 00:19:43.470 ⇒ 00:19:44.070 JanieceGarcia: Okay.
130 00:19:44.070 ⇒ 00:19:44.870 Amber Lin: Alright. Perfect.
131 00:19:44.870 ⇒ 00:19:46.760 YvetteRuiz: Alright, thanks, Amber, have a good rest of your week.
132 00:19:46.980 ⇒ 00:19:47.949 Amber Lin: You too, bye.
133 00:19:47.950 ⇒ 00:19:48.540 YvetteRuiz: Bye.