Meeting Title: Brainforge x ABC Home and Commercial: Weekly Project Check Date: 2025-09-11 Meeting participants: read.ai meeting notes, JanieceGarcia, MattBurns, Uttam Kumaran, Amber Lin
WEBVTT
1 00:00:36.780 ⇒ 00:00:40.799 JanieceGarcia: Where do I fix my view so I don’t look like I’m so close?
2 00:00:41.410 ⇒ 00:00:45.629 JanieceGarcia: Why is my… yeah, my view always seems like I’m so close.
3 00:00:52.950 ⇒ 00:00:54.290 JanieceGarcia: Did you get me out of here?
4 00:00:59.250 ⇒ 00:01:00.150 JanieceGarcia: Hi, Matt!
5 00:01:00.320 ⇒ 00:01:02.120 MattBurns: Hi, Janiece, how are you?
6 00:01:02.390 ⇒ 00:01:03.679 JanieceGarcia: Good, how are you?
7 00:01:04.030 ⇒ 00:01:04.730 MattBurns: Good.
8 00:01:04.910 ⇒ 00:01:07.210 JanieceGarcia: Good, good. I’m gonna shut the door really quick.
9 00:01:16.490 ⇒ 00:01:17.750 JanieceGarcia: Bye, Udom!
10 00:01:30.400 ⇒ 00:01:31.580 Amber Lin: Hello!
11 00:01:33.290 ⇒ 00:01:34.170 Uttam Kumaran: Hello.
12 00:01:34.610 ⇒ 00:01:36.220 JanieceGarcia: Hello, Amber?
13 00:01:36.220 ⇒ 00:01:39.900 Amber Lin: Nice to see you again. Do we know if it’s coming today?
14 00:01:40.370 ⇒ 00:01:43.059 JanieceGarcia: I don’t believe she is, she’s in another meeting at the moment.
15 00:01:44.090 ⇒ 00:01:45.120 JanieceGarcia: My division.
16 00:01:47.540 ⇒ 00:01:54.750 Amber Lin: updates on the transcript, and then on the zip code side, and then I’ll send her the weekly update email, and she’ll have the recording as well.
17 00:01:57.060 ⇒ 00:01:58.120 Amber Lin: Okay.
18 00:01:58.530 ⇒ 00:02:00.600 Amber Lin: Let’s get started.
19 00:02:15.400 ⇒ 00:02:33.870 Amber Lin: So, starting off with a usage update, I checked this morning, so we might have got a little bit more by now. We’re pretty close to our goal. Our goal, so per week, is going to be around 460 sessions. That will bring us to about 2,000 sessions per month.
20 00:02:33.870 ⇒ 00:02:43.209 Amber Lin: And now, right now, we’re decently close. I think this month, we will be hitting the 2,000 target, especially now that we have
21 00:02:43.230 ⇒ 00:03:02.070 Amber Lin: home improvement and lawn included. So I’m looking at this usage list here, and actually, I… I do see quite a few new names, so meaning that they’re not from pest, not from lead line, not from mechanical. I… Janice, if you read this with me, I think.
22 00:03:02.070 ⇒ 00:03:06.620 JanieceGarcia: Lauren Barnes is mechanical, Lauren Anderson is lead line.
23 00:03:07.920 ⇒ 00:03:09.320 Amber Lin: Chrissy is…
24 00:03:09.320 ⇒ 00:03:10.190 JanieceGarcia: Lead line.
25 00:03:10.190 ⇒ 00:03:12.570 Amber Lin: need line, clever.
26 00:03:12.570 ⇒ 00:03:15.290 JanieceGarcia: Clarissa’s Dispatch Mechanical.
27 00:03:15.610 ⇒ 00:03:23.070 Amber Lin: And so, going down here, Veridiana is also a new name.
28 00:03:23.590 ⇒ 00:03:36.369 JanieceGarcia: She’s mechanical, so since we’ve opened it up completely to mechanical, now you’re seeing more, mechanical, but then I also noticed there is Scott Glandin, which, is in our home improvement.
29 00:03:37.200 ⇒ 00:03:43.500 JanieceGarcia: So, at least the testing and stuff, has definitely helped, and yes, mechanical is in there.
30 00:03:43.500 ⇒ 00:03:46.390 Amber Lin: Yeah, totally. And they’re reaching…
31 00:03:46.580 ⇒ 00:03:57.149 Amber Lin: I think it says Mechanical’s new, they’re even surpassing Pest a little bit, so if we want to host a little internal competition, I think that will be pretty fun, now that we have all the departments coming in.
32 00:03:57.750 ⇒ 00:03:58.760 JanieceGarcia: That would be cool.
33 00:03:58.760 ⇒ 00:04:17.870 Amber Lin: Yeah. So we’re in the first week of September. We’ve already launched mechanical, home improvement RN testing, we have made good progress with the transcripts, we also have a lot more usage, and right now we have
34 00:04:17.980 ⇒ 00:04:22.840 Amber Lin: The ticketing system, with… which helps with,
35 00:04:22.950 ⇒ 00:04:38.099 Amber Lin: completing the feedback loop when CSR send in things and improving this central dock, and then we also have the zip code database, which I want to show you guys in a bit. So I think we’re making good progress based on the plan that we had in July.
36 00:04:39.290 ⇒ 00:04:40.200 JanieceGarcia: Awesome.
37 00:04:40.200 ⇒ 00:04:50.709 Amber Lin: Yeah, so mainly I want to run through two main updates. So, for one for the zip code database, and one for transcripts. As for the departments,
38 00:04:50.710 ⇒ 00:05:06.630 Amber Lin: Mechanical’s fully rolled out, and we’re having working sessions with the new CSRs, and then for home improvement and lawn, they are in testing to make sure that the documents are at a reasonable level when we give it out to everybody.
39 00:05:07.840 ⇒ 00:05:09.230 Amber Lin: And so…
40 00:05:09.840 ⇒ 00:05:26.749 Amber Lin: I want to show you, this video that Sam recorded. I don’t have access to the dashboard yet, because it’s still in development, but it looked really cool when I saw it a little bit earlier today, and I just want to play this video so we can look at it together.
41 00:05:27.530 ⇒ 00:05:35.669 Amber Lin: Here we go. So, what I threw together here is a little dashboard that pulls data from 8x8.
42 00:05:36.090 ⇒ 00:05:39.579 Amber Lin: These numbers, these totals are not accurate, it’s just pulling…
43 00:05:39.720 ⇒ 00:05:45.139 Amber Lin: a limited sample, so that I don’t have everything coming in, right now, but you can see I have a bunch of stuff here.
44 00:05:45.270 ⇒ 00:06:00.969 Amber Lin: What I can do is filter by different interaction types, for example, look at these calls. When I click into the details, you can get the transcript here, a bunch of information from 8.8, which is great. If I come back, what’s really exciting?
45 00:06:01.240 ⇒ 00:06:16.819 Amber Lin: to do with this is to go into our logs. So this is Andy Logs, it should say there, but once we’re connected to the database, I can load a bunch of them. This is just a random sampling right now, but if I click into one of these, you can see it does a quick search.
46 00:06:17.150 ⇒ 00:06:18.479 Amber Lin: Through the 8x8 data.
47 00:06:18.840 ⇒ 00:06:24.020 Amber Lin: So what’s really exciting here is that, you know, I can find,
48 00:06:24.270 ⇒ 00:06:27.210 Amber Lin: a bunch of users from 8x8,
49 00:06:27.480 ⇒ 00:06:30.940 Amber Lin: Eventually what I’m looking to do is tie those to the ANDI data.
50 00:06:31.220 ⇒ 00:06:43.270 Amber Lin: And so what I made really quickly just to show this is that I can look up all these users in the data for Andy. Again, if it’s Andy. Again, it’s just a subsampling. You can see that there’s a bunch of them here.
51 00:06:43.360 ⇒ 00:06:56.630 Amber Lin: I don’t think these errors are accurate, because I’m having a hard time filtering by them, so I think the next step would be to… I’m skipping this part because his audio was breaking up. We can do that kind of ongoing.
52 00:06:57.180 ⇒ 00:07:10.219 Amber Lin: So, yeah, I think that’s really all I have right now to show off, but hopefully, in the next day or two, I’ll be able to actually match these things up and see, side-by-side different, interactions.
53 00:07:13.440 ⇒ 00:07:19.909 Amber Lin: Yeah, so that’s what we have for the transcripts. I personally think that’s very, very exciting, because
54 00:07:20.090 ⇒ 00:07:27.400 Amber Lin: It’s a… it’s a lot easier to navigate when there is a dashboard, and also…
55 00:07:27.830 ⇒ 00:07:45.420 Amber Lin: we’ll be able to match the records to Andy, so that we know, okay, for this type of call, what are people asking questions? Because they ask questions when they don’t know. And so, when we match those two records together, we can see, okay, for all cancellation calls.
56 00:07:45.420 ⇒ 00:07:58.060 Amber Lin: people seem to not know what to say when, specifically people are moving, so we can have a training session specifically for that. Or people are asking a lot about, how do I…
57 00:07:58.060 ⇒ 00:08:08.150 Amber Lin: schedule a, say, a cage follow-up, and that tells us, okay, a lot of times when people get that call, they don’t know what to do, and then I think that will help us
58 00:08:08.230 ⇒ 00:08:25.240 Amber Lin: improve training a lot, and that will help us identify the gaps. So next week, hopefully, we’ll have this and Andy connected. I’ll ask them if we can, share this dashboard for… to you guys to do an internal, to poke around and test it together.
59 00:08:25.240 ⇒ 00:08:37.539 Uttam Kumaran: Yeah, so one of the goals that we had is basically, can we bring the transcript up for the call right next to, like, how the chat interface is being used? Ideally, for me, it’s interesting to see, like.
60 00:08:38.820 ⇒ 00:08:54.179 Uttam Kumaran: what are the calls where they’re not being used? Are there still further opportunities for AMD to get used in calls? And maybe, again, people just aren’t deciding to use it? Additionally, I want to make sure that we use the live transcripts as sort of like a testbed.
61 00:08:54.180 ⇒ 00:09:03.939 Uttam Kumaran: I want to kind of get a sense of, are the questions that are being asked things that Andy can’t answer? Right? I think there’s still going to be a little bit of gap for what people think
62 00:09:04.020 ⇒ 00:09:07.710 Uttam Kumaran: They can answer, but as usage goes up and up, they’re gonna…
63 00:09:07.800 ⇒ 00:09:16.000 Uttam Kumaran: really try to push it, and I want to make sure our system is actually matched towards, like, real-life scenarios. So this is kind of, like, one thing that we wanted to try to
64 00:09:16.150 ⇒ 00:09:22.960 Uttam Kumaran: to bring together. And then also, again, like, I think it’ll be easy, we’re gonna be meeting with the 8x8 folks next week.
65 00:09:22.960 ⇒ 00:09:40.789 Uttam Kumaran: But we’ll also get a sense that we’re not trying to rebuild, like, 8x8 here, so I want to make sure that we’re just doing the things where we can match Andy directly with transcripts, and sort of give a… improve ANDI itself, and then also give, you know, trainers a little bit of a better understanding of, like, where it’s getting used and why.
66 00:09:41.840 ⇒ 00:09:42.400 JanieceGarcia: Yep.
67 00:09:42.760 ⇒ 00:09:43.520 JanieceGarcia: Okay.
68 00:09:43.930 ⇒ 00:09:45.380 MattBurns: Makes sense, good.
69 00:09:46.850 ⇒ 00:09:47.300 Amber Lin: Yeah.
70 00:09:47.300 ⇒ 00:09:47.990 JanieceGarcia: Don’t.
71 00:09:48.590 ⇒ 00:10:09.990 Amber Lin: And then the next one, also very exciting. Okay, so just send this to me, or just before this meeting. So, we’ve completed the zip code database, we’re doing… we did the internal validations, and right now, what we’re doing, is connecting the zip code database to Andy. So I want to show you a few things that’s…
72 00:10:09.990 ⇒ 00:10:34.920 Amber Lin: capable through this database that we weren’t able to do before. So, right now, this is what we always had of, okay, who is the inspector, for this zip code? And, of course, is able to answer this way, but it’s also able to answer, who does this person cover? We’ve got a lot of feedback already on, hey, I asked about, oh, who this current person covers, and
73 00:10:34.920 ⇒ 00:10:53.119 Amber Lin: Andy’s not able to answer it, but we will be able to do that with this database now, because it’s a lot more flexible, and we can say, okay, Robert covers, all these zips, so we can make sure, oh, maybe now Robert covers more zips and less zips, and it’ll be a lot easier to update that way.
74 00:10:53.760 ⇒ 00:10:55.120 JanieceGarcia: Awesome. Okay.
75 00:10:55.120 ⇒ 00:11:02.339 Amber Lin: Yeah, and this one is also something that we were asked for. This is also very important of who…
76 00:11:02.390 ⇒ 00:11:11.009 Amber Lin: are the inspectors for that quadrant. And this helps us do backup. Right now, I know you guys have to look at the map.
77 00:11:11.010 ⇒ 00:11:22.870 Amber Lin: a physical map to see, okay, what are the zip codes next to it, and then go look for an inspector in that zip code. But if we are able to do quadrants, we can say, okay, for this quadrant.
78 00:11:22.870 ⇒ 00:11:31.830 Amber Lin: here are all the inspectors. This person’s not available, then go get that person. And so this is gonna be very, very helpful for the people that’s scheduling.
79 00:11:33.450 ⇒ 00:11:34.839 MattBurns: Yeah, no, that looks…
80 00:11:35.510 ⇒ 00:11:36.210 JanieceGarcia: awesome.
81 00:11:37.210 ⇒ 00:11:56.969 Amber Lin: And this one is, sometimes, I think specifically for chem-free, we get a… we get questions about, okay, who are the chem-free inspectors? I think something very similar is commercial inspectors. Who are the commercial inspectors? So this just gives it a lot more flexibility, whatever…
82 00:11:57.000 ⇒ 00:12:15.959 Amber Lin: column you see here, we can do, all the texts, all the services, the zips, by the branch, by the town. So this just gives us so much more flexibility and, a lot easier to update, because you can say, okay, let’s find all the services for chem-free, and then… and then we can
83 00:12:15.960 ⇒ 00:12:25.809 Amber Lin: add any zips that belongs to Campfree. We don’t have to search it up in a spreadsheet, we can just type in a question, and it’ll give that, and we can update it.
84 00:12:26.870 ⇒ 00:12:31.600 Uttam Kumaran: Yeah, so part of the thing we had here that was difficult before is
85 00:12:31.780 ⇒ 00:12:47.279 Uttam Kumaran: even, you know, having AI over a spreadsheet is a lot more difficult than having it just query a database. So I’m kind of glad that we’re able to sort of mold it to here. So, Amber, for new updates, like, how are we getting updates made to… to this?
86 00:12:47.510 ⇒ 00:12:50.660 Uttam Kumaran: Like, when people change, or we’re adding new people?
87 00:12:51.780 ⇒ 00:13:06.649 Amber Lin: Right now, so we’re connecting, currently we’re… Casey’s working on connecting queries to… so, essentially, how this… this database interprets asks into human questions, and…
88 00:13:06.700 ⇒ 00:13:17.140 Amber Lin: I can… I think once we do that, or in parallel, we’ll also do the… connected to the Google Doc, the Google Form, because essentially we need fields.
89 00:13:17.140 ⇒ 00:13:21.239 Uttam Kumaran: As long as the fields match, we’ll be able to update this.
90 00:13:21.240 ⇒ 00:13:25.780 Amber Lin: We’ve just been focusing on getting this out this week, so we haven’t looked into that yet.
91 00:13:26.560 ⇒ 00:13:27.160 Uttam Kumaran: Okay.
92 00:13:27.490 ⇒ 00:13:36.360 JanieceGarcia: So, can I ask, too, because I know yesterday when we met with the working session, several of the questions was the zip codes for the inspectors, and.
93 00:13:37.390 ⇒ 00:13:43.859 JanieceGarcia: I was sending you those screenshots again, so are those all going to be updated now in this database and not in the actual sheet?
94 00:13:44.260 ⇒ 00:13:54.119 Amber Lin: Right now, I will update it in the sheet, just to make sure that the transition still goes, but I’ll just make sure that we also update it in a new sheet.
95 00:13:54.430 ⇒ 00:13:55.790 JanieceGarcia: Okay, okay.
96 00:14:00.180 ⇒ 00:14:01.150 JanieceGarcia: Perfect.
97 00:14:01.150 ⇒ 00:14:06.630 Amber Lin: Sounds good. Let me note that down. Okay.
98 00:14:07.100 ⇒ 00:14:22.399 Amber Lin: Sounds good. And then, next, I know we… we’re talking about the new UI that’s not based in Google Chat, and Utem, I want to, I’ll give the floor to you, and then you guys can talk about what’s our plan there.
99 00:14:22.830 ⇒ 00:14:26.799 Uttam Kumaran: Yeah, so I know we… we’re… we’re still kind of figuring out the…
100 00:14:26.880 ⇒ 00:14:43.689 Uttam Kumaran: sort of the demo UI, I think it’s been a little bit, time-consuming to figure out the 8x8 API, and so what we… we worked… I worked with Sam internally on sort of, like, what a… a project plan could look like just around, you know, UI that’s outside of Google Chat.
101 00:14:43.690 ⇒ 00:14:59.100 Uttam Kumaran: So we have some estimates, so Matt, I think I can probably grab some time with you. I was planning on doing it last week, but I was kind of pushing on the team to get something visual that we can sort of demo a couple of those features that we’re talking about. In particular, what’s really interesting
102 00:14:59.100 ⇒ 00:15:03.819 Uttam Kumaran: Interesting for me is, one, is there a clear way for,
103 00:15:04.140 ⇒ 00:15:17.760 Uttam Kumaran: the trainers to search and look at all the transcripts associated with, the people that are making calls, and is that easier or harder to do than 8x8? Second, the biggest thing is, can it be another
104 00:15:17.760 ⇒ 00:15:36.760 Uttam Kumaran: a better experience than the existing Andy, meaning you could go into a better chat experience, it can pull from past transcripts, past answers, and actually also direct you to where in the documents things are. Again, like, this is sort of… would be like an Andy 2.0.
105 00:15:36.760 ⇒ 00:15:44.449 Uttam Kumaran: It’s sort of how we’re thinking about it. It really, really depends on us getting this transcript data, I think. Like, we can replicate
106 00:15:44.670 ⇒ 00:16:04.579 Uttam Kumaran: the chat interface, but having the transcripts, I think, really improves, how the trainers can identify more things that Andy can’t answer, and also start to get more feedback. So, I think I’d rather… would prefer to grab some time next week with you, and maybe we can walk you through, sort of, like, a proposal around
107 00:16:04.640 ⇒ 00:16:08.960 Uttam Kumaran: You know, like this, this additional project.
108 00:16:09.760 ⇒ 00:16:17.050 MattBurns: Okay. Probably want to put Steven on this, too, but yeah, I’m gonna keep… I want to keep Steven in the loop, too. So, yeah, next week should be good, Udom.
109 00:16:17.250 ⇒ 00:16:18.200 Uttam Kumaran: Okay, perfect.
110 00:16:19.780 ⇒ 00:16:26.709 Uttam Kumaran: Yeah, and we’re meeting with the 8x8 folks on Wednesday, so I’ll see if we can do it sometime maybe around this time next week.
111 00:16:26.710 ⇒ 00:16:27.590 MattBurns: Okay.
112 00:16:28.390 ⇒ 00:16:29.220 Uttam Kumaran: Perfect.
113 00:16:32.730 ⇒ 00:16:36.190 MattBurns: Okay, awesome. That’s all on my side.
114 00:16:36.190 ⇒ 00:16:48.389 Amber Lin: I think next week we will continue our working sessions with the CSR, so we have new folks on the mechanical side who we haven’t met with yet, and also some people testing for
115 00:16:48.390 ⇒ 00:17:07.960 Amber Lin: home improvement and lawn, and I’ll probably grab a time with the leaders on home improvement and lawn to make sure they know how to update the central dock. They seem to be using it pretty well, but I just want to double check and make sure. And so far, our feedback system has been doing really great. We haven’t dropped
116 00:17:07.960 ⇒ 00:17:17.310 Amber Lin: Anything and everything has been addressed or is in progress. And so it… and it helps us collaborate with the leaders a lot better, too.
117 00:17:19.349 ⇒ 00:17:23.509 MattBurns: Good. Well, good to see the progress, particularly with mechanical.
118 00:17:23.849 ⇒ 00:17:30.479 MattBurns: And yeah, bringing home improvement and lawn on, good stuff, yeah.
119 00:17:30.780 ⇒ 00:17:49.749 Amber Lin: Yeah. Once we have everyone on, I talked to Denise about this, we want to combine the documents for these, for these departments, especially things that’s already similar across the board, such as billing, such as the different accounts. So once we have that, I think it will…
120 00:17:50.060 ⇒ 00:17:57.270 Amber Lin: Allow people to cross-train a lot easier, because then it will only be the service-specific scheduling items.
121 00:17:58.740 ⇒ 00:18:01.609 MattBurns: Agreed, yeah, no, that’s… that makes sense, but…
122 00:18:01.870 ⇒ 00:18:09.370 MattBurns: in terms of, yeah, both cross-training, training new people, yeah, this should be a big help.
123 00:18:09.370 ⇒ 00:18:24.739 Uttam Kumaran: Yeah, and also one thing that I want to start to look at is, like, the time for people to ramp up, right? And we kind of… one of the hopes that we talked about originally is that, you know, it takes months for people to ramp up, and sort of what is the longevity of folks on the team.
124 00:18:24.740 ⇒ 00:18:31.439 Uttam Kumaran: And so one thing is, like, can we start to find ways that… to measure, you know, looking at the transcripts,
125 00:18:31.560 ⇒ 00:18:51.689 Uttam Kumaran: And one is, like, can we give better feedback? So, I know typically in call center training, and even in sales training, you look at the real scenarios, right? You’re looking at the real back and forth and giving feedback. I think this is a lot better way to not only have those in front of you, but also for the trainers to use AI to find those scenarios that they can probably give training on.
126 00:18:51.690 ⇒ 00:18:56.479 Uttam Kumaran: And second, we want to see that, like, ramp-up time shorten, right? With the use of Andy.
127 00:18:56.480 ⇒ 00:19:11.510 Uttam Kumaran: We want to see that maybe someone that comes in just brand new, they can actually hit the ground running at a higher level because they’re using Andy, you know, during their onboarding, and the ability… the impact of the training continues to happen.
128 00:19:11.510 ⇒ 00:19:27.340 Uttam Kumaran: So, yeah, I think it’s gonna be a huge unlock to sort of get these transcripts directly in the platform. And then lastly, this is the feedback loop we were talking about, where if we get the exact information that people are asking.
129 00:19:27.470 ⇒ 00:19:38.990 Uttam Kumaran: there is a natural feedback loop of understanding, okay, what other opportunities were there for people to use ANDI that maybe they didn’t? Either they made a mistake, or they put someone on hold, and how can we attack those?
130 00:19:38.990 ⇒ 00:19:51.499 Uttam Kumaran: you know, and say, hey, there was an… there was an opportunity for you to use Andy. And again, people may not understand that it can answer certain questions, people may just have forgotten, so it gives us real-life
131 00:19:51.530 ⇒ 00:19:53.330 Uttam Kumaran: You know, use cases.
132 00:19:53.370 ⇒ 00:19:56.569 Uttam Kumaran: So that’s something that I’m really, really excited for.
133 00:19:57.840 ⇒ 00:19:59.710 MattBurns: Oh, agreed, agreed.
134 00:20:01.030 ⇒ 00:20:03.599 MattBurns: Good. Janiece, anything else? I’m your…
135 00:20:03.600 ⇒ 00:20:04.270 JanieceGarcia: I’m…
136 00:20:04.990 ⇒ 00:20:22.839 JanieceGarcia: I’m really… I’m really excited for the… the inspector platform, the new way of even looking at things if we do go that route, but the usage, that was one big thing that I did ask Amber when we were in our working session yesterday, was to see if our usage went up, and she had…
137 00:20:22.840 ⇒ 00:20:27.729 JanieceGarcia: I already knew that good news, so, I did see that, but…
138 00:20:28.040 ⇒ 00:20:29.140 MattBurns: But yeah, no, I think.
139 00:20:29.140 ⇒ 00:20:30.100 JanieceGarcia: Getting the feedback is good.
140 00:20:30.100 ⇒ 00:20:32.569 MattBurns: That’s pretty common, isn’t it, Janice? I mean.
141 00:20:33.120 ⇒ 00:20:36.000 MattBurns: in terms of the inspectors, I mean, we’re…
142 00:20:36.930 ⇒ 00:20:38.799 MattBurns: If you don’t get that ride, that…
143 00:20:39.820 ⇒ 00:20:40.390 JanieceGarcia: abs…
144 00:20:40.390 ⇒ 00:20:48.220 MattBurns: takes a lot of time, too, to figure it out, and if it’s put right there in front of you in that format, it seems like that’s a time saver, you know?
145 00:20:48.540 ⇒ 00:21:05.499 JanieceGarcia: Huge time saver, but it also… and once we’re able to connect that Google Form and be able to automatically update those as well for us, it’s… it’s gonna be huge for us, because those… I mean, that’s everything to start out with the customer service experience, so… For sure.
146 00:21:06.460 ⇒ 00:21:08.240 MattBurns: Well, good stuff, guys, yeah.
147 00:21:09.620 ⇒ 00:21:10.400 JanieceGarcia: Awesome.
148 00:21:10.850 ⇒ 00:21:13.210 JanieceGarcia: Yay. Thank you guys.
149 00:21:13.210 ⇒ 00:21:13.900 Amber Lin: Sweek.
150 00:21:14.300 ⇒ 00:21:14.910 JanieceGarcia: Yes.
151 00:21:14.910 ⇒ 00:21:15.720 Amber Lin: Thank you.
152 00:21:15.720 ⇒ 00:21:16.230 Uttam Kumaran: Thanks.
153 00:21:16.230 ⇒ 00:21:16.720 JanieceGarcia: Bye!
154 00:21:17.710 ⇒ 00:21:18.180 Uttam Kumaran: B.
155 00:21:18.180 ⇒ 00:21:18.790 MattBurns: Bye-bye.