Meeting Title: ABC | Standup Date: 2025-06-24 Meeting participants: Casie Aviles, Awaish Kumar, Amber Lin, Mustafa Raja
WEBVTT
1 00:00:58.990 ⇒ 00:01:00.210 Amber Lin: Hello!
2 00:01:01.300 ⇒ 00:01:06.400 Casie Aviles: Amber Mustafa will might like might be late. He’s just.
3 00:01:06.400 ⇒ 00:01:06.760 Amber Lin: Okay.
4 00:01:06.760 ⇒ 00:01:08.300 Casie Aviles: Something internally.
5 00:01:08.300 ⇒ 00:01:08.650 Amber Lin: Okay.
6 00:01:08.650 ⇒ 00:01:09.250 Casie Aviles: Okay.
7 00:01:09.550 ⇒ 00:01:17.370 Amber Lin: Okay, no worries I wish. Let’s start. If you’re any blockers on your current item.
8 00:01:20.220 ⇒ 00:01:22.509 Awaish Kumar: Nope, I’m working on that.
9 00:01:22.700 ⇒ 00:01:24.689 Awaish Kumar: Let me know when it’s done.
10 00:01:25.838 ⇒ 00:01:27.910 Amber Lin: When do you estimate it’s gonna be done.
11 00:01:29.880 ⇒ 00:01:32.859 Awaish Kumar: Yeah, I’m not getting enough time to work on that. So.
12 00:01:40.190 ⇒ 00:01:43.799 Awaish Kumar: I don’t like, after all the
13 00:01:44.810 ⇒ 00:01:50.200 Awaish Kumar: meetings and all, I get very little to actually go in and work on something.
14 00:01:50.360 ⇒ 00:02:02.639 Awaish Kumar: Oh, but I will try to get it out by maybe today. Yeah, that means, by
15 00:02:04.140 ⇒ 00:02:08.380 Awaish Kumar: that means. When we are
16 00:02:08.530 ⇒ 00:02:16.029 Awaish Kumar: okay, like by the next stand up. So I will work on that today and in the morning.
17 00:02:16.580 ⇒ 00:02:17.369 Awaish Kumar: In my time.
18 00:02:17.370 ⇒ 00:02:17.800 Amber Lin: I see.
19 00:02:17.800 ⇒ 00:02:18.360 Awaish Kumar: So.
20 00:02:18.410 ⇒ 00:02:19.630 Amber Lin: Oh, I see.
21 00:02:20.540 ⇒ 00:02:27.050 Amber Lin: Yeah, I wish I wanted to ask you about that. I know you. You are pretty short on time, like, do you think it’s
22 00:02:27.703 ⇒ 00:02:33.369 Amber Lin: like? Do you think you should hand it off? I mean, this is a little bit pressed on time so.
23 00:02:33.370 ⇒ 00:02:33.770 Awaish Kumar: Or.
24 00:02:33.770 ⇒ 00:02:41.200 Amber Lin: We might still need you to do this one, but we probably should onboard someone. So you don’t get caught in this situation again.
25 00:02:43.116 ⇒ 00:02:46.883 Awaish Kumar: Yeah, for ae work. We are going to get somebody
26 00:02:47.260 ⇒ 00:02:47.690 Amber Lin: Okay.
27 00:02:47.690 ⇒ 00:02:50.860 Awaish Kumar: But for data engineering tasks like this one
28 00:02:51.750 ⇒ 00:02:54.380 Awaish Kumar: for me or order some. We are only 2.
29 00:02:55.370 ⇒ 00:02:56.090 Awaish Kumar: Thank you.
30 00:02:56.988 ⇒ 00:02:59.030 Amber Lin: I see, I see. Okay.
31 00:02:59.200 ⇒ 00:03:09.040 Amber Lin: okay, let’s let’s talk, Tom, about this, because I feel like your time is very short. And I there’s a lot of stuff that you need to do, so I’ll remember to raise that to Tom.
32 00:03:09.330 ⇒ 00:03:13.420 Awaish Kumar: Yeah, I don’t. Wanna. I don’t wanna press you for it like when you don’t have time.
33 00:03:14.590 ⇒ 00:03:20.330 Awaish Kumar: Yeah. But that’s okay, like these, these tasks are not going to come as frequent as data modeling work.
34 00:03:22.024 ⇒ 00:03:29.130 Awaish Kumar: So unless we have more tasks, like data engineering tasks, then we need to escalate. But
35 00:03:29.350 ⇒ 00:03:33.370 Awaish Kumar: if it’s just the one, this one, and then only the data modeling work. Then.
36 00:03:33.370 ⇒ 00:03:34.010 Amber Lin: Hmm.
37 00:03:34.010 ⇒ 00:03:35.789 Awaish Kumar: We bring in an ae.
38 00:03:36.290 ⇒ 00:03:36.890 Amber Lin: Hmm.
39 00:03:37.540 ⇒ 00:03:38.100 Awaish Kumar: Yep.
40 00:03:38.560 ⇒ 00:03:48.480 Amber Lin: Okay, sounds good. So I’ll say, we’re aiming to get this done by tomorrow. Stand up complete.
41 00:03:49.880 ⇒ 00:03:54.350 Amber Lin: Right? Okay. Sounds good.
42 00:03:54.720 ⇒ 00:03:58.960 Amber Lin: Let me know if you need any more requirements, or anything on that.
43 00:04:01.190 ⇒ 00:04:05.950 Awaish Kumar: Okay and like,
44 00:04:07.240 ⇒ 00:04:13.045 Awaish Kumar: I don’t know like, why we get to this point of building these ourselves
45 00:04:14.690 ⇒ 00:04:20.079 Awaish Kumar: like, why didn’t we ask any vendor to do do that like polytomic or somebody.
46 00:04:21.160 ⇒ 00:04:23.150 Amber Lin: To oh.
47 00:04:23.150 ⇒ 00:04:26.110 Awaish Kumar: The pipeline. Yeah, like this Connector.
48 00:04:27.970 ⇒ 00:04:34.789 Amber Lin: Do you think it’s? I mean, we never really had anyone to talk about it. What do you think? It’s better to get polytoma to build it.
49 00:04:37.020 ⇒ 00:04:48.639 Awaish Kumar: Like, it’s not about better. It’s like we offload our some because we already have a polyatomic instance, where we run some of our ingestions and
50 00:04:49.070 ⇒ 00:04:49.390 Amber Lin: Okay.
51 00:04:49.390 ⇒ 00:04:55.419 Awaish Kumar: We could just ask them to build, build this out for us. So kind of data engineering work goes
52 00:04:56.540 ⇒ 00:04:58.959 Awaish Kumar: offloaded to to them like.
53 00:04:59.450 ⇒ 00:05:05.110 Amber Lin: Yeah, I mean, if you haven’t started on that, do you think it’s a good time to ask them to do that for us?
54 00:05:05.880 ⇒ 00:05:13.114 Awaish Kumar: Because it’s about like the urgency. So like, do you need
55 00:05:14.230 ⇒ 00:05:18.220 Awaish Kumar: like today or tomorrow? Then it’s it. It is me who can do that?
56 00:05:18.769 ⇒ 00:05:25.000 Awaish Kumar: But yeah, like we can ask them to put in, put it in their roadmap and
57 00:05:26.560 ⇒ 00:05:30.299 Awaish Kumar: and maintain it like in future, like for something like that.
58 00:05:31.980 ⇒ 00:05:34.389 Awaish Kumar: Like for the numbers we can ask them
59 00:05:34.983 ⇒ 00:05:39.060 Awaish Kumar: but like, if we are short on the time, then, like we are.
60 00:05:39.260 ⇒ 00:05:41.949 Awaish Kumar: then it will probably me, will be building that.
61 00:05:42.680 ⇒ 00:05:45.500 Amber Lin: Yeah, I don’t know how long it would take.
62 00:05:46.260 ⇒ 00:05:54.029 Amber Lin: because we had something that we asked polyatomic to build on the urban stem side, and it was taking quite a few quite a few weeks.
63 00:05:56.410 ⇒ 00:06:01.730 Awaish Kumar: Yeah, like that. They will text weeks because they will put it in backlog and then bring it in like.
64 00:06:04.030 ⇒ 00:06:09.110 Amber Lin: I see, okay, hmm.
65 00:06:11.120 ⇒ 00:06:12.890 Amber Lin: how long would this take? You.
66 00:06:15.380 ⇒ 00:06:18.439 Awaish Kumar: Sorry, but that’s what I said. I will just
67 00:06:18.640 ⇒ 00:06:22.060 Awaish Kumar: try to get it done by like
68 00:06:22.790 ⇒ 00:06:24.909 Awaish Kumar: tomorrow’s a stand up like if I done it
69 00:06:25.020 ⇒ 00:06:27.690 Awaish Kumar: before that I will let I will update you.
70 00:06:28.840 ⇒ 00:06:29.470 Amber Lin: Okay.
71 00:06:29.660 ⇒ 00:06:31.110 Awaish Kumar: But yeah, I’m hoping.
72 00:06:31.910 ⇒ 00:06:34.440 Awaish Kumar: Get it done by tomorrow.
73 00:06:34.670 ⇒ 00:06:35.480 Awaish Kumar: Stand up.
74 00:06:35.480 ⇒ 00:06:42.929 Amber Lin: I see, I see. Okay, let’s, I’ll note that down discuss with Utam as well. But I think for now let’s do it.
75 00:06:43.540 ⇒ 00:06:49.508 Amber Lin: let’s do it internally, because we do need the data. And Annie needs it to do some
76 00:06:50.600 ⇒ 00:07:05.249 Amber Lin: to do some visualizations. But other than that I think, when it there will be a few. One more endpoint that comes in, or there will be other systems that we might need to integrate in the future. And if we get that, we should ask polyatomic.
77 00:07:06.775 ⇒ 00:07:07.380 Awaish Kumar: Okay.
78 00:07:07.620 ⇒ 00:07:12.067 Amber Lin: Hmm, yeah, I’ll note that down. Thank you.
79 00:07:15.160 ⇒ 00:07:16.290 Amber Lin: Let’s see.
80 00:07:16.290 ⇒ 00:07:18.270 Mustafa Raja: Hey? Sorry for joining late.
81 00:07:18.650 ⇒ 00:07:26.409 Amber Lin: Hi, I think any quick updates on your.
82 00:07:26.410 ⇒ 00:07:40.940 Mustafa Raja: Yeah, I moved the move. The 30 30 second investigates did thing in over here. So what what it now is? It works very similar to how our client hubs are working in slack.
83 00:07:41.669 ⇒ 00:07:44.060 Amber Lin: Okay, so good.
84 00:07:44.600 ⇒ 00:07:49.350 Mustafa Raja: So if you want, I can share my screen quickly, quickly. Show.
85 00:07:51.260 ⇒ 00:08:05.930 Amber Lin: it’s okay. I think you’ll you can put the updates here. I think I just have one question, because I trust you on the development progress. I just want to know when you think this can be put into production like, when can say me or the trainers use this.
86 00:08:08.183 ⇒ 00:08:09.036 Mustafa Raja: I’ll
87 00:08:10.410 ⇒ 00:08:14.499 Mustafa Raja: I’ll I’ll try to have it done by this by by this cycle.
88 00:08:15.440 ⇒ 00:08:20.290 Amber Lin: Hmm, okay. So by the end, let me see how much is left.
89 00:08:20.710 ⇒ 00:08:26.819 Amber Lin: Okay. So by end, this Friday, we should be able to use it in production.
90 00:08:26.820 ⇒ 00:08:27.250 Mustafa Raja: Yeah.
91 00:08:27.570 ⇒ 00:08:38.320 Amber Lin: Okay, do you need me to test because today’s Tuesday, we have Wednesday, Thursday, Friday. Can I get like a test version sometime between before Friday.
92 00:08:38.640 ⇒ 00:08:39.979 Mustafa Raja: Yeah, yeah, give any feedback.
93 00:08:39.980 ⇒ 00:08:47.429 Mustafa Raja: Yeah. Once I’m once I’m done with this Google Google Docs. Api, this, this should be this should be ready for testing.
94 00:08:47.790 ⇒ 00:08:50.330 Amber Lin: Okay, awesome. So I will save
95 00:08:50.540 ⇒ 00:08:52.660 Amber Lin: end of this week. All right.
96 00:08:53.192 ⇒ 00:09:01.039 Amber Lin: Casey, I will give you more requirements for the spreadsheet. Maybe we can stay after together to talk about it.
97 00:09:01.960 ⇒ 00:09:02.750 Casie Aviles: Yes. Okay.
98 00:09:02.750 ⇒ 00:09:05.420 Amber Lin: Okay, yeah. Sounds good.
99 00:09:05.740 ⇒ 00:09:14.539 Amber Lin: I mean, that’s every that’s everything in here. Let me ask Annie about this task. But other than that, thank you. Everybody for coming here.
100 00:09:17.060 ⇒ 00:09:17.889 Mustafa Raja: Thank you.
101 00:09:18.270 ⇒ 00:09:18.980 Amber Lin: Thanks.
102 00:09:19.750 ⇒ 00:09:21.230 Mustafa Raja: Okay. Bye-bye.
103 00:09:21.900 ⇒ 00:09:22.680 Amber Lin: Bye.
104 00:09:26.650 ⇒ 00:09:29.079 Amber Lin: Casey, did you get time to
105 00:09:31.570 ⇒ 00:09:33.040 Amber Lin: Look at the.
106 00:09:33.040 ⇒ 00:09:33.770 Casie Aviles: Good.
107 00:09:33.900 ⇒ 00:09:34.660 Casie Aviles: The video.
108 00:09:34.660 ⇒ 00:09:36.010 Amber Lin: That I sent.
109 00:09:36.010 ⇒ 00:09:37.960 Casie Aviles: Oh, video, no, no, I I.
110 00:09:37.960 ⇒ 00:09:47.729 Amber Lin: Oh, I see it’s no all good. It’s the. It’s in the email that I sent to you that I didn’t point it out. So I don’t expect you to have seen it
111 00:09:48.390 ⇒ 00:09:51.630 Amber Lin: essentially. Let me share my whole screen.
112 00:09:52.607 ⇒ 00:09:59.369 Amber Lin: Oh, a wish. If you want to hop off, it’s okay. I’m just gonna look at one single ticket with Casey together.
113 00:10:00.000 ⇒ 00:10:01.420 Awaish Kumar: Okay, sure. Thank you.
114 00:10:01.420 ⇒ 00:10:02.780 Amber Lin: Yeah, thank you.
115 00:10:05.320 ⇒ 00:10:10.439 Amber Lin: So let me pull it up.
116 00:10:12.120 ⇒ 00:10:22.550 Amber Lin: So this is the spreadsheet. You see, this is the spreadsheet that we want to incorporate, and, as you can see, there is, there’s a lot of random stuff.
117 00:10:22.660 ⇒ 00:10:31.699 Amber Lin: and we talked about it. You also agreed that when we have to put it in. You have to make special rules for each of them, and I think that’s
118 00:10:32.590 ⇒ 00:10:37.169 Amber Lin: too treacherous of a task and very error prone for AI.
119 00:10:37.170 ⇒ 00:10:37.910 Casie Aviles: Yeah.
120 00:10:37.910 ⇒ 00:10:40.240 Amber Lin: So what I wanted to do is
121 00:10:40.819 ⇒ 00:10:42.970 Amber Lin: let me show you here.
122 00:10:43.870 ⇒ 00:10:55.800 Amber Lin: I did it in the back, so I I found I took this one. So this is a copy of the call station Tab. So right here, there’s a copy of this tab.
123 00:10:56.660 ⇒ 00:11:04.430 Amber Lin: and I essentially flattened it out right. So the the zip codes over here.
124 00:11:04.960 ⇒ 00:11:17.999 Amber Lin: I flatten it out right here and then see where it says residential pests. These are the inspectors that can do residential pests, so I put their names down there
125 00:11:18.260 ⇒ 00:11:19.730 Amber Lin: and then.
126 00:11:20.220 ⇒ 00:11:29.930 Amber Lin: Commercial pest is now is then another, you know, like another column, and everything in there is Brandon Cook. So making sure that all these zip codes have
127 00:11:30.040 ⇒ 00:11:43.419 Amber Lin: Brandon cook for commercial pests, and similar for these like these would apply for everything. So I split it up for okay, residential wdis would be this person that applies to all the zip codes.
128 00:11:44.000 ⇒ 00:11:47.020 Amber Lin: So this is essentially how I flattened.
129 00:11:47.440 ⇒ 00:11:51.320 Amber Lin: I flatten everything and
130 00:11:52.820 ⇒ 00:11:55.700 Amber Lin: I’m gonna pause here. If this makes sense.
131 00:11:58.060 ⇒ 00:12:01.379 Casie Aviles: Yeah, this is also kind of what I was thinking of
132 00:12:01.770 ⇒ 00:12:05.360 Casie Aviles: in terms of you know how how we want to flatten it out.
133 00:12:05.360 ⇒ 00:12:09.779 Amber Lin: Yeah, yeah, totally great. We’re on the same page. And so.
134 00:12:10.700 ⇒ 00:12:21.830 Amber Lin: this just this is just all the services that they have. I know it’s a big document, but essentially it’s the same that we saw here just in order.
135 00:12:22.340 ⇒ 00:12:26.869 Amber Lin: So we have. Each row is a different Zip code.
136 00:12:27.290 ⇒ 00:12:35.499 Amber Lin: And then each each column is a different service, and we have. So these are all.
137 00:12:35.670 ⇒ 00:12:53.079 Amber Lin: And then these are all pest services, as you can see on the top, I said, Okay, they’re pest. And then between pests we have commercial versus residential. So I also labeled it. So when the AI is searching for it. It’s probably easier when we labeled each one
138 00:12:53.310 ⇒ 00:12:55.060 Amber Lin: and then some of them.
139 00:12:55.330 ⇒ 00:13:14.229 Amber Lin: And so these are all the different services we have residential ones. We have commercial ones, and then special ones like term measure, come free. Same for lawn. There’s residential and commercial. I’m going to do the same for the Home Improvement department. They both have residential, commercial.
140 00:13:14.930 ⇒ 00:13:18.377 Amber Lin: and then for each of these services we would.
141 00:13:18.940 ⇒ 00:13:24.109 Amber Lin: I did like a vlookup to make sure that it just it applies the right
142 00:13:24.636 ⇒ 00:13:46.729 Amber Lin: inspector in each of the columns. So that say, and I think if we do this for all of the different zip codes, it’ll be pretty easy when we use AI, because then somebody search for a specific zip code. Okay, we’re like, okay, this is the Zip code. We’re searching for what service are they looking for? Oh, they’re looking for cur residential bedbug. So that would be this, this person
143 00:13:47.955 ⇒ 00:13:49.880 Amber Lin: does that make sense? Okay.
144 00:13:50.020 ⇒ 00:14:06.529 Amber Lin: awesome? I I do think we probably need to ask for some spreadsheet help, or maybe use AI to make sure that we have everything right. But I like, I think this is a good framework for us to start. I just don’t think I can. I can handle all of the different.
145 00:14:06.530 ⇒ 00:14:07.820 Casie Aviles: Yeah, sure. Sure.
146 00:14:08.310 ⇒ 00:14:08.640 Amber Lin: Yeah.
147 00:14:08.640 ⇒ 00:14:17.669 Casie Aviles: I can continue, I guess, for that. Can you walk me through like what exactly I need to do? I could share my screen, and then.
148 00:14:18.260 ⇒ 00:14:19.660 Casie Aviles: of course.
149 00:14:19.660 ⇒ 00:14:25.820 Amber Lin: Yeah, let me pull up one more document which will be helpful for you, and then let me send all of the links
150 00:14:26.040 ⇒ 00:14:31.929 Amber Lin: to you. So where I got the zip codes is in there
151 00:14:32.380 ⇒ 00:14:37.820 Amber Lin: service area. So that’s all of their complete zip codes that they have.
152 00:14:38.880 ⇒ 00:14:51.240 Amber Lin: So I think that would be. That would be a good starting point. And essentially, I got all of the college station once, and I did it just by I did it by area, because that’s kind of how
153 00:14:51.620 ⇒ 00:14:56.549 Amber Lin: the the inspector she is based off of.
154 00:14:57.510 ⇒ 00:15:06.419 Amber Lin: So yeah, let me here, I’ll stop share.
155 00:15:07.310 ⇒ 00:15:11.740 Amber Lin: Let me share the links with you service area.
156 00:15:13.098 ⇒ 00:15:18.179 Amber Lin: I’ll send it in slack as we probably need it.
157 00:15:19.895 ⇒ 00:15:33.510 Amber Lin: Spread sheet thread. Okay, I’m gonna send it in our channel so that one.
158 00:15:39.010 ⇒ 00:15:39.690 Casie Aviles: Okay.
159 00:15:44.220 ⇒ 00:15:49.909 Casie Aviles: so the target is this one or this one.
160 00:15:51.130 ⇒ 00:15:52.959 Casie Aviles: This is the one we’re building out right.
161 00:15:53.600 ⇒ 00:16:00.480 Amber Lin: Yeah, this is what we’re building out. And then the target is the inspector. Zip codes.
162 00:16:00.900 ⇒ 00:16:05.580 Casie Aviles: Inspector. So this we will get this from here.
163 00:16:07.710 ⇒ 00:16:16.470 Amber Lin: Yes, we’ll get all the zip codes. We probably should just get the 1st A B and C columns from this. From this spreadsheet.
164 00:16:18.090 ⇒ 00:16:19.060 Casie Aviles: Okay.
165 00:16:19.060 ⇒ 00:16:27.990 Amber Lin: Yeah, you can make a new document. New document if you want she.
166 00:16:35.440 ⇒ 00:16:36.493 Casie Aviles: All right,
167 00:16:37.640 ⇒ 00:16:41.812 Casie Aviles: And then I will just fill out the names here, for example,
168 00:16:47.850 ⇒ 00:17:12.489 Amber Lin: Yeah, I think. Let me demonstrate. Let me demonstrate. Let’s do a new one together. So it makes sense. And I mean, this meeting will be recorded so we can watch it together again. I’ll share my screen, and then I can walk you through. How I did a new one. It’s probably not the most optimal way. But I just think I don’t want you to have to fill in each name manually
169 00:17:12.670 ⇒ 00:17:13.280 Amber Lin: so.
170 00:17:13.290 ⇒ 00:17:14.109 Casie Aviles: Sure.
171 00:17:14.450 ⇒ 00:17:29.990 Amber Lin: Say, I have. Let’s see here. Okay, let’s do corpus. Let’s do Corpus. And so I’m gonna go here under Corpus Christi. I’m gonna copy all of these.
172 00:17:31.720 ⇒ 00:17:33.569 Amber Lin: These zip codes.
173 00:17:34.020 ⇒ 00:17:39.279 Amber Lin: I come back here and I paste it in.
174 00:17:39.420 ⇒ 00:17:44.710 Amber Lin: Okay, and then and then I go. See here.
175 00:17:46.790 ⇒ 00:17:56.599 Amber Lin: Okay, let me probably need to.
176 00:17:59.200 ⇒ 00:18:00.340 Amber Lin: The
177 00:18:04.690 ⇒ 00:18:09.740 Amber Lin: probably need to separate split into columns.
178 00:18:10.110 ⇒ 00:18:20.550 Amber Lin: And then I would transpose these okay oops.
179 00:18:21.630 ⇒ 00:18:28.529 Amber Lin: And then I think we can see here that for pests? Rodent.
180 00:18:28.860 ⇒ 00:18:29.890 Amber Lin: Okay.
181 00:18:30.320 ⇒ 00:18:40.219 Amber Lin: I think this is lawn cleaning, and this is lawn mowing. I don’t actually know what they mean, but that’s my assumption. And so
182 00:18:41.390 ⇒ 00:18:51.850 Amber Lin: I would put this here, but probably need to split them up later. And based on that. We know that. Okay, Larry, large and covers all these zip codes. So I’m gonna put his name here
183 00:18:53.700 ⇒ 00:18:57.890 Amber Lin: and then, and I fill it in for all of that.
184 00:18:58.060 ⇒ 00:19:02.559 Amber Lin: And then we can see that. Okay, he can do mosquito system estimates.
185 00:19:02.890 ⇒ 00:19:08.429 Amber Lin: So we’re going to say he can do that as well.
186 00:19:10.902 ⇒ 00:19:17.660 Amber Lin: For he can to Wdi reports. Okay, he can do that as well.
187 00:19:21.680 ⇒ 00:19:28.939 Amber Lin: Let’s check okay, so and then I’m gonna do. I’m just gonna quickly do
188 00:19:29.300 ⇒ 00:19:31.310 Amber Lin: this one as well.
189 00:19:33.130 ⇒ 00:19:35.029 Amber Lin: Let’s let’s do
190 00:19:38.980 ⇒ 00:19:40.049 Amber Lin: all right.
191 00:19:41.380 ⇒ 00:19:42.330 Amber Lin: Oh.
192 00:19:52.620 ⇒ 00:19:57.530 Amber Lin: A special transpose. Oh, gosh, okay.
193 00:20:03.040 ⇒ 00:20:06.339 Amber Lin: And I’m gonna say for this one, it’s
194 00:20:06.440 ⇒ 00:20:13.270 Amber Lin: Michelle Lopez. He can do all of them this these ones
195 00:20:14.199 ⇒ 00:20:24.440 Amber Lin: but he cannot do mosquito estimates. So I’m gonna leave that blank. I’m gonna say, Wdi reports he can do that. So I’m gonna fill that in.
196 00:20:27.020 ⇒ 00:20:29.410 Amber Lin: And then, same for.
197 00:20:30.130 ⇒ 00:20:30.530 Casie Aviles: Larry.
198 00:20:31.153 ⇒ 00:20:38.200 Amber Lin: Yeah. So I’m gonna say, data split to columns.
199 00:20:45.710 ⇒ 00:20:52.720 Amber Lin: So same for so that’s gonna be both of them.
200 00:20:58.340 ⇒ 00:21:01.410 Amber Lin: So that would be okay.
201 00:21:05.980 ⇒ 00:21:13.250 Amber Lin: that would be for these, right? So that now we have covered all this parts, then we can go look at
202 00:21:15.670 ⇒ 00:21:22.380 Amber Lin: Okay, let’s look at this. Okay, we say, holiday holiday lights.
203 00:21:22.730 ⇒ 00:21:30.590 Amber Lin: Okay, I’m gonna delete this one. So there’s no duplicates in there. So I’m gonna add one that says holiday lights. And it says
204 00:21:30.770 ⇒ 00:21:32.820 Amber Lin: these people.
205 00:21:33.390 ⇒ 00:21:37.280 Amber Lin: Oh, so I’m gonna put it here. And it says it’s for
206 00:21:37.480 ⇒ 00:21:47.710 Amber Lin: all of these zip codes. So I’m gonna just fill that in. So all of these are now covered. Okay, let’s now look at this one. It says.
207 00:21:47.960 ⇒ 00:21:50.730 Amber Lin: dry wood termite fumigation.
208 00:21:51.360 ⇒ 00:21:54.680 Amber Lin: So I’m just gonna say, Oh.
209 00:21:59.190 ⇒ 00:22:10.449 Amber Lin: and that would be these, and that also covers
210 00:22:10.710 ⇒ 00:22:21.339 Amber Lin: all of that. So that lines also done. Okay, right? So probably
211 00:22:21.510 ⇒ 00:22:27.529 Amber Lin: see, though, these are like, each of them, are a little bit. Each of them have their little complications, so that probably
212 00:22:27.530 ⇒ 00:22:30.540 Amber Lin: it means we should break down.
213 00:22:31.920 ⇒ 00:22:39.319 Amber Lin: Oh, gosh! That’s so confusing. Okay, let’s take. Let’s take this one out. We can have one that says, say.
214 00:22:43.660 ⇒ 00:22:44.900 Amber Lin: gonna take.
215 00:22:45.610 ⇒ 00:22:58.840 Amber Lin: You see? That’s why I broke I broke it down in this spreadsheet to very specific ones, because this says it doesn’t do residential pets. So in there we could have just took, took it out and said.
216 00:22:59.310 ⇒ 00:23:05.581 Amber Lin: Oh, gosh, okay, I’m doing it in the wrong. I’m doing it in their table. Let me take that out first.st
217 00:23:06.609 ⇒ 00:23:13.320 Amber Lin: I shouldn’t be doing it there, because this is the table. They operate in oopsies.
218 00:23:15.550 ⇒ 00:23:18.100 Amber Lin: Okay, my bad.
219 00:23:32.030 ⇒ 00:23:33.180 Amber Lin: anyways.
220 00:23:35.580 ⇒ 00:23:40.340 Amber Lin: Okay, great. No trace left. So I’m going to copy this.
221 00:23:41.940 ⇒ 00:23:43.550 Amber Lin: Go back here.
222 00:23:55.790 ⇒ 00:24:02.800 Amber Lin: Yeah. So essentially say, I’ll make another column here. That’s residential pest.
223 00:24:03.150 ⇒ 00:24:07.180 Amber Lin: And I’ll say, Okay, no.
224 00:24:07.310 ⇒ 00:24:13.490 Amber Lin: And also residential termite.
225 00:24:13.850 ⇒ 00:24:17.920 Amber Lin: Now go find these zip codes, and we’ll say there’s none.
226 00:24:19.920 ⇒ 00:24:31.360 Amber Lin: So I’m gonna copy these over first.st And let’s say, Okay, this one is, where is that?
227 00:24:32.640 ⇒ 00:24:39.099 Amber Lin: Okay? So that’s not even that’s not even there. So I guess if that’s that applies when we
228 00:24:39.530 ⇒ 00:24:43.900 Amber Lin: put it into, you know, when we have all of the
229 00:24:44.490 ⇒ 00:24:49.649 Amber Lin: all of the zip codes here, we would find that and say, Okay, the resident. We don’t do
230 00:24:51.080 ⇒ 00:25:04.060 Amber Lin: residential term. I there, anyways. And then we would say, Okay, for mosquito systems
231 00:25:04.690 ⇒ 00:25:09.980 Amber Lin: is all on Larry Lardin, so that would he would do everything.
232 00:25:13.110 ⇒ 00:25:18.979 Amber Lin: And bed. Bugs and commercial residential will put in another column that says that.
233 00:25:19.110 ⇒ 00:25:25.640 Amber Lin: and then we can enter in like that. This applies to okay.
234 00:25:25.640 ⇒ 00:25:29.880 Amber Lin: One. Yeah. I was thinking.
235 00:25:30.130 ⇒ 00:25:40.049 Amber Lin: you know what I think. It might be nicer if we complete, you know, if we do this, this little table first, st and then we take that table
236 00:25:40.420 ⇒ 00:25:44.399 Amber Lin: we go to here, and we do like a
237 00:25:45.400 ⇒ 00:25:48.680 Amber Lin: vlookup to fill in stuff first, st and then.
238 00:25:48.910 ⇒ 00:25:54.619 Amber Lin: and then you know these sort of apply to everything so we could base it off of
239 00:25:55.220 ⇒ 00:25:58.270 Amber Lin: these spreadsheets. I don’t know if that was clear.
240 00:26:01.200 ⇒ 00:26:04.310 Amber Lin: No, sorry. It’s okay.
241 00:26:06.310 ⇒ 00:26:26.859 Amber Lin: you know what? Let give it a try and then let me know. And we can also just work on things together. Now this is a little bit complicated, and probably why they haven’t changed it for the past 5 years. So we’re taking on a big task here. So no worries. If it doesn’t make sense, or if you need help, just leave, leave any comments
242 00:26:26.950 ⇒ 00:26:37.699 Amber Lin: on items that you’re not too sure on. And also don’t do what I did, because this is what their people use, so probably should copy it somewhere else. First.st
243 00:26:38.720 ⇒ 00:26:43.640 Casie Aviles: Yeah, yeah, I’ll give it a shot first.st Then, if I have any questions I’ll I’ll just make sure to ask. Yeah.
244 00:26:43.640 ⇒ 00:26:48.480 Amber Lin: Okay, yeah, I think it will be easier to start with.
245 00:26:49.865 ⇒ 00:27:00.400 Amber Lin: I don’t. Probably these like the zip codes, like, maybe Waco austin residential.
246 00:27:01.200 ⇒ 00:27:03.769 Amber Lin: That’s Corpus Christi.
247 00:27:04.200 ⇒ 00:27:04.960 Amber Lin: Yeah.
248 00:27:05.270 ⇒ 00:27:12.569 Amber Lin: just pick the ones you think makes more sense. And just ping me anytime you need help, or we can just call again, and walk through it together.
249 00:27:13.260 ⇒ 00:27:18.280 Casie Aviles: Okay, yeah, yeah. I don’t wanna take more time of your time right now. But yeah, I’ll just.
250 00:27:18.280 ⇒ 00:27:18.820 Amber Lin: All good.
251 00:27:18.820 ⇒ 00:27:20.060 Casie Aviles: I got it. Yeah, okay.
252 00:27:20.060 ⇒ 00:27:22.320 Amber Lin: Okay, yeah, thank you.
253 00:27:22.700 ⇒ 00:27:29.080 Casie Aviles: And yeah, just one last question for this week. Right? This is the only this is the only sheet that we have to worry for now or.
254 00:27:29.080 ⇒ 00:27:38.869 Amber Lin: Yeah, this is the only one. This is quite a big one. So I don’t even know if we can get it done this week, but we’ll try it. We’ll aim to get it all incorporated this week.
255 00:27:39.670 ⇒ 00:27:41.159 Casie Aviles: Okay. Sure. Sure. Sure.
256 00:27:41.160 ⇒ 00:27:42.969 Amber Lin: Yeah, thank, you.
257 00:27:42.970 ⇒ 00:27:43.880 Casie Aviles: Thanks, amber.
258 00:27:44.260 ⇒ 00:27:46.119 Amber Lin: Of course, bye, bye.
259 00:27:46.280 ⇒ 00:27:46.950 Casie Aviles: Bye.