Meeting Title: Brainforge x ABC Home and Commercial: Weekly Project Check Date: 2025-05-02 Meeting participants: Uttam Kumaran, Amber Lin, Steven, Janiecegarcia
WEBVTT
1 00:00:40.180 ⇒ 00:00:41.830 Amber Lin: Hi, Steven!
2 00:00:41.830 ⇒ 00:00:43.100 Steven: Hi! How are you?
3 00:00:43.370 ⇒ 00:00:47.579 Amber Lin: Pretty good. I just finished up another meeting with
4 00:00:48.690 ⇒ 00:00:58.060 Amber Lin: with the team, and we are doing a lot of internal AI progress as well, so it’s really exciting to see see things come together.
5 00:00:58.060 ⇒ 00:00:59.740 Steven: Cool. That’s good.
6 00:01:00.400 ⇒ 00:01:02.940 Amber Lin: What about you? I know you were on holiday.
7 00:01:03.370 ⇒ 00:01:08.059 Steven: Yeah, yeah, we took our took our kids to seaworld. Actually, last Friday.
8 00:01:08.330 ⇒ 00:01:09.020 Amber Lin: Thank you.
9 00:01:09.720 ⇒ 00:01:11.290 Amber Lin: What’s in seaworld.
10 00:01:11.530 ⇒ 00:01:21.489 Steven: So seaworld is. There’s 1 here in San Antonio, and I think there’s 1 in Florida, too. It’s like an amusement park, but it’s it’s the one based with the killer whales like Shamu and all that they do. They have dogs
11 00:01:22.530 ⇒ 00:01:22.940 Steven: so.
12 00:01:22.940 ⇒ 00:01:23.469 Amber Lin: What’s in there.
13 00:01:23.470 ⇒ 00:01:25.800 Steven: Like roller coasters, and all that kind of stuff.
14 00:01:25.800 ⇒ 00:01:33.310 Amber Lin: What? So it’s like a like a outdoor aquarium, like kind of like a zoo. But then you also get like.
15 00:01:33.450 ⇒ 00:01:35.379 Steven: Yeah, I live with everything. Yeah, it’s pretty cool.
16 00:01:35.550 ⇒ 00:01:36.810 Amber Lin: Wow!
17 00:01:36.980 ⇒ 00:01:37.289 Steven: Yeah.
18 00:01:38.320 ⇒ 00:01:39.480 Amber Lin: Hi Janice.
19 00:01:39.900 ⇒ 00:01:40.810 JanieceGarcia: Morning.
20 00:01:41.210 ⇒ 00:01:45.630 Amber Lin: Good morning. Do you know, if Yvette is coming to this meeting.
21 00:01:45.940 ⇒ 00:01:58.940 Steven: Yeah, she’d email just a few minutes ago. Said she wouldn’t be able to make it. She’d she’d said we could reschedule, for she had a few action items that she’s working on today so we could either reschedule or just have a quick catch up and then catch up.
22 00:01:59.240 ⇒ 00:02:10.639 Amber Lin: Cool. I mean, we met with her yesterday as well. So she knows most of the stuff that’s going on. I’ll just share a quick update. We can keep this.
23 00:02:11.350 ⇒ 00:02:18.340 Amber Lin: Yeah, let me share my screen. And they can go through here.
24 00:02:19.090 ⇒ 00:02:19.930 Amber Lin: Right?
25 00:02:33.260 ⇒ 00:02:34.400 Amber Lin: So
26 00:02:35.020 ⇒ 00:02:48.780 Amber Lin: mostly today, I wanted to run this through a quick April recap. Denise. You already know about this, because this is what we talked about with. Give it yesterday, and then I have a quick few
27 00:02:48.890 ⇒ 00:02:59.090 Amber Lin: updates and also for the data team cool. So this is the this is the
28 00:02:59.340 ⇒ 00:03:02.350 Amber Lin: Kpi dashboard that we also have.
29 00:03:02.781 ⇒ 00:03:07.410 Amber Lin: I don’t know, Stephen, if you have access to it. I know your vet. Engineer needs to have access so.
30 00:03:07.410 ⇒ 00:03:12.070 Steven: Yeah, I think I’ve been. I haven’t. I haven’t looked down in a while, but I think I had access to it. I looked at it before.
31 00:03:12.240 ⇒ 00:03:16.220 Amber Lin: Fantastic. So this is for the month of April.
32 00:03:16.470 ⇒ 00:03:29.920 Amber Lin: So and and let’s see, yeah, this is all should be all bot metrics.
33 00:03:30.200 ⇒ 00:03:36.360 Amber Lin: And so this is everything, every conversation that has used Andy and
34 00:03:36.710 ⇒ 00:03:41.610 Amber Lin: down here. So we have around like 700 exchanges.
35 00:03:41.830 ⇒ 00:03:50.140 Amber Lin: And down here you can see who’s who has used it the most this month, so we can see that.
36 00:03:50.250 ⇒ 00:03:51.060 Amber Lin: Pardon me.
37 00:03:51.470 ⇒ 00:03:59.759 Amber Lin: Joy, use it a lot, Janice, use it a lot, lean and Amy, and this is sort of like a small leader board for this month
38 00:04:01.320 ⇒ 00:04:02.540 Amber Lin: and
39 00:04:04.901 ⇒ 00:04:29.449 Amber Lin: I can point. I’ll point out the average execution time later, but we can see here that the average execution time on Janice’s questions is a lot longer on Joy’s questions, and I think that would be because of the type of questions that they’re asking, because Janice is really tackling those very, very hard questions, and it makes sense that sometimes it takes a lot more time.
40 00:04:30.940 ⇒ 00:04:34.130 Amber Lin: So that’s 1 insight that we can find on this page.
41 00:04:34.630 ⇒ 00:04:39.870 Amber Lin: And this view is what we went over with Yvette
42 00:04:40.140 ⇒ 00:04:47.839 Amber Lin: yesterday. So this view composes of residential pest and free, and the callbacks for these 2.
43 00:04:48.050 ⇒ 00:05:00.190 Amber Lin: And we can see that in this month we we have data until, like April 23.rd The rest we haven’t received from Brian yet, but even on that we can see that
44 00:05:00.230 ⇒ 00:05:23.759 Amber Lin: the average handling time is about 5 min. And when we look at this graph. We can see. Okay, here’s a average handling time over time. So the average of each day. And then we can see on the right. We can see Max handling time. So what was the call that took the longest that day.
45 00:05:24.230 ⇒ 00:05:36.449 Amber Lin: and we can see comparing these charts, we’ll know, hey? Was it because there was an very, very long call outlier that made the average handling time a lot more
46 00:05:36.640 ⇒ 00:05:37.929 Amber Lin: that day.
47 00:05:38.400 ⇒ 00:05:50.180 Amber Lin: So by the Max, only time we can probably know. Okay, maybe on April 8, th and maybe on April 10th there was 2 calls or a few calls that took very, very long, as you can see here in the spike.
48 00:05:54.089 ⇒ 00:06:09.029 Amber Lin: Here is more of a very interesting view. This is the calls that used Andy. So this is from our data team that connected those data together, and we can see that from
49 00:06:09.170 ⇒ 00:06:15.570 Amber Lin: started April to around April 23, rd we had a hundred ish calls that
50 00:06:16.340 ⇒ 00:06:24.340 Amber Lin: where Andy was used. And I want to point our attention to average handling time right here.
51 00:06:24.770 ⇒ 00:06:40.210 Amber Lin: we want to. We’ll include more metrics as we get more data right now what we can get, what Brian sent us is a little bit limited, and we’re hoping that when we get the Api set up. But we’ll get a lot more information.
52 00:06:40.280 ⇒ 00:06:57.970 Amber Lin: And yesterday we were talking about what metrics we can use to compare of maybe using the average holding time instead of the average handling time, because Andy is helpful to reduce the time that clients are put on hold.
53 00:06:58.110 ⇒ 00:07:04.066 Amber Lin: And we can look at also what type of calls
54 00:07:05.010 ⇒ 00:07:12.480 Amber Lin: what type of questions were asked in these calls, or what type of calls they actually are. And then to figure out, okay.
55 00:07:12.580 ⇒ 00:07:33.259 Amber Lin: same time, last year, when we had new agents. This, the average time was this, but this year with Andy, did it actually change. So, comparing it, maybe to last year the same time what actually changed. So there’s a lot of things we can do with a dashboard here, and we’ve scheduled a
56 00:07:33.717 ⇒ 00:07:43.309 Amber Lin: weekly review or bi-weekly review with the event to go over these kpis and to know what what we can action on them.
57 00:07:47.130 ⇒ 00:07:51.379 Amber Lin: Here’s a interesting metric that we found
58 00:07:51.530 ⇒ 00:08:06.359 Amber Lin: with these Bot assisted calls. And so we can see that comparing, say, Joy and Cameron, they have a different levels experience. And on average, Joy’s calls take longer.
59 00:08:06.900 ⇒ 00:08:19.830 Amber Lin: Her calls take about 15 min versus for Cameron. Her calls. Take 5 min, and I think that’s an important factor. We want to take remember, when we compare
60 00:08:20.170 ⇒ 00:08:24.979 Amber Lin: these average handling times of who’s actually taking the calls.
61 00:08:25.540 ⇒ 00:08:39.680 Amber Lin: And actually, Janice has helped us to give us a list of people’s tenures to know, hey? Who’s more experienced. And who should we expect their calls to be faster? And if it’s not, then what’s gonna what’s happening right there?
62 00:08:43.417 ⇒ 00:08:58.089 Amber Lin: So this this view is for this week. So this week our average usage per day is actually lower than last week. And that’s kind of what I wanted to talk to you guys about? Of
63 00:08:58.470 ⇒ 00:09:10.319 Amber Lin: how do we improve usage? Because all of Andy’s a lot of Andy’s like benefits come from more usage and come from the Csrs actually using it in their calls.
64 00:09:11.230 ⇒ 00:09:13.689 Amber Lin: and we can see that
65 00:09:14.366 ⇒ 00:09:24.020 Amber Lin: it’s still the same people on the leaderboard. And over here I have a few people that have not used Andy at all this week.
66 00:09:24.570 ⇒ 00:09:32.150 Amber Lin: So if you can see in the middle the non-users on the right of total exchanges as of today, for this week.
67 00:09:32.270 ⇒ 00:09:35.910 Amber Lin: These are the people who haven’t used it.
68 00:09:41.390 ⇒ 00:09:47.709 Amber Lin: So a few things we want to think about of. How do we improve usage
69 00:09:48.090 ⇒ 00:10:07.179 Amber Lin: we talked about. I know if that is gonna get back to us on Csr office hours. I was mentioning that to her. Creating a slack channel helping to roll this out to the overflow agents, and also continue iterating on the issues
70 00:10:07.390 ⇒ 00:10:11.679 Amber Lin: and improving on the training part as well, so that we can
71 00:10:11.870 ⇒ 00:10:18.289 Amber Lin: help improve the usage. But I kind of wanted to get an idea from you guys as well. Of what do you think
72 00:10:18.470 ⇒ 00:10:23.960 Amber Lin: will improve that? How? What can we do to make more people use handy.
73 00:10:25.890 ⇒ 00:10:32.440 JanieceGarcia: I know truthfully right now I think the biggest thing is the errors
74 00:10:32.640 ⇒ 00:10:51.270 JanieceGarcia: so, and having cause even me going back through like I told you before, Amber, even me going back through and saying, Okay, well, this is in the central dock. Why is it not coming out so I think, in formatting, and everything is really aligned. And they’re getting the responses they need.
75 00:10:52.380 ⇒ 00:11:07.540 JanieceGarcia: It’s it’s that confidence we have to keep that confidence up, that they’re gonna be receiving what they need to receive. And you know, like we’ve shown, and like I’ve been putting in whether it’s in slack or it’s in the other documents.
76 00:11:07.910 ⇒ 00:11:11.890 JanieceGarcia: There’s stuff in there, but it’s not coming out. It’s not.
77 00:11:12.920 ⇒ 00:11:15.649 JanieceGarcia: you know. So how we get that improved.
78 00:11:17.690 ⇒ 00:11:39.430 Amber Lin: Sounds good. I haven’t got a chance to check on the error sheet. So we’ll also check on. Okay. Are those errors that surfaced are they improved on yet? And making sure that in updates we make are actually reflected in the bot. So I think we can think about a workflow, or we can
79 00:11:39.530 ⇒ 00:11:45.189 Amber Lin: have a period that we meet a lot more frequently where we dedicate that time to make sure that it happens.
80 00:11:46.510 ⇒ 00:11:51.669 Amber Lin: Hmm, Steven. Any ideas from your side of how we can implement.
81 00:11:51.670 ⇒ 00:11:58.480 Steven: You know, I think the confidence to Janice that I haven’t been in it much, but if they’re getting errors it’s gonna push people away then the time.
82 00:11:58.680 ⇒ 00:12:08.699 Steven: you know, I think some of them were average 5 seconds, you know, Denise, have you seen it? What yours was like up at 14 or 15 is is that it’s truly taken that long on some of them. The harder questions.
83 00:12:09.290 ⇒ 00:12:20.930 JanieceGarcia: It is on some of them, but it’s not a lot and I’ve shared a couple other sheets, and I made sure that they’ve had that
84 00:12:21.650 ⇒ 00:12:35.800 JanieceGarcia: brain forge is actually had, like the up to date documents, especially when it comes to. Okay. What technician can do this in this zip code and not having the updated information on a regular basis. That’s.
85 00:12:35.930 ⇒ 00:12:42.639 JanieceGarcia: you know, we just need to make sure that everything that we’re submitting and we’re putting in there is really coming out.
86 00:12:42.880 ⇒ 00:12:56.480 JanieceGarcia: And at this time I’ve noticed. And so the longer questions it is, the harder questions. But those are the things that I know is going to be coming from the Csrs because they’re asking. They’re starting to ask me
87 00:12:56.961 ⇒ 00:13:05.369 JanieceGarcia: to double check on them, you know, and even them going and asking Andy. They’re like, Well, I’m putting a thumbs down. I’ve asked this before, but
88 00:13:06.090 ⇒ 00:13:14.329 JanieceGarcia: you know I’m trying to get the errors corrected. But like I said, until that actually reflects in Andy.
89 00:13:15.160 ⇒ 00:13:16.350 JanieceGarcia: it’s not.
90 00:13:16.770 ⇒ 00:13:20.130 JanieceGarcia: Their confidence is going to go down, so they’re not going to use it as much.
91 00:13:21.063 ⇒ 00:13:41.660 Amber Lin: Totally, I think it just it means that we as a team need to meet more frequently and so that will be you me and the team to sort of really go and work through those errors, because I noticed that this week we didn’t have too much progress on the
92 00:13:41.780 ⇒ 00:13:46.589 Amber Lin: on the error sheet, and that’s something that I think we should improve on next week.
93 00:13:47.350 ⇒ 00:13:48.730 JanieceGarcia: Yeah, for sure.
94 00:13:49.140 ⇒ 00:14:04.670 Steven: It’ll just take some time, you know. Obviously some of those people aren’t using it all. There’s not comfortable, probably haven’t even tried yet, and it’ll just take some time. We feel more confident and really push. Then it’ll just be continue pushing, hey, for? Don’t forget, don’t forget, don’t forget, and it’ll get more usage as time goes on. I think.
95 00:14:05.940 ⇒ 00:14:26.670 Amber Lin: Totally. And I’m very confident about this, because it’s already a up down process, and we’re not sort of building from the ground up. We already have great examples and leadership who’s using it like you guys. And I think this will. This will work out. And it does take some time and special attention. To get everybody using it.
96 00:14:27.350 ⇒ 00:14:27.715 Steven: Cool.
97 00:14:28.080 ⇒ 00:14:34.320 Amber Lin: This is the sheet, as you can tell, not too many colors this week, which means we haven’t really addressed most of them.
98 00:14:35.620 ⇒ 00:15:00.729 Amber Lin: and last week we provided this formatting guideline. And this week we actually have made a ui that will help format documents that you sort of paste into it, it will help format it in the bot. Friendly guideline. We’re still testing it to make sure it gives the right results. But we’ll share this very soon, either in a email later or email, early next week.
99 00:15:00.890 ⇒ 00:15:11.610 Amber Lin: And we can go in and experiment with, does this really help with the formatting? And if yes, we can use this to help improve the formatting of the existing Central Doc.
100 00:15:11.790 ⇒ 00:15:22.580 Amber Lin: which Denise say will help the issues of Oh, why are things not coming out from the central dock, and most of the times is because the bot doesn’t understand what’s going on.
101 00:15:24.050 ⇒ 00:15:27.079 Uttam Kumaran: Hey, team? Sorry. I’m just a little bit late, just coming back from a meeting.
102 00:15:27.440 ⇒ 00:15:28.210 Steven: Hey, Tom!
103 00:15:28.560 ⇒ 00:15:29.110 JanieceGarcia: I would talk.
104 00:15:29.110 ⇒ 00:15:31.809 Uttam Kumaran: Hi! Hope everyone’s well.
105 00:15:32.480 ⇒ 00:15:33.409 JanieceGarcia: You as well.
106 00:15:33.620 ⇒ 00:15:34.405 Uttam Kumaran: Yes.
107 00:15:35.370 ⇒ 00:16:01.649 Amber Lin: We’re doing a quick little catch up today, cause we already talked about a lot of stuff we give it. Yesterday we went through a recap of April, and we talked about how we can improve the usage for next week. And right now I’m showing an update of what our engineers delivered for helping with formatting of the bot, and
108 00:16:02.000 ⇒ 00:16:06.420 Amber Lin: how this is going to be helpful is that this is going to help format
109 00:16:06.590 ⇒ 00:16:23.799 Amber Lin: the existing Central Doc and any additional updates, so that the bot actually retrieves correctly. What’s in there? Because Janice brought up a problem of it’s in a central doc. But Annie’s not answering, based on the central doc. So I think this will be really helpful.
110 00:16:24.950 ⇒ 00:16:45.109 Uttam Kumaran: Yeah, I think I think also, you know this, not only asking for more information as well before it completes the format, saying, Hey, you may have missed this one or 2 details. Can I get that? So I can round this out? You know, would be really, really helpful. And yeah, I know when we talked last week, Janice, the formatting was something that I just think quickly. We wanted to
111 00:16:45.230 ⇒ 00:17:14.149 Uttam Kumaran: just help to sort of close out. But I know that’s part of a larger sort of like, how do we get? Answers back in the central Doc faster. And then, yeah, I think broadly. We notice a couple of things this week on, just hey, it’s in the document, but it’s not being retrieved by Andy, or sometimes it does. Sometimes it doesn’t. I think that’s really helpful feedback for the AI team to basically nail down. How do we prioritize certain piece of information? How can we improve the retrieval of the agents, so that there are no situations where
112 00:17:14.290 ⇒ 00:17:21.140 Uttam Kumaran: answer is there, and and it’s not being retrieved. I think this is also something we should add to the evaluation
113 00:17:21.280 ⇒ 00:17:25.239 Uttam Kumaran: set amber so that we can begin to make sure that
114 00:17:25.359 ⇒ 00:17:33.610 Uttam Kumaran: from anything that’s like very obvious in the sheet, or repeated multiple times, even to the smallest thing that we’re able to confirm, that it’s it’s being
115 00:17:34.220 ⇒ 00:17:40.670 Uttam Kumaran: it’s being shared out. You know, or why it isn’t right what steps happening. That’s limiting that.
116 00:17:41.690 ⇒ 00:17:43.820 JanieceGarcia: Yeah, thank, you.
117 00:17:44.090 ⇒ 00:17:45.000 Amber Lin: Sounds good.
118 00:17:46.250 ⇒ 00:18:04.859 Amber Lin: And so the quick next step of adding that feature that interviews the trainers, of asking additional questions, to get all the necessary information. And next up for the data team, we’re doing weekly Kpi reviews.
119 00:18:04.970 ⇒ 00:18:19.990 Amber Lin: And we’re gonna invite David as well, because I know internally, the ABC data team is also building very similar stuff. Of how do we access the Apis or get all the call data and how to extract insights from there? And so I think there’s a
120 00:18:20.430 ⇒ 00:18:39.859 Amber Lin: that’s a great place for us to work together, because we work with data day in, day out. And it’s a great opportunity to make sure from the start that we’re aligned on different business logics and help each other to know. Okay, what kind of insights are important and what questions should we ask? Based on the data we have.
121 00:18:40.810 ⇒ 00:18:49.060 Amber Lin: That’s all. From this week it’s a pretty quick update. And any questions from you guys.
122 00:18:50.210 ⇒ 00:18:57.860 Steven: No, yeah, like I said no event was busy, but she said she had a few things she was working on today and would catch us up on that all next week.
123 00:18:58.630 ⇒ 00:19:04.359 Steven: yeah, no, I think so. I think it’s just, continued Rollout, getting a little bit better, a little bit better, and then continuing work with our
124 00:19:04.470 ⇒ 00:19:08.979 Steven: people, making sure they’re don’t forget about it, and utilize as much as possible.
125 00:19:09.530 ⇒ 00:19:16.409 Amber Lin: Yeah, that sounds great. I’ll send out the email later with Tom. You were saying.
126 00:19:16.410 ⇒ 00:19:19.649 Uttam Kumaran: Yeah, one thing, Amber, I think now that we’ve sort of hit the
127 00:19:19.900 ⇒ 00:19:23.370 Uttam Kumaran: sort of 1st milestone we’ve had, which is just rolling it out to past. I think
128 00:19:23.850 ⇒ 00:19:32.320 Uttam Kumaran: I think I want to set a second milestone of like these sort of usage numbers we’re trying to hit as well as like the quality. Basically, I wanna
129 00:19:32.490 ⇒ 00:19:55.890 Uttam Kumaran: see that, you know, issues coming up either from what we’re noticing or from Janice, are are going down over time, or at least the major ones are going down over time. That way. And then the basically, the next phase is like beginning to talk through how we roll this out to other divisions. So I would like to even just start to think about those as the next
130 00:19:56.000 ⇒ 00:20:06.429 Uttam Kumaran: milestones, if that those make sense right like. So how do we increase adoption? Currently? How do we in in parallel for that? How do we
131 00:20:06.620 ⇒ 00:20:16.589 Uttam Kumaran: mitigate existing issues and show that? And then 3, rd and then after that, I think we’re we’re we will be pretty well equipped to. Then bring the solution.
132 00:20:16.690 ⇒ 00:20:18.270 Uttam Kumaran: you know, a little bit broadly.
133 00:20:21.290 ⇒ 00:20:21.930 Steven: Yeah.
134 00:20:22.270 ⇒ 00:20:27.066 JanieceGarcia: Awesome can I ask you? Really quick for
135 00:20:28.200 ⇒ 00:20:30.549 JanieceGarcia: oh, my goodness! I totally lost my train of thought.
136 00:20:31.220 ⇒ 00:20:32.080 JanieceGarcia: Wow!
137 00:20:32.873 ⇒ 00:20:40.269 Uttam Kumaran: The only other thing I was gonna mention is that the technician Doc, is on our team’s plate to do. That’s 1 thing we we talked about
138 00:20:40.390 ⇒ 00:20:42.110 Uttam Kumaran: when I was on site.
139 00:20:42.260 ⇒ 00:20:43.030 Uttam Kumaran: That was like.
140 00:20:43.420 ⇒ 00:20:44.630 Amber Lin: So yeah.
141 00:20:44.630 ⇒ 00:20:59.870 Amber Lin: we figure that out. Actually, it turns out we were. We didn’t have access to the document that was updated. Janice has helped us and shared this with us. So, Casey, I think he has already changed the source in our system. I can check with him.
142 00:21:00.090 ⇒ 00:21:06.730 JanieceGarcia: That’s what I was actually going to ask was because the documents I had noticed that it was a copy
143 00:21:06.940 ⇒ 00:21:25.799 JanieceGarcia: that I guess Casey had made from before. So it was a copy of the actual document, and not the actual document itself. But I did make sure that you guys are commenters on that. So to open it up a little bit for for your access. But then, also what? There was the Pest Directory.
144 00:21:25.960 ⇒ 00:21:44.470 JanieceGarcia: because there’s been some questions that have come back from the Csr. Is like, who is this person’s manager? And so the Directory that’s going to be able to pull that as well. So it has to do with all the technicians and their managers or their supervisors. So I got you guys commenter access on that as well to to open that up.
145 00:21:44.470 ⇒ 00:21:44.940 Amber Lin: But they.
146 00:21:44.940 ⇒ 00:21:47.550 Uttam Kumaran: Thank you. That’s what I was going to bring up. Okay.
147 00:21:47.550 ⇒ 00:21:49.950 Amber Lin: Perfect, so so for these.
148 00:21:50.150 ⇒ 00:22:00.129 Uttam Kumaran: These external documents outside of the Central Doc Amber. Can we just put a note that we can start to document these in our like master, like the platform document spreadsheet.
149 00:22:01.191 ⇒ 00:22:12.410 Uttam Kumaran: You know, I I sort of see this really aligned with also the work that we’re planning. And we, Janice, we talked about, which is basically starting to deprecate
150 00:22:12.480 ⇒ 00:22:28.160 Uttam Kumaran: the old documents. Yep, you know. And I I know that’s just that’s something on our side that we’re we just have tracked as work. And, Steven, for your context, where we want to start to move people actually away from sourcing information from the old docs that we’ve cleaned up.
151 00:22:28.491 ⇒ 00:22:48.490 Uttam Kumaran: To start sourcing it from the one central document. We have a little bit of a phased approach where we’ll just sort of communicate it out. Basically start to lock it from edits. And like kind of put highlights that hey? This is going to be archived. Go to this other document and then over time, basically also, just like remove access to those and just keep them in archive.
152 00:22:48.550 ⇒ 00:22:50.930 Steven: So continuing to streamline.
153 00:22:51.050 ⇒ 00:23:13.420 Uttam Kumaran: That like. If people have questions, it should come from one document, I think that phase approach is something that I’ve seen work. But for any other document that we’re sort of not archiving, but it is still a core source outside of the Central Doc. I just want to have a really good pulse on that and then so we can just avoid yeah, if they shipped around. Or if we want to update those that’s just listed somewhere.
154 00:23:13.610 ⇒ 00:23:20.809 JanieceGarcia: And that should be like on the pest side. That should be those 2 and those 2 only
155 00:23:21.304 ⇒ 00:23:28.220 JanieceGarcia: until we get to the other ones, cause. That is part of like the operations. And they update those those sheets so.
156 00:23:28.220 ⇒ 00:23:28.810 Uttam Kumaran: Yes.
157 00:23:29.460 ⇒ 00:23:29.870 JanieceGarcia: So, yeah.
158 00:23:30.960 ⇒ 00:23:31.880 Uttam Kumaran: Okay.
159 00:23:33.410 ⇒ 00:23:33.850 Steven: Cool.
160 00:23:35.800 ⇒ 00:23:36.320 Amber Lin: Alright!
161 00:23:36.320 ⇒ 00:23:36.940 JanieceGarcia: Awesome.
162 00:23:37.210 ⇒ 00:23:38.330 Amber Lin: Well, perfect!
163 00:23:38.330 ⇒ 00:23:39.370 Amber Lin: The meeting.
164 00:23:39.370 ⇒ 00:23:40.329 Steven: Yeah. Appreciate. Y’all.
165 00:23:40.330 ⇒ 00:23:44.590 JanieceGarcia: Thanks. Appreciate you guys, thanks. Bye.
166 00:23:44.590 ⇒ 00:23:45.570 Amber Lin: Bye-bye.