Meeting Title: Brainforge x ABC Project Check-in Date: 2026-03-27 Meeting participants: Pranav Narahari, JanieceGarcia
WEBVTT
1 00:04:30.590 ⇒ 00:04:35.870 JanieceGarcia: So sorry. I know, I’m in with you. But it’s both of us. Oh.
2 00:04:36.280 ⇒ 00:04:37.110 Pranav Narahari: Hey!
3 00:04:37.560 ⇒ 00:04:39.840 Pranav Narahari: How’s it going? All good, all good.
4 00:04:40.930 ⇒ 00:04:44.190 Pranav Narahari: Are you guys usually in the same office? You are, right?
5 00:04:44.190 ⇒ 00:04:46.249 JanieceGarcia: Well, we’re both in San Antonio, yeah.
6 00:04:46.250 ⇒ 00:04:49.460 Pranav Narahari: Right, right. Okay, but, like, in nearby… okay, gotcha.
7 00:04:49.650 ⇒ 00:04:50.730 Pranav Narahari: Okay, cool.
8 00:04:50.730 ⇒ 00:04:54.200 JanieceGarcia: Kind of nearby. I sit on the other side of the hall. Yep.
9 00:04:54.710 ⇒ 00:04:56.400 Pranav Narahari: Okay, cool, cool.
10 00:04:56.720 ⇒ 00:04:57.980 Pranav Narahari: How are you listening?
11 00:04:57.980 ⇒ 00:04:58.670 JanieceGarcia: Friday?
12 00:05:00.220 ⇒ 00:05:08.450 Pranav Narahari: Yeah, happy Friday. Today was busy. This morning, we saw some more, I think, QA. Yesterday, we were back and forth, so…
13 00:05:08.750 ⇒ 00:05:11.449 Pranav Narahari: It’s a good way to close out the… the project.
14 00:05:11.720 ⇒ 00:05:20.669 JanieceGarcia: I haven’t told Yvette, though, about the usage. I was fixing to. I was like, I’m kind of excited, I want to see if Pranav talks about our usage, what we have Andy with.
15 00:05:20.670 ⇒ 00:05:22.730 Pranav Narahari: Oh, is it a little bit of a surprise? Perfect.
16 00:05:23.030 ⇒ 00:05:24.280 JanieceGarcia: Okay.
17 00:05:25.480 ⇒ 00:05:29.520 Pranav Narahari: Let me share my screen real quick.
18 00:05:29.520 ⇒ 00:05:32.860 JanieceGarcia: I don’t know about Steven and Matt. Yeah.
19 00:05:32.980 ⇒ 00:05:37.730 JanieceGarcia: I’m gonna move you to another screen so we can see what you share.
20 00:05:38.090 ⇒ 00:05:39.270 Pranav Narahari: Okay, perfect.
21 00:05:49.210 ⇒ 00:05:49.980 Pranav Narahari: Okay.
22 00:05:51.510 ⇒ 00:06:10.850 Pranav Narahari: So, yeah, kind of just wanted to go over, like, you know, today’s the big day of migration being complete, and then also we have the added, weekly usage report that we were talking about, and so we need to dive into the weekly usage report, and then on Monday next week,
23 00:06:11.200 ⇒ 00:06:23.550 Pranav Narahari: I’m gonna kind of go into, like, a lot of the wins with, like, the migration, and I think, you know, just having this weekend, too, to kind of go over things, and maybe even a little bit of Monday, and then we’ll have just more data to kind of talk about with the migration.
24 00:06:23.660 ⇒ 00:06:40.599 Pranav Narahari: And then lastly, just starting Monday, we’re just gonna kick off on… immediately on the central.copilot, and, a little bit of, like, the department-based insights as well. So, I’ll kind of give you kind of, like, a more concrete version of, like, week-to-week, what that’ll look like.
25 00:06:41.250 ⇒ 00:06:45.249 Pranav Narahari: And, also factoring in the feedback that we got on Wednesday.
26 00:06:45.890 ⇒ 00:06:46.500 JanieceGarcia: Okay.
27 00:06:46.910 ⇒ 00:06:57.629 Pranav Narahari: And so, yeah, just kind of wanted to show this, like, how we’re right on schedule with everything we want to do for the migration. And so, yeah, basically since I joined, like, kind of…
28 00:06:57.650 ⇒ 00:07:12.879 Pranav Narahari: we’ve re… reorganized a little bit of, like, what are the deliverables week to week. I wanted to kind of make it more clear what we are working on behind the scenes, and so… if there’s any questions on specific ones of these, I can kind of help describe and…
29 00:07:13.220 ⇒ 00:07:23.539 Pranav Narahari: relay what exactly is the business case to a lot of this stuff. But the important part here is, like, nothing is really red, everything’s green.
30 00:07:23.930 ⇒ 00:07:37.010 JanieceGarcia: How’s the accuracy? What do you think on the accuracy? I think it’s been… it’s been a lot better. Once we did the migration, we started testing the QA, we’ve seen a lot better, and I… and Pranav.
31 00:07:37.120 ⇒ 00:07:54.730 JanieceGarcia: please, please, please leave those Amy tickets, because I want to double check and make sure that we’re not reducing information on those, because I’m working closely with her, being a new trainer on the pest side. So don’t let the team mess with those, but for everybody else, it’s been very minimal.
32 00:07:54.730 ⇒ 00:08:05.200 JanieceGarcia: I have a question, and I’m sorry, I can’t really see the screen, so if there’s any numbers in there, forgive me. Is there any way that we can start measuring that for now?
33 00:08:05.460 ⇒ 00:08:07.900 JanieceGarcia: Like, accuracy? Yeah.
34 00:08:08.290 ⇒ 00:08:10.069 Pranav Narahari: Yeah, so,
35 00:08:10.230 ⇒ 00:08:25.159 Pranav Narahari: there’s one way that I can think of doing this, which is basically, in real, we can see the percent of thumbs down coming in per week. And so, yeah, that sounds like a pretty good way of…
36 00:08:25.320 ⇒ 00:08:44.600 Pranav Narahari: assessing whether things are progressing forward as well, or less. I think the… the total amount may stay kind of stagnant coming forward, because we’re just going to see increased usage, so let’s just look at, like, what that percentage is going, over time. And then also, with just, like, this migration of the central docs, we are kind of…
37 00:08:44.710 ⇒ 00:08:51.960 Pranav Narahari: getting all eyes onto the Central Docs for the first time in a while, I think, and so we were able to see, like, a lot of updates there, so…
38 00:08:51.960 ⇒ 00:08:52.680 JanieceGarcia: Yeah.
39 00:08:52.680 ⇒ 00:08:53.280 Pranav Narahari: But I figured…
40 00:08:53.280 ⇒ 00:08:57.540 JanieceGarcia: Oh, I’m so sorry, I’m so sorry. I was just gonna say, it would be very…
41 00:08:57.750 ⇒ 00:09:09.360 JanieceGarcia: good to see, you know, that number, but along with, like, okay, was that on our end, things that, you know, the thumbs down that were not clean, you know what I mean? So, I just… just to really get a good insight, yeah.
42 00:09:09.600 ⇒ 00:09:32.100 Pranav Narahari: Yeah, totally. And so, a little bit of the central dock, co-pilot that we have set up is going to be that daily memo that goes out, and in that daily memo, we’ll be able to assess all of the different triage tickets that come in, which ones are on ABC to update the central dock, which ones are on us, because
43 00:09:32.100 ⇒ 00:09:36.490 Pranav Narahari: Andy wasn’t functioning properly, wasn’t retrieving the correct information from the central doc.
44 00:09:36.490 ⇒ 00:09:49.949 Pranav Narahari: and then whatever other buckets there are as well in there. Like, there’s sub-buckets of the DB issue, central dock issue, etc. So, yeah, my goal is to, like, as we work on all this stuff, have it all displayed in real.
45 00:09:50.830 ⇒ 00:09:52.789 JanieceGarcia: Perfect, perfect, that’ll be excellent.
46 00:09:53.170 ⇒ 00:09:57.710 JanieceGarcia: And then you’ll work with me on how to pull information like that too, right?
47 00:09:58.530 ⇒ 00:10:05.460 JanieceGarcia: Pharrell, we have the dash… Are you going to be sending me the information? I know we talked about having a recording or something.
48 00:10:05.810 ⇒ 00:10:07.790 Pranav Narahari: Yes, yeah, so,
49 00:10:08.870 ⇒ 00:10:18.730 Pranav Narahari: Casey just worked on a bunch of just, like, revamping of Real, and he’s also working on a new dashboard as well for these weekly reports. And so…
50 00:10:19.420 ⇒ 00:10:30.439 Pranav Narahari: once that comes out, I think Rill would be in a really good place for me to show it to you guys, and kind of give a quick training on. And, let’s, like, let’s plan for that for next week.
51 00:10:31.630 ⇒ 00:10:32.420 JanieceGarcia: Sounds good.
52 00:10:32.900 ⇒ 00:10:33.500 Pranav Narahari: Cool.
53 00:10:34.350 ⇒ 00:10:39.640 Pranav Narahari: And so, with usage, right? So, this is what Janiece was talking about in the beginning.
54 00:10:39.640 ⇒ 00:10:40.920 JanieceGarcia: I’m excited.
55 00:10:40.920 ⇒ 00:10:45.340 Pranav Narahari: Yeah, so what we saw with usage, and let me…
56 00:10:45.700 ⇒ 00:10:48.970 Pranav Narahari: Let me flip my screen share real quick.
57 00:10:55.510 ⇒ 00:11:02.629 Pranav Narahari: So, right here, it says 700 total exchanges, and this is after excluding all of the QA testing that happened.
58 00:11:03.380 ⇒ 00:11:06.079 JanieceGarcia: Wow, that’s still, so far.
59 00:11:06.080 ⇒ 00:11:21.959 Pranav Narahari: I think this might be the most so far in terms of just production Andy usage, and it’s funny that it happened on the same week that we were also stress testing Andy for QA. So, if I were to remove all of these names besides Mustafa…
60 00:11:30.910 ⇒ 00:11:31.730 Pranav Narahari: Yeah.
61 00:11:32.350 ⇒ 00:11:33.830 Pranav Narahari: We’ll let this reload.
62 00:11:34.200 ⇒ 00:11:34.710 Pranav Narahari: Yeah.
63 00:11:35.030 ⇒ 00:11:37.070 Pranav Narahari: 1.2 thousand exchanges this week.
64 00:11:38.040 ⇒ 00:11:41.919 JanieceGarcia: Wow! That’s amazing. Who’s driving that?
65 00:11:42.300 ⇒ 00:11:56.819 JanieceGarcia: That was the Andy QA, so that right there tells you that there was about, what, 500, 600? That was just the trainers? Just the trainers, and everything else is… Is the agents. The agents, okay, all right. What division is Robby?
66 00:11:58.410 ⇒ 00:11:59.280 JanieceGarcia: Pest.
67 00:11:59.490 ⇒ 00:12:00.550 JanieceGarcia: passes? Yep.
68 00:12:02.260 ⇒ 00:12:19.420 JanieceGarcia: Did you… nope, so we haven’t updated the one, so I’ll actually… I’m gonna go in there before we share the numbers, because we still have the wrong ones in the… Yeah, screen… share that with me, because I’ll put a message out to the entire team on this. Okay.
69 00:12:20.030 ⇒ 00:12:27.110 JanieceGarcia: I can actually get that. Will you send that to me, Printoff? I mean, I can log into REAL and pull it, I just didn’t know if it was…
70 00:12:28.150 ⇒ 00:12:36.710 JanieceGarcia: I want to just… I want to start getting into the happening. Sharing it. I mean, that’s what I did in the beginning, when we first had kicked it off, I would share the data.
71 00:12:37.820 ⇒ 00:12:39.000 JanieceGarcia: With everybody?
72 00:12:43.980 ⇒ 00:12:48.680 Pranav Narahari: Yeah, and what’s cool here is we can also see a lot of the difference per…
73 00:12:49.230 ⇒ 00:12:51.490 Pranav Narahari: department as well. And so…
74 00:12:52.350 ⇒ 00:12:58.430 Pranav Narahari: Yeah. I don’t know if you want to share the data of the QA testing plus production, like…
75 00:12:58.430 ⇒ 00:13:00.669 JanieceGarcia: Production, just production, yeah.
76 00:13:00.850 ⇒ 00:13:06.770 Pranav Narahari: Okay, so then I will re-add all of these filters. Let’s see if there’s an easy way to do that.
77 00:13:07.130 ⇒ 00:13:11.090 Pranav Narahari: Yeah. I’ll do that right after this call, and then.
78 00:13:11.090 ⇒ 00:13:11.610 JanieceGarcia: Nope.
79 00:13:12.130 ⇒ 00:13:13.880 Pranav Narahari: It should be all set up for you guys.
80 00:13:13.880 ⇒ 00:13:25.579 JanieceGarcia: That’s excellent, though. Looks like we are driving some results there. Now that we can… and with that, we can start looking at accuracy and see what that looks like.
81 00:13:25.690 ⇒ 00:13:34.750 JanieceGarcia: And then all the other… all the other tools, I mean, all the things that we’ve talked about as well, but I think those two are going to be really good, because accuracy is really going to be where the trust
82 00:13:35.080 ⇒ 00:13:39.620 JanieceGarcia: You know, and making sure that that information is… is correct, yeah.
83 00:13:40.050 ⇒ 00:13:40.649 Pranav Narahari: Yep, that’s.
84 00:13:40.650 ⇒ 00:13:41.370 JanieceGarcia: Silicon?
85 00:13:41.850 ⇒ 00:13:46.689 Pranav Narahari: I also wanted to show you guys the category breakdown for this week.
86 00:13:48.760 ⇒ 00:13:49.380 Pranav Narahari: Yeah.
87 00:13:49.520 ⇒ 00:13:54.640 Pranav Narahari: So, let me see if I can zoom in a little bit. I’m using a different…
88 00:13:54.640 ⇒ 00:13:58.669 JanieceGarcia: enough to look up? Okay, so we’re looking for who… who’s who?
89 00:13:58.990 ⇒ 00:14:02.370 JanieceGarcia: Staff lookup and assignments, that’s zip code assignments, right?
90 00:14:03.830 ⇒ 00:14:05.280 Pranav Narahari: Yes, yeah.
91 00:14:05.920 ⇒ 00:14:14.879 Pranav Narahari: So, staff lookup and assignment is mostly zip codes, and what I kind of wanted to show here is just the categories, and then I have some sample questions per category, if we wanted.
92 00:14:14.880 ⇒ 00:14:15.910 JanieceGarcia: Okay, perfect.
93 00:14:16.390 ⇒ 00:14:16.930 Pranav Narahari: Yeah.
94 00:14:16.930 ⇒ 00:14:17.739 JanieceGarcia: pricing and building.
95 00:14:17.740 ⇒ 00:14:18.110 Pranav Narahari: Nice.
96 00:14:18.110 ⇒ 00:14:19.910 JanieceGarcia: It’s interesting, okay.
97 00:14:20.800 ⇒ 00:14:22.580 JanieceGarcia: Service requests, what?
98 00:14:22.910 ⇒ 00:14:23.940 JanieceGarcia: Shorthand.
99 00:14:24.310 ⇒ 00:14:28.599 JanieceGarcia: Right? Oh yeah, service request shorthand. What is that? Yeah. Turn off.
100 00:14:29.090 ⇒ 00:14:32.369 Pranav Narahari: Yeah, so let me hop into…
101 00:14:33.920 ⇒ 00:14:35.100 JanieceGarcia: Teaching questions.
102 00:14:35.260 ⇒ 00:14:37.260 Pranav Narahari: Let me hop into the sample questions.
103 00:14:41.980 ⇒ 00:14:43.200 Pranav Narahari: And then…
104 00:14:44.920 ⇒ 00:14:47.939 JanieceGarcia: So, yeah, inspectors.
105 00:14:49.490 ⇒ 00:14:54.089 JanieceGarcia: Do we fix fountains? Is that what there was?
106 00:14:54.480 ⇒ 00:14:56.740 Pranav Narahari: And so, yeah, here’s a few…
107 00:14:57.310 ⇒ 00:15:03.290 Pranav Narahari: I think it’s just, like, they’re asking for certain… and they’re just, like, really using just, like, keywords sometimes, sometimes they’re.
108 00:15:03.290 ⇒ 00:15:08.579 JanieceGarcia: I get it. Okay. So they’re not asking the question, they’re just kind of…
109 00:15:09.270 ⇒ 00:15:17.819 JanieceGarcia: Trying. Short, like, okay, so that makes exactly what he put, shorthand. Question. So, is this, this even… what… what does Andy spit out?
110 00:15:18.080 ⇒ 00:15:19.890 JanieceGarcia: Random stuff I’ve seen.
111 00:15:19.920 ⇒ 00:15:38.620 JanieceGarcia: Not really random, but trying… so, something like, okay, this right here… This would be super helpful to go in there and, like, just copy-paste and throw that into Andy. Yep. This right here, because I did it because Jenny in Mechanical, she was doing that, and she was like, Janice, why is it coming up with telling me that there’s no initial?
112 00:15:38.620 ⇒ 00:15:43.430 JanieceGarcia: I said, well, I need a screenshot of your actual questions, and this is what she would do.
113 00:15:43.430 ⇒ 00:15:50.869 JanieceGarcia: Termite inspector in the zip code. Then she would ask you residential or commercial, and she would say, oh, residential.
114 00:15:51.030 ⇒ 00:16:01.710 JanieceGarcia: And then she wouldn’t get what she was looking for, because he would come back and say, okay, well, here’s all your termite inspectors. And then she goes, oh, customer’s canceling safe.
115 00:16:02.660 ⇒ 00:16:11.890 JanieceGarcia: Or cancel, save, something like that. It was very one to two words, that’s it. And then it said, it would give her save tactics. Moving.
116 00:16:12.870 ⇒ 00:16:14.949 JanieceGarcia: Okay, well, now what?
117 00:16:15.070 ⇒ 00:16:20.809 JanieceGarcia: you know, where are they moving? And he tries to go in there and help, but then it’s…
118 00:16:21.650 ⇒ 00:16:26.340 JanieceGarcia: all… all in the end, it came out with… Andy came out with,
119 00:16:26.410 ⇒ 00:16:43.449 JanieceGarcia: to save the customer, we want to… we want to go ahead and create the account and offer to waive their initial fee. And I said, Jenny, but if you actually ask customers canceling from current home, but staying in the service area.
120 00:16:44.130 ⇒ 00:16:48.720 JanieceGarcia: it gave the exact right answer. But that’s gonna be my question, so…
121 00:16:48.830 ⇒ 00:16:54.250 JanieceGarcia: That’s my question, right? Because I know this came up before, like… one…
122 00:16:55.770 ⇒ 00:16:59.610 JanieceGarcia: how big of a question, right? Because I’m having to type this, I’m on the phone, I’m doing that.
123 00:16:59.810 ⇒ 00:17:05.430 JanieceGarcia: So… How are we training? How should they be asking the questions?
124 00:17:05.700 ⇒ 00:17:06.500 Pranav Narahari: Yes.
125 00:17:06.500 ⇒ 00:17:12.810 JanieceGarcia: or shorthand is, no, no, no. You’ve got to… you’ve got to ask the questions like…
126 00:17:12.940 ⇒ 00:17:15.230 JanieceGarcia: to make it make sense. So…
127 00:17:15.230 ⇒ 00:17:19.020 Pranav Narahari: Yeah, so I just kind of showed two examples here, and I think…
128 00:17:19.430 ⇒ 00:17:38.650 Pranav Narahari: what I want to show here is the difference, is that I actually think Andy may have performed okay with these service requests that are shorthand. However, there’s another grouping here for short and ambiguous inputs. And so things like this, these are things that definitely need to be, like, improved upon. Okay.
129 00:17:38.770 ⇒ 00:17:44.300 Pranav Narahari: And so… And if I go back to the category breakdown.
130 00:17:45.390 ⇒ 00:17:50.400 Pranav Narahari: This is actually, like, a pretty… it’s a non-zero percentage of…
131 00:17:50.540 ⇒ 00:17:52.690 Pranav Narahari: the amount of requests Andy’s getting.
132 00:17:52.800 ⇒ 00:18:05.430 Pranav Narahari: So, here, short and ambiguous inputs is 8%, which is 150 inputs. So, it’s not a huge amount, but it is the…
133 00:18:05.430 ⇒ 00:18:11.810 JanieceGarcia: This is a learning opportunity, this is great, this is what we’re asking for, right? This is what we want, to be able to go back and say, okay.
134 00:18:12.120 ⇒ 00:18:17.199 JanieceGarcia: We need to make sure that they’re… they know, they’re trained how to ask the right way, not…
135 00:18:17.510 ⇒ 00:18:22.340 JanieceGarcia: Right, the, like, this, this example. This was her screenshot that she sent me.
136 00:18:22.890 ⇒ 00:18:38.100 JanieceGarcia: That’s the Jenny one. Oh, okay, okay. And so what I did was I… because she was like, Janiece, I keep asking it the same question. I said, okay, well, I need to see how you’re asking that. Yeah. So then I sent her the whole screenshot, and all I said was, customer wants.
137 00:18:38.100 ⇒ 00:18:53.549 JanieceGarcia: to start service at new home. Yeah, but you hear where I’m going with this, right? Because if you have a new hire that comes in, you have to train them. Like, how am I supposed to? Because if not, they think that this is okay to do that, and then they’re gonna say, well, it doesn’t work for me.
138 00:18:53.550 ⇒ 00:19:06.439 JanieceGarcia: Right. So that’s what I really want to drill down on. This is… this is excellent stuff, because that’s like, okay, what is… what are they… what are we… what are they asking? How are we responding to it, and where do we bridge any gaps that need to be bridged?
139 00:19:06.720 ⇒ 00:19:12.439 Pranav Narahari: Yeah, and Yvette, what I’m hearing from you, too, is, like, with each of these questions that are being asked, like.
140 00:19:13.100 ⇒ 00:19:20.389 Pranav Narahari: what was the response by Andy, so we can assess, like, what is the level of detail that needs to be added, right?
141 00:19:20.390 ⇒ 00:19:22.460 JanieceGarcia: Yes, right, exactly.
142 00:19:22.640 ⇒ 00:19:36.259 Pranav Narahari: Yeah, and so when we have things set up in Reel, which Casey’s working on right now, all of that information’s gonna be tied together. So we won’t have just, like, stand-alone, like, Google Sheet reports like this. Everything will be in there. You’ll be able to click on certain questions and get…
143 00:19:36.260 ⇒ 00:19:45.150 Pranav Narahari: More understanding of even execution time, because sometimes if you’re getting ambiguous inputs, the execution time is going to take a lot longer as well.
144 00:19:45.160 ⇒ 00:19:47.180 Pranav Narahari: So… Yeah.
145 00:19:47.180 ⇒ 00:19:50.700 JanieceGarcia: And I think this is a good exercise, too, once you get out with your trainers.
146 00:19:50.940 ⇒ 00:20:07.579 JanieceGarcia: Yes, definitely, and this is something, and I use, like, the questions like this for Jenny. This will be something that I use with Rayanne, but Rayanne’s already all… already noticed this. Okay, oh, got you. Yeah, because all of her questions, she pretty much does… Yeah.
147 00:20:07.600 ⇒ 00:20:27.390 JanieceGarcia: And again, this… when you hire someone, this is where the stuff is important, because you could… you already know, like, when I have a new hire, what am I… what should… because remember, Andy should… part of what we wanted, the goal is, when I hire someone, my training should shave down, right? So, we should already start building. What does…
148 00:20:27.790 ⇒ 00:20:37.210 JanieceGarcia: when a new hire comes in, what’s the level of training that we’re doing with Andy, and how do they know how to answer the questions, all that stuff.
149 00:20:37.730 ⇒ 00:20:43.940 JanieceGarcia: I forget about Turing Deer asking me about, chelsea.
150 00:20:44.420 ⇒ 00:20:51.159 JanieceGarcia: Oh, with Andy? Okay. Oh, I think she chatted me, but I was in another meeting. All good.
151 00:20:52.120 ⇒ 00:20:52.910 JanieceGarcia: Yay.
152 00:20:52.910 ⇒ 00:20:53.240 Pranav Narahari: So…
153 00:20:54.380 ⇒ 00:21:02.910 Pranav Narahari: One other thing that I just wanted to bring up here is that 25% of the usage of ANDI is for things that are pulling from the DB.
154 00:21:03.030 ⇒ 00:21:05.150 Pranav Narahari: And so…
155 00:21:05.350 ⇒ 00:21:15.169 Pranav Narahari: I think, Janiece, you and I have talked about how just, like, just that DB being more accurate is gonna be super important for certain departments more so than other departments, like.
156 00:21:15.170 ⇒ 00:21:15.500 JanieceGarcia: What do you mean.
157 00:21:15.500 ⇒ 00:21:24.789 Pranav Narahari: test, for example, is like, they really need that DB to be working, and then if that’s working, like, they’re gonna see a huge spike in usage there.
158 00:21:24.790 ⇒ 00:21:38.639 JanieceGarcia: But still, what are still some of the… the gaps that we have on… on… On the database, yeah. It’s… it’s not getting the correct data, it’s the fact that it’s not in there, but do we have it? Okay. So, pest…
159 00:21:38.640 ⇒ 00:21:51.699 JanieceGarcia: inspectors, those are pretty good. The ones that are, oh no, we just need to open up all of our techs, or you just put it… like, mechanical, for example, we do dispatch… dispatch bins, but in the database.
160 00:21:51.780 ⇒ 00:22:14.200 JanieceGarcia: Tara and Cass worked really hard with Brain Forge in saying, okay, well, these are the licenses for the technicians, this is what we have, and that’s in there too, so it may not show, okay, you’re going to schedule on this specific person, but you’re able to figure out who’s our plumbers, who’s our master plumbers, who’s our apprentices, those type of things. We don’t have that for
161 00:22:14.200 ⇒ 00:22:15.509 JanieceGarcia: Comment from that side.
162 00:22:15.530 ⇒ 00:22:30.870 JanieceGarcia: For all of them. We do for San Antonio. Maybe you need to work… maybe you need to work with Steve. Maybe you need to show him how Andy works and what we’re trying to accomplish here. Yeah. Because you’re going to work with the… With that, the person, the one that’s… yeah.
163 00:22:30.910 ⇒ 00:22:45.380 JanieceGarcia: I will say the irrigation stuff, I did get confirmation that we’re good on the areas, and who the irrigators are, so I’m gonna go ahead and add that into the database, so you guys don’t have to. I don’t know if you saw my chat.
164 00:22:45.760 ⇒ 00:22:46.400 Pranav Narahari: Yep.
165 00:22:46.590 ⇒ 00:22:48.650 Pranav Narahari: Right. Okay. Okay. Perfect.
166 00:22:48.860 ⇒ 00:22:57.769 Pranav Narahari: And yeah, I’m happy that, you know, Casey worked on that, too, for you to kind of be able to update that information and not have to kind of go through triage.
167 00:22:58.460 ⇒ 00:23:08.839 JanieceGarcia: Right, I love it, because I can pull the triage ticket that comes through, or I have, you know, so-and-so’s zip codes are updating, perfect, then let me update it, and it’s 2 seconds.
168 00:23:09.020 ⇒ 00:23:09.880 Pranav Narahari: Yep, yep.
169 00:23:09.880 ⇒ 00:23:10.450 JanieceGarcia: It’s no time.
170 00:23:10.450 ⇒ 00:23:11.900 Pranav Narahari: Fantastic. Yeah.
171 00:23:13.030 ⇒ 00:23:20.220 Pranav Narahari: And we also worked on that issue that was, Janice, I think you were saying you would add something into the DB, and then…
172 00:23:20.440 ⇒ 00:23:29.980 Pranav Narahari: you would ask a follow-up question immediately, and it wouldn’t show the new information that you just added, so now that’s fixed as well. So we’ve basically made it…
173 00:23:30.590 ⇒ 00:23:32.760 JanieceGarcia: Sorry, I didn’t mean to cut you off. Go ahead.
174 00:23:32.760 ⇒ 00:23:38.750 Pranav Narahari: Oh, no, I was just gonna say that that should be fixed now, but if you’re noticing anything off there…
175 00:23:38.920 ⇒ 00:23:41.390 Pranav Narahari: Or I’m not sure if you’ve tested that again.
176 00:23:42.390 ⇒ 00:23:48.550 JanieceGarcia: I have not tested that again, but I did want to ask you, do I still need to use the link that’s in
177 00:23:48.870 ⇒ 00:23:53.449 JanieceGarcia: Our chat, or are we good?
178 00:23:53.590 ⇒ 00:23:57.319 JanieceGarcia: Because I noticed that the old link is not working. Whoops.
179 00:23:57.860 ⇒ 00:23:58.700 JanieceGarcia: Still.
180 00:23:59.700 ⇒ 00:24:03.780 Pranav Narahari: Okay, I sent you one link a few days ago, and that one isn’t working again?
181 00:24:03.780 ⇒ 00:24:08.720 JanieceGarcia: No, that one is, so do I just need to save that one and bookmark that one in replace of my other one?
182 00:24:09.170 ⇒ 00:24:24.410 Pranav Narahari: Yeah, just bookmark that one for now. I think next week we’ll have… that’s technically, like, our dev, like, link, but we just wanted you to have a link, and it really does not make a big difference in this case if it’s dev or production.
183 00:24:24.410 ⇒ 00:24:24.960 JanieceGarcia: Okay.
184 00:24:25.110 ⇒ 00:24:30.360 Pranav Narahari: But in the future, we will probably send you a… we will send you a different link for production.
185 00:24:30.930 ⇒ 00:24:31.879 JanieceGarcia: Okay, that’s fine.
186 00:24:32.340 ⇒ 00:24:36.709 JanieceGarcia: Cool. I just want to get those updates done, so… and that’s the one I’ve been using, so… just making sure.
187 00:24:36.710 ⇒ 00:24:42.410 Pranav Narahari: Perfect. Perfect, perfect. Okay, let me hop back into… Slide deck.
188 00:24:43.810 ⇒ 00:24:47.420 Pranav Narahari: It’s, like, end of Friday, and I just have so many tabs open.
189 00:24:49.700 ⇒ 00:24:54.870 JanieceGarcia: We do, too. Yvette’s probably staring at my other screen, like, what the hell does she have over?
190 00:24:54.870 ⇒ 00:24:55.250 Pranav Narahari: Yep.
191 00:24:55.250 ⇒ 00:25:07.100 JanieceGarcia: When David comes over to my office, he’s all like, can we close these? Yeah, he knows that I’m, like, notorious for that. Wait a minute, not that one.
192 00:25:08.790 ⇒ 00:25:16.349 Pranav Narahari: Yeah, so we talked a little bit on Wednesday, I believe, just kind of on these different projects, and
193 00:25:16.670 ⇒ 00:25:23.999 Pranav Narahari: got a little bit of feedback from you guys on the central.coPilot, how an end-of-day memo would be better than an end-of-week memo, so I updated.
194 00:25:24.000 ⇒ 00:25:24.570 JanieceGarcia: Yep.
195 00:25:25.170 ⇒ 00:25:33.930 Pranav Narahari: really doesn’t make much of a difference on, like, the technical approach for us, but I’m glad that, you know, we could have that 24-hour SLA.
196 00:25:33.930 ⇒ 00:25:34.460 JanieceGarcia: Yep.
197 00:25:34.770 ⇒ 00:25:46.410 Pranav Narahari: And the idea of just, like, a bunch of triage tickets just getting piled on top of each other and having to assess, are there duplicates even in the triage? Now that won’t be much of an issue with just having it
198 00:25:46.560 ⇒ 00:25:48.250 Pranav Narahari: Run every single day.
199 00:25:48.700 ⇒ 00:25:54.790 Pranav Narahari: And so here, it’s like a pretty quick turnaround on, I think, this triage…
200 00:25:54.900 ⇒ 00:26:05.080 Pranav Narahari: workflow update. Basically, as of end of next week, we’ll have a whole, just… we’ll have a documentation of just, like, how this new process should look.
201 00:26:05.150 ⇒ 00:26:13.520 Pranav Narahari: What I am noticing that isn’t great with the current process is that there’s a lot of stale linear tickets from even last year.
202 00:26:13.520 ⇒ 00:26:27.759 Pranav Narahari: That are just, like, sitting in there, because it’s just, like, we don’t know… and I don’t… maybe they’ve already been completed, but there just was never an end state defined, I think, anywhere. I don’t think, Janiece, anybody’s given you, like, any documentation about, like, okay, at this stage, it should be…
203 00:26:27.760 ⇒ 00:26:31.610 Pranav Narahari: Looking like this in linear, and then when it’s completed, it should look like this in linear.
204 00:26:31.610 ⇒ 00:26:50.460 Pranav Narahari: And there are a lot of different paths that it can go down, right? You, Janiece, you might assign it to a trainer internally at ABC, you also might assign it to Casey, you might even assign it to yourself if you’re gonna make the update in the zip code yourself. So, all of that stuff is going to be…
205 00:26:50.600 ⇒ 00:27:06.129 Pranav Narahari: I think at this point, we… I have a really good understanding of just, like, that whole approach. I’ll talk to you as well as I’m developing this new workflow, and you can let me know if there’s any edge cases that aren’t defined. But the idea is, by end of next week, we’ll have it all defined.
206 00:27:06.540 ⇒ 00:27:25.279 Pranav Narahari: everything in linear, the status should… will make sense. And then once the automations are in place, nothing should be there for more than a day. And if it is in there for more than a day, then you guys will be alerted to know that, okay, this one has not been looked at in the last 24 hours.
207 00:27:25.320 ⇒ 00:27:28.260 Pranav Narahari: I think just having that level of flagging.
208 00:27:28.460 ⇒ 00:27:31.669 Pranav Narahari: It’s gonna help us a lot with just keeping linear tidy.
209 00:27:32.540 ⇒ 00:27:48.800 JanieceGarcia: Yeah, no, for that, I agree. Yeah. I agree 100%. I did tell you, any vet on Wednesday when we met that I would go through all my linear tickets and complete and update what needed to be. So I have done that. So my issues, everything that was assigned to me.
210 00:27:49.020 ⇒ 00:28:03.099 JanieceGarcia: is now under completed. But like I said, that’s why I was telling you, leave the Amy ones, because I still have those in triage, because I didn’t want to move them over. I’m going to look at them before anything to actually see what the request is.
211 00:28:03.630 ⇒ 00:28:17.279 Pranav Narahari: Perfect, yeah, and then at some point next week, we’ll assess, like, all of the historical triage tickets, and then we’ll… we’ll just start kind of clean a little bit there, or just start from a place where they’re all in statuses that make sense for the new workflow.
212 00:28:17.980 ⇒ 00:28:18.750 JanieceGarcia: Perfect.
213 00:28:18.910 ⇒ 00:28:33.780 Pranav Narahari: And so, the idea here is that we can implement this new workflow, and it’s gonna be kind of in production starting next Friday. However, with, like, the placement automation, which is gonna help whoever the ticket’s assigned to.
214 00:28:33.780 ⇒ 00:28:49.770 Pranav Narahari: where it’s going to be added into the central dock, and then also assessing whether information is duplicate or conflicting in the central dock, that’s gonna happen the following week. And so, yeah, April 10th is where we’re looking at in terms of targeting for that to be as part of the workflow as well.
215 00:28:49.770 ⇒ 00:28:55.539 Pranav Narahari: And then lastly, just having that working end-to-end, we’re gonna…
216 00:28:55.540 ⇒ 00:29:15.790 Pranav Narahari: probably want to refine the sensitivity of assessing, okay, is this the right placement in the central doc? And then also the sensitivity of, like, is this information duplicate or conflicting? And so that’s what that last week is for, as well as just developing a memo, which is gonna be, what were the updates to the central dock.
217 00:29:16.700 ⇒ 00:29:24.310 Pranav Narahari: what are the thumbs up, thumbs downs, the accuracy that we’re seeing, week to week? That’s what’s gonna happen in that final week.
218 00:29:24.820 ⇒ 00:29:25.390 JanieceGarcia: Okay.
219 00:29:26.010 ⇒ 00:29:27.720 JanieceGarcia: So, yay.
220 00:29:28.040 ⇒ 00:29:45.719 JanieceGarcia: Yeah, I’m so excited. I know, lots of… and the usage is just great. I mean, again, taking out the QA, and just looking at the actual agent usage, I mean, that’s already a win right there, so kudos to you, ma’am, and Hernal, for helping, and keeping… and then the…
221 00:29:45.720 ⇒ 00:29:53.179 JanieceGarcia: the whole two questions, I think that’s going to be another layer that’s really, really going to help us. I mean, just looking at what right now, I mean, that’s…
222 00:29:53.810 ⇒ 00:29:58.790 JanieceGarcia: eye-opening on some of the stuff that they’re asking, because even, like, that one, I think there was a question, do we do…
223 00:29:59.160 ⇒ 00:30:07.400 JanieceGarcia: Fountains. Fountains. Like, I mean, because that’s… that’s, like, important stuff, like, those are the type of questions that are asked, and…
224 00:30:07.950 ⇒ 00:30:24.619 JanieceGarcia: Do we? Do we not? I mean… Right, you don’t know. Is there data there? I mean… Exactly, and is it plumbing, or is it handyman? Well, do we even know, is it residential, or commercial, or… I mean, there’s so much. We’re gonna learn from this and really, really,
225 00:30:25.230 ⇒ 00:30:38.269 JanieceGarcia: tweak it and fix it the way it needs to be to provide those right answers, because, I mean, how much more professional are you going to sound with the customer? Right. …knowing these things, or how do I direct it, or if I need to refer it? Right, exactly, yep.
226 00:30:38.910 ⇒ 00:30:57.419 Pranav Narahari: Yeah, and it’s honestly… I’m really excited about this next project for that reason, because we’re gonna have, in this, like, fourth milestone here, the trainer loop, we’re going to be pulling in all of the questions being asked in the transcript, so not even just what’s being asked to Andy, but what are the customers actually asking?
227 00:30:57.870 ⇒ 00:31:12.590 Pranav Narahari: pulling the questions there, grouping them into certain categories, and then we’re not just gonna talk about it in this call, we’re gonna make a report where the trainers can actually assess, okay, what was Andy’s, answer, and is it correct?
228 00:31:12.590 ⇒ 00:31:13.090 JanieceGarcia: Yep.
229 00:31:13.400 ⇒ 00:31:20.400 Pranav Narahari: Because, like I said, these are not necessarily going to be questions. Most of the questions here aren’t going to be asked to Andy, so we’re going to manually.
230 00:31:20.400 ⇒ 00:31:22.710 JanieceGarcia: Exactly. On the transcripts, right?
231 00:31:22.710 ⇒ 00:31:23.430 Pranav Narahari: Yeah, that’s true.
232 00:31:23.430 ⇒ 00:31:23.790 JanieceGarcia: That’s cool.
233 00:31:23.790 ⇒ 00:31:38.649 Pranav Narahari: Exactly. And so, kind of going back to, like, how this looks like in stages, is that starting next week, I’m going to look into how do we first source all these transcripts and organize them in a way that we can start the automation.
234 00:31:38.840 ⇒ 00:31:46.250 Pranav Narahari: And then that’s going to include also a spike on our end for figuring out how do we extract and,
235 00:31:46.750 ⇒ 00:31:59.979 Pranav Narahari: anonymize the… the PII data. And so, Google Workspace, which is where we’re already having all of our, like, ANDI backend, technology, is…
236 00:32:00.420 ⇒ 00:32:12.740 Pranav Narahari: the same place where this PII redaction is going to be happening as well. And so we’ve done this for other projects before, so it’ll be pretty straightforward here as well. Can I ask the question?
237 00:32:12.740 ⇒ 00:32:20.670 JanieceGarcia: I’m sorry, this is just because you’re in the PI piece of it, right? So…
238 00:32:21.740 ⇒ 00:32:33.159 JanieceGarcia: I think you’re in the loop, I don’t know if you’re in the loop. Again, these are things that we talked about way in the beginning that are now coming into fruition, if you will. So,
239 00:32:33.490 ⇒ 00:32:46.039 JanieceGarcia: and Sam was in a meeting that we had earlier this week, and it’s with Evolve, which is one of the CRM that we use for scheduling, booking all our work and all. And so, currently, right now, we’re working to
240 00:32:46.040 ⇒ 00:32:59.790 JanieceGarcia: have screen pop, so when the phone… when a call comes in through our 8x8 CRM, it’s integrated with Evolve, and it should populate the customer’s information, right, instead of us having to search the database, right?
241 00:32:59.940 ⇒ 00:33:07.409 JanieceGarcia: And so, the other layer to that is going to be the transcript of the call being summarized.
242 00:33:08.010 ⇒ 00:33:13.180 JanieceGarcia: and plugged into Evolve. And part of what…
243 00:33:13.700 ⇒ 00:33:24.709 JanieceGarcia: Tim worked with Utum and them regarding what is when we… because you guys were going to start working on the transcript, is… because y’all are already going through and getting our transcripts.
244 00:33:25.290 ⇒ 00:33:28.710 JanieceGarcia: how… I guess, I don’t even know how…
245 00:33:28.970 ⇒ 00:33:44.410 JanieceGarcia: you guys would be the one that would be doing all the cleanup, if you will, you know what I mean? Like, I don’t want to say cleanup, but, like, the PI stuff, you’re keeping that away, so anything with the credit card information, all we’re, you know, we’re really just looking… I don’t even know how to even say how that’s going to be done.
246 00:33:45.390 ⇒ 00:33:47.400 JanieceGarcia: the cleaning piece of it. But how…
247 00:33:48.820 ⇒ 00:33:51.979 JanieceGarcia: I’m just trying to wrap around this in my… how would that…
248 00:33:52.320 ⇒ 00:33:55.260 JanieceGarcia: How are you guys the middlemen to plug that into
249 00:33:55.470 ⇒ 00:34:01.850 JanieceGarcia: A evolve. Does that make sense, what I’m asking? I know I’m kind of all over the map. Have you been looped into any of this?
250 00:34:02.180 ⇒ 00:34:21.229 Pranav Narahari: Yeah, so Sam has looped me in a little bit into that. I guess my… so, in terms of what we’re trying to show here in terms of department-based insights, Evolve isn’t, like, a key part of that, right? Because we’re just getting the transcripts from them. Right.
251 00:34:21.330 ⇒ 00:34:31.459 Pranav Narahari: But putting them back into Evolve, in terms of, like, the summary of the transcript, is not… that’s not gonna help with… in terms of, like, insights for the trainers. So I guess…
252 00:34:31.460 ⇒ 00:34:37.389 JanieceGarcia: Yeah, no, no, totally agree. I’ll just… I know, and I didn’t want to group this all together, I just wanted to see
253 00:34:37.739 ⇒ 00:34:46.319 JanieceGarcia: I guess I shouldn’t even have asked it during this question right here. It’s kind of more of a separate thing. I’m just trying to get an understanding of how that would work, not with this piece of it, but, like.
254 00:34:46.320 ⇒ 00:34:46.929 Pranav Narahari: That’s true.
255 00:34:47.280 ⇒ 00:34:51.639 Pranav Narahari: Yeah, so, I mean, it could definitely work. It could definitely work,
256 00:34:52.620 ⇒ 00:35:05.760 Pranav Narahari: So, yeah, I guess my question to you is because, yeah, Sam did talk to me about this, and he was wondering, actually, kind of the question that… maybe the same topic of what you’re talking about is, how do we map that back into Evolve?
257 00:35:05.760 ⇒ 00:35:06.830 JanieceGarcia: Yup. Yup.
258 00:35:07.540 ⇒ 00:35:17.600 Pranav Narahari: how are we feel… so, the end state of this project is going to be that weekly report, and it’s going to be in the real dashboard. Okay.
259 00:35:18.180 ⇒ 00:35:19.340 Pranav Narahari: So…
260 00:35:19.750 ⇒ 00:35:29.319 Pranav Narahari: Why that’s important is that these transcripts aren’t going to be, analyzed in real time to then be updated back into…
261 00:35:29.320 ⇒ 00:35:33.790 JanieceGarcia: See, that’s what I was thinking, like, how is that gonna all work? How would it work, yeah.
262 00:35:33.790 ⇒ 00:35:36.180 Pranav Narahari: That can definitely happen, but…
263 00:35:36.700 ⇒ 00:35:40.379 Pranav Narahari: You can kind of see, like, with the, like, target for, like, timeframe here, like.
264 00:35:40.380 ⇒ 00:35:40.790 JanieceGarcia: We’re trying.
265 00:35:41.080 ⇒ 00:35:43.580 Pranav Narahari: as quick as possible. Yeah.
266 00:35:43.880 ⇒ 00:35:46.450 Pranav Narahari: I think, though, that that would be…
267 00:35:46.730 ⇒ 00:36:00.459 Pranav Narahari: insanely high value. If they don’t even need to use Andy, it just kind of goes through Andy, and then it pops up for them. But I think that’s probably next. Like, let’s… like, it’s probably next after this.
268 00:36:00.640 ⇒ 00:36:06.680 Pranav Narahari: Because I think when Utam was here, like, a couple weeks ago, he was even saying, like, hey, why don’t we just do this all at the same time?
269 00:36:06.680 ⇒ 00:36:07.769 JanieceGarcia: That makes sense.
270 00:36:07.770 ⇒ 00:36:11.990 Pranav Narahari: And I was thinking, like, let’s make sure accuracy and execution…
271 00:36:11.990 ⇒ 00:36:26.210 JanieceGarcia: No, for all I know, we still gotta stick to our goals. I didn’t mean to jump out, because… but again, this is, again, stuff that we’ve been talking about. Now it’s kind of coming into fruition, and I’m just like, okay, what is that going to do now? Like, is that.
272 00:36:26.210 ⇒ 00:36:26.820 Pranav Narahari: So…
273 00:36:27.190 ⇒ 00:36:42.669 Pranav Narahari: I would say this is all building on top of each other, which is good. It’s not like we’re going down one path and we can’t go down that other path later. And these are all things that, like, I have in the back of my mind, too, of like, okay, after these two projects, what’s project 3, 4, and 5?
274 00:36:43.170 ⇒ 00:36:46.139 Pranav Narahari: And that is something that we’ve discussed for sure.
275 00:36:46.370 ⇒ 00:36:50.440 JanieceGarcia: Okay, alright. Well, I didn’t mean to squirrel, I just… that wasn’t, like, front line, and I.
276 00:36:50.440 ⇒ 00:36:53.990 Pranav Narahari: No, no, no, I love these conversations. I’m glad you brought that up, because.
277 00:36:53.990 ⇒ 00:37:12.250 JanieceGarcia: Yeah, I talked to Matt yesterday, and he was all like, you know, I heard your Brainforge meeting went well with 8x8, and I’m just… and he goes, are we good with the PI information, Tim, so I’m like, I believe that we’re good. I said, I have a meeting with Pranov, too, I just need to make sure that everyone’s in the know.
278 00:37:12.250 ⇒ 00:37:26.820 Pranav Narahari: Yeah, yeah, yeah, yeah. And what we’ll… I’ll learn a lot more about that process next week, too. I know we’ve already sourced in transcripts, so I know for this project itself, we’re in a good standing.
279 00:37:27.000 ⇒ 00:37:38.350 Pranav Narahari: Now, for future projects, this will also be good research as well, because I’ll be able to know, like, okay, what is the technical complexity of doing that full loop of sending the information back to Evolve?
280 00:37:38.730 ⇒ 00:37:39.570 JanieceGarcia: Gotcha, okay.
281 00:37:39.570 ⇒ 00:37:40.100 Pranav Narahari: Yep.
282 00:37:40.100 ⇒ 00:37:42.210 JanieceGarcia: Right. Well, I’m sorry, I just… I sometimes.
283 00:37:42.210 ⇒ 00:37:43.940 Pranav Narahari: No, no, not a podcast, yeah, yeah.
284 00:37:43.940 ⇒ 00:37:45.700 JanieceGarcia: Of all that.
285 00:37:45.700 ⇒ 00:37:58.870 Pranav Narahari: Yeah, no, I, I’m glad, actually, you’re in the loop on that as well, because I wasn’t sure who was in these conversations fully, with Evolve, so Sam was telling me about this, and so, yeah, I’m glad you brought that up.
286 00:37:58.870 ⇒ 00:38:03.539 JanieceGarcia: I had to… I had to make… I had to ask that I be put in there, because we all connect, and…
287 00:38:03.810 ⇒ 00:38:04.430 Pranav Narahari: Yeah.
288 00:38:04.430 ⇒ 00:38:10.680 JanieceGarcia: I want to make sure that I stay in the know, and we’re all aligned. Aligned. Yeah. Anyhow, thank you so much.
289 00:38:10.910 ⇒ 00:38:13.050 Pranav Narahari: Totally, yeah.
290 00:38:13.200 ⇒ 00:38:26.989 Pranav Narahari: So, yeah, kind of for the next milestone, just after we’re able to source that data into a place that can… the automation can run from, we’re going to start pulling in those transcripts on a weekly basis.
291 00:38:26.990 ⇒ 00:38:42.509 Pranav Narahari: and sorting them based on department, and we can do even further sorting without any additional technical complexity, based on trainer as well. Because at the end of the day, we want to provide individual reports to trainers, right?
292 00:38:42.670 ⇒ 00:38:56.529 Pranav Narahari: So, department is great, right? We don’t want just, like, overall, across all departments, just one type of report. We want to separate between departments, but then, if we can separate further into trainers, I think it’s just…
293 00:38:56.830 ⇒ 00:39:04.459 Pranav Narahari: another… another more niche report that we’re able to give, and it’s just gonna be even more actionable. Right.
294 00:39:04.760 ⇒ 00:39:05.760 Pranav Narahari: So…
295 00:39:05.890 ⇒ 00:39:15.809 Pranav Narahari: That’s something we can talk about, too, like, we’ll see with the type of transcripts that we’re getting, like, does it make sense to do it on a department level or on a trainer level?
296 00:39:15.810 ⇒ 00:39:16.469 JanieceGarcia: And I’m like…
297 00:39:16.540 ⇒ 00:39:24.530 JanieceGarcia: Yeah. And that’s kind of where to invest their time in. Exactly. Like, what specifically do we need to work with?
298 00:39:24.530 ⇒ 00:39:37.010 JanieceGarcia: Yeah, I mean, that just makes total sense. And when you pull the transcripts, are you pulling and looking at trainers based upon where the CSR is, or based upon the conversation?
299 00:39:37.110 ⇒ 00:39:55.019 JanieceGarcia: What do you mean, the queue? For the transcript. So, like, say, okay… No, I mean, it’d be… it’d be you two, because I’m thinking overflow agents. Yeah. And I wouldn’t want, like, V, for example. V is in mechanical underneath Tara.
300 00:39:55.150 ⇒ 00:40:06.119 JanieceGarcia: But if she’s got a transcript, like, hey, why didn’t you ask Andy this, and it was pressed… But if I’m not mistaken, in the transcript, and you can correct me if I’m wrong, I think you guys are pulling off the queue.
301 00:40:06.420 ⇒ 00:40:07.310 JanieceGarcia: Right?
302 00:40:08.680 ⇒ 00:40:09.340 JanieceGarcia: Not often.
303 00:40:09.680 ⇒ 00:40:16.360 JanieceGarcia: It’s… I mean, I think that’s… I mean, I don’t know if you will, but I think it’s very easy, because I did this exercise
304 00:40:17.600 ⇒ 00:40:18.930 JanieceGarcia: When did I do it?
305 00:40:19.320 ⇒ 00:40:33.919 JanieceGarcia: Oh, when I was talking to Brainfort, I mean, when I was talking to Evolve and them, because that’s one of the things that I specifically said, that I wanted the tag to have the queue, because if I needed to pin it back to what CSR, because we do have overflow agents that answer calls, so I would think that we would…
306 00:40:34.080 ⇒ 00:40:42.500 JanieceGarcia: follow the same, when you’re pulling this data for the trainers, it would reference the queue. The queue, okay, okay. And I don’t.
307 00:40:42.500 ⇒ 00:40:42.850 Pranav Narahari: Yeah.
308 00:40:43.980 ⇒ 00:41:00.049 Pranav Narahari: So, yeah, each transcript will definitely be, will be tagged with that CSR that had the conversation. And so, I think maybe, Janiece, I’m seeing, kind of, like, the two paths we could go down, because we do have…
309 00:41:00.710 ⇒ 00:41:05.490 Pranav Narahari: there’s a CSR that is… there’s a trainer per the CSR, right?
310 00:41:05.680 ⇒ 00:41:06.520 Pranav Narahari: And then…
311 00:41:06.520 ⇒ 00:41:16.819 JanieceGarcia: So, Pranav, so, yes, the name’s gonna be important, but what we’re trying to say is, like, if Janice, Janiece has overflow skills for all our queues.
312 00:41:17.290 ⇒ 00:41:24.829 JanieceGarcia: So, in order… if you’re gonna… if we’re gonna get granular to give our trainers, you know what I mean? Like, we have to be able to…
313 00:41:24.990 ⇒ 00:41:42.020 JanieceGarcia: get it by the queue that they… is that correct, Janiece? Am I asking that? Yes, I mean, I kind of would think we would want it… You would walk past it. I mean, you would… if you carried an overflow, you would want Amy to look at that, because… Right. Because she’s the one that’s training for that. Does that make sense, what we’re saying?
314 00:41:42.290 ⇒ 00:41:44.809 Pranav Narahari: Oh, okay, okay. So, if…
315 00:41:44.810 ⇒ 00:41:45.160 JanieceGarcia: because…
316 00:41:45.160 ⇒ 00:41:46.550 Pranav Narahari: Yeah.
317 00:41:47.130 ⇒ 00:42:07.080 JanieceGarcia: And to give you a little bit more, it’s like, right now, if something comes in, and there’s a question, or there’s an issue, and it comes to me, or I see it, it’s like, well, that’s an overflow agent. Okay, they’re sending it to that overflow agent’s manager, but that manager may not even know anything about the department, so how are they going to train them on…
318 00:42:07.320 ⇒ 00:42:10.690 JanieceGarcia: what’s happening. Does that make sense? Does that make more…
319 00:42:11.830 ⇒ 00:42:12.170 Pranav Narahari: Yeah.
320 00:42:12.170 ⇒ 00:42:12.969 JanieceGarcia: Oh, miss.
321 00:42:13.360 ⇒ 00:42:20.500 Pranav Narahari: And at that level, too, like, we’ll just be able to assess what is the customer asking, and then map that to what department is the
322 00:42:21.240 ⇒ 00:42:22.149 Pranav Narahari: conversation, right?
323 00:42:22.150 ⇒ 00:42:25.809 JanieceGarcia: And then have the tag of who the CSR was, so we can be…
324 00:42:26.140 ⇒ 00:42:31.690 JanieceGarcia: Yeah, regardless, don’t have the CSR, and just, I figured that we would…
325 00:42:32.540 ⇒ 00:42:45.900 JanieceGarcia: categorize them by the queue, and again, like, to Pranal’s point, I mean, you can go by the transcript, but if it went in that queue, it’s more likely one of those calls. One of those calls, yeah. Mechanical, mechanical, plumbing, whatever, all that. Yep.
326 00:42:46.360 ⇒ 00:42:52.440 Pranav Narahari: Okay, yeah. And I think when we get this data, too, we’ll have another touchpoint of, like.
327 00:42:52.860 ⇒ 00:42:58.910 Pranav Narahari: do we look at the queue? Do we look at… we’ll obviously bring the CSR as a tag as well, and then…
328 00:42:59.420 ⇒ 00:43:02.760 Pranav Narahari: Actually, when we’re… so kind of the…
329 00:43:02.990 ⇒ 00:43:06.370 Pranav Narahari: The next part of this is, we’re going to batch this, and
330 00:43:06.440 ⇒ 00:43:21.599 Pranav Narahari: the idea here was to batch it per a trainer, so each individual trainer can have a list of questions that they can assess are correct or not. And so we can talk about how do we match those specific questions to a trainer.
331 00:43:21.620 ⇒ 00:43:32.620 Pranav Narahari: I know, like, from my understanding right now, there’s trainers per department, and so how I would think about it is just, like, we’re going to be able to assess the
332 00:43:32.620 ⇒ 00:43:45.609 Pranav Narahari: the customer’s intent and match that to a department. And then, basically, we just take that department list of questions, we can then chunk that into… in between however many trainers there are per department.
333 00:43:45.620 ⇒ 00:43:46.590 Pranav Narahari: Yep.
334 00:43:46.970 ⇒ 00:43:55.650 Pranav Narahari: That is… yeah, that’s like a for-sure way that this will work, but then we can go down the additional path of…
335 00:43:55.670 ⇒ 00:44:14.420 Pranav Narahari: okay, even of the department questions, maybe we can group those and refine those further into subcategories, based on which trainers should focus on which category. So, like, Janiece, you’re like, this trainer should focus on specific questions within mechanical, then we can do that as well.
336 00:44:15.300 ⇒ 00:44:17.240 JanieceGarcia: Fair. Okay. Sweet.
337 00:44:17.860 ⇒ 00:44:22.720 Pranav Narahari: And, yeah. So, after we have that first set of…
338 00:44:23.040 ⇒ 00:44:37.030 Pranav Narahari: feedback from you guys. We’ll then implement all of those changes, basically, like, okay, where Andy was correct, or Andy wasn’t correct, into the central docs, and then we’ll run the cycle again to show you, like.
339 00:44:37.080 ⇒ 00:44:41.290 Pranav Narahari: The percent answered correctly versus the percent answered incorrectly.
340 00:44:41.290 ⇒ 00:44:59.109 Pranav Narahari: And for the percent answered correctly, we’ll be able to say, hey, this is not where Andy’s currently being used, but we’re seeing that there’s a ton of these questions being asked, and it’s basically just, like, Andy’s just waiting to answer those questions, and it’s answering them properly. So that’s gonna be, like.
341 00:44:59.110 ⇒ 00:45:03.090 Pranav Narahari: that first easy win report that we provide to each department.
342 00:45:03.190 ⇒ 00:45:17.590 Pranav Narahari: And so, I just kind of said, like, top question themes. We’ll probably, kind of like how we showed today, the categories as well as sample questions that, that weren’t asked to Andy, but should have been asked to Andy.
343 00:45:18.970 ⇒ 00:45:19.730 JanieceGarcia: Awesome.
344 00:45:20.040 ⇒ 00:45:23.839 Pranav Narahari: Cool. And then, the last part is just…
345 00:45:24.230 ⇒ 00:45:29.739 Pranav Narahari: how do we then have this automated weekly, and then in a real dashboard? So…
346 00:45:29.860 ⇒ 00:45:38.159 Pranav Narahari: We can not have to worry about… so we can just work on the next project, but then there’s still gonna be great insights that we’re gonna get from this every single week.
347 00:45:38.300 ⇒ 00:45:45.600 Pranav Narahari: So, I really think this feedback loop that we’re building here is what’s really gonna take Andy to, like, the next level, and then…
348 00:45:45.760 ⇒ 00:45:49.950 Pranav Narahari: Coming from that, like, full picture of, like, okay, then how do we just…
349 00:45:50.400 ⇒ 00:46:04.219 Pranav Narahari: pull the questions straight from the… when the customer’s talking about it, redact all the personal information, throw that into Andy, and then bring that back into the platform so that they can, yeah, into Evolve, so…
350 00:46:04.510 ⇒ 00:46:05.810 JanieceGarcia: Yeah. Yep. Yeah.
351 00:46:05.810 ⇒ 00:46:10.190 Pranav Narahari: That sounds like a great, like, follow opportunity to this.
352 00:46:10.810 ⇒ 00:46:13.670 Pranav Narahari: And so, yeah, maybe starting in June, we work on that.
353 00:46:13.810 ⇒ 00:46:15.279 JanieceGarcia: Yeah, no, for sure, yeah.
354 00:46:15.790 ⇒ 00:46:18.320 JanieceGarcia: Yeah. I love it Man.
355 00:46:18.640 ⇒ 00:46:20.599 JanieceGarcia: It’s a lot, I know.
356 00:46:21.220 ⇒ 00:46:28.900 JanieceGarcia: Right? Yeah. It’s all good stuff. I mean, yeah, it’s just all kind of evolving, which is great.
357 00:46:29.140 ⇒ 00:46:29.940 Pranav Narahari: Yeah.
358 00:46:30.090 ⇒ 00:46:30.569 JanieceGarcia: muted about.
359 00:46:31.560 ⇒ 00:46:40.710 JanieceGarcia: Yeah, I know, I know. It’s like, when we talked to Ronald, I felt like we came again to that plateau, like, okay, where are we at? But now the ball’s rolling again, so…
360 00:46:41.080 ⇒ 00:46:44.990 Pranav Narahari: Yeah, no, I’m glad. I’m glad we’re able to say migration is done.
361 00:46:45.380 ⇒ 00:46:48.740 JanieceGarcia: Yes, check it off the list.
362 00:46:48.740 ⇒ 00:46:52.789 Pranav Narahari: Yeah, yeah, migration is… it just sounds so boring.
363 00:46:52.790 ⇒ 00:46:53.230 JanieceGarcia: It doesn’t…
364 00:46:53.230 ⇒ 00:47:03.900 Pranav Narahari: No, like, this type of stuff, department-based insights, like, central.copilot, like, I think there’s just more excitement to this. Like, that’s how I feel. So I’m glad we can get started on this.
365 00:47:04.630 ⇒ 00:47:05.240 JanieceGarcia: Good.
366 00:47:05.450 ⇒ 00:47:10.300 JanieceGarcia: Yeah. Alrighty, well… You got plans for the weekend for Nov?
367 00:47:11.180 ⇒ 00:47:13.479 Pranav Narahari: Someone else asked me this this morning, and I was like…
368 00:47:14.120 ⇒ 00:47:22.459 Pranav Narahari: Oh my gosh, like, work is just, like, during the week, I’m just, like, all-consuming, and then I’m just like, okay, Friday I log off, and I’m like, oh, I can actually go do something else.
369 00:47:22.460 ⇒ 00:47:23.529 JanieceGarcia: Yeah, there you go.
370 00:47:23.530 ⇒ 00:47:35.940 Pranav Narahari: But I don’t think I have plans yet. So, two weekends from now, though, I’m gonna be coming to Austin, actually, just for a weekend. And so…
371 00:47:36.200 ⇒ 00:47:50.140 Pranav Narahari: Yeah, I think I just need to plan some of this stuff with, like, my friends out there, figure out, you know, some reservations, things like that. And it’s finally good weather out here in Massachusetts. It was, like, freezing this past week.
372 00:47:50.310 ⇒ 00:47:53.410 Pranav Narahari: So, I know you guys can’t relate, but yeah.
373 00:47:53.410 ⇒ 00:47:57.259 JanieceGarcia: We’ve got a cold front coming in this weekend, which… We do.
374 00:47:57.660 ⇒ 00:47:59.550 JanieceGarcia: Yeah.
375 00:48:00.120 ⇒ 00:48:05.639 JanieceGarcia: in the 70s, we’re all gonna be bundled up in our hoodies and our sweatpants.
376 00:48:05.640 ⇒ 00:48:06.370 Pranav Narahari: Oh, man.
377 00:48:07.070 ⇒ 00:48:09.760 Pranav Narahari: Ugh, 70 sounds great.
378 00:48:09.760 ⇒ 00:48:13.200 JanieceGarcia: Good outside weather, for sure. Yep.
379 00:48:13.200 ⇒ 00:48:15.839 Pranav Narahari: Awesome. Yeah. Do you guys have any plans?
380 00:48:17.210 ⇒ 00:48:31.260 JanieceGarcia: I have my 8-year-old’s baseball game tonight, and then we have a birthday party for his friend. I’m glad it’s only one this weekend. She had 3 last week.
381 00:48:31.260 ⇒ 00:48:32.480 Pranav Narahari: 3 birthday parties.
382 00:48:32.750 ⇒ 00:48:35.690 JanieceGarcia: Yes. Kid birthday parties. Kid birthday parties. Not fun!
383 00:48:35.690 ⇒ 00:48:36.010 Pranav Narahari: Of course.
384 00:48:36.010 ⇒ 00:48:36.770 JanieceGarcia: Day parties.
385 00:48:36.770 ⇒ 00:48:39.610 Pranav Narahari: Right, right. So it’s, like, more of a story, yeah.
386 00:48:39.610 ⇒ 00:48:40.700 JanieceGarcia: Yeah.
387 00:48:40.700 ⇒ 00:48:43.330 Pranav Narahari: Oh, God.
388 00:48:43.810 ⇒ 00:48:50.899 JanieceGarcia: I’ll be dress shopping, so… Yeah… I get married in two weeks, 2 weeks, whatever.
389 00:48:50.900 ⇒ 00:48:51.870 Pranav Narahari: Yeah, two weeks.
390 00:48:51.870 ⇒ 00:48:52.750 JanieceGarcia: Yeah.
391 00:48:52.960 ⇒ 00:48:55.189 Pranav Narahari: Oh, wow, congrats! That’s awesome.
392 00:48:55.190 ⇒ 00:49:11.130 JanieceGarcia: Thank you, thank you. It’s… I’ve been married, Pranav, for 28 years already, but I’m now going to do it. I’m completing my sacraments. I’m going to now do it through the church, so, okay. It’s… it’s… that’s what I’m planning for here in the next couple of weeks.
393 00:49:11.130 ⇒ 00:49:14.590 Pranav Narahari: That’s awesome, that’s awesome. Is that gonna happen in San Antonio?
394 00:49:14.590 ⇒ 00:49:15.569 JanieceGarcia: Yeah, it’ll happen instantly.
395 00:49:15.570 ⇒ 00:49:16.130 Pranav Narahari: Okay.
396 00:49:16.130 ⇒ 00:49:25.129 JanieceGarcia: Yes. Yeah, it’s not gonna be anything big, it’s just more of… it’s big, yes, because of the way we’re doing it, right? Like, it’s now more meaningful, I mean, it’s just… it’s very different.
397 00:49:25.130 ⇒ 00:49:27.520 Pranav Narahari: I think you took the stress out of it, you know?
398 00:49:27.520 ⇒ 00:49:28.490 JanieceGarcia: It’s like…
399 00:49:28.490 ⇒ 00:49:30.680 Pranav Narahari: That’s been, like, the last couple of weeks, really.
400 00:49:30.850 ⇒ 00:49:31.470 Pranav Narahari: Yeah.
401 00:49:31.470 ⇒ 00:49:33.830 JanieceGarcia: Yes. But yeah.
402 00:49:34.210 ⇒ 00:49:39.910 Pranav Narahari: That’s awesome. Okay, well, I’m glad we ended up Friday on, like, a good note, you know? We got all the…
403 00:49:39.910 ⇒ 00:49:40.430 JanieceGarcia: house.
404 00:49:40.430 ⇒ 00:49:43.889 Pranav Narahari: QA headache out of the way, and migration’s good.
405 00:49:44.180 ⇒ 00:49:45.309 JanieceGarcia: I’m good. Yeah.
406 00:49:45.480 ⇒ 00:49:50.180 JanieceGarcia: All right. Well, I hope you have a good weekend. We’ll talk Monday!
407 00:49:50.180 ⇒ 00:49:52.040 Pranav Narahari: We’ll talk Monday. Alright.
408 00:49:52.040 ⇒ 00:49:53.010 JanieceGarcia: Bye, bye.