Meeting Title: Hannah <> Casie: n8n Case Study Date: 2025-09-04 Meeting participants: Casie Aviles, Hannah Wang
WEBVTT
1 00:00:28.960 ⇒ 00:00:29.770 Casie Aviles: Hey.
2 00:00:30.790 ⇒ 00:00:32.190 Hannah Wang: Hi, how’s it going?
3 00:00:33.760 ⇒ 00:00:34.679 Casie Aviles: It’s doing good.
4 00:00:34.980 ⇒ 00:00:35.810 Casie Aviles: I love this.
5 00:00:37.400 ⇒ 00:00:45.120 Hannah Wang: Good. I have a quick question. Is my mic okay? Like, my audio? Is it fuzzy, or is it clear?
6 00:00:45.120 ⇒ 00:00:46.150 Casie Aviles: Yeah, it’s clear.
7 00:00:46.500 ⇒ 00:00:53.360 Hannah Wang: Oh, okay. I was having issues yesterday, so… that was weird, but…
8 00:00:54.030 ⇒ 00:00:57.449 Hannah Wang: Okay, so… I feel like we’ve done…
9 00:00:57.600 ⇒ 00:01:07.250 Hannah Wang: a lot of these, which is good. So, it’ll be the same, structure and format. So, let’s get started with…
10 00:01:07.800 ⇒ 00:01:13.160 Hannah Wang: The NAN case study,
11 00:01:13.940 ⇒ 00:01:18.810 Hannah Wang: Yeah, UTAM… well, usually for case studies.
12 00:01:19.140 ⇒ 00:01:26.419 Hannah Wang: I get, like, a clear, oh, this is the project that we want to demo and make a case study for, but I think.
13 00:01:26.910 ⇒ 00:01:29.610 Hannah Wang: We just need something N8N.
14 00:01:30.000 ⇒ 00:01:32.560 Hannah Wang: Related and focused, so…
15 00:01:32.810 ⇒ 00:01:41.509 Hannah Wang: Yeah, were you able to think about a case study or, like, a project that you did that was, like, primarily NAN?
16 00:01:41.700 ⇒ 00:01:46.070 Hannah Wang: like, heavy on NAN, and… Stuff like that.
17 00:01:47.560 ⇒ 00:01:51.470 Casie Aviles: Yeah, I think, I think we can do…
18 00:01:52.270 ⇒ 00:01:55.600 Casie Aviles: ABC there, because we use NHN.
19 00:01:56.210 ⇒ 00:02:04.709 Casie Aviles: For ABC, so… I can… Walk through the… The workflow that we have.
20 00:02:06.200 ⇒ 00:02:13.049 Hannah Wang: Yeah, so before we start that, I… we already have an ABC.
21 00:02:13.050 ⇒ 00:02:14.229 Casie Aviles: Oh, okay.
22 00:02:14.230 ⇒ 00:02:18.009 Hannah Wang: study, but it’s for Andy, so I don’t know if this is different.
23 00:02:18.910 ⇒ 00:02:21.479 Casie Aviles: Yeah, it’s going to be the same.
24 00:02:22.620 ⇒ 00:02:23.600 Hannah Wang: Okay.
25 00:02:23.800 ⇒ 00:02:24.530 Hannah Wang: Okay.
26 00:02:25.880 ⇒ 00:02:26.990 Hannah Wang: Let me think.
27 00:02:39.140 ⇒ 00:02:40.950 Hannah Wang: So this is for Andy, right?
28 00:02:42.130 ⇒ 00:02:45.950 Casie Aviles: Yes, what else can I…
29 00:02:46.080 ⇒ 00:02:53.850 Casie Aviles: So, sorry, I thought it was going to be, like… Talking about the tool, so…
30 00:02:55.090 ⇒ 00:02:57.949 Hannah Wang: I see. I… yeah, I think… I do think…
31 00:02:58.230 ⇒ 00:03:06.719 Hannah Wang: talking about the tool is helpful, but I think probably within the context of client work is probably the best.
32 00:03:07.130 ⇒ 00:03:12.649 Hannah Wang: Use of it.
33 00:03:13.530 ⇒ 00:03:18.559 Casie Aviles: Okay, I think I can do one of the clients that I worked on
34 00:03:19.210 ⇒ 00:03:21.560 Casie Aviles: Which is… off the record.
35 00:03:22.980 ⇒ 00:03:31.630 Hannah Wang: Okay, let me see… We already have… let’s see, is it… so, I already have case studies for off the record.
36 00:03:31.630 ⇒ 00:03:32.040 Casie Aviles: Wonderful.
37 00:03:32.040 ⇒ 00:03:38.590 Hannah Wang: Or a query agent, and then… One for CRM automation.
38 00:03:38.730 ⇒ 00:03:40.990 Hannah Wang: Were you gonna talk about those already?
39 00:03:43.690 ⇒ 00:03:50.559 Casie Aviles: Yeah, if we have a case study there, then yeah, that’s pretty much what I was going to talk about for off the record.
40 00:03:51.490 ⇒ 00:03:54.539 Hannah Wang: Let me try to think.
41 00:03:54.760 ⇒ 00:04:01.490 Hannah Wang: I mean, like, for both of those case studies, for both ABC and Off the Record, like, it’s not…
42 00:04:02.190 ⇒ 00:04:07.150 Hannah Wang: like… I just mentioned…
43 00:04:07.970 ⇒ 00:04:16.480 Hannah Wang: N-A-N, like, and there’s, like, a section for tools used, and I just, like, slapped the logo on there, but, like.
44 00:04:16.829 ⇒ 00:04:21.330 Hannah Wang: Within the actual… case study, like, I didn’t…
45 00:04:22.450 ⇒ 00:04:30.309 Hannah Wang: Explicitly go into detail, like, oh, this is how we used N-A-N.
46 00:04:31.280 ⇒ 00:04:32.630 Casie Aviles: Hmm, okay.
47 00:04:33.290 ⇒ 00:04:41.059 Hannah Wang: Yeah, so maybe… It might be worth it to… it’s okay if, like, we redo…
48 00:04:41.330 ⇒ 00:04:45.169 Hannah Wang: The same case study, but maybe this time, like, you can just…
49 00:04:45.620 ⇒ 00:04:52.950 Hannah Wang: heavily emphasize NAN, because, for example, for Andy, like, it’s very high level.
50 00:04:55.530 ⇒ 00:04:56.250 Casie Aviles: Okay.
51 00:04:57.840 ⇒ 00:05:04.329 Casie Aviles: Yeah. Did we also… have we also done, like, the client hub agents that we have?
52 00:05:06.020 ⇒ 00:05:12.969 Hannah Wang: Client hub agent… could you give me an example of what those are?
53 00:05:13.800 ⇒ 00:05:20.390 Casie Aviles: So these are agents that… That, like, have… access to Slack.
54 00:05:21.240 ⇒ 00:05:25.220 Casie Aviles: Slack messages and Zoom meeting recordings.
55 00:05:27.050 ⇒ 00:05:29.530 Hannah Wang: I see, like the Zoom meeting platform.
56 00:05:30.160 ⇒ 00:05:31.260 Hannah Wang: And…
57 00:05:32.370 ⇒ 00:05:42.820 Casie Aviles: Yeah, it’s kind of like that, but this one is more like, consolidating… plant-specific, info.
58 00:05:44.740 ⇒ 00:05:49.859 Hannah Wang: Could you, like, just demo it for me, real quick? Or, like, show me what it looks like?
59 00:05:50.970 ⇒ 00:05:59.830 Casie Aviles: Yes, so… we have some… I believe we have some… Tests…
60 00:06:01.060 ⇒ 00:06:03.669 Casie Aviles: Yeah, here, like, going stuff I use.
61 00:06:05.300 ⇒ 00:06:06.480 Hannah Wang: Oh…
62 00:06:07.470 ⇒ 00:06:14.960 Casie Aviles: So, like, for example, I would ask… I think default.
63 00:06:15.870 ⇒ 00:06:19.130 Casie Aviles: What were the… Passed.
64 00:06:19.860 ⇒ 00:06:25.070 Casie Aviles: Or, like… Can you give… Can you assign me off of it.
65 00:06:28.320 ⇒ 00:06:29.950 Hannah Wang: I see. Something like that.
66 00:06:30.290 ⇒ 00:06:31.110 Hannah Wang: Okay.
67 00:06:31.330 ⇒ 00:06:34.320 Hannah Wang: I don’t think we did that yet.
68 00:06:34.820 ⇒ 00:06:45.050 Hannah Wang: Yeah, I only have, like… for internal projects, I only have… like…
69 00:06:45.230 ⇒ 00:06:52.600 Hannah Wang: lead… enriching lead automation, Zoom meeting platform… MQS… .
70 00:06:53.230 ⇒ 00:06:53.950 Casie Aviles: Okay.
71 00:06:54.280 ⇒ 00:06:57.040 Casie Aviles: Yeah, we also did something like this.
72 00:06:58.810 ⇒ 00:06:59.590 Hannah Wang: Okay.
73 00:07:01.070 ⇒ 00:07:03.879 Hannah Wang: And is it, like, helpful and being used?
74 00:07:04.110 ⇒ 00:07:08.130 Hannah Wang: within… The teams or, like, clients?
75 00:07:08.330 ⇒ 00:07:10.529 Hannah Wang: Or is this just internal for us?
76 00:07:11.260 ⇒ 00:07:13.380 Casie Aviles: Yeah, this is only internally,
77 00:07:14.840 ⇒ 00:07:19.369 Casie Aviles: Adoption is not yet, like, it’s not being widely used.
78 00:07:19.980 ⇒ 00:07:23.130 Casie Aviles: But we do have it on the platform as well.
79 00:07:23.710 ⇒ 00:07:24.690 Casie Aviles: And…
80 00:07:26.100 ⇒ 00:07:34.409 Casie Aviles: Yeah, like, basically it’s for, like… the idea is to help the team, like, if they have questions about
81 00:07:35.390 ⇒ 00:07:39.610 Casie Aviles: You know, if we have to onboard someone to a new client, and then…
82 00:07:39.740 ⇒ 00:07:42.289 Casie Aviles: Ideally, the client hub agent would help.
83 00:07:43.110 ⇒ 00:07:45.490 Casie Aviles: Onboard them much faster.
84 00:07:47.260 ⇒ 00:07:48.030 Hannah Wang: Okay.
85 00:07:48.420 ⇒ 00:07:51.499 Hannah Wang: Sure, let’s… Let’s do that.
86 00:07:52.290 ⇒ 00:08:00.189 Hannah Wang: Sorry, let me… give me one second. I’m forgetting what…
87 00:08:01.040 ⇒ 00:08:07.100 Hannah Wang: Our next case study is gonna be about… oh, it’s measuring AI agents, right? That’s our…
88 00:08:07.100 ⇒ 00:08:07.790 Casie Aviles: Yes.
89 00:08:07.960 ⇒ 00:08:10.180 Casie Aviles: Or the dash, I believe the dashboard.
90 00:08:10.690 ⇒ 00:08:26.120 Hannah Wang: Yeah, the ABC dashboard. Okay, yeah. Alright, so for this one, we can focus on NAN. Okay, so I think we can try the client hub, and then we’ll just see, like, what Utam says. So, let me pull up the questions.
91 00:08:28.980 ⇒ 00:08:39.350 Hannah Wang: Okay, so I guess first question is, how long did this project take? I know it’s still, like, in progress, but how long have you guys been working on it for?
92 00:08:41.350 ⇒ 00:08:44.260 Casie Aviles: I believe it’s been… Oh, well, and I think…
93 00:08:45.670 ⇒ 00:08:50.739 Casie Aviles: I don’t really remember, like, the first time we did this anymore, but I think it was,
94 00:08:51.110 ⇒ 00:08:52.410 Casie Aviles: Around March?
95 00:08:53.160 ⇒ 00:08:53.900 Hannah Wang: Okay
96 00:08:56.260 ⇒ 00:09:06.680 Hannah Wang: Okay, and then who primarily worked on this? I… I know Miguel probably worked on it, but did you also work on it, too?
97 00:09:07.530 ⇒ 00:09:12.330 Casie Aviles: Yeah, so it’s, yeah, it was mostly Miguel at first.
98 00:09:12.680 ⇒ 00:09:18.529 Casie Aviles: And then I also started to come in and work on…
99 00:09:18.820 ⇒ 00:09:23.469 Casie Aviles: integrating the agents here on Slack, like, adding them to Slack.
100 00:09:25.290 ⇒ 00:09:27.759 Casie Aviles: And then Miguel worked primarily on…
101 00:09:29.370 ⇒ 00:09:38.670 Casie Aviles: I’m, like, getting the knowledge base for… yeah, for this… yeah, getting the knowledge base working for these agents, so…
102 00:09:40.400 ⇒ 00:09:45.720 Casie Aviles: That’s where he did some RAG for these agents.
103 00:09:46.630 ⇒ 00:09:47.400 Hannah Wang: Okay.
104 00:09:47.820 ⇒ 00:09:56.560 Hannah Wang: Okay, cool. So, let’s go over the context next. So, I guess before…
105 00:09:57.340 ⇒ 00:10:06.679 Hannah Wang: creating these client agents. What… was the environment like? Like…
106 00:10:06.980 ⇒ 00:10:10.700 Hannah Wang: Or what was, like, the primary…
107 00:10:11.520 ⇒ 00:10:18.499 Hannah Wang: Like, issue that made you guys want to start developing this tool, or the… the agent?
108 00:10:19.190 ⇒ 00:10:21.990 Casie Aviles: Yeah, so I think kind of similar to…
109 00:10:22.220 ⇒ 00:10:25.419 Casie Aviles: what I mentioned before for the Zoom platform.
110 00:10:27.100 ⇒ 00:10:34.080 Casie Aviles: Sorry, so the idea is that, there are some questions, like, Client-specific questions that…
111 00:10:36.100 ⇒ 00:10:38.650 Casie Aviles: That are some… are being asked, and…
112 00:10:39.220 ⇒ 00:10:41.940 Casie Aviles: You know, sometimes they’re not getting answered.
113 00:10:43.910 ⇒ 00:10:50.159 Casie Aviles: Or, like, it’s kind of hard to… so it’s… yeah, it’s kind of hard to look for the information, so it’s similar…
114 00:10:50.440 ⇒ 00:10:57.340 Casie Aviles: idea behind it, behind the platform, where we want to consolidate the the information, so…
115 00:10:58.200 ⇒ 00:11:05.580 Casie Aviles: Like, before our… the plan was to consolidate the information from
116 00:11:06.060 ⇒ 00:11:11.040 Casie Aviles: not just Slack and Zoom, but also GitHub, and then…
117 00:11:13.090 ⇒ 00:11:18.109 Casie Aviles: What else? Notion, yeah, and… Even emails, so…
118 00:11:19.230 ⇒ 00:11:22.889 Casie Aviles: That that was the idea, like, we wanted to get everything there.
119 00:11:23.540 ⇒ 00:11:27.240 Casie Aviles: So the agent would be able to answer questions.
120 00:11:27.570 ⇒ 00:11:28.910 Casie Aviles: Much easily.
121 00:11:30.180 ⇒ 00:11:33.120 Casie Aviles: So yeah, that was the idea behind it.
122 00:11:34.140 ⇒ 00:11:39.190 Hannah Wang: But currently, it only pulls info… info from Slack.
123 00:11:41.120 ⇒ 00:11:45.550 Casie Aviles: Yeah, right now it’s just those.
124 00:11:46.690 ⇒ 00:11:53.670 Casie Aviles: We’ve… yeah, we have. In the past, we’ve tried to work on the other data sources, but it was kind of…
125 00:11:53.910 ⇒ 00:11:55.540 Casie Aviles: It… it got… it got…
126 00:11:55.950 ⇒ 00:12:00.879 Casie Aviles: Difficult quickly, because, you know, it was… there are a lot of sources, it’s kind of…
127 00:12:01.200 ⇒ 00:12:03.490 Casie Aviles: Hard to make sure that the bot is…
128 00:12:03.900 ⇒ 00:12:06.840 Casie Aviles: Pulling from the right sources, so that’s where we.
129 00:12:06.840 ⇒ 00:12:07.300 Hannah Wang: Yeah.
130 00:12:07.450 ⇒ 00:12:09.649 Casie Aviles: That’s a challenge that we face then.
131 00:12:11.070 ⇒ 00:12:21.149 Hannah Wang: Okay, and it’s still… like, are there plans to try to do that again? Like, integrate with Notion and email, or is it still gonna be Slack and Zoom?
132 00:12:21.150 ⇒ 00:12:24.050 Casie Aviles: Yeah, I think there’s still,
133 00:12:24.590 ⇒ 00:12:29.110 Casie Aviles: I… we still want to do that, because, there’s…
134 00:12:29.500 ⇒ 00:12:34.740 Casie Aviles: like, Slap and Zoom is just… Like, it’s not…
135 00:12:35.630 ⇒ 00:12:41.070 Casie Aviles: what do you call this? The context is not just there, we also have…
136 00:12:41.690 ⇒ 00:12:47.120 Casie Aviles: Ideally, we also want to have Notion, because we have a lot of documentation there, like.
137 00:12:48.050 ⇒ 00:12:52.050 Casie Aviles: So we want to be able to get those as well.
138 00:12:53.330 ⇒ 00:12:55.020 Hannah Wang: Okay, yes.
139 00:12:55.020 ⇒ 00:12:55.620 Casie Aviles: Yeah.
140 00:12:55.730 ⇒ 00:12:56.719 Casie Aviles: Thank you very much.
141 00:12:57.650 ⇒ 00:13:03.310 Hannah Wang: Okay, and I guess before these agents were made,
142 00:13:04.560 ⇒ 00:13:15.410 Hannah Wang: like, basically, one person who had the context… like, if someone was asking questions, like, if there was a new person onboarding to a client, I’m assuming before…
143 00:13:15.690 ⇒ 00:13:31.499 Hannah Wang: this agent, like, either Utam or whoever was, like, PMing, the client would have to, like, type out or, like, send a bunch of documentation to the new person onboarding, and that was just, like, really time-consuming, right?
144 00:13:31.500 ⇒ 00:13:31.829 Casie Aviles: Thank you.
145 00:13:31.830 ⇒ 00:13:34.349 Hannah Wang: Kind of makes it easier for…
146 00:13:34.710 ⇒ 00:13:40.410 Hannah Wang: it to be, like, a bot to answer those questions instead of, like, UTAM, for example.
147 00:13:40.590 ⇒ 00:13:41.990 Casie Aviles: Yeah, exactly, Lynn.
148 00:13:42.180 ⇒ 00:13:51.420 Casie Aviles: That’s the main pain point that we were trying to address, where, you know, someone does not become a bottleneck for…
149 00:13:52.670 ⇒ 00:13:53.690 Casie Aviles: For this…
150 00:13:55.350 ⇒ 00:13:56.910 Hannah Wang: Yeah, that makes sense.
151 00:14:02.050 ⇒ 00:14:03.610 Casie Aviles: Yeah, I’m just… Jeez.
152 00:14:10.190 ⇒ 00:14:14.739 Casie Aviles: Yeah, okay, yeah, hmm, anything else?
153 00:14:16.970 ⇒ 00:14:22.959 Hannah Wang: Okay, so that’s the context, and I guess you kind of answered the challenge already.
154 00:14:23.240 ⇒ 00:14:29.470 Hannah Wang: It’s just that people were being bottlenecks, basically, and it was hard to…
155 00:14:29.940 ⇒ 00:14:38.299 Hannah Wang: answer client-specific questions, to new people, to the… to people who are new to the client, and that just, like, slowed the team down.
156 00:14:38.690 ⇒ 00:14:39.780 Hannah Wang: Mmm.
157 00:14:40.660 ⇒ 00:14:44.700 Hannah Wang: Okay, so getting into the solution…
158 00:14:44.840 ⇒ 00:14:52.790 Hannah Wang: here’s where we can make N8N shine. So, I guess, what was the solution and the setup and infra?
159 00:14:53.900 ⇒ 00:14:55.210 Casie Aviles: Okay.
160 00:14:55.940 ⇒ 00:14:59.710 Casie Aviles: So, I can show one of the workflows.
161 00:15:03.240 ⇒ 00:15:05.380 Casie Aviles: Let me just look for one of them.
162 00:15:06.040 ⇒ 00:15:06.820 Casie Aviles: Bye.
163 00:15:09.660 ⇒ 00:15:14.110 Casie Aviles: So, we have this, workflow,
164 00:15:14.560 ⇒ 00:15:20.820 Casie Aviles: So I’m not gonna go through each node, but… The idea here is,
165 00:15:22.180 ⇒ 00:15:29.309 Casie Aviles: this is, like, the core of it, like, this is the AI agent that’s… receiving the input.
166 00:15:30.410 ⇒ 00:15:33.620 Casie Aviles: Performing actions and generating output.
167 00:15:36.070 ⇒ 00:15:41.880 Casie Aviles: So… The good thing about N810 is it already comes built
168 00:15:42.040 ⇒ 00:15:44.859 Casie Aviles: In with a lot of integrations, so…
169 00:15:45.500 ⇒ 00:15:49.080 Casie Aviles: As opposed to, like, if we were to do this…
170 00:15:49.500 ⇒ 00:15:53.320 Casie Aviles: like, I guess from scratch, or, like, completely from code.
171 00:15:54.230 ⇒ 00:15:59.489 Casie Aviles: We’d have to implement our own connectors or integrations to Slack.
172 00:15:59.880 ⇒ 00:16:02.960 Casie Aviles: So that’s definitely gonna be more time-consuming.
173 00:16:03.250 ⇒ 00:16:09.829 Casie Aviles: And… also, like, There’s going to be a lot of work to be done for that.
174 00:16:09.960 ⇒ 00:16:10.700 Casie Aviles: And…
175 00:16:11.300 ⇒ 00:16:19.370 Casie Aviles: What’s good about NA10 is these integrations are already available for us, so we just… they come out of the box, so…
176 00:16:19.550 ⇒ 00:16:26.410 Casie Aviles: We just have to add a Slack node, and just set the… I guess the credentials to…
177 00:16:27.330 ⇒ 00:16:31.450 Casie Aviles: To our… to one of our accounts, and then, yeah, that should be good to go, and…
178 00:16:31.880 ⇒ 00:16:35.670 Casie Aviles: So that’s one good thing, and
179 00:16:35.910 ⇒ 00:16:37.519 Casie Aviles: So the idea is we want
180 00:16:37.830 ⇒ 00:16:41.100 Casie Aviles: the AI agents to live where we do the work.
181 00:16:41.840 ⇒ 00:16:45.660 Casie Aviles: So, wherever the team is, most…
182 00:16:45.920 ⇒ 00:16:50.000 Casie Aviles: Or, like, where we spend time the most, so, which is probably on Slack.
183 00:16:50.460 ⇒ 00:16:54.530 Casie Aviles: So that’s the idea why we wanted to have it on Slack, and then…
184 00:16:55.920 ⇒ 00:17:01.549 Casie Aviles: Additionally, since we developed our, the internal platform that we have now.
185 00:17:03.600 ⇒ 00:17:10.640 Casie Aviles: And A10 also, makes it easier for us to integrate it to our own front end because of.
186 00:17:11.720 ⇒ 00:17:15.330 Casie Aviles: The triggers that… that come with N8N.
187 00:17:15.480 ⇒ 00:17:26.019 Casie Aviles: So we can… activate this agent, via webhooks, so… Essentially, we’re just,
188 00:17:26.579 ⇒ 00:17:29.339 Casie Aviles: Yeah, we’re just sending out, requests.
189 00:17:29.480 ⇒ 00:17:35.840 Casie Aviles: And… which is… which are received by this, workflow, and then the agent will just…
190 00:17:35.970 ⇒ 00:17:40.150 Casie Aviles: Generate an output, so that’s what makes it… convenient.
191 00:17:40.270 ⇒ 00:17:46.979 Casie Aviles: So we could do something, yeah, so the agent setup is not the most…
192 00:17:47.680 ⇒ 00:17:53.870 Casie Aviles: challenging part, I guess, it would be the… the vectorization.
193 00:17:54.560 ⇒ 00:18:01.079 Casie Aviles: But yeah, the good thing about… another good thing about NA10 is… It has these…
194 00:18:02.070 ⇒ 00:18:06.709 Casie Aviles: vector database tools, or RAG tools.
195 00:18:06.950 ⇒ 00:18:11.640 Casie Aviles: So… We also don’t have to implement our own I mean…
196 00:18:12.780 ⇒ 00:18:15.480 Casie Aviles: At least for, like, the retrieval.
197 00:18:15.630 ⇒ 00:18:18.260 Casie Aviles: part. We don’t have to implement that.
198 00:18:18.770 ⇒ 00:18:23.950 Casie Aviles: We just use Supabase, which is another integration that comes with N80.
199 00:18:24.560 ⇒ 00:18:32.729 Casie Aviles: And then we just… Plug in these models, so… Yeah, I think that’s… I think that’s what…
200 00:18:34.170 ⇒ 00:18:39.989 Casie Aviles: That’s what… those are the good things about N80 that made this possible for us.
201 00:18:42.510 ⇒ 00:18:48.919 Hannah Wang: Maybe I should have asked this before we started talking, but what exactly is N8N?
202 00:18:49.080 ⇒ 00:18:56.900 Casie Aviles: Oh, okay. Yeah, so NA10 is, like, an agent builder, but… or a workflow builder as well, like, you know.
203 00:18:57.240 ⇒ 00:19:07.030 Casie Aviles: So… It’s… it’s kind of… it lets us do, like, automations in a node-based manner, so…
204 00:19:07.730 ⇒ 00:19:13.740 Casie Aviles: Basically, we have these… Little nodes here that we can string together.
205 00:19:14.070 ⇒ 00:19:19.509 Casie Aviles: And… It lets us build automation, so…
206 00:19:19.640 ⇒ 00:19:27.070 Casie Aviles: And additionally, we can also build, like, workflows where AI could be added.
207 00:19:27.560 ⇒ 00:19:28.710 Casie Aviles: So…
208 00:19:29.500 ⇒ 00:19:36.310 Casie Aviles: Yeah, if we want… if we have steps that need to be AI-generated, like, if we want to automate
209 00:19:36.650 ⇒ 00:19:39.969 Casie Aviles: email generation.
210 00:19:40.230 ⇒ 00:19:48.040 Casie Aviles: or, like, email draft generation, then we could have, like, a step, an LLM step, that We’ll generate the…
211 00:19:49.150 ⇒ 00:19:53.329 Casie Aviles: the draft for us. So, yeah, that’s what pretty much…
212 00:19:53.730 ⇒ 00:19:58.180 Casie Aviles: N8N is. It’s like a workflow builder, and also a…
213 00:19:59.170 ⇒ 00:20:03.490 Casie Aviles: With AI features and with a lot… with a lot of integrations.
214 00:20:04.460 ⇒ 00:20:06.209 Hannah Wang: Okay. Kind of like Zapier.
215 00:20:07.060 ⇒ 00:20:07.790 Hannah Wang: Got it.
216 00:20:08.100 ⇒ 00:20:12.290 Hannah Wang: Zapier and, I think, Make is another workflow builder, right?
217 00:20:12.420 ⇒ 00:20:13.220 Hannah Wang: those two.
218 00:20:14.820 ⇒ 00:20:29.130 Hannah Wang: Okay, that… I understand that a little more now, because I’ve… I’ve used Make before, so… N-A-N, yeah, it looks similar, it’s just workflow building, basically. Yeah. Okay. And…
219 00:20:29.590 ⇒ 00:20:36.989 Hannah Wang: Perhaps adoption hasn’t been that great, but…
220 00:20:37.260 ⇒ 00:20:37.770 Casie Aviles: Yeah.
221 00:20:37.770 ⇒ 00:20:39.179 Hannah Wang: even, like.
222 00:20:39.670 ⇒ 00:20:50.839 Hannah Wang: with the small amount of usage that we got, I guess, was there any feedback that you guys got for the agent?
223 00:20:50.980 ⇒ 00:20:55.739 Hannah Wang: either from Utam or from Amber, I feel like those are the names.
224 00:20:55.740 ⇒ 00:20:56.430 Casie Aviles: two people.
225 00:20:56.430 ⇒ 00:20:57.499 Hannah Wang: that use it.
226 00:20:58.170 ⇒ 00:21:06.170 Casie Aviles: Yeah, that… yeah, those are, like, the main two people that… that use the client hubs, so…
227 00:21:06.320 ⇒ 00:21:10.180 Casie Aviles: like, the positive feedback, I guess, would be…
228 00:21:10.930 ⇒ 00:21:16.840 Casie Aviles: It’s helped Amber before, like, for ABC, when she was starting out.
229 00:21:17.030 ⇒ 00:21:26.219 Casie Aviles: It’s helped her generate, like, email summaries for the work that we’ve done, and… also asked.
230 00:21:26.560 ⇒ 00:21:33.150 Casie Aviles: Questions, or, you know, things that… She doesn’t have context on…
231 00:21:33.640 ⇒ 00:21:38.200 Casie Aviles: Because she, she was, I think…
232 00:21:38.470 ⇒ 00:21:42.000 Casie Aviles: She came later, to the project.
233 00:21:43.680 ⇒ 00:21:48.049 Casie Aviles: So, me and Miguel were there first, so we built this for her.
234 00:21:48.270 ⇒ 00:21:50.120 Casie Aviles: When she was new, so…
235 00:21:51.520 ⇒ 00:21:54.619 Casie Aviles: Yeah, I think that’s the positive side to it.
236 00:21:56.120 ⇒ 00:22:04.310 Casie Aviles: But then, it’s also… when we built this, it was also very challenging, because we had a lot of,
237 00:22:04.470 ⇒ 00:22:06.020 Casie Aviles: Quality issues.
238 00:22:06.660 ⇒ 00:22:09.169 Casie Aviles: Then, because we haven’t figured out, like.
239 00:22:09.630 ⇒ 00:22:16.450 Casie Aviles: the best way to feed the data to this AI agent
240 00:22:16.850 ⇒ 00:22:20.860 Casie Aviles: we were kind of also learning how to do RAG as we did
241 00:22:21.570 ⇒ 00:22:25.700 Casie Aviles: These client hub agents, so… I guess that’s what…
242 00:22:25.880 ⇒ 00:22:29.620 Casie Aviles: That’s, like, the negative feedback that we got, which is…
243 00:22:30.870 ⇒ 00:22:34.480 Casie Aviles: It was kind of hard to, like, trust this agent.
244 00:22:34.960 ⇒ 00:22:35.800 Casie Aviles: Yeah.
245 00:22:36.370 ⇒ 00:22:44.709 Casie Aviles: Especially since… It doesn’t give the data, sometimes it doesn’t give the data, the info that is…
246 00:22:45.220 ⇒ 00:22:50.520 Casie Aviles: That we’re looking for, and… You’ve got incomplete data, and sometimes it’s just…
247 00:22:50.760 ⇒ 00:22:55.260 Casie Aviles: hallucinating. So we had a lot of technical challenges with
248 00:22:55.620 ⇒ 00:22:59.259 Casie Aviles: Building this, primarily because of, like, the…
249 00:23:00.410 ⇒ 00:23:08.350 Casie Aviles: Yeah, the data part, like, the knowledge base, so… fabulous, yeah.
250 00:23:08.350 ⇒ 00:23:09.899 Hannah Wang: I guess…
251 00:23:10.850 ⇒ 00:23:14.179 Casie Aviles: Yeah, I was just gonna say that was a lot of,
252 00:23:14.940 ⇒ 00:23:19.750 Casie Aviles: iterations, and on… so we were working on this on and off as well, so…
253 00:23:19.750 ⇒ 00:23:20.410 Hannah Wang: Yeah.
254 00:23:21.690 ⇒ 00:23:22.070 Casie Aviles: Yeah.
255 00:23:22.070 ⇒ 00:23:22.690 Hannah Wang: Got it.
256 00:23:23.390 ⇒ 00:23:35.200 Hannah Wang: And, I guess after building, this workflow, did it help you like, build other…
257 00:23:35.840 ⇒ 00:23:43.979 Hannah Wang: tools? Like, did… was it… like, for example, like, I know you integrated this into the Zoom platform, right? So I’m just…
258 00:23:43.980 ⇒ 00:23:44.430 Casie Aviles: Yes.
259 00:23:44.430 ⇒ 00:23:46.950 Hannah Wang: Yeah, asking, like, oh, was this…
260 00:23:47.240 ⇒ 00:23:51.960 Hannah Wang: Building this, like, a helpful foundation for other projects that you guys worked on.
261 00:23:52.440 ⇒ 00:23:54.899 Casie Aviles: Yeah, actually, we did learn a lot from
262 00:23:55.120 ⇒ 00:23:59.310 Casie Aviles: Building the client hub agents, so…
263 00:23:59.580 ⇒ 00:24:03.149 Casie Aviles: There, like, for example, the email…
264 00:24:03.740 ⇒ 00:24:07.930 Casie Aviles: like, summary generation, we decided to just…
265 00:24:08.120 ⇒ 00:24:11.230 Casie Aviles: Create, like, an entirely separate workflow for that.
266 00:24:13.050 ⇒ 00:24:14.060 Casie Aviles: And…
267 00:24:14.290 ⇒ 00:24:24.680 Casie Aviles: Yeah, so that’s what… something that, Mustafa also worked on. But yeah, we kind of specialized it from there.
268 00:24:25.870 ⇒ 00:24:30.959 Casie Aviles: And so, yeah, like, It’s… Ideally, this agent would have done…
269 00:24:31.140 ⇒ 00:24:35.600 Casie Aviles: a lot of the stuff, but we decided to just compartmentalize, I guess.
270 00:24:35.670 ⇒ 00:24:37.140 Hannah Wang: For now.
271 00:24:37.220 ⇒ 00:24:44.219 Casie Aviles: But yeah, also a lot of the challenges we faced building this Has been good.
272 00:24:44.370 ⇒ 00:24:51.259 Casie Aviles: at least a good learning experience. We were able to build, like, other more Complex workflows, for instance,
273 00:24:51.600 ⇒ 00:24:58.269 Casie Aviles: like, with… even with ABC, we… we would, from… based on what we learned here, we were able to, like.
274 00:24:58.680 ⇒ 00:25:01.600 Casie Aviles: Make some improvements to other clients.
275 00:25:02.200 ⇒ 00:25:04.590 Casie Aviles: Existing client workflows that we have.
276 00:25:04.860 ⇒ 00:25:08.269 Casie Aviles: So… Yeah, definitely, I think.
277 00:25:08.400 ⇒ 00:25:15.020 Casie Aviles: Even though the… there were a lot of issues that we faced, we learned a lot here.
278 00:25:15.560 ⇒ 00:25:16.160 Hannah Wang: Hmm.
279 00:25:17.420 ⇒ 00:25:18.330 Hannah Wang: Cool.
280 00:25:18.510 ⇒ 00:25:21.760 Hannah Wang: Okay, I think this is…
281 00:25:21.950 ⇒ 00:25:36.430 Hannah Wang: This is good. We’ll see how the case study comes out. But yeah, I think we can hop on to the next meeting to talk about, measuring agents with the ABC dashboard. So, I’ll see you in that meeting.
282 00:25:37.280 ⇒ 00:25:37.980 Casie Aviles: Okay.
283 00:25:38.460 ⇒ 00:25:39.020 Casie Aviles: Bye-bye.