Meeting Title: Brainforge AI Projects Check-in Date: 2025-08-08 Meeting participants: Mustafa Raja, Giselle Agot
WEBVTT
1 00:00:23.500 ⇒ 00:00:25.260 Giselle Agot: Hi! Mustafa!
2 00:00:26.590 ⇒ 00:00:32.439 Giselle Agot: Hello, Hi! Mustafa! Can you hear me?
3 00:00:34.690 ⇒ 00:00:36.250 Mustafa Raja: Hey? Hello! Sorry.
4 00:00:36.918 ⇒ 00:00:49.469 Giselle Agot: Okay, Hello, thank you for attending. Today’s call. This is will just be a quick one. I know you’re super busy. I just wanted to check in with you, and also Amber asked me to
5 00:00:49.977 ⇒ 00:00:57.340 Giselle Agot: have a 1 on one call with you to discuss about interlude and default, though I have already attended
6 00:00:57.540 ⇒ 00:01:11.089 Giselle Agot: some of the calls that you have with with the client and also with Adam. I just wanted to ask about your AI work like how it, how it is just to give me an understanding.
7 00:01:12.450 ⇒ 00:01:19.859 Mustafa Raja: Okay, so do you have a do you have understanding of what? What interlude wants us to build?
8 00:01:20.180 ⇒ 00:01:21.580 Mustafa Raja: I can walk you through it.
9 00:01:23.036 ⇒ 00:01:28.849 Giselle Agot: Actually, I’m trying to understand. But I don’t have, because when you had the call, it was just like
10 00:01:29.324 ⇒ 00:01:30.820 Giselle Agot: like, it’s so.
11 00:01:30.820 ⇒ 00:01:31.460 Mustafa Raja: Yeah.
12 00:01:31.460 ⇒ 00:01:39.030 Giselle Agot: The workflow. So I I did not have like an understanding. I I did not also see the project scope. That was what I was asking. Amber.
13 00:01:39.630 ⇒ 00:01:42.750 Mustafa Raja: Oh, yeah, let me share my screen, actually.
14 00:01:45.440 ⇒ 00:01:46.260 Mustafa Raja: And
15 00:01:48.720 ⇒ 00:02:14.569 Mustafa Raja: so what interlude does, at least for now? What’s related to us is they have clients that need some sort of funding, and so what they do is they create decks for them. You see, these are all fundraising decks, and so let’s take. And let’s take a look at one of these examples.
16 00:02:17.910 ⇒ 00:02:18.760 Mustafa Raja: Yeah.
17 00:02:19.660 ⇒ 00:02:44.559 Mustafa Raja: So what? So? So this is the interlude. CEO. Going to Claude and giving Claude context of everything about their client, and what their client wants, what their client is going to present, and all, and what the what the what they’re expecting out. If it is a purposeful deck that their client
18 00:02:44.560 ⇒ 00:03:03.920 Mustafa Raja: can present to potential investors to get funding. And so. And this is this is the 1st task that we have taken over. We are automating this process for them. And most of our AI stuff is going to be built on this tool named na, 10
19 00:03:04.356 ⇒ 00:03:27.469 Mustafa Raja: and so this is an agent. That I have built for them. And we are improving it over to make sure that they will at the end would not have to change anything or do some minor little changes and hand it over to the client or their design team to set up slides for their client.
20 00:03:28.110 ⇒ 00:03:41.279 Mustafa Raja: So this is sort of a short example of what AI, really, AI team really does. Have you got the chance to use the linear linear tickets? Creator tool.
21 00:03:42.020 ⇒ 00:03:43.009 Giselle Agot: Yeah, I have.
22 00:03:43.010 ⇒ 00:03:50.680 Mustafa Raja: Yeah. So the that is also another use case. So what? That? Let me open that to workflow also.
23 00:03:52.160 ⇒ 00:03:55.579 Mustafa Raja: Give me a few moments. Let me find it.
24 00:03:58.510 ⇒ 00:03:59.190 Mustafa Raja: Yeah.
25 00:03:59.730 ⇒ 00:04:26.225 Mustafa Raja: So what what what this really does is it takes in the client meeting transcript the meeting transcript that we would have no in the platform. And then we parse it through. A few agents and the purpose of we also fetch the data from linear or with which people belong to which team so it
26 00:04:26.640 ⇒ 00:04:47.319 Mustafa Raja: so it only assigns the ticket to to the people that are in that particular team. It also decides which team does this ticket belong to. And this is all based on the title of the meeting and the transcript and the participants of the meeting. So these 3,
27 00:04:48.330 ⇒ 00:04:49.480 Mustafa Raja: oh, God!
28 00:04:52.830 ⇒ 00:04:53.940 Mustafa Raja: These 3,
29 00:04:57.650 ⇒ 00:05:08.250 Mustafa Raja: the comp building components as our ticketing client hub is essentially that we have stored kind meeting
30 00:05:11.072 ⇒ 00:05:39.249 Mustafa Raja: in a vector database. A vector database is something that an AI agent would understand would be able to search through. So when we talk about vector, database, it’s essentially the knowledge base of an agent. So so the agent knows, sort of the things that we have built already for the client. So it adds in that context, and throws out the tickets.
31 00:05:39.420 ⇒ 00:05:56.199 Mustafa Raja: So this is the sort of work that AI team would be doing we have one other member in the AI team. That’s Casey. I I think you’d be able to meet him in future projects or so, if you haven’t met him yet.
32 00:05:57.740 ⇒ 00:05:58.910 Giselle Agot: Oh, yeah, I haven’t.
33 00:05:58.910 ⇒ 00:05:59.840 Mustafa Raja: Yeah, you’re.
34 00:05:59.840 ⇒ 00:06:03.180 Giselle Agot: I was. I met him just earlier when he had our team meeting.
35 00:06:03.180 ⇒ 00:06:11.850 Mustafa Raja: Yeah, yeah, you’ll you’ll see him around. He takes in a lot of the work that you’re seeing here. It’s only my second month
36 00:06:12.220 ⇒ 00:06:13.960 Mustafa Raja: in Brain Forge.
37 00:06:14.270 ⇒ 00:06:15.470 Giselle Agot: Oh, it’s your second month.
38 00:06:15.470 ⇒ 00:06:18.803 Mustafa Raja: Yeah, yeah, he’s he’s been over here for
39 00:06:19.900 ⇒ 00:06:23.360 Mustafa Raja: of, I think of, I think, almost a year now.
40 00:06:23.360 ⇒ 00:06:26.509 Giselle Agot: Okay, so where do you learn all this? You’re an engineer, right?
41 00:06:26.510 ⇒ 00:06:27.836 Mustafa Raja: Yeah. So
42 00:06:29.059 ⇒ 00:06:37.849 Mustafa Raja: I was, I’m a recent graduate. It’s been almost 4 months. I graduated my undergrad computer science.
43 00:06:38.887 ⇒ 00:06:47.140 Mustafa Raja: So what I used to do is I used to do freelancing while I was studying and the main thing that I freelance in was
44 00:06:47.812 ⇒ 00:07:12.580 Mustafa Raja: web development and mobile development. And when I was in my 6th semester the the AI started to pop off and that is when I needed to build a project in Pakistan. Before graduation a year before graduation. What we need to do is we need to work work on something innovative.
45 00:07:13.961 ⇒ 00:07:34.049 Mustafa Raja: That’s also somewhat of a new technology. So this AI agents they had just recently came out. So that is when I started AI engineering, and I saw that it had a lot of scope. And then I started freelancing in it, and that is how I met with them.
46 00:07:35.370 ⇒ 00:07:36.850 Giselle Agot: Oh, okay.
47 00:07:36.850 ⇒ 00:07:40.499 Mustafa Raja: Yeah, yeah, that is sort of my story.
48 00:07:41.370 ⇒ 00:07:44.699 Giselle Agot: So did you also program linear? Did you have program, linear or.
49 00:07:46.520 ⇒ 00:07:54.669 Mustafa Raja: Well, the linear is we we are subscribed to linear, I believe.
50 00:07:54.870 ⇒ 00:07:55.860 Giselle Agot: Hmm, okay.
51 00:07:55.860 ⇒ 00:07:57.160 Mustafa Raja: It’s not ours.
52 00:07:57.350 ⇒ 00:07:58.150 Giselle Agot: Okay.
53 00:07:59.790 ⇒ 00:08:05.179 Mustafa Raja: Yeah, let me know. Let me know if you have any questions about about interlude or default.
54 00:08:05.990 ⇒ 00:08:10.010 Giselle Agot: Yeah. Interlude is a new client, right in my understanding.
55 00:08:10.953 ⇒ 00:08:16.460 Mustafa Raja: Default is also new client. It’s only been almost a week.
56 00:08:16.830 ⇒ 00:08:19.549 Mustafa Raja: or I think 2 weeks to both of them.
57 00:08:19.990 ⇒ 00:08:23.160 Giselle Agot: Now observing the meetings that we had with them this week.
58 00:08:24.121 ⇒ 00:08:27.969 Giselle Agot: We’re basically already setting up right.
59 00:08:27.970 ⇒ 00:08:28.970 Mustafa Raja: No.
60 00:08:29.690 ⇒ 00:08:31.769 Giselle Agot: So that was what you were presenting.
61 00:08:31.770 ⇒ 00:08:32.500 Mustafa Raja: Yeah.
62 00:08:33.230 ⇒ 00:08:33.615 Giselle Agot: Hey!
63 00:08:34.490 ⇒ 00:08:49.349 Mustafa Raja: We started. I guess we started a week or 2 before. The meeting that you joined for interlude was the second demo, and for me. The meeting you joined for default was the the 1st demo for them.
64 00:08:50.490 ⇒ 00:08:51.340 Giselle Agot: Okay.
65 00:08:51.530 ⇒ 00:08:54.420 Giselle Agot: And yeah, I’ll just ask Amber. But
66 00:08:54.600 ⇒ 00:09:04.680 Giselle Agot: I’m I’m just going to look at the timeline of the project. I’m not really sure. I have to really understand what the project is about. And actually, it’s my 4th day
67 00:09:05.663 ⇒ 00:09:06.370 Giselle Agot: I I.
68 00:09:06.370 ⇒ 00:09:13.260 Mustafa Raja: Yeah, I understand the 1st week is a very is is really hectic. You you need to take in so much of context.
69 00:09:13.700 ⇒ 00:09:17.036 Giselle Agot: Yeah, I have to understand what you guys are talking about.
70 00:09:17.340 ⇒ 00:09:17.990 Mustafa Raja: Yeah, yeah.
71 00:09:17.990 ⇒ 00:09:25.659 Giselle Agot: I’m used to is just website and development, that technic, that not really this technical, it’s really super technical. So I.
72 00:09:25.660 ⇒ 00:09:26.150 Mustafa Raja: Yeah, yeah.
73 00:09:26.400 ⇒ 00:09:49.500 Giselle Agot: Understand. But process, wise project management. I have that. It’s just that I really need to understand the what the business is about, because if I will be handling those calls, I will sound not very confident, especially if it’s a very technical question from the client, or they needed something to explain to. So I I can see how autumn handled those calls. So
74 00:09:49.700 ⇒ 00:09:54.059 Giselle Agot: I’m hoping that I’ll be able to catch up as soon as possible.
75 00:09:54.640 ⇒ 00:09:55.430 Mustafa Raja: Yeah.
76 00:09:57.280 ⇒ 00:10:21.543 Giselle Agot: Okay, I think that’s just what I need. Amber just asked me to ask about AI work and also include and default. I was thinking enrollment default was an you know, a current project that I had to transition covering for autumn, because autumn said he wants to move out from the project. So it’s also a new project. So probably I’ll
77 00:10:22.260 ⇒ 00:10:30.710 Giselle Agot: I’ll just read through the scope. And then there are some meetings. Let’s say, earlier, I did see we have ABC data, insomnia stand up. I was not.
78 00:10:30.710 ⇒ 00:10:31.280 Mustafa Raja: Yeah, yeah.
79 00:10:31.280 ⇒ 00:10:35.190 Giselle Agot: And what was were you? Was the meeting? Did the meeting push through.
80 00:10:36.250 ⇒ 00:10:55.349 Mustafa Raja: Yeah. So Apc and insomnia are 2 other clients that we are working on and amber handles that insomnia? I believe it. We just started working on insomnia. Oh, maybe maybe you’re you’re working on insomnia right?
81 00:10:56.010 ⇒ 00:11:01.940 Giselle Agot: No, I’m actually, I’m not working. I’m just assisting Amber for some of the tasks.
82 00:11:02.420 ⇒ 00:11:07.280 Mustafa Raja: Okay, okay. Maybe you can ask Amber to add you in the insomnia meetings.
83 00:11:07.720 ⇒ 00:11:08.810 Giselle Agot: Yeah, I am.
84 00:11:08.810 ⇒ 00:11:23.459 Mustafa Raja: So maybe she maybe she forgot to add you in there. It’s it was actually 1st insomnia. My, at least my 1st insomnia meeting with the with the team I’m assisting, assisting on the automation side.
85 00:11:24.330 ⇒ 00:11:25.500 Giselle Agot: Yeah, okay, oh, we.
86 00:11:25.500 ⇒ 00:11:28.513 Mustafa Raja: I don’t know why this isn’t loading up.
87 00:11:28.890 ⇒ 00:11:29.510 Giselle Agot: Tweet.
88 00:11:29.510 ⇒ 00:11:30.800 Mustafa Raja: It’s been so long.
89 00:11:31.160 ⇒ 00:11:31.920 Mustafa Raja: Yeah.
90 00:11:32.180 ⇒ 00:11:39.499 Giselle Agot: And then for the default meeting, there is a default bi-weekly grooming meeting. What is? I’ll just ask Amber. We have a meeting.
91 00:11:39.500 ⇒ 00:11:57.569 Mustafa Raja: Yeah, yeah. The grooming meeting is we talk about the tickets that have been created. If we have any questions about them. So the grooming is, so that the person who’s assigned the ticket has enough context on the ticket to work on it.
92 00:11:59.140 ⇒ 00:11:59.820 Giselle Agot: Yeah.
93 00:11:59.820 ⇒ 00:12:00.990 Mustafa Raja: That that’s all.
94 00:12:01.540 ⇒ 00:12:02.880 Giselle Agot: Okay, got it?
95 00:12:02.880 ⇒ 00:12:09.260 Mustafa Raja: Yeah, and the other day I I believe Uttam wanted you to move these to block right.
96 00:12:10.300 ⇒ 00:12:11.169 Giselle Agot: I’m sorry.
97 00:12:11.691 ⇒ 00:12:17.659 Mustafa Raja: These 2 tickets for Henry. I feel these were Utah wanted these to move, to blocked no.
98 00:12:19.180 ⇒ 00:12:20.689 Giselle Agot: The one from.
99 00:12:21.490 ⇒ 00:12:22.109 Giselle Agot: Let’s see.
100 00:12:22.110 ⇒ 00:12:22.750 Mustafa Raja: Delete it.
101 00:12:23.500 ⇒ 00:12:24.270 Giselle Agot: Henry.
102 00:12:24.540 ⇒ 00:12:25.400 Mustafa Raja: Yeah, yeah.
103 00:12:25.400 ⇒ 00:12:31.020 Giselle Agot: Is it from Henry? Yeah, that that was. I was trying to understand also.
104 00:12:31.620 ⇒ 00:12:33.580 Mustafa Raja: Yeah, let me know. I can walk you through it.
105 00:12:33.580 ⇒ 00:12:34.640 Giselle Agot: Okay. Please.
106 00:12:35.270 ⇒ 00:12:36.140 Mustafa Raja: Oh.
107 00:12:37.110 ⇒ 00:12:45.149 Mustafa Raja: yeah, all issues. So you see, status, we have all these statuses, and if we want to move it to blog, we can say.
108 00:12:46.800 ⇒ 00:12:50.980 Mustafa Raja: Over here. I believe you misclicked into the backlog.
109 00:12:52.260 ⇒ 00:12:53.929 Giselle Agot: Oh! Depending, which I think.
110 00:12:54.570 ⇒ 00:12:55.470 Mustafa Raja: Yeah, yeah.
111 00:12:57.176 ⇒ 00:13:04.560 Mustafa Raja: I’ll just move it back over here. I don’t know. I don’t know. What’s the state of these? I I see that Henry has followed up with something.
112 00:13:05.920 ⇒ 00:13:06.550 Giselle Agot: Yes.
113 00:13:06.810 ⇒ 00:13:07.670 Mustafa Raja: Facility.
114 00:13:09.298 ⇒ 00:13:12.610 Mustafa Raja: I see that you assigned some tickets to me.
115 00:13:13.660 ⇒ 00:13:17.520 Giselle Agot: I did. That was based on the transcript from the call yesterday.
116 00:13:17.840 ⇒ 00:13:19.800 Mustafa Raja: Yeah, those were good. The tickets were good.
117 00:13:19.800 ⇒ 00:13:25.352 Giselle Agot: Okay, the tickets are good. I just took it from the transcript in the platform. So.
118 00:13:25.700 ⇒ 00:13:26.130 Mustafa Raja: Oh!
119 00:13:26.130 ⇒ 00:13:26.860 Giselle Agot: The timeline.
120 00:13:26.860 ⇒ 00:13:27.454 Mustafa Raja: Should
121 00:13:28.050 ⇒ 00:13:28.590 Giselle Agot: Yeah.
122 00:13:28.830 ⇒ 00:13:29.760 Giselle Agot: Sorry.
123 00:13:29.760 ⇒ 00:13:34.320 Mustafa Raja: Use the did you use the linear ticket to create those tickets?
124 00:13:36.390 ⇒ 00:13:45.940 Giselle Agot: No, I just manually add it, because when I test it out using the transcript and add it to linear, it goes to a no project board, so it.
125 00:13:46.348 ⇒ 00:13:48.800 Mustafa Raja: It went to something somewhere else.
126 00:13:48.800 ⇒ 00:13:50.300 Giselle Agot: Yes, yes, so it.
127 00:13:50.300 ⇒ 00:13:55.099 Mustafa Raja: It was very confusing yesterday. So I just manually, okay, okay, okay, okay, okay.
128 00:13:55.560 ⇒ 00:14:03.079 Mustafa Raja: okay. Let me let me show how. If if it misclassifies the tickets. How can we fix that.
129 00:14:05.000 ⇒ 00:14:07.100 Mustafa Raja: Let’s it was this meeting right?
130 00:14:07.280 ⇒ 00:14:09.280 Giselle Agot: Yes, yeah, that’s correct.
131 00:14:10.190 ⇒ 00:14:13.900 Mustafa Raja: Yeah, let me. Let’s let it. Let let it cook up some
132 00:14:14.180 ⇒ 00:14:30.249 Mustafa Raja: tickets, and then we can create an example. One, I did saw that it created. The wrong meetings. Oh, sorry. Associated the meetings with the wrong team. Yeah. So if it does this the this is the team. Actually, this.
133 00:14:30.360 ⇒ 00:14:37.099 Mustafa Raja: this is the team that that’s associated with the ticket. We can just edit. Go over here and say.
134 00:14:37.100 ⇒ 00:14:37.630 Giselle Agot: Wonderful.
135 00:14:37.630 ⇒ 00:14:38.240 Mustafa Raja: World.
136 00:14:38.440 ⇒ 00:14:41.429 Mustafa Raja: Okay? I, that’s what does not interlude. It’s.
137 00:14:41.430 ⇒ 00:14:42.100 Giselle Agot: Okay. Okay.
138 00:14:42.100 ⇒ 00:14:42.700 Mustafa Raja: Fold.
139 00:14:43.150 ⇒ 00:14:43.620 Giselle Agot: Okay.
140 00:14:43.620 ⇒ 00:14:45.400 Mustafa Raja: And let’s
141 00:14:46.710 ⇒ 00:14:48.659 Mustafa Raja: This is a test.
142 00:14:51.240 ⇒ 00:14:52.240 Mustafa Raja: So that’s good.
143 00:14:52.240 ⇒ 00:14:55.240 Giselle Agot: That’s what actually missed yesterday.
144 00:14:56.700 ⇒ 00:14:57.609 Giselle Agot: You thank you.
145 00:14:57.610 ⇒ 00:15:01.409 Mustafa Raja: If we create it. If we see it in linear, it’s in default.
146 00:15:01.790 ⇒ 00:15:05.310 Giselle Agot: Okay, got it. Okay. Thank you. Now I get it. Now, thank you.
147 00:15:05.310 ⇒ 00:15:10.299 Mustafa Raja: Yeah, any other questions, any other thing that I can help you with? Let me know.
148 00:15:10.300 ⇒ 00:15:13.985 Giselle Agot: Yeah, okay, I’ll let you know, probably the technical stuff, because.
149 00:15:14.620 ⇒ 00:15:15.509 Mustafa Raja: Yeah, yeah, yeah.
150 00:15:15.710 ⇒ 00:15:16.035 Giselle Agot: Then.
151 00:15:16.360 ⇒ 00:15:19.309 Mustafa Raja: I’ll be very. I’ll be very happy to
152 00:15:19.440 ⇒ 00:15:22.499 Mustafa Raja: to assist you in anything that I can.
153 00:15:22.830 ⇒ 00:15:24.899 Giselle Agot: Okay, thank you. Thank you so much. Mustafa.
154 00:15:24.900 ⇒ 00:15:25.870 Mustafa Raja: Thank you.
155 00:15:25.870 ⇒ 00:15:27.290 Giselle Agot: Thank you. Have a great day.
156 00:15:27.290 ⇒ 00:15:28.570 Mustafa Raja: Have a good day. Bye, bye.