1 00:00:00,359 --> 00:00:03,159 Hello, welcome to the XMACNA podcast. 2 00:00:03,159 --> 00:00:05,600 I'm Gustavo Fechus, and today 3 00:00:05,600 --> 00:00:06,879 we are here once again to 4 00:00:06,879 --> 00:00:09,440 talk with Lucca Carvalho and 5 00:00:09,440 --> 00:00:12,519 Rômulo Carneiro. Well, Lucca, Rômulo, we 6 00:00:12,519 --> 00:00:14,440 have some important issues to 7 00:00:14,440 --> 00:00:17,000 discuss here. I 8 00:00:17,000 --> 00:00:20,600 wanted to start by saying the following, and 9 00:00:20,600 --> 00:00:24,680 we, in interactions with the market, have already 10 00:00:24,680 --> 00:00:28,119 noticed a certain confusion, Lucca, about 11 00:00:28,119 --> 00:00:31,000 the fact that chatbots already exist 12 00:00:31,000 --> 00:00:33,200 out there, right? I mean, I think 13 00:00:33,200 --> 00:00:36,360 unfortunately, who hasn't 14 00:00:36,360 --> 00:00:38,079 had an experience, let's say, 15 00:00:38,079 --> 00:00:41,000 horrible or terrible with a chatbot that 16 00:00:41,000 --> 00:00:43,480 cast the first stone. But what I 17 00:00:43,480 --> 00:00:46,120 wanted to point out, Rômulo and Lucca, is the 18 00:00:46,120 --> 00:00:48,960 following: XMACNA aims to 19 00:00:48,960 --> 00:00:50,960 develop the concept of a digital 20 00:00:50,960 --> 00:00:53,000 employee in its various 21 00:00:53,000 --> 00:00:56,559 applications. And I think that in no way should it 22 00:00:56,559 --> 00:00:58,120 um 23 00:00:58,120 --> 00:01:01,760 be confused with the idea of a chatbot, right? 24 00:01:01,760 --> 00:01:04,360 So let's start by making that distinction 25 00:01:04,360 --> 00:01:06,640 first, so that, who knows, with 26 00:01:06,640 --> 00:01:09,159 some luck, we’ll never again 27 00:01:09,159 --> 00:01:11,560 get this question. And then we 28 00:01:11,560 --> 00:01:13,439 can move on later to other 29 00:01:13,439 --> 00:01:15,080 parts of this story, at least if 30 00:01:15,080 --> 00:01:16,360 someone asks, we’ll have this clip ready. 31 00:01:16,360 --> 00:01:18,920 Send this, send this exact link. 32 00:01:18,920 --> 00:01:21,680 Alright, go ahead. So Lucca, right? Well, I think... 33 00:01:21,680 --> 00:01:23,880 This answer, if you allow me to take two... 34 00:01:23,880 --> 00:01:26,280 Steps back. By the way, thank you... 35 00:01:26,280 --> 00:01:27,720 For the audience, thank you for being here... 36 00:01:27,720 --> 00:01:29,920 With us today. Thank you for the invitation. 37 00:01:29,920 --> 00:01:32,720 Thank you, Rômulo, for being here with us. Well... 38 00:01:32,720 --> 00:01:35,439 XMACNA was born within the Gir network... 39 00:01:35,439 --> 00:01:36,840 Right? It started as a spinoff from a... 40 00:01:36,840 --> 00:01:39,520 Education company, and in the Gir network we... 41 00:01:39,520 --> 00:01:41,000 We were already working with artificial intelligence... 42 00:01:41,000 --> 00:01:44,960 AI, we had been doing research for years, and... 43 00:01:44,960 --> 00:01:47,479 To help with our core activity... 44 00:01:47,479 --> 00:01:50,759 Which is correcting essays, teaching... 45 00:01:50,759 --> 00:01:53,079 Students to write better. 46 00:01:53,079 --> 00:01:56,320 Well, the first digital employee... 47 00:01:56,320 --> 00:01:58,119 Of XMACNA... 48 00:01:58,119 --> 00:02:00,719 Who still works, it was for the Gir network and I... 49 00:02:00,719 --> 00:02:02,079 Actually, I think our employee... 50 00:02:02,079 --> 00:02:05,280 Of the year some years ago was Bárbara, who... 51 00:02:05,280 --> 00:02:06,799 Is not a chatbot, it has nothing to do with... 52 00:02:06,799 --> 00:02:08,759 A chatbot. When we started to... 53 00:02:08,759 --> 00:02:10,000 Work with these artificial intelligences... 54 00:02:10,000 --> 00:02:11,239 When we started to work with language models... 55 00:02:11,239 --> 00:02:12,920 To work with language models, we... 56 00:02:12,920 --> 00:02:14,720 Realized that we would be able to... 57 00:02:14,720 --> 00:02:16,760 Automate some cognitive processes... 58 00:02:16,760 --> 00:02:19,319 Inside companies, eliminating a step... 59 00:02:19,319 --> 00:02:20,800 That before had to be done by some... 60 00:02:20,800 --> 00:02:23,080 Human being. Usually, it's a step... 61 00:02:23,080 --> 00:02:24,599 It's very boring, and a step that humans 62 00:02:24,599 --> 00:02:26,400 didn't want to do, freeing up the human 63 00:02:26,400 --> 00:02:27,720 to be able to do more 64 00:02:27,720 --> 00:02:29,360 interesting things. Give an example of what 65 00:02:29,360 --> 00:02:32,200 you're talking about. In the case of Redigir, 66 00:02:32,200 --> 00:02:34,879 Bárbara, she works by doing the 67 00:02:34,879 --> 00:02:37,040 guidance note, the final comment 68 00:02:37,040 --> 00:02:39,360 on the essays, which is extra work for the 69 00:02:39,360 --> 00:02:42,040 grader. The human grader, flesh and blood, 70 00:02:42,040 --> 00:02:43,560 has to read a text and make a 71 00:02:43,560 --> 00:02:45,560 series of annotations and then assign a grade. 72 00:02:45,560 --> 00:02:47,239 After that he has to do a 73 00:02:47,239 --> 00:02:49,440 review of everything he marked and 74 00:02:49,440 --> 00:02:51,599 explain in Portuguese to the student how 75 00:02:51,599 --> 00:02:53,640 they have to, how they can correct 76 00:02:53,640 --> 00:02:56,000 that mistake or those 77 00:02:56,000 --> 00:02:58,840 mistakes. What happens is our 78 00:02:58,840 --> 00:03:01,319 graders do this at scale. We 79 00:03:01,319 --> 00:03:04,400 grade thousands of essays a day. A, a 80 00:03:04,400 --> 00:03:06,280 professional grader who's there 81 00:03:06,280 --> 00:03:08,920 at the front line, the first essay they grade with 82 00:03:08,920 --> 00:03:11,640 standards, the second with a certain 83 00:03:11,640 --> 00:03:13,400 final comment. It decreases a bit by 84 00:03:13,400 --> 00:03:16,799 the 12th, the 30th, and that keeps getting a little 85 00:03:16,799 --> 00:03:18,959 lazier, because patience runs out, right? 86 00:03:18,959 --> 00:03:23,560 We created a copilot that comes in 87 00:03:23,560 --> 00:03:26,319 right at this part, taking away the 88 00:03:26,319 --> 00:03:28,239 boring part of the grader's work 89 00:03:28,239 --> 00:03:30,120 so the human grader gains 90 00:03:30,120 --> 00:03:33,159 speed, quality, and quality of life. 91 00:03:33,159 --> 00:03:35,360 time and earning more money for the 92 00:03:35,360 --> 00:03:36,720 same type of work. In fact, 93 00:03:36,720 --> 00:03:38,480 we remove a boring part of the job 94 00:03:38,480 --> 00:03:40,680 for them. They can be more productive and still 95 00:03:40,680 --> 00:03:42,319 earn more for the same amount of time worked. 96 00:03:42,319 --> 00:03:45,120 This was a turning point within 97 00:03:45,120 --> 00:03:48,480 XMACNA and we realized that, well, 98 00:03:48,480 --> 00:03:50,480 just as XMACNA had a Bárbara, 99 00:03:50,480 --> 00:03:52,000 there was actually room for several 100 00:03:52,000 --> 00:03:53,760 Bárbaras inside. Then others started appearing, 101 00:03:53,760 --> 00:03:55,959 like the Oracle, who is a programmer, 102 00:03:55,959 --> 00:03:57,840 he makes requests to our database 103 00:03:57,840 --> 00:03:59,760 using natural language, for example. 104 00:03:59,760 --> 00:04:01,799 Then there’s Keber, who is our 105 00:04:01,799 --> 00:04:04,120 data scientist, who analyzes the 106 00:04:04,120 --> 00:04:05,439 performance of a school and makes a 107 00:04:05,439 --> 00:04:07,200 suggestion for a pedagogical intervention, 108 00:04:07,200 --> 00:04:09,159 which is a suggestion for a teacher 109 00:04:09,159 --> 00:04:10,560 to apply in their classroom. 110 00:04:10,560 --> 00:04:13,720 We realized that every type of 111 00:04:13,720 --> 00:04:15,799 company could have Bárbaras, could have 112 00:04:15,799 --> 00:04:17,560 cognitive processes that could be 113 00:04:17,560 --> 00:04:20,959 automated and, while the market 114 00:04:20,959 --> 00:04:22,479 later, we realized what 115 00:04:22,479 --> 00:04:23,880 was going to happen. It’s natural that 116 00:04:23,880 --> 00:04:26,320 it happened. People or companies that 117 00:04:26,320 --> 00:04:28,880 worked with chatbots saw in 118 00:04:28,880 --> 00:04:31,160 artificial intelligence a great 119 00:04:31,160 --> 00:04:35,720 ally for chatbots, and it is well suited 120 00:04:35,720 --> 00:04:37,680 for this role. Without 121 00:04:37,680 --> 00:04:38,440 Problem. 122 00:04:38,440 --> 00:04:42,160 Yeah, but there are a lot of Frankensteins out there, right? 123 00:04:42,160 --> 00:04:43,759 People who worked at companies, 124 00:04:43,759 --> 00:04:46,800 who worked with... had an entire 125 00:04:46,800 --> 00:04:48,240 system set up to handle the 126 00:04:48,240 --> 00:04:51,479 chatbot. The chatbot, it has a... it 127 00:04:51,479 --> 00:04:54,160 assumes a very rigid flow. It 128 00:04:54,160 --> 00:04:56,880 assumes knowing the questions and the 129 00:04:56,880 --> 00:04:58,639 answers. Well, no need to explain here, 130 00:04:58,639 --> 00:05:00,759 right? Everyone has already had a chance 131 00:05:00,759 --> 00:05:03,440 to get frustrated with a chatbot. And a 132 00:05:03,440 --> 00:05:05,400 digital employee, when placed 133 00:05:05,400 --> 00:05:08,600 in this sales position, it revolutionizes. 134 00:05:08,600 --> 00:05:11,000 It's something completely different, it's 135 00:05:11,000 --> 00:05:12,720 a different experience, right? A different experience. 136 00:05:12,720 --> 00:05:15,160 But it doesn't stop there, because the process 137 00:05:15,160 --> 00:05:18,160 the cognitive process involved there 138 00:05:18,160 --> 00:05:21,080 is that the salesperson has to perform, not 139 00:05:21,080 --> 00:05:24,160 it's not just about talking, it's not limited to 140 00:05:24,160 --> 00:05:26,000 being there interacting with the public 141 00:05:26,000 --> 00:05:28,160 there are a number of other 142 00:05:28,160 --> 00:05:30,319 back office activities that are 143 00:05:30,319 --> 00:05:32,919 repetitive, that take a lot of time, that 144 00:05:32,919 --> 00:05:35,160 no salesperson likes to do, and that 145 00:05:35,160 --> 00:05:36,720 our artificial intelligences also 146 00:05:36,720 --> 00:05:40,120 are involved and optimizing, saving 147 00:05:40,120 --> 00:05:42,160 the salesperson's life, right? We say that the 148 00:05:42,160 --> 00:05:45,600 CRM is the main ally and the 149 00:05:45,600 --> 00:05:47,639 main enemy of the salesperson, because when 150 00:05:47,639 --> 00:05:50,479 filled out properly, it's, it's, it's a miracle 151 00:05:50,479 --> 00:05:53,160 If it's filled out wrong, it sabotages you. So the 152 00:05:53,160 --> 00:05:55,840 our solution, the solutions from XMACNA 153 00:05:55,840 --> 00:05:57,039 they don't start out like the 154 00:05:57,039 --> 00:05:59,639 Frankenstein of some other system, 155 00:05:59,639 --> 00:06:00,680 a system that later adds 156 00:06:00,680 --> 00:06:02,360 artificial intelligence in the middle. It 157 00:06:02,360 --> 00:06:06,080 is AI-first from the start. So we 158 00:06:06,080 --> 00:06:09,199 designed the whole process, thought through every 159 00:06:09,199 --> 00:06:11,400 step, understanding the 160 00:06:11,400 --> 00:06:12,639 potential of Artificial 161 00:06:12,639 --> 00:06:14,759 Intelligence, and we created something that, well, 162 00:06:14,759 --> 00:06:17,720 doesn't exist in the market. Which, well, I think 163 00:06:17,720 --> 00:06:20,000 I'll hand it over to Rômulo because he's 164 00:06:20,000 --> 00:06:22,479 the one who leads the factory and is there with 165 00:06:22,479 --> 00:06:24,560 an army of Autobots. We call the 166 00:06:24,560 --> 00:06:26,039 Autobots our digital employees 167 00:06:26,039 --> 00:06:27,800 who are in the back office. He's 168 00:06:27,800 --> 00:06:29,160 there configuring these Autobots. You can 169 00:06:29,160 --> 00:06:31,520 tell us about the revolution it is in a 170 00:06:31,520 --> 00:06:33,800 company that needs to serve at 171 00:06:33,800 --> 00:06:35,919 scale. So, how is it, Romulo? Of course, 172 00:06:35,919 --> 00:06:38,360 guys, here's the thing: taking a step 173 00:06:38,360 --> 00:06:40,280 back in this point that Lucca 174 00:06:40,280 --> 00:06:42,319 was bringing up, I think it's interesting 175 00:06:42,319 --> 00:06:44,720 for us to understand a difference of ours at 176 00:06:44,720 --> 00:06:48,000 XMACNA compared to chatbots, even 177 00:06:48,000 --> 00:06:51,000 chatbots that work with AI today, is that 178 00:06:51,000 --> 00:06:53,840 the idea at XMACNA always starts in 179 00:06:53,840 --> 00:06:56,160 cognitive processes. So I think the 180 00:06:56,160 --> 00:06:58,599 first stage of our work with 181 00:06:58,599 --> 00:07:00,319 company, which we call the meeting of 182 00:07:00,319 --> 00:07:02,599 KickOff, that initial meeting, is to say 183 00:07:02,599 --> 00:07:04,280 like this: Gustavo, you who are 184 00:07:04,280 --> 00:07:06,720 hiring us, right? What is the process 185 00:07:06,720 --> 00:07:08,400 that you want to automate within your 186 00:07:08,400 --> 00:07:10,960 company? What is your complete workflow like 187 00:07:10,960 --> 00:07:13,960 around this stage of the process, where 188 00:07:13,960 --> 00:07:15,680 your fun... where the digital employee 189 00:07:15,680 --> 00:07:17,639 will act? How does he 190 00:07:17,639 --> 00:07:20,080 process within your entire funnel? So, 191 00:07:20,080 --> 00:07:23,639 there is a very thorough analysis 192 00:07:23,639 --> 00:07:26,440 individualized of what the steps are 193 00:07:26,440 --> 00:07:28,960 of your company specifically. So, it's not 194 00:07:28,960 --> 00:07:30,800 something that will just talk about 195 00:07:30,800 --> 00:07:33,240 the generalist knowledge that AIs 196 00:07:33,240 --> 00:07:35,800 often have. So, there is a cut. 197 00:07:35,800 --> 00:07:38,120 There are layers that are applied 198 00:07:38,120 --> 00:07:39,720 within artificial intelligence to 199 00:07:39,720 --> 00:07:42,120 make it extremely assertive 200 00:07:42,120 --> 00:07:44,319 within your workflow. I have already participated in 201 00:07:44,319 --> 00:07:47,479 some of these meetings a few times and 202 00:07:47,479 --> 00:07:50,400 there's something cool, Lucca, that 203 00:07:50,400 --> 00:07:52,319 Rômulo, as the factory director, is 204 00:07:52,319 --> 00:07:55,280 funny to say factory director, right? 205 00:07:55,280 --> 00:07:57,120 A lot of oil, a lot... it gives the impression 206 00:07:57,120 --> 00:07:58,520 like we hear some metals 207 00:07:58,520 --> 00:08:02,440 clanging and such. I hear it, you hear it, but 208 00:08:02,440 --> 00:08:05,360 it's because I've seen us talk like that. 209 00:08:05,360 --> 00:08:07,080 So, you who are full of these crazy ideas 210 00:08:07,080 --> 00:08:10,000 let's not start there, there are 211 00:08:10,000 --> 00:08:12,039 You have to start here, you know? That's the 212 00:08:12,039 --> 00:08:14,919 initial process. So, there is a 213 00:08:14,919 --> 00:08:17,759 reading of someone else's business that we 214 00:08:17,759 --> 00:08:21,080 have obviously already learned how to do. 215 00:08:21,080 --> 00:08:24,120 Which, by the way, sets a starting point 216 00:08:24,120 --> 00:08:25,680 for us, right? Sometimes, people 217 00:08:25,680 --> 00:08:29,080 have a somewhat vague idea of 218 00:08:29,080 --> 00:08:32,279 what AI does, you know? No, it's not 219 00:08:32,279 --> 00:08:34,440 magic, on the contrary, right? We need to 220 00:08:34,440 --> 00:08:36,599 explain some obvious things to everyone and 221 00:08:36,599 --> 00:08:39,159 really understand these conditions of where 222 00:08:39,159 --> 00:08:40,599 we should start so that 223 00:08:40,599 --> 00:08:42,120 certain cognitive processes 224 00:08:42,120 --> 00:08:45,040 are already solved, so that later we 225 00:08:45,040 --> 00:08:47,279 can move on to the second stage of these 226 00:08:47,279 --> 00:08:49,480 projects, to the third stage of these 227 00:08:49,480 --> 00:08:52,519 projects. This is a quality, in a way, 228 00:08:52,519 --> 00:08:54,480 in the sense that we need to have 229 00:08:54,480 --> 00:08:56,920 this understanding of the business to 230 00:08:56,920 --> 00:08:59,880 be able to make the best assessment 231 00:08:59,880 --> 00:09:02,720 of where and for what reason we 232 00:09:02,720 --> 00:09:05,200 implement an FD first, right? Exactly. 233 00:09:05,200 --> 00:09:07,440 I think this idea of where fits with the 234 00:09:07,440 --> 00:09:10,320 question Lucca asked me, because many 235 00:09:10,320 --> 00:09:13,040 times we see AI there as 236 00:09:13,040 --> 00:09:16,120 just that chat on WhatsApp, right? 237 00:09:16,120 --> 00:09:18,640 All those AIs that Lucca mentioned from the 238 00:09:18,640 --> 00:09:20,519 Girra network, they're not on WhatsApp. 239 00:09:20,519 --> 00:09:22,560 They're inside the system, doing 240 00:09:22,560 --> 00:09:24,360 automated processes that no one is 241 00:09:24,360 --> 00:09:26,279 I see it happening in the vast majority of 242 00:09:26,279 --> 00:09:28,880 times, right? So when Lucca asks me 243 00:09:28,880 --> 00:09:31,560 about the Autobots, for example, about 244 00:09:31,560 --> 00:09:34,680 these backoffice AIs, this AI that stays 245 00:09:34,680 --> 00:09:37,839 running inside the systems, they are 246 00:09:37,839 --> 00:09:39,320 essential. We recently had 247 00:09:39,320 --> 00:09:41,240 recently in southern Brazil as 248 00:09:41,240 --> 00:09:43,760 a partner company of ours, right? A CRM that we 249 00:09:43,760 --> 00:09:45,640 are working on to automate 250 00:09:45,640 --> 00:09:47,640 practically all the processes within 251 00:09:47,640 --> 00:09:51,240 the CRM. So, the solution starts with the SDR 252 00:09:51,240 --> 00:09:53,399 which is the qualifier there. After 253 00:09:53,399 --> 00:09:56,200 it qualifies, all the stage movements 254 00:09:56,200 --> 00:09:58,360 of qualification. If this client is already 255 00:09:58,360 --> 00:10:00,800 with high probability of closing, low 256 00:10:00,800 --> 00:10:03,079 probability of closing, what are the 257 00:10:03,079 --> 00:10:04,880 products they want to buy, all the 258 00:10:04,880 --> 00:10:07,440 fields that the human salesperson would need 259 00:10:07,440 --> 00:10:08,839 to fill in, this is already filled 260 00:10:08,839 --> 00:10:11,200 automatically by the Autobots. Then 261 00:10:11,200 --> 00:10:14,800 it already schedules an appointment in the CRM calendar 262 00:10:14,800 --> 00:10:17,720 of the salesperson. And then the salesperson can still 263 00:10:17,720 --> 00:10:19,560 have a copilot. They click on a 264 00:10:19,560 --> 00:10:21,920 little button there, opens a button and they 265 00:10:21,920 --> 00:10:24,360 talk to the AI to resume a 266 00:10:24,360 --> 00:10:26,440 service, for example, with a client 267 00:10:26,440 --> 00:10:28,480 who was left with low qualification and 268 00:10:28,480 --> 00:10:31,079 sends one to automatic. There's a whole 269 00:10:31,079 --> 00:10:34,040 post-sales work they can do 270 00:10:34,040 --> 00:10:36,519 using AI. Anyway, the 271 00:10:36,519 --> 00:10:38,360 processes we've been creating with 272 00:10:38,360 --> 00:10:41,360 partner companies, they go far beyond 273 00:10:41,360 --> 00:10:43,720 WhatsApp, they go far beyond 274 00:10:43,720 --> 00:10:46,160 what's right in front of us. Of course, this 275 00:10:46,160 --> 00:10:48,480 stage of a qualifier, as you well 276 00:10:48,480 --> 00:10:51,200 said, Gustavo, it's usually one of the 277 00:10:51,200 --> 00:10:53,279 first steps we set up 278 00:10:53,279 --> 00:10:55,800 within the automation process, right 279 00:10:55,800 --> 00:10:57,519 when the digital employee is 280 00:10:57,519 --> 00:11:00,040 linked to sales, to reaching the public 281 00:11:00,040 --> 00:11:02,600 in general. Cool, Lucca, do you want to add 282 00:11:02,600 --> 00:11:05,399 something? In your speech, there is, uh, it 283 00:11:05,399 --> 00:11:07,639 caught my attention, sometimes we have to 284 00:11:07,639 --> 00:11:09,240 say no to the client, saying: Look, 285 00:11:09,240 --> 00:11:11,160 you want to start here, but this is not 286 00:11:11,160 --> 00:11:13,200 the first step. There's a big trap 287 00:11:13,200 --> 00:11:16,200 for engineers, you know, of 288 00:11:16,200 --> 00:11:18,880 smart ones, to automate a 289 00:11:18,880 --> 00:11:20,639 part, a process that didn't need to 290 00:11:20,639 --> 00:11:22,639 exist or that wasn't optimized 291 00:11:22,639 --> 00:11:24,560 so sometimes we rack our brains 292 00:11:24,560 --> 00:11:26,600 for a long time trying to create a 293 00:11:26,600 --> 00:11:28,200 automated solution, a solution that 294 00:11:28,200 --> 00:11:31,399 runs completely autonomously, 295 00:11:31,399 --> 00:11:33,720 when often that part of the 296 00:11:33,720 --> 00:11:36,519 workflow could be deleted because 297 00:11:36,519 --> 00:11:38,560 it's necessary with humans, but 298 00:11:38,560 --> 00:11:40,160 when you add technology to the 299 00:11:40,160 --> 00:11:41,760 process, sometimes it's not necessary 300 00:11:41,760 --> 00:11:43,920 or the way it's done today, it's not 301 00:11:43,920 --> 00:11:45,680 It's the most efficient way. So, why 302 00:11:45,680 --> 00:11:48,320 would I spend a lot of time putting it there 303 00:11:48,320 --> 00:11:50,920 technology that will repeat without making mistakes a 304 00:11:50,920 --> 00:11:53,560 process that is wrong from the start? So, 305 00:11:53,560 --> 00:11:55,639 this maturity to look at the processes, 306 00:11:55,639 --> 00:11:57,360 understand where we start, validate 307 00:11:57,360 --> 00:11:59,680 these processes so that later we just optimize and 308 00:11:59,680 --> 00:12:02,560 accelerate. This is something we bring 309 00:12:02,560 --> 00:12:05,200 to the table and, like, it's a diff... it's a 310 00:12:05,200 --> 00:12:06,560 very big differential. Consulting 311 00:12:06,560 --> 00:12:09,800 free. Well, ex, Lucca, just to wrap up, 312 00:12:09,800 --> 00:12:10,760 it costs 313 00:12:10,760 --> 00:12:14,440 expensive? Look, I think it's very cheap. 314 00:12:14,440 --> 00:12:16,480 Very cheap. It should cost much more 315 00:12:16,480 --> 00:12:18,760 expensive, but our mission is to put 316 00:12:18,760 --> 00:12:20,320 artificial intelligence in all 317 00:12:20,320 --> 00:12:22,600 companies in the world. We want a 318 00:12:22,600 --> 00:12:24,440 modest mission. It's a modest mission, a 319 00:12:24,440 --> 00:12:26,079 modest mission starting with a 320 00:12:26,079 --> 00:12:27,760 little country called Brazil with 200 million 321 00:12:27,760 --> 00:12:29,440 people. 322 00:12:29,440 --> 00:12:31,839 Look, for you to put an AI to 323 00:12:31,839 --> 00:12:34,399 work in your company tomorrow, starting 324 00:12:34,399 --> 00:12:38,360 from R$360 per month. That's it, folks, and then, 325 00:12:38,360 --> 00:12:40,880 let's suppose, I don't know, it's Christmas and you 326 00:12:40,880 --> 00:12:43,160 need to scale your customer service. 327 00:12:43,160 --> 00:12:46,360 You're running a campaign on Facebook, it 328 00:12:46,360 --> 00:12:47,880 scales naturally. You need 329 00:12:47,880 --> 00:12:50,639 more Santa Claus, for example, we clone 330 00:12:50,639 --> 00:12:52,839 from one moment to the next. Imagine if the 331 00:12:52,839 --> 00:12:55,000 If Santa Claus had a printer of 332 00:12:55,000 --> 00:12:58,000 elves that make the gifts, let's 333 00:12:58,000 --> 00:12:59,920 let's think about this logic 334 00:12:59,920 --> 00:13:01,600 Christmas logic here and 335 00:13:01,600 --> 00:13:04,360 yeah, no, I don't need it anymore, I'll download 336 00:13:04,360 --> 00:13:06,560 so, will I have to fire that 337 00:13:06,560 --> 00:13:07,920 person I trained? No, no, no, the 338 00:13:07,920 --> 00:13:09,079 digital employee can 339 00:13:09,079 --> 00:13:11,240 simply be reduced, it stays there 340 00:13:11,240 --> 00:13:13,240 almost hibernating and then eventually 341 00:13:13,240 --> 00:13:15,600 comes into play again when called, it's 342 00:13:15,600 --> 00:13:17,720 fantastic, really cool. Lucca, thanks 343 00:13:17,720 --> 00:13:19,959 for your explanations. Rômulo, director of the 344 00:13:19,959 --> 00:13:22,639 factory. Listen to the metals. 345 00:13:22,639 --> 00:13:25,279 Exactly. It was a pleasure having you here. 346 00:13:25,279 --> 00:13:27,839 To you as well, to you who listen to us, thank you very 347 00:13:27,839 --> 00:13:30,000 much. Follow us on all our 348 00:13:30,000 --> 00:13:31,440 social networks. We have been doing a 349 00:13:31,440 --> 00:13:34,720 really cool job, bringing information and 350 00:13:34,720 --> 00:13:37,160 education around the topic of 351 00:13:37,160 --> 00:13:38,920 artificial intelligence to all of 352 00:13:38,920 --> 00:13:43,000 Brazil. A hug, see you next time.