WEBVTT 00:00:00.000 --> 00:00:02.060 If you run a business, or I mean, even if you 00:00:02.060 --> 00:00:04.540 just need specialized data, you always face the 00:00:04.540 --> 00:00:08.619 same uncomfortable trade -off. You need a custom 00:00:08.619 --> 00:00:11.740 tool, right? A specific lead scraper or some 00:00:11.740 --> 00:00:14.140 kind of unique content engine, but you're forced 00:00:14.140 --> 00:00:16.300 to choose. Yeah. What are the options? Do you 00:00:16.300 --> 00:00:18.359 pay a hundred bucks a month for a sauce product 00:00:18.359 --> 00:00:20.239 that is just going to constantly limit your usage? 00:00:20.379 --> 00:00:24.239 Right. Or do you dedicate months to learning 00:00:24.239 --> 00:00:26.500 Python and digging through API documentation? 00:00:27.160 --> 00:00:29.460 It's the classic paradox of specialization. You 00:00:29.460 --> 00:00:31.460 need custom performance, but that barrier to 00:00:31.460 --> 00:00:34.159 entry is just so steep. Right. But our sources 00:00:34.159 --> 00:00:36.340 today, they detail this kind of, I guess you'd 00:00:36.340 --> 00:00:38.700 call it a hidden loophole. It's a way to use 00:00:38.700 --> 00:00:41.579 normal conversation, not code, to build exactly 00:00:41.579 --> 00:00:44.240 what you need by strategically combining Google 00:00:44.240 --> 00:00:48.060 AI Studio and the automation platform NIN. Welcome 00:00:48.060 --> 00:00:51.000 to the deep dive. Our mission today is to unpack 00:00:51.000 --> 00:00:53.299 a guide that honestly, it promises to deliver 00:00:53.299 --> 00:00:56.780 the true no -code dream. Yeah. We're looking 00:00:56.780 --> 00:00:59.460 at how to combine two extremely powerful platforms. 00:00:59.719 --> 00:01:01.820 Yeah. One to handle the application's appearance, 00:01:02.119 --> 00:01:04.920 its face, and one to handle its complex automation 00:01:04.920 --> 00:01:07.519 brain. So first, we're going to establish why 00:01:07.519 --> 00:01:09.859 this specific integration really changes everything 00:01:09.859 --> 00:01:12.519 for the average user. Then we're going to walk 00:01:12.519 --> 00:01:15.519 through the six crucial steps you need to build 00:01:15.519 --> 00:01:20.099 a real -world Google Maps lead scraper. And successfully 00:01:20.099 --> 00:01:23.099 establish that live, functional data link between 00:01:23.099 --> 00:01:26.140 the two platforms. Exactly. Okay, let's start 00:01:26.140 --> 00:01:28.340 with the fundamental problem this whole approach 00:01:28.340 --> 00:01:31.920 is trying to solve. Sounds good. So the longstanding 00:01:31.920 --> 00:01:35.379 frustration for a curious learner or, you know, 00:01:35.379 --> 00:01:38.359 a small business has always been the inherent 00:01:38.359 --> 00:01:41.379 limitations of off the shelf software. You always 00:01:41.379 --> 00:01:43.599 hit a wall. Always. You need more data or you 00:01:43.599 --> 00:01:45.500 need a slightly different feature. And suddenly 00:01:45.500 --> 00:01:49.780 that 50 or 100 dollar subscription is it's functionally 00:01:49.780 --> 00:01:52.450 worthless. You're on the limit's treadmill. That's 00:01:52.450 --> 00:01:54.469 a great way to put it. And even if you look at 00:01:54.469 --> 00:01:57.109 so -called no -code platforms, they often feel 00:01:57.109 --> 00:01:59.010 like they're just proprietary coding languages 00:01:59.010 --> 00:02:01.989 with, you know, colorful boxes. You spend all 00:02:01.989 --> 00:02:04.069 that time learning how to string together a dozen 00:02:04.069 --> 00:02:07.010 obscure nodes, and you haven't actually avoided 00:02:07.010 --> 00:02:08.810 the steep learning curve. You've just traded 00:02:08.810 --> 00:02:11.389 Python for their own syntax. That's why this 00:02:11.389 --> 00:02:13.830 architectural workaround, this loophole, is so 00:02:13.830 --> 00:02:16.509 compelling. It seems to completely bypass that 00:02:16.509 --> 00:02:19.430 choice between you know, high subscription fees 00:02:19.430 --> 00:02:22.270 or months of learning low -level code. Because 00:02:22.270 --> 00:02:24.009 the strength here is the essential separation 00:02:24.009 --> 00:02:27.210 of duties. Okay. We use Google AI Studio, which 00:02:27.210 --> 00:02:29.830 is powered by Gemini, to build the application's 00:02:29.830 --> 00:02:32.389 face, the user interface. We just talk to it. 00:02:32.590 --> 00:02:35.949 Then we connect that interface to N8n, which 00:02:35.949 --> 00:02:38.310 is the powerful open source automation bridge. 00:02:38.569 --> 00:02:41.669 And that combination lets you do... It means 00:02:41.669 --> 00:02:44.069 you can build and automate almost any tool you 00:02:44.069 --> 00:02:46.490 can imagine without ever touching a command line. 00:02:46.610 --> 00:02:48.650 It's really democratization through architecture. 00:02:49.030 --> 00:02:51.330 So if traditional no -code fails because it's 00:02:51.330 --> 00:02:53.949 still too complex, how does simply connecting 00:02:53.949 --> 00:02:57.229 two tools bypass that steep learning curve? One 00:02:57.229 --> 00:02:59.150 tool handles the interface easily. The other 00:02:59.150 --> 00:03:01.389 handles all the complex plumbing. Let's focus 00:03:01.389 --> 00:03:03.710 on those roles then. Google AI Studio handles 00:03:03.710 --> 00:03:06.150 the creative part. The guide we're looking at 00:03:06.150 --> 00:03:09.409 refers to this process as vibe coding. Vibe coding. 00:03:09.550 --> 00:03:11.789 I like that. It's defining what you want in plain 00:03:11.789 --> 00:03:14.469 English and then letting the Gemini AI generate 00:03:14.469 --> 00:03:16.370 a functional web app from it. That's exactly 00:03:16.370 --> 00:03:18.889 right. Think of it like telling a hyper -efficient 00:03:18.889 --> 00:03:21.409 intern exactly what inputs and outputs you need. 00:03:21.569 --> 00:03:23.870 And what do you get back? You get a fully functional 00:03:23.870 --> 00:03:28.009 user interface, buttons, forms, and the backend 00:03:28.009 --> 00:03:32.180 logic you need to execute API calls. And crucially, 00:03:32.280 --> 00:03:35.280 it comes with built -in Google superpowers. Meaning? 00:03:35.340 --> 00:03:38.139 Meaning immediate, easy access to Google search 00:03:38.139 --> 00:03:42.300 maps or sheets. Okay, but this brilliant, instantly 00:03:42.300 --> 00:03:46.259 generated app has a critical flaw, which is why 00:03:46.259 --> 00:03:49.759 we need the second tool, an ADAN. It's a ghost 00:03:49.759 --> 00:03:52.560 app. It is. The data just evaporates when you 00:03:52.560 --> 00:03:54.740 close the browser. It lacks any kind of permanent 00:03:54.740 --> 00:03:56.740 memory or a database. It's like a whiteboard. 00:03:56.840 --> 00:03:59.300 It's a perfect analogy. It's a brilliant one 00:03:59.300 --> 00:04:01.800 -day whiteboard sketch. It works perfectly while 00:04:01.800 --> 00:04:03.819 you're looking at it, but unless you immediately 00:04:03.819 --> 00:04:06.439 save that drawing, which is NANN's job, it just 00:04:06.439 --> 00:04:09.659 vanishes forever. Which brings us to NANN. The 00:04:09.659 --> 00:04:11.979 indispensable plumbing that handles the persistence 00:04:11.979 --> 00:04:14.240 and the complexity. Right. GenAN is a workflow 00:04:14.240 --> 00:04:16.379 automation platform. It can connect over 400 00:04:16.379 --> 00:04:18.540 different services. Its core function in the 00:04:18.540 --> 00:04:20.879 setup is receiving data via a webhook. And the 00:04:20.879 --> 00:04:23.670 webhook is... What exactly? It's essentially 00:04:23.670 --> 00:04:26.269 just a simple, unique URL that acts like a private, 00:04:26.389 --> 00:04:28.649 unlisted mailbox for your application. Okay. 00:04:28.850 --> 00:04:31.170 Once the app fires the data into that mailbox, 00:04:31.589 --> 00:04:34.810 N8N catches it, processes it, and then routes 00:04:34.810 --> 00:04:38.189 it to any permanent destination, a CRM, a Slack 00:04:38.189 --> 00:04:40.879 channel, or in our case, a Google Sheet. And 00:04:40.879 --> 00:04:43.000 I guess the reverse limitation is true, right? 00:04:43.079 --> 00:04:45.379 Right. Trying to build a highly specialized scraper 00:04:45.379 --> 00:04:49.540 or a custom UI inside N8n gets incredibly complex 00:04:49.540 --> 00:04:51.800 for a beginner. Oh, absolutely. I still wrestle 00:04:51.800 --> 00:04:53.939 with prompt drift myself when I try to force 00:04:53.939 --> 00:04:56.500 a single tool to do both the front -end job and 00:04:56.500 --> 00:04:59.060 the complex data structuring. It's better to 00:04:59.060 --> 00:05:01.019 separate those jobs. So the combination is the 00:05:01.019 --> 00:05:03.939 key. It's seamless. AI Studio builds the tailored 00:05:03.939 --> 00:05:06.519 app face, and the submit button on that app just 00:05:06.519 --> 00:05:09.120 fires the data payload to the N8n webhook URL. 00:05:09.740 --> 00:05:12.339 N8n then does all the heavy lifting of storage 00:05:12.339 --> 00:05:15.560 and routing. So since AI Studio is using Gemini, 00:05:15.720 --> 00:05:17.879 does it automatically clean and structure the 00:05:17.879 --> 00:05:19.860 data before sending it? It structures the request, 00:05:20.160 --> 00:05:23.079 but the data often arrives messy for N8n to handle. 00:05:23.279 --> 00:05:25.839 Okay, let's pivot to the actionable steps. We'll 00:05:25.839 --> 00:05:28.160 use the guide's real -world example. Okay. Building 00:05:28.160 --> 00:05:30.930 a Google Maps late scraper. Perfect. Step one 00:05:30.930 --> 00:05:33.250 is fast. You just set up the structure in AI 00:05:33.250 --> 00:05:35.930 Studio. You sign in, click Build, and immediately 00:05:35.930 --> 00:05:38.569 grant the app its necessary superpowers. And 00:05:38.569 --> 00:05:40.629 for a lead scraper, that means you have to select 00:05:40.629 --> 00:05:43.149 Google Search and Google Maps Access right from 00:05:43.149 --> 00:05:45.870 the start. Yes, that tells the AI what tools 00:05:45.870 --> 00:05:48.370 it's allowed to use. Then step two is the architecture. 00:05:49.050 --> 00:05:52.329 the blueprint writing the initial prompt and 00:05:52.329 --> 00:05:54.529 the guide really stresses that specificity here 00:05:54.529 --> 00:05:58.589 is the key to forcing the ai to output structured 00:05:58.589 --> 00:06:01.930 data not just you know a paragraph of text this 00:06:01.930 --> 00:06:04.629 is absolutely non -negotiable if you want machine 00:06:04.629 --> 00:06:07.670 readable data you tell the ai the inputs search 00:06:07.670 --> 00:06:11.399 query city country simple enough But then you 00:06:11.399 --> 00:06:13.459 have to be rigorous about the output. Extremely. 00:06:13.459 --> 00:06:15.860 You don't just ask for info. You demand discrete 00:06:15.860 --> 00:06:18.480 columns, company name, address, phone, email, 00:06:18.680 --> 00:06:21.459 website. But here's the key. You also include 00:06:21.459 --> 00:06:24.000 challenging, specialized columns like coordinates, 00:06:24.120 --> 00:06:26.720 latitude, and longitude, and even a subjective 00:06:26.720 --> 00:06:29.180 quality score with some reasoning. That level 00:06:29.180 --> 00:06:31.680 of detail, listing over 20 columns, it forces 00:06:31.680 --> 00:06:33.779 the AI to structure the extraction up front. 00:06:34.040 --> 00:06:35.740 And that's going to be crucial for the mapping 00:06:35.740 --> 00:06:38.259 process later. It's everything. So while AIS 00:06:38.259 --> 00:06:40.199 Studio is prepping that, we establish the destination. 00:06:40.579 --> 00:06:44.819 Step three is creating the NAN webhook, the mailbox. 00:06:45.199 --> 00:06:47.759 So you're in a new NAN workflow. Yep. You drop 00:06:47.759 --> 00:06:51.259 in the webhook node first. You set the HTTP method 00:06:51.259 --> 00:06:54.139 to post. That just means it's configured specifically 00:06:54.139 --> 00:06:56.420 to receive data that's being sent to it. And 00:06:56.420 --> 00:06:58.920 the output of that node is a unique URL. A unique 00:06:58.920 --> 00:07:01.949 production URL. That specific address is the 00:07:01.949 --> 00:07:04.430 private target the AI Studio app is going to 00:07:04.430 --> 00:07:07.730 send the scraped data payload to. It's the direct, 00:07:07.850 --> 00:07:10.449 unlisted mailing address for your application's 00:07:10.449 --> 00:07:12.509 data. You just copy it and hold on to it for 00:07:12.509 --> 00:07:14.930 a second. So why is specifying all those columns 00:07:14.930 --> 00:07:16.910 in the prompt so important before we even see 00:07:16.910 --> 00:07:19.389 the app? It forces the AI to structure the data 00:07:19.389 --> 00:07:22.259 extraction up front. All right, now for the quick 00:07:22.259 --> 00:07:24.899 connection phase. This is step four, letting 00:07:24.899 --> 00:07:27.420 AI Studio actually build the app. You go back 00:07:27.420 --> 00:07:28.839 to the prompt you were writing in AI Studio, 00:07:29.000 --> 00:07:31.360 you paste that AI and production URL where the 00:07:31.360 --> 00:07:33.879 placeholder was, and you just hit build. And 00:07:33.879 --> 00:07:36.980 the speed here is... It's genuinely impressive. 00:07:37.300 --> 00:07:40.439 I mean, whoa. Imagine a complex multi -field 00:07:40.439 --> 00:07:43.180 application UI being built and integrated in 00:07:43.180 --> 00:07:45.959 under 20 seconds. That's scaling customized tools 00:07:45.959 --> 00:07:48.300 instantly. It's functionally brilliant. Gemini 00:07:48.300 --> 00:07:51.079 delivers the full functioning application, the 00:07:51.079 --> 00:07:53.839 input fields, a scrape leads button, and all 00:07:53.839 --> 00:07:56.420 the backend logic, and it's all pre -wired to 00:07:56.420 --> 00:07:59.680 hit your private N8n URL. So the front end is 00:07:59.680 --> 00:08:02.319 built and connected. Now step five is back in 00:08:02.319 --> 00:08:05.439 NNN to set up the storage. We add a Google Sheets 00:08:05.439 --> 00:08:07.480 node immediately after the webhook. Right. You 00:08:07.480 --> 00:08:09.699 set the operation to append row. You connect 00:08:09.699 --> 00:08:12.319 your account, select your target sheet. Now, 00:08:12.319 --> 00:08:14.300 here is the crucial moment for anyone trying 00:08:14.300 --> 00:08:16.300 this. When those Google Sheets column headers 00:08:16.300 --> 00:08:18.879 appear in the NNN node, do not fill them in yet. 00:08:18.959 --> 00:08:21.519 Why not? This is where 90 % of beginners fail. 00:08:21.660 --> 00:08:23.660 They try to guess the data structure. You can't 00:08:23.660 --> 00:08:25.120 guess the structure. You need to see what the 00:08:25.120 --> 00:08:27.779 actual incoming data looks like first before 00:08:27.779 --> 00:08:30.939 you can map it to your nice, clean columns. Precisely. 00:08:31.300 --> 00:08:34.399 Which brings us to step six. Test the data flow. 00:08:34.480 --> 00:08:36.539 We can call this the messy middle. You have to 00:08:36.539 --> 00:08:40.320 activate the N8N workflow first, then open the 00:08:40.320 --> 00:08:42.299 executions panel. That's basically your mission 00:08:42.299 --> 00:08:45.320 control. And now, back in the AI Studio app, 00:08:45.480 --> 00:08:48.360 you run your test query. For example, search 00:08:48.360 --> 00:08:51.340 marketing agencies in New York, United States. 00:08:51.419 --> 00:08:53.419 And you watch Mission Control. It just lights 00:08:53.419 --> 00:08:55.899 up green immediately. You can click on that execution 00:08:55.899 --> 00:09:00.360 and you can see the exact raw JSON data that 00:09:00.360 --> 00:09:03.220 AI Studio packaged up and fired across the bridge. 00:09:03.379 --> 00:09:05.990 And JSON is just. JavaScript object notation. 00:09:06.250 --> 00:09:08.090 It's just the universal language of the web. 00:09:08.190 --> 00:09:10.309 It's how the app structures all that specific 00:09:10.309 --> 00:09:12.250 data is scraped. And that's it. The bridge is 00:09:12.250 --> 00:09:14.690 built. The custom AI front end is scraping. And 00:09:14.690 --> 00:09:17.169 the powerful back end is receiving the data successfully. 00:09:17.509 --> 00:09:20.090 So now that we've seen the raw JSON data, what's 00:09:20.090 --> 00:09:22.830 the immediate next challenge we face? We need 00:09:22.830 --> 00:09:25.490 to map that messy data into our clean spreadsheet 00:09:25.490 --> 00:09:28.490 columns. So to recap, we successfully replaced 00:09:28.490 --> 00:09:31.549 the limitations of commercial saws or the complexity 00:09:31.549 --> 00:09:34.570 of learning Python with a simple two -part architecture. 00:09:34.789 --> 00:09:37.629 We used conversation in AI Studio to build a 00:09:37.629 --> 00:09:41.110 bespoke Google Maps scraping app. And we connected 00:09:41.110 --> 00:09:44.309 it via a webhook to the robust automation plumbing 00:09:44.309 --> 00:09:47.509 of NAN. And while the data is flowing, we saw 00:09:47.509 --> 00:09:50.059 that it's currently just... disorganized chaos 00:09:50.059 --> 00:09:53.500 inside of NAN. Right. And this sets up the critical 00:09:53.500 --> 00:09:56.519 part two of the workflow, learning how to precisely 00:09:56.519 --> 00:10:01.000 map that inconsistent JSON data into clean Google 00:10:01.000 --> 00:10:03.879 Sheet columns, troubleshooting missing elements 00:10:03.879 --> 00:10:06.059 like LinkedIn links, and then refining the app 00:10:06.059 --> 00:10:08.620 itself using just natural language prompts. Just 00:10:08.620 --> 00:10:10.960 think about the genuine flexibility this offers 00:10:10.960 --> 00:10:13.240 you. You're no longer constrained by the fixed 00:10:13.240 --> 00:10:15.159 feature set of some off -the -shelf software. 00:10:15.320 --> 00:10:17.139 Not at all. You can build extremely specific, 00:10:17.340 --> 00:10:19.860 tailored tools that perfectly fit your unique 00:10:19.860 --> 00:10:22.460 internal data workflows and then scale that capability 00:10:22.460 --> 00:10:25.059 instantly. The real power is moving beyond just 00:10:25.059 --> 00:10:27.139 basic lead scraping. Go and build a tool that 00:10:27.139 --> 00:10:29.039 integrates five different services you never 00:10:29.039 --> 00:10:30.759 thought you could connect before. We'll catch 00:10:30.759 --> 00:10:31.940 you on the next Deep Dive.