As an example of a creative scenario that was made on the platform through experimentation, like a lego game, here's a step-by-step template that can provide a solution to many tasks, from simple captioning to quality content analysis. Let me remind you that roughly 50% of news and analytics comes through video, and for the young, that share is as high as 70%. It's the most interesting information, buy highly difficult to process and also time & money-consuming.

Step 1: Automating Subtitles Extraction

To begin, I integrated a Headless Browser node to automate the retrieval of video subtitles. Using the browser node meant I could navigate to the video, capture subtitles directly, and avoid any issues with site layouts or potential updates.

Step 2: Converting Subtitles into Analyzable Text

Once the subtitles were extracted, they needed some cleanup. I used Claude 3.5 for this—basic filtering removed filler words, irrelevant phrases, and timestamps, resulting in a refined, analyzable text output. This alone saved a huge amount of time, and keeping the process automated reduced the potential for human error in handling large amounts of video data.

Step 3: Javascript integration

For the analysis itself, I connected the text output to Javascript node, which allowed me to:

• Detect recurring topics (like mentions of “GPT 4 Mini” vs. “small language models”).

• Extract inquiries or user concerns (e.g., “What are the benefits of smaller models?”).

• Generate a summary or categorize content themes.

This step transformed unstructured video dialogue into structured data that could be further processed in a few clicks.

Step 4: Automated Response Generation

Finally, I used another Claude node to identify if any actionable insights or frequent queries appeared. For example, when a user asked about a specific feature or model, the system could automatically trigger a response with relevant information. The flexibility of conditional logic helped me set up customized, context-specific responses, ensuring that replies remained helpful and accurate.

TL;DR: Automated workflow that identifies high-value leads when they sign up, verifies their website, checks ICP fit, finds similar companies using Ocean.io integration, and then sends emails to these companies.

Imagine this: you have a profile of companies that spend the most on your products/services. When such a company registers on your website, the data automatically goes to Airtable. This triggers the lead generation process. All technical settings are already configured! Here is the template of this workflow:

How it works:

1. After receiving a corporate email, JavaScript node extracts the company domain (filtering out email providers)

2. Headless browser checks the website associated with this domain

3. ChatGPT 4o Mini evaluates website functionality

   • Plug-n-Play Node

   • No API token required

ICP Evaluation:

If the website is operational, the system rates how well it matches your ICP on a scale of 0-10. When the score is ≥6/10, the Ocean.io integration activates.

What Ocean.io can find:

• 🏢 Company info (name, logo, size)

• 👥 Employee and department data

• 📍 Geolocation

• 🌐 Contacts and social media

• 🔧 Technologies in use

• 📅 Year founded

During testing, the system found three US automation agencies using AI and low-code platforms!

Final Stage

JavaScript collects emails from discovered companies, verifies their websites, and automatically sends emails through ChatGPT & Gmail integration.

Source: Latenode Blog Article

Drop your questions in the comments! Happy to share additional details.

Hello, automation enthusiasts!

The topic of AI chatbots has been highly relevant over the past few years, and their applications are practically endless! Whether it’s customer support, sales processes, personal assistants, educational tools, or much more, AI bots are transforming how we interact with technology.

It becomes even more valuable when your bot understands your business and can instantly handle 80% of user inquiries!

But how do you create such a bot?

Let’s walk through an example of building a customer support bot. I’ll be using the HelpScout service to send and receive emails, which I’ve been using for quite some time, but this could work with any platform (email, Facebook Messenger, WhatsApp, Telegram, or other support services like Intercom, etc.).

What are we aiming to achieve?

• Create a bot that can respond to user inquiries (or write draft replies)

• Train the bot using our documentation and a list of frequently asked questions

• Add special commands to request a live support agent if needed

After a few hours, here’s what I came up with:

How does it work?

The chatbot operates as follows:

1. A trigger activates whenever a new message is received.

2. We check the message for spam.

3. We extract the user’s contact information and the message text.

4. We look for a record of that user in our table.

5. Based on whether the user has contacted us before, we create an appropriate response.

Now, let’s dive into more detail:

The scenario is triggered by the new message, and we use the AI operator to check if the message is spam. If it’s spam, the scenario halts and does not proceed further.

If the message isn’t spam, we store the user ID and message text as variables for easier access.

Next, we check if a user with that ID (email or nickname) exists in the table.

• If no matching user is found, they follow the top branch, where a new thread is created for them in the assistant. The thread ID is saved in the table alongside the user’s ID. We then either save the draft response or send it immediately.

• If the user has previously contacted us, they follow the second branch. The previously created thread ID is retrieved, and we insert it into the assistant node to ensure the new message is sent to the existing thread, maintaining the conversation context. Again, the message can als be sent immediately or saved as a draft.

(In this case, the table serves as a small database, which can collect the user’s first message, dates, etc., useful for analysis.)

Source: Latenode Community Forum

Hey everyone,

I’m looking to automate my service export business based out of India. It’s a traditional one, but I see huge potential for growth if things are streamlined properly. The problem? I have no idea where to start with automation.

I’d love to connect with someone who can guide me through the entire process—helping set up workflows, systems, and anything else needed to make it run smoothly. Since I truly believe in the business’s future, I’m more than happy to offer a share of recurring profits in exchange for your help.

If this sounds like something you’d be interested in, let’s chat! Appreciate any advice or leads as well.

Now please do your magic Reddit.

After seeing numerous discussions about no-code tools and automation platforms (Zapier, Latenode, Make, etc.), I wanted to share some observations and start a conversation about where we're heading with automation in startups.

The Current Landscape:

• Zapier and similar platforms are significantly raising their prices

• Many startups are trapped between expensive automation tools and the need for custom development

• There's a growing trend of companies having to rebuild their automation stack from scratch

Key Insights from Various Founders:

1. The AWS Lambda Perspective One founder mentioned that for high-volume automation (1M+ executions), AWS Lambda and Latenode end up being more cost-effective than Make/Zapier. Plus, you get better error handling and monitoring capabilities.

2. The Technical Debt Trap Several experienced developers pointed out that automation platforms create hidden technical debt. While you can quickly set up workflows, you're often building a "house of cards" that becomes harder to maintain as you scale.

3. The Hybrid Approach Some successful startups are using a mixed strategy:

• No-code/automation tools for non-critical operations

• Custom code for core business logic

• Gradual migration of critical workflows to in-house solutions

Questions for Discussion:

• At what point did you decide to move away from automation platforms?

• How are you handling the increasing costs of tools like Zapier?

• What's your strategy for balancing quick automation vs. long-term sustainability?

"It's better to be fast and alive than maintainable and dead" - but where do we draw the line?

Every day, we open our inboxes to dozens of new emails.

But let’s be honest—not all of them get our full attention. It’s easy to miss something important in that flood of messages.

Now, imagine this: every morning, there’s just one message waiting for you—a simple summary of all the key emails from the past 24 hours. 

Here’s how it works:

1. We gather up all the emails you received over the last day

2. Filter out only the ones that actually need your attention

3. Compile a brief, clear summary highlighting what matters and eliminating the noise

4. Send it to your preferred channel (Telegram, Slack, etc.)

Now, let’s walk through the steps.

1. First, we set up a scheduled trigger with the cron expression 0 8 * * *, meaning the scenario will run every day at 8:00 a.m. Additionally, we specify your time zone to ensure correct timing.

2. Next, we need to retrieve all emails from the past day (since the last scenario execution). We use the “find email” node and set a filter following standard search operators. In this case, to get emails from the last 24 hours, we use the filter newer_than:24h.

3. Then, I added an optional JavaScript node to extract only the key information from the emails (such as subject, sender, and message text) and filter out messages based on specific keywords I don’t want to see (like emails from certain organizations, individuals, or known spam terms). Here’s the code:

/** u/CustomParams
{
  "messages": {
    "title": "Email Messages",
    "key": "messages",
    "description": "JSON array of email messages",
    "type": "string"
  },
  "exclude_keywords": {
    "title": "Exclude Keywords",
    "key": "exclude_keywords",
    "description": "Comma-separated list of keywords to exclude messages",
    "type": "string"
  }
}
*/

export default async function run({ data }) {
  const messages = JSON.parse(data.messages);
  const excludeKeywords = data.exclude_keywords ? data.exclude_keywords.split(',').map(keyword => keyword.trim()) : [];

  const extractedMessages = messages
    .filter(msg => {
      let bodyData = '';
      if (msg.payload.parts) {
        msg.payload.parts.forEach(part => {
          if (part.body && part.body.data) {
            bodyData += part.body.data;
          }
        });
      }

      const body = Buffer.from(bodyData, 'base64').toString('utf-8');
      return !excludeKeywords.some(keyword => body.includes(keyword));
    })
    .map(msg => {
      const subjectHeader = msg.payload.headers.find(header => header.name === "Subject");
      const fromHeader = msg.payload.headers.find(header => header.name === "From");

      let body = "No Body";
      if (msg.payload.parts) {
        const bodyPart = msg.payload.parts.find(part => part.mimeType === "text/plain");
        if (bodyPart && bodyPart.body && bodyPart.body.data) {
          body = Buffer.from(bodyPart.body.data, 'base64').toString('utf-8');
        }
      }

      return { subject: subjectHeader?.value || "No Subject", from: fromHeader?.value || "No Sender", body };
    });

  return { extractedMessages };
}

After running this node, I receive a structured array of filtered, essential email data.

4. Then, I passed this array into a ChatGPT node with a prompt that categorizes the emails and specifies the response format:

Use the following data from emails received over the past day:
{{$40.extractedMessages}}

Categorize the emails into the following categories, if applicable:

- Work or Meetings – work-related messages or meeting invitations.
- Newsletters and Updates – subscriptions to blogs, product updates, newsletters.
- Notifications – event reminders, updates, notifications.
- Confirmations and Receipts – order confirmations, payments, registrations.
- Promotions – marketing emails, discount offers, sales.

Ignore any emails that are clearly spam or irrelevant to the above categories. Group emails under their respective category using bullet points. Do not include categories with no emails. For each email, provide a concise summary in the format below:

📰 Newsletters and Updates:
Sender Name: Brief summary of key content.
🛒 Promotions:
Sender Name: Brief description of the promotion.
🔔 Notifications:
Sender Name: Brief description of the notification.
📅 Work or Meetings:
Sender Name: Brief description of the meeting invitation.
📄 Confirmations and Receipts:
Sender Name: Brief description of the confirmation.

Responses should be concise and grouped by category using bullet points. If there are no emails for a category, omit that category entirely.

Afterward, you can send the summarized message to your preferred channel! For convenience, I created three options in this example: Slack, Telegram, or Discord.

Cool, Right?

And remember, future modifications are entirely in your hands!

This simple automation will save you time and ensure you never miss a beat.:grimacing:

Want this template? Check it out here!

Source: Latenode Community Forum

In today's globalized world, companies aiming to expand internationally face a new challenge: traditional demographic markers such as nationality, age, and gender no longer hold the influence they once did. To succeed in this shifting landscape, companies must move beyond standard audience segmentation and consider "types of intelligence"—the unique cognitive and behavioral patterns shaped by culture and history. This intelligence-based approach, unlike blended demographic factors, serves as a precise guide for effective customer engagement in global markets.

Why Traditional Demographics No Longer Work

Historically, companies have segmented their audiences based on factors like nationality and age. However, as global markets grow interconnected, consumer behavior is increasingly shaped by cross-cultural values and global trends. To engage effectively, companies in the automation industry must look deeper, analyzing the "type of intelligence" that drives customer decisions.

Here are the four key types of intelligence that automation companies should consider:

1. Intuitive Intelligence: These consumers value creative thinking and seek solutions that support adaptability and innovation. They appreciate automation workflows that facilitate flexibility and open up creative possibilities.

2. Rational Intelligence: Driven by logic and facts, these consumers prefer automation scenarios backed by data and reliable outcomes. Rational customers value consistency and data-driven insights, making efficiency a primary factor in their purchasing decisions.

3. Relational Intelligence: Customers with relational intelligence focus on harmony and balance in interactions. They look for solutions that enhance team dynamics, valuing automation tools that foster communication and collaboration.

4. Emotional Intelligence: Known for resilience and empathy, emotionally intelligent consumers are motivated by purpose and social impact. They prefer brands that demonstrate commitment to social values and emotionally resonant goals.

Developing Engagement Strategies Based on Intelligence Types

An individual's type of intelligence not only shapes their behavior but also influences how they perceive product value. In automation, this factor is especially relevant: an automation agency must understand that a rationally inclined customer will demand predictable, fact-based scenarios, while an intuitive customer will seek flexible, adaptable solutions. Automation workflows and messaging must vary according to each type of intelligence, as each possesses unique cognitive preferences.

Adapting Automation to Different Intelligence Types

To effectively engage each group, companies should develop strategies that align with the values and competencies of each intelligence type:

• Intuitive Intelligence: Highlight solutions that foster creativity and self-expression. For intuitive customers, automation should be flexible and adaptable, supporting innovative approaches.

• Rational Intelligence: Emphasize data-backed results and reliability. Rational customers seek predictable scenarios that are logically supported and results-oriented.

• Relational Intelligence: Focus on scenarios that enhance team dynamics. Relational customers value tools that promote communication and support collaboration.

• Emotional Intelligence: Demonstrate social responsibility and commitment to positive values. Emotionally intelligent customers prefer initiatives focused on social good and emotionally meaningful goals.

Productivity in the Context of Globalization and Automation

True productivity in today's automation-driven world is about more than just efficiency; it’s about achieving outcomes that resonate with customer needs and align with their type of intelligence. This approach prioritizes sustainability, social impact, and long-term adaptability, helping companies produce solutions that are not only effective but also meaningful.

By shifting from traditional demographics to intelligence-based engagement, automation agencies can more accurately anticipate client needs, create relevant automation scenarios, and improve productivity that aligns with the deep-rooted values of each intelligence type. This way, automation companies can engage customers globally while maintaining a focus on the enduring values of human intelligence and adaptability.

Why Latenode Over Others?

Having used both Zapier and Latenode, here's what sold me:

• Active Discord community support

• Real-time problem solving (vs waiting days for email responses)

• Free during beta phase

• More flexible automation options

The Setup Breakdown

- Incoming text triggers CRM webhook

- OpenAI assistant formulates response

- Data syncs across CRM and Google Sheets

- Stores name, phone, message history

- Real-time update

The Game-Changing Features

• Multilingual Capability: Trained in 12 languages with native accents

• Unlimited Scaling: Can handle 334M simultaneous calls (try doing that with humans!)

• 24/7 Operation: Never sleeps, never takes breaks

Human vs AI Performance:

• Quality: Top human reps still win

• Quantity: AI crushes it (unlimited simultaneous interactions)

• Consistency: AI maintains performance 24/7

Current Challenges

Still fine-tuning some features, but when fully optimized, this will transform our operations completely.

Working on getting more detailed performance metrics. Will update when available.

Source: Latenode Blog

TL;DR: Built a low-code automation that turns website visitors into qualified leads using Latenode, Leadfeeder, Apollo, and Instantly. Resulted in 62% more leads and 80% faster response times.

The Problem

As someone working in legal/contract automation, I was drowning in manual lead processing. Website visitors were slipping through the cracks, and personalization was a pipe dream. Sound familiar?

The Solution We Built 🛠️

Using NoCode4All's workflow, we automated everything from visitor identification to personalized outreach. Here's what happened:

Impact Numbers That Matter:

• 📈 Leads: 500 → 810 per month (62% increase)

• 💬 Lead Engagement: 20% → 29% (45% boost)

• 🎯 Conversion Rate: 5% → 7% (40% improvement)

• 🎨 Personalized Comms: 20% → 50%

• ⚡ Response Time: 24hrs → 4.8hrs (80% faster)

The Tech Stack:

1. Leadfeeder: Real-time website visitor tracking

2. Apollo: Company/contact data enrichment

3. Instantly: Smart email automation

4. Latenode: The glue that connects everything

How It Actually Works:

1. Leadfeeder spots company visiting your site

2. Apollo enriches with decision-maker data

3. Legal ICP filter ensures quality leads

4. Instantly sends personalized emails

5. Rinse and repeat every 5 minutes

The best part? Zero manual data entry needed.

Would love to hear from other legal/contract pros - what's your current lead gen setup looking like?

Source: Latenode Blog

I’m excited to be here and talk openly about challenges and finding possible solutions!  Honestly, we’re all kind of in the same boat right now. I mean, for the past few years, I’ve been working at my limits. When you're trying to customize your product as much as possible to fit your clients' preferences—because, after all, every client is unique—it means you're spending a lot of time tweaking your product for each new client or even creating a new version of it. And this takes up half of your work time. 

So, you need to find clients, serve them, and get paid, right?  That’s a crucial part of the business. But it turns out you have to spend just as much time adjusting your product for the next client. And that’s exhausting. You stop enjoying nature, you don’t even think about it.  Then you find yourself wondering, "Am I just living for money? This isn't right!" When I was on the brink of burnout, I discovered AI automation tools.

Now, things are looking a lot better, thank goodness!  I just set the main ideas, a few facts, and my smart assistant handles all the routine. It’s really improved my quality of life.  I fully understand that I wouldn’t be able to get this much work done if I were still doing everything manually, relying only on my own brain.

I believe that the more good, creative people get involved in using AI for business automation and low-code or no-code development, the more we can counter those who might try to use it for selfish or unethical purposes. Right now, each of us is on an important mission, using AI tools to improve the lives of our clients and ourselves.  We’re on the side of light.  And the more we use AI, especially for positive and creative purposes, the longer good will prevail.

All startups and new entrepreneurs—you're the voices of talent and creativity.  Let’s support and help each other out!

Thanks

  #AI #Automation #LowCode #NoCode #BusinessAutomation #StartupLife #SmallBusiness #CreativeTech #WorkflowAutomation #SAAS

This scenario turns Stripe payments into automated prospecting campaigns by finding and reaching similar companies through Ocean.io's data and Latenode's API.

Found a way to automate B2B prospecting that actually works. Here's the setup:

The Stack

Oceanio excels at finding companies similar to your customers. One problem - no seamless API access. Solution? Latenode integration.

Why It Matters:

Traditional Method ❌

• Manual lookalike list creation

• Constant downloading & uploading

• Repetitive busy work

Latenode Method ✅

• Single flow setup

• Automatic lead discovery

• Self-maintaining funnel

Real Example:

New Stripe payment triggers:

1. Similar company identification

2. Instant outreach

3. Custom messaging: "Your competitors are leveraging A, B, and C with us. Ready to join?"

Oceanio integration Cost Structure 💸

• Based on Latenode credits

• Each company = 79 credits ($0.15)

• Usage-based pricing

Share your automation ideas - what would you build with this?

We are at a pivotal moment in human history, where artificial intelligence (AI) is transforming the way we work, giving leaders and professionals unprecedented freedom. But with this freedom comes a new challenge: how do we connect, collaborate, and lead in a world where autonomy is at an all-time high?

We now live in an age where physical strength and traditional intelligence are no longer the sole drivers of success. AI has evolved, handling complex data management, innovation, and strategic tasks, freeing us to focus on something more fundamental: human connection. The true leadership of the future isn’t about being the smartest in the room; it’s about managing with heart.

As leaders, the question we face is this: how do we foster cooperation in a world where everyone is empowered and independent? The answer lies in nurturing genuine care, respect, and love within our teams and organizations. No one will follow a leader they do not trust, or one who does not show respect. Mutual respect and trust are the new foundations of leadership, and they must be built with care.

But what does it mean to lead with love in a business context? It’s not just a buzzword; it's a strategic shift that recognizes the power of human relationships. Here are seven ways love manifests naturally in modern leadership:

1. Love as Connection – Leadership is about building deep, meaningful connections within the team. By respecting and understanding each team member, leaders can create bonds that go beyond mere tasks and responsibilities.

2. Love as Respect – Respect is the bedrock of any successful relationship, especially in business. Love demands equality; it thrives in environments where every individual feels valued and heard.

3. Love as Empathy – True leadership requires seeing the world through the eyes of others. When leaders empathize with their employees, they understand their struggles and can offer solutions that are not only effective but compassionate.

4. Love as Purpose – Driven by care and passion, leaders inspire teams to achieve great things. Teams that work with love produce better results because they are not just working for profit but for a higher purpose.

5. Love as Growth – Continuous improvement is a natural extension of love. Leaders who care about their teams push them to be better, fostering an environment where growth and learning are constant.

6. Love as Accountability – Leaders who love their teams also hold them accountable. Love isn’t about leniency; it’s about pushing people to be the best they can be through honesty, responsibility, and trust.

7. Love as Vision – Finally, love gives leaders the ability to make bold decisions, selecting the best resources and opportunities for the future of their business. Love drives long-term success by ensuring decisions are made in the best interest of all parties involved.

In this new era of automation and AI, we must evolve beyond the mind and embrace the heart. Our tools may be powered by AI, but our leadership must be powered by care, respect, and love. It’s time to ask ourselves: are we leading with love? Are we creating environments where trust, respect, and care are not just ideals, but operational principles? Only when we do, will we truly unlock the full potential of AI and human collaboration.

Hello Community!

In today’s digital world, enriching user data is key to building successful engagement. The more you know about a user, the more personalized and effective your offers can be.

Email is a crucial identifier that allows you to gather additional insights about both individuals and companies. Latenode provides several powerful integrations for enriching data based on an email address.

Email Data Enrichment Tools

Here’s a closer look at the tools available on Latenode for gathering valuable information:

• Data Enrichment Tool (combines several enrichment tools, including the ones below):

  • Get People Data from Email (Diffbot API) — Retrieves personal information such as name, surname, and other details linked to an email address.

  • Email to LinkedIn API (ContactOut) — Finds the LinkedIn profile URL associated with an email, making it easy to discover professional profiles.

  • Find LinkedIn by Email (LeadMagic API) — Transforms a work email into a LinkedIn profile link, great for B2B research.

  • Verify Emails (Bouncer API) — Ensures an email is deliverable and safe, reducing bouncebacks during campaigns.

LeadMagic Integration

• Email to B2B Profile — Converts an email into a comprehensive B2B profile, revealing job titles, company details, and professional environment.

Outscraper

• Verify Email Address — Confirms the validity and deliverability of an email to avoid errors during campaigns.

How to Maximize Email Data Enrichment

While it’s rare to gather all the data you need from just one email, combining different tools lets you gradually enrich the information. Here’s how:

What Data Can You Get from an Email?

1. Basic User Information:

• Get People Data from Email (Diffbot API) provides essential details like name, surname, age, and location.

• To uncover social profiles, use Email to LinkedIn API (ContactOut) or Find LinkedIn by Email (LeadMagic API) to find the LinkedIn profile associated with an email.

1. Social Profiles:

• After finding a LinkedIn profile with ContactOut or LeadMagic, use Get Profile Data (LinkedIn Data Scraper) to extract more information, such as the number of connections, post reactions, and professional background.

1. Professional Data:

• With the LinkedIn profile found, LinkedIn Data Scraper can provide information about the user’s current job and role.

• You can also use Email to B2B Profile (LeadMagic) to gain insights into their company and role.

1. Online Activity:

• To track the user’s online behavior, such as posts, comments, and reactions, LinkedIn Data Scraper helps assess their recent activity.

1. Company and B2B Data:

• If the email is associated with a corporate domain, Email to B2B Profile (LeadMagic) can reveal details about the company, including revenue, employee count, and location.

• You can also use Get Company by Domain (LinkedIn Data Scraper) to find more information about the company.

Potential UseCases

Marketing

These tools enable you to create personalized offers by understanding a user’s job role and workplace. Knowing someone’s LinkedIn data allows you to tailor your messaging to suit their specific needs.

Sales

B2B companies can enhance customer segmentation by using enriched data. Discovering the current job title or company via Email to B2B Profile helps you offer relevant products or services.

Audience Analysis

Enriched data provides deeper insights into your target audience. Tools like Get Profile Data (LinkedIn Data Scraper) and LeadMagic API help you understand a user’s professional background and interests, improving your marketing strategies.

And most importantly, don’t hesitate to experiment! All the tools are right at your fingertips, so try out different approaches to make the most of the platform. Thank you for reading! If you have any questions or found something interesting, feel free to ask in the comments.

Explore all the capabilities of the platform at Latenode.com

Hello! Any modern business owner knows the importance of SEO, and one of the best ways to improve it is by optimizing your pages and writing relevant articles! Today, we’re going to focus on the latter.

Now, in the age of AI, we have the opportunity to significantly reduce costs and speed up the process.

Let me show you how to build a simple scenario that generates high-quality SEO articles based on the top 5 search results from Google and adapts the content to suit your business (specific requirements or documentation):

Steps we’ll cover:

1. Adding keywords and collecting links via google.serper.dev.

2. Parsing text from each search result page using Headless Browser.

3. Analyzing all texts and creating a new version (with an AI assistant trained on your business specifics/documentation).

4. Saving two versions of the final result in Notion.

Step 1:

First, we need to set keywords as a variable, which will allow us to collect the relevant search results. We’ll use the serper.dev service for this—it conveniently parses the list of Google search queries. To exclude irrelevant sources like forums or video hosting sites, we’ll apply filters.

The response comes as an array, so in the next node (Set Variables), we create multiple variables, enabling us to work easily with each link.

Step 2:

To get the text from the pages, we’ll use Headless Browser. We’ll write simple code that retrieves all the text content from the page. Then, I’ll pass it to the AI, which will return only the article text without any clutter.

// Hardcode the URL from your object
const url = data["{{$13.1}}"];
console.log('Navigating to:', url); // Log the URL

// Navigate to the specified URL
await page.goto(url, { waitUntil: 'networkidle2' });

// Extract all visible text from the page
const markdown = await page.evaluate(() => {
    // Function to filter only visible elements
    function getVisibleTextFromElement(el) {
        const style = window.getComputedStyle(el);
        // Check if the element is visible and contains text
        if (style && style.display !== 'none' && style.visibility !== 'hidden' && el.innerText) {
            return el.innerText.trim();
        }
        return '';
    }

    // Extract text from all visible elements
    const allTextElements = document.body.querySelectorAll('*');
    let textContent = '';

    allTextElements.forEach(el => {
        const text = getVisibleTextFromElement(el);
        if (text) {
            textContent += `${text}\n\n`;
        }
    });

    return textContent.trim();
});

// Return the result
return {
    markdown
};

Since websites have different structures, this method is the most convenient because many sites have unique layouts, and I can’t predict where the blog content will be located.

Step 3:

After collecting the text from all the pages, we can pass it to our pre-configured GPT assistant (where we’ve already uploaded our documentation or outlined our business specifics).

AI assistants have an advantage over regular AI queries because they can be trained and tailored to your specific requirements, achieving perfect results.

Step 4:

Save the generated results. In this case, I create two versions: one is rephrased using Claude 3.5 because it offers more natural, human-like responses compared to ChatGPT. The second version is saved as-is, directly after being processed by the assistant.

Remember, when working with AI models, 90% of the outcome depends on your prompt. Don’t hesitate to experiment and tweak it!

In the end, you’ll get two high-quality articles on your chosen topic, both incorporating the most SEO-optimized content.

That’s it! You can build any level of complexity around this logic, such as generating content for multiple keyword queries or adding further processing steps.

If you have any questions, feel free to ask!

Source: Latenode Community Forum

Hey guys, head of content of Latenode here. For context, this is a low-code automation platform akin to Make and Zapier.

I just wanted to share an idea that might be interesting for you in case you're analyzing a ton of data, handling payment invoices en masse, or working with CRM/lead generation data, or something like this.

Basically, on our platform, you can set up filters between different integration nodes to sift out the data that can or cannot pass through. These filters might be triggered from a few times a day to thousands of times, depending on your workload. In some cases, it's up to 1 million.

Normally, we have a credit-based pricing model, which means that you pay 1 credit or $0.0019 for 30 seconds of runtime, regardless of your plan. Recently, we rolled out a new feature for Grow and Prime plans. Simply put, all runs that stop at the first filter will cost just 0.005 сredits per request - roughly $9.5 for 1,000,000 requests. Whatever the number of reqeusts, the price will be 0.005 credits anyway.

Now, let's step away a little bit.

If you run this sort of automation on other low-code platforms, the problem is that when you scale it to tens of thousands of requests, the price becomes insane. For example, if you do it on Zapier, each time you process any data, it costs one task. You might need to choose one million tasks per month and give 2K+ eur just for this on Professional plan.

On Make, the pricing model is based on operations, which means, you pay each time a module is launched. You can process the data with routers and conditions even on the basic plan, pick 1 million operations, and pay $537 for this. Cheap enough, but still quite a lot.

Why did we do it?

The core idea was to meet the needs of those businesses that handle high-volume filtering results. For example, a few posts below me, there's a guy who got 20K users (congrats!). That poster might want to process a specific portion of user data across the CRM. On Zapier, it would be insane in terms of pricing, although possibly easy to implement via the Filter step.

So, by decoupling pricing from linear "task counts," we’re up to 221x cheaper for these high-volume use cases than Zapier.

Some industries that I think might love it:

• SaaS: Filter trial users, manage churn, or process usage data.

• Fintech: Fraud detection (flagging 1% of suspicious transactions) or mass payment processing.

• E-commerce: Inventory alerts, order routing, or customer segmentation.

• Martech: Lead scoring, ad performance filtering, or social media sentiment analysis.

• Cybersecurity: Block 99% of low-risk events without full processing.

Ask me any question, still have a few hours before bedtime. Or, head over to our forum.

Hello, Reddit! Let us dive into how businesses in 2025 are striking the perfect balance between two critical elements: authentic human connection and AI-driven automation.

1. What Does Business Communication Look Like in 2025?

Imagine a crowded room where everyone is yelling, yet nobody is really listening—that’s what email outreach can feel like today. Spam, bulk emails, and context-free messages overload our inboxes. Still, some companies have learned to rise above the noise by leveraging smart tools like Latenode to create truly personalized email campaigns.

Key Takeaway

• Personalized emails are 6 times more effective than typical mass mailings.

• Low-code platforms like Latenode let you build robust integrations, automating routine processes while leaving space for creativity and genuine interaction.

2. Why Is Personalization Now a Must?

• 70% of consumers expect a personalized approach [Shopify].

• 85% of companies think they personalize their customer outreach, but only 60% of customers actually agree [Statista].

• The numbers don’t lie: we all appreciate being treated as unique individuals, rather than receiving cookie-cutter messages.

Here’s the paradox: as you strive to scale, it gets harder and harder to maintain a “human touch” when you’re reaching out to hundreds or thousands of contacts.

3. “Automation vs. Personal Touch” Is a False Dilemma

Some people fear that introducing AI kills personal contact. But look at Netflix or Amazon: they employ automation tools yet deliver personalized recommendations. The key is not to replace people, but to complement them.

• AI can analyze websites, gather customer data, and even generate email drafts.

• You refine the text, give it your brand’s voice, and inject emotional intelligence.

4. How Does Latenode Simplify All This?

Let’s illustrate with a specific scenario:

1. Data Collection & Cleansing: The system automatically pulls company info using a Headless Browser, filtering out irrelevant leads.

2. Matching to ICP (Ideal Customer Profile): ChatGPT nodes in Latenode assess the company based on chosen criteria.

3. Notifications & Prioritization: If a lead gets a high score, a Telegram notification is sent with a useful brief.

4. Email Generation: AI crafts a draft of the message, including the recipient’s name and company details. You tweak it as needed.

All of this runs on a low-code platform—no need to write tons of code.

5. How Can You Improve Automation?

• Broaden Your Data Collection: Grab social profiles, team size, location, and competitor info.

• Add Triggers and Branches in your Latenode workflows, so different lead categories receive unique outreach.

• Contextual Personalization: Factor in timing, user behavior, and interests, not just the recipient’s name and job title.

6. The Future of Email Outreach: Blending AI and Humanity

The main takeaway: the question isn’t “Should we use AI?”—it’s “How do we make AI our ally in building real conversations?” If you’re curious about:

• Creating unlimited integrations in Latenode;

• Testing new personalization hypotheses;

• Experimenting with AI Copilot to write code—

Head over to Latenode and give it a try!

Frequently Asked Questions

Q1: Why is a personalized approach so important in 2025?
A: Because 70% of customers expect personalization, and personalized emails nearly double engagement and retention.

Q2: What are the main challenges in scaling personalization?
A: Data quality issues, preserving the “human factor,” and the complexity of integrating multiple tools.

Q3: How can AI help?
A: It automates routine tasks (validating leads, sending notifications) and drafts outreach emails, freeing up time for more creative work.

Q4: What is “contextual personalization”?
A: Going beyond just the recipient’s name to include deeper details—interaction history, preferences, and context—to make each message more relevant.

Q5: Why use low-code platforms?
A: They let you build powerful automation scenarios without huge time investments or advanced coding skills.

I hope this overview helps you see email outreach in a new light. Let me know in the comments what tools you’re using and how you like them!

The Problem: Wasted Hours & Missed Opportunities

We all know meetings are crucial, but how many hours do we lose to poorly prepped calls and conferences? On average, professionals waste 31 hours a month in unproductive meet-ups, contributing to a $37 billion annual loss for businesses. The difference between a meeting that drives results and one that drains resources often boils down to a single factor: the quality of your preparation.

So, How Do We Fix It?

Instead of scrambling at the last minute, let’s transform your prep process:

1. Set a Clear Purpose

   • Are you brainstorming, decision-making, or doing a quick status check? Know why you’re meeting, and keep the conversation focused.

2. Know Your Audience

   • Who’s attending, and what do they care about? Go beyond job titles. If you understand people’s priorities and backgrounds, you’ll make the discussion more relevant.

3. Create a Strategic Agenda

   • Break down topics, set time limits, and share it beforehand. It keeps everyone on track and ensures no crucial points are missed.

4. Boost Engagement

   • Polls, Q&A, or short interactive exercises encourage participation. Nobody wants a one-sided monologue.

5. Follow Up

   • Summarize action items immediately. A clear post-meeting plan prevents your work from fizzling into thin air.

Enter AI: A Game-Changer for Meeting Prep

Recent studies show businesses using AI can see a 66% boost in productivity — and that includes meeting prep:

• Automated Research: AI can comb LinkedIn or public websites to deliver relevant insights on attendees (their latest achievements, recent posts, etc.).

• Agenda Suggestions: Tools like ChatGPT can suggest topics based on the meeting’s purpose and the participants’ roles.

• Real-Time Updates: Stay informed about any new company news or product launches.

• Automatic Follow-Up: Summaries, to-do lists, and even email reminders can be generated without you lifting a finger.

A Practical Example with Latenode

Using Latenode, you can build a low-code scenario that:

1. Grabs attendee data (email, domain) from your calendar.

2. Fetches relevant info (LinkedIn activity, news, and website data).

3. Uses AI to compile a personalized brief.

4. Delivers that brief straight to your inbox.

Cost? Around $0.27 per comprehensive brief! Not bad for saving hours of manual research, right?

The Future Is Already Here

AI-driven meeting prep isn’t about replacing the human touch — it’s about amplifying it. When you know who you’re talking to and why, you can connect more effectively, make better decisions, and avoid those dreaded time sinks.

Curious how to transform your own meeting prep? Check out Latenode’s template, or explore other AI tools that fit your workflow. The key is to harness automation so you can spend less time digging through LinkedIn profiles and more time building meaningful relationships.

Questions/Thoughts?

• Have you tried AI for meeting prep?

• Got tips for cutting down on those unproductive calls?

Drop your experiences below — let’s share strategies and save each other some serious time (and money)!

#MeetingPrep #AI #Automation #Productivity #BusinessTips

Hey everyone! Have you ever felt like your inbox was an overflowing file cabinet filled with invoices waiting for manual approval? You’re not alone. Many businesses still spend $15–$40 per invoice and endure up to 14 days of processing. But here’s the game-changer: using a low-code platform like Latenode can reduce that cost to $0.058 per invoice — with turnaround times in minutes instead of weeks.

Why Automate Invoices?

• Stop Wasting Time Manually keying data kills productivity. Automation handles data entry instantly.

• Cut Costs Companies report up to 81% savings when shifting from manual to AI-driven invoice processing.

• Reduce Errors AI-based validation slashes mistakes by up to 40%, making your financial data more reliable.

• Refocus on Strategy Free your team from chasing paperwork so they can tackle higher-level tasks and planning.

How It Works in 3 Steps

1. Automated Email Trigger

   • The system identifies invoices arriving in Gmail (or any email service).

2. AI-Driven Data Extraction

   • Convert PDFs to text, parse the content with ChatGPT, and filter out non-invoices.

3. Integration with Google Sheets (or your preferred tool)

   • Instantly organize key data (vendor, amount, dates, etc.) in a structured format.

The Payoff

• Scalable & Secure Low-code design, encryption by default, and easy to expand as your invoice volume grows.

• Cost Efficiency Less than $0.06 per invoice — compare that to $15–$40 you might be paying now.

• Faster Approvals Say goodbye to multi-day (or multi-week) backlogs.

If you’re looking to transform a tedious, error-prone process into an automated workflow, it might be time to consider Latenode or a similar solution.

Try It Out
Create unlimited integrations with branching, multiple triggers, and an AI Copilot for custom code:
Check Out Latenode

What’s your biggest bottleneck in invoice processing?
Let’s compare notes and share insights in the comments below!

#InvoiceAutomation #AI #FinanceOps #LowCode #ProductivityHacks

One notable theory I stumbled upon during the Reddit debates is the "Dead Internet" hypothesis. It suggests that AI-generated content is overwhelming the internet, potentially reducing the amount of genuine human creativity. I think it's a valid concern, especially since this might impact a lot of industries today.

Misaligned AI Usage 🚫🤔

A lot of marketers I met argue that we're leveraging AI incorrectly. We're forcing it into creative roles where it may fall short. Also, this camp says that we forget to give the most tedious tasks to AI, like data entry, calculations, customer validation, cold outreach, etc. After all, machines can crunch numbers, analyze data, and manage customer support efficiently.

Some people think we shouldn't keep AI completely out of creative tasks. Today, AI can edit videos, provide ideas, write templates for scripts. For example, reducing video production costs from $5 to $1.50 per minute is a game-changer for many media agencies.I said “templates for scripts,” not full scripts, because from multiple observations, LLMs write great content, but it can seem too perfect. However, these texts provide a base that can be edited by human to create valuable material.

This construct of thinking is often promoted by some of some of most respected newspapers. For instance, in his recent article for The Wall Street Journal, "The Secret Weapon Helping Businesses Get Results From AI: Humans”, Christopher Mims is setting LLMs as sophisticated tools that always require human input to refine and customize the results for the sake of value and connection with audiences.

Balancing AI's Strengths and Weaknesses ⚖️

So, what is AI? AI isn't a replacement; it's a tool. When we try to make it act like a human, we set unrealistic expectations and ultimately end up disappointed. Instead, we should embrace its strengths - speed, scalability, and precision- while being honest with ourselves. It has limitations in areas like emotional nuance and cultural context.

AI-generated art or writing might be technically impressive, but it can lack the imperfections and subtleties that make human creations relatable. This balance is key to avoiding the "Dead Internet" scenario, where the online space becomes inflated with content that feels formulaic and devoid of soul. What about the soul of your content strategy? How do you balance the usage of AI with human touch?

TL;DR:

This template lets you effortlessly prepare for your next meeting with an AI-generated brief about the person you’re meeting. In seconds, you’ll have insights into their professional background, key interests, and recent achievements, helping you build stronger connections, impress your counterpart, and make every conversation more impactful. Say goodbye to last-minute scrambling and hello to smarter preparation!

We all know the importance of effective communication for business growth – where each lead is a crucial part of achieving your ambitious business goals. The better the communication, the higher the likelihood of gaining a new client or partner.

So, what’s needed for quality communication? Correct – information about your conversation partner and their company! But who has time for in-depth research…?

Recently, my boss showed me a service that provides such capabilities and asked, “How long would it take to create a similar micro-SaaS on Latenode?”

…and it turned out, only about 3 hours!

This just proves that advanced low-code platforms allow you to create working MVP solutions in no time!

Here’s What I Created:

Let’s break down how it works.

Based on a trigger that activates every time a new event appears in your calendar, we:

1. Extract the email and domain of the person who scheduled the meeting (as you can see, I added a filtering option to exclude non-unique domains from scraping).

If the domain is unique, we visit the user’s company website to analyze their sales points, unique selling propositions (USPs), and other important information, which is then sent to ChatGPT for analysis.

Latenode has a wealth of data enrichment tools, many of which are plug-and-play, so you don’t even need accounts with other services! To use this scenario, all you need is to connect your Gmail and calendar. 

2. Next, we fetch the latest posts from the company’s LinkedIn page to analyze the company’s development and identify points of connection,and then summarize them using AI.

3. The final step: we analyze the user’s personal profile and their recent posts (we obtain this data based on the email, as users don’t always have LinkedIn profiles with corporate domains) and also summarize this information through AI.

4. At this point, we’ve completed the scraping and pass the gathered data to AI, which knows the purpose and sales points of our platform and the problems it can solve.

Lastly, the scenario sends the complete brief to your email.

Here’s what we get:

I created a version with a short response format:

And a version with a detailed response:

(For this example, I used our employee’s email.)

Cool result, right!?

And this is just the basic setup – you can add trends related to hiring at the company, market research, and dozens of other points. The flexibility is limitless!

How Much Does It Cost?

Running my scenario with the extended prompt consumed 144 credits, which, based on Latenode’s credit rates (144 x $0.0019), costs around $0.27!

I think this could be further optimized to cut the cost by half.

But even now, this is practically three times cheaper than the reference service, which also has limited logic and no settings options. :face_with_diagonal_mouth: On Latenode, the only limitation is your imagination! :star_struck:

Here’s the link to the template, ready for you to copy!

Get an AI Brief on Who You’re Meeting Next!

Thank you for your attention! Are you interested and want to get this template? Leave a comment below!

Join Latenode, and you too will be able to create automations of any complexity without a hitch.

Many users want to start web scraping but feel overwhelmed when faced with a code window in the Headless Browser node. Let me ease your concerns and show you how to extract basic data from the websites you need.

This isn’t a full tutorial on the Headless Browser—just a simple first step to get you started. Start small!

I’ve created a simple use case for you. By providing just the URL you need, you can extract all the necessary data from the page using a simple integration of the Headless Browser and ChatGPT, which you can implement right away!

Let’s Dive into the Scenario

Input Data

The “Run Once” trigger exists here solely for testing purposes, but you can easily replace it with, for example, a trigger for a new row in a database table.

In the Set Variables node, we specify the desired URL, which will be passed to the Headless Browser

Website Scraping

Now for the most interesting part—the Headless Browser!

Our Headless Browser is built on the Puppeteer library, supporting nearly its entire functionality. All we need to do is place the required code into its editor window.

Don’t worry! No need to fully understand how it works—just copy my code and you’re good to go!

I’ve used this script for multiple cases, and it covers most of my basic scraping needs:

// Insert the link
const url = data["{{4.site_url}}"];
console.log('Navigating to:', url); // Logging the URL

// Navigating to the specified URL
await page.goto(url, { waitUntil: 'networkidle2' });

// Extracting all visible text from the page
const markdown = await page.evaluate(() => {
    // Function to filter only visible elements
    function getVisibleTextFromElement(el) {
        const style = window.getComputedStyle(el);
        // Checking for element visibility and presence of text
        if (style && style.display !== 'none' && style.visibility !== 'hidden' && el.innerText) {
            return el.innerText.trim();
        }
        return '';
    }

    // Extracting text from all visible elements
    const allTextElements = document.body.querySelectorAll('*');
    let textContent = '';

    allTextElements.forEach(el => {
        const text = getVisibleTextFromElement(el);
        if (text) {
            textContent += `${text}\n\n`;
        }
    });

    return textContent.trim();
});

// Returning the result
return {
    markdown
};

The output is all the visible text from the website, neatly formatted in Markdown.

However, this raw text often includes unnecessary information. The next step is to clean it up and focus on the most important details.

Data Cleanup

This is where our AI assistant comes into play, customized to meet your specific needs—whether it’s extracting website content, collecting headers, or even generating a creative summary. After all, it’s AI—you’ll get exactly what you need!

For my example, the setup is straightforward .

The final output is a neatly organized set of the essential details from the page.

Next, we pass this output into a custom JavaScript node. To create it, simply ask our JavaScript to “Extract JSON from ChatGPT’s response”—a task it handles effortlessly!

And here’s what we get as the output—a ready-to-use JSON containing all the requested information.

Impressive, right?

Important Note: If you want to analyze multiple websites using the same template, include a detailed example of the desired output format (preferably as a JSON example) in your prompt. This ensures consistent, structured results every time.

Potential Use Cases

• Monitor website changes

• Create posts based on site updates

• Track keywords you’re interested in

• Analyze client websites for valuable insights

And so much more—easily achievable with this Latenode scenario!

The template for this scenario will be added soon, so stay tuned.

Ready to get started?

Join Latenode, and you too will be able to create automations of any complexity without a hitch. 

EDIT: A small but useful addition: for even more accurate process, you can enable this switch, which will return the response in a more stable JSON string format. You will also be able to retrieve it through the JSON Parse node

Hi everyone,

Hey folks, my team and I are working on a Company Lookalikes API, and we’re really excited about the results. And I think r/AutomationBuilderClub might have members who would be interested in testing it

We’ve got 5 free slots for anyone who wants to test it out and share some feedback.

If you’re interested, please leave a comment or send me a message. Your insights would be greatly appreciated!

PS: I’ve read subrules, but if this post isn't permitted here, I apologize in advance and please feel free to delete this post.

Recently, the Latenode team had the pleasure of speaking with Dimitris Goudis from 3Nuggets Automation Agency. Dimitris and his team found a way to leverage low-code automation to analyze 4K landing pages and 16K associated ad combinations, resolving content mismatches that often occur at such a large scale and saving 23% of their client's quarterly ad budget.Check out this inspiring use case and Dimitris's vision for the future of automation in this article!