Lean startup: what is it and why is management simpler?
6 min
Estimated reading time: 9 minutes
Starting a business from scratch can seem very complex. Given the countless planning steps that are suggested, you may feel lost as to which guidance to follow. But what if I told you that there was a simple way to start your own business?
The concept of lean startupemerges as an innovative approach that challenges traditional methods of business planning and development. It's a way of create, test and release products and services through process optimization and focus on agile interactions with customers.
In this content, we will delve deeper into the characteristics of this model, presenting the fundamental pillars and the advantages offered to companies. If you are interested in the subject, be sure to read this content in full.
What is it lean startup and what are its characteristics?
Lean can be translated as “lean”, so, in a free translation, lean startup is technology-based company with high scalability potential, but lean.
Thus, while conventional models emphasize the elaboration of detailed plans, lean startup adopts a condensed approach, focused on hypotheses and experiments. The method aims to reduce resource waste and deliver value to customers, from the beginning.
Continuous experimentation, iterative learning and constant adaptation are highly valued. And unlike traditional approaches that often involve extensive planning and analysis before launch, lean startup believe in start small and evolve quickly.
Features of lean startup
Below, see the main aspects of this approach:
MVP (Minimum Viable Product)
The concept of MVP is essential for the proposed lean startup. Instead of spending months or years developing a complete product, companies create a minimum, viable version that contains only the essential features.
This MVP is then released to the market to collect feedback customers and validate hypotheses. This not only saves resources but also allows the company to obtain insights valuable from the start.
With the use of tools no-code, it is possible to facilitate the construction of the MVP, as they offer greater agility and lower cost. This saves time and resources to propose, develop and test projects in real time.
Not afraid to start over
The methodology encourages companies to be agile enough to recognize when something is not working as planned. If the data and feedback indicate that the product is not doing well or that there is a better opportunity, the company can make a significant change in strategy. This may involve changes to the value proposition, the target audience or even the business model.
Validated learning
Knowledge acquired through experiments and feedback real customers is highly valued. By validating this learning, companies can make more informed decisions and direct their resources more efficiently.
Pillars Lean Startup
Continue reading to learn the pillars of this methodology and its advantages for your startup!
To better understand how the lean startup, it is essential to understand its three fundamental pillars:
customer development
agile development
low-cost technology platform.
These three elements interact collaboratively to create a framework that saves resources, accelerates innovation, and improves decision-making. Understand more about each of these pillars:
Customer development
The term means customer development In practice, it is based on a proactive relationship with the public, from the beginning of the process.
In addition to simply listening to customers, lean startup, there is a concern to actively involve them in validating hypotheses and refining the product.
Some ways in which the customer development contributes to the success of the methodology include:
Validation of hypotheses: By interacting directly with customers, companies can certify or disprove their assumptions about market needs. This avoids building products that no one wants.
Feedback continuous: customers have valuable insight into the product, and its feedback helps shape renewals along the way. Continuously carrying out this process allows the company to better understand and meet market needs.
Building Relationships: Customer development focuses on building long-term relationships with customers, enabling loyalty.
Low-cost technology platform
It is considered a fundamental piece to support the methodologyean startup. This type of platform is aligned with the idea of eliminating waste and allocating resources effectively. A practical example of this pillar is the use of cloud services, such as AWS, Azure or Google Cloud.
Based on this pillar, companies evaluate operations as necessary, eliminating the need for large initial investments in infrastructure.
It is worth highlighting that the no-code language makes it possible to create applications and softwares more cheaply. This is because it is easier and faster to create a functional product without the need to use code, work that is not restricted to professional developers and can be carried out by anyone interested in learning about knowledge in the area.
In this way, with the use of open source tools, it is possible to reduce the development costs of software. Process automation allows you to save time and resources.
Agile development
It is the third pillar of lean startup and complements the other two. Focuses on flexibility, adaptation and renewal during the development processO. This technique focuses on short cycles, when small parts of the product are developed and implemented. This allows the team to quickly respond to changes and feedback.
Priorities can change as new information emerges. Agile development allows the team to reevaluate and adjust their goals and tasks over time. That way, products are not considered “finished”. Instead, are always evolvingo, as the team constantly looks for ways to improve them based on feedback customer and market needs.
The no-code tools can be allies in the process. After all, from them, there is greater agility for the development of softwares and applications that the company needs, which contributes to the desired improvement being carried out more quickly and also at a lower cost.
Lean startup advantages
The methodology allows organizations to strike a balance between innovation and efficiency. This way, they gain advantages such as:
Greater connection with the customer
One of the fundamental principles of lean startup is to have the customer at the center of all decisions. This is not just a nice idea, it is a strategy that can be transformative for business.
Collect feedbacks is essential as many businesses fail due to the disconnect between what they think customers want and what they actually want.
When customers see that their opinions matter and that companies are committed to meeting their needs, it creates loyalty.
Loyal customers not only buy again, but also become brand advocates, recommending it to others.
Better acceptance of products and services
The lean approach to lean startup allows the creation of essential products or services. This means that each element is carefully considered and any unnecessary components are eliminated.
Eliminating unnecessary elements results in a more focused product or service. Customers appreciate simplicity and ease of use, which can lead to better market acceptance.
Additionally, leaner products tend to be developed and delivered more quickly, allowing companies enter the market earlier.
Waste reduction
The lean approach eliminates waste time, money and other resources. By focusing only on what is needed for the MVP and making data-driven decisions, companies reduce the likelihood of investing in directions that will not yield returns.
You resources are directed only to activities that add real value to the product or service. This prevents them from being wasted on projects that have no potential.
Simplified management
Through a focus on experimentation and continuous learning, the lean startup simplifies management. Decisions are based on feedbacks and real data, reducing dependence on uncertain forecasts.
You may be wondering how to effectively implement these principles in your business and ensure simplified management. Here comes a powerful tool: O bubble.io.
O Bubble is a no-code platform that allows program alone create complete web applications and systems, without the need for prior programming knowledge. And best of all: the No-Code Startup offers a free Bubble course!
This course not only allows you to acquire valuable skills in using Bubble, but also provides a solid foundation for applying the principles of lean startup in your projects. You will learn how to create prototypes, test ideas quickly, collect feedback customers and create solutions effectively – all without the need for coding.
If you are looking for a Free AI Agent Course for Beginners, you have come to the right place! After all, we are in the era of automation and artificial intelligence. In this context, companies and professionals are looking, above all, for intelligent solutions to optimize processes. In addition, they are looking to reduce costs and, consequently, improve the user experience.
In this context, AI agents stand out for their ability to automate complex tasks, interact naturally with users and integrate multiple systems without the need for constant supervision.
Continue reading this article and discover how the Free AI Agent Course can transform the way you work with artificial intelligence. Understand why AI agents are so powerful and learn how to start creating your own agent from scratch, using accessible and efficient tools, without needing any programming experience. Enjoy your reading!
From Zero to AI Agent: Learn how it works Free AI Agent Course for Beginners
If you want to learn for free and create your own AI Agent, the first step is to learn about the structure of the Free AI Agent Course for Beginners from NoCode Startup. If you want to start from scratch and develop your own Artificial Intelligence Agent, this content was made for you, in a complete material you will learn:
fundamentals of Artificial Intelligence Agents to build a solid foundation;
a step-by-step guide to creating practical agents, even without prior experience;
integrations with platforms such as Telegram, enabling the creation of interactive and dynamic agents.
Remember that the AI Agent course was developed so that anyone, even without prior programming knowledge, can create intelligent and scalable solutions. In other words, even if you have never programmed before, you can start without fear!
Why are AI agents so powerful?
Before understanding how to create your own AI Agent, it is essential to understand why these tools have become indispensable in different sectors. Therefore, it is worth reflecting: why is the use of these solutions growing so much? How do they impact the efficiency of processes?
Furthermore, understanding these aspects can reveal new opportunities for optimization and growth.
1. Integration with custom data (RAG)
One of the main reasons for the power of AI agents lies in the technique known as RAG (Retrieval-Augmented Generation). This methodology allows the AI model to be combined with personalized user or company data. This means that the agent can be trained to access specific information from:
PDF files;
corporate websites;
spreadsheets and databases;
Through this customization, the agent becomes able to perform advanced queries, access specific documents and respond accurately based on the available information.
2. Ability to execute actions (Function Calling)
In addition to RAG, another distinguishing feature of AI agents is the function called Function Calling (or tools), which allows the agent to not only analyze data, but also perform actions on different platforms. For example, among the main functions, the following stand out:
access and edit the calendar (schedule meetings, check events);
send, read and reply to emails;
interact with spreadsheets and databases (consult and update information);
perform direct tasks via corporate applications.
This capability turns the agent into a true virtual assistant. Imagine being able to send a simple message on WhatsApp, and the agent automatically accesses different systems, queries databases and sends comprehensive reports, all without direct human interaction.
Learn how to create AI Agents for different businesses
AI agents aren’t limited to basic tasks or simple interactions. In the Free AI Agents for Beginners Course, you’ll master tools like RAG and Function Calling and learn how to create intelligent solutions for different industries, without needing technical experience and without paying anything!
Scheduling agents, for example, can automate appointment scheduling, eliminating the need for direct human interaction. Key application examples include:
medical consultations: the agent checks available times, schedules the appointment and sends confirmation to the patient;
barbershops and beauty salons: the agent manages the professionals' schedule and allows clients to choose times directly via WhatsApp or Instagram;
classes and events: Want to schedule an adventure class or special event? The agent automates the process and confirms details with participants;
restaurants and snack bars: the agent acts as an intermediary between the customer and the establishment, optimizing orders and integrating with the restaurant system;
e-commerces: manage orders, inventory and customer service in an automated way, using agents integrated with the main sales platforms;
veterinary clinics: allow appointment scheduling, vaccination control and automatic notifications for customers;
gyms and studios: the agent manages class reservations, waitlists and sends automatic reminders to students.
Understand the Architecture of an AI Agent
Creating an AI Agent is more than just programming a chatbot. It’s about developing an intelligent, autonomous solution that transforms processes!
To do this, it is essential to understand the architecture that supports these agents, ensuring that they are capable of performing complex tasks, interacting with different platforms and delivering accurate and contextualized responses.
Below, learn more about this framework and how each component contributes to the advanced performance of AI agents.
Input Layer: where the agent receives information from the user through different channels (WhatsApp, Instagram, email or website), whether in text, voice or specific commands;
natural language processing (NLP): responsible for interpreting messages, understanding intentions and extracting relevant information, such as dates, times and user preferences;
connectors and APIs (Function Calling): allow the agent to perform real actions, such as checking available times, consulting menus or accessing internal systems, through external integrations;
RAG (Retrieval-Augmented Generation): combines natural language generation with external data retrieval, allowing the agent to search for information in databases or on the internet in real time before responding;
decision making and automation: After processing the information, the agent performs actions such as scheduling appointments, forwarding orders or sending notifications;
real-time feedback: keeps the user informed about the status of the service, sending automatic updates at each stage of the process.
N8N: The most complete tool for creating AI agents
n8n complete tool to create AI agents
Creating AI agents goes far beyond just setting up simple bots. There are robust tools on the market that allow you to build complex, interactive, and fully automated agents. Choosing the right tool makes all the difference in the performance and possibilities of your project.
In this way, the N8N stands out for integrating two essential worlds: advanced automation and creation of AI agents.
Originally designed for complex automation, the platform has evolved and today offers a powerful framework for creating intelligent and scalable agents. Among the main differentiators of N8N are:
creation of complex automations and integrations on a single platform;
integration with multiple AI models such as GPT, Llama, Claude and Gemini;
ability to host the system on your own servers, reducing costs;
intuitive interface with support for the “No-Code” concept, ideal for beginners'
integration with external tools such as calendars, spreadsheets, emails and databases.
Additionally, N8N offers a visual interface for creating automation flows, making the job easier even for those with no prior programming experience. And best of all, you can take a 14-day free trial with credits included to use OpenAI's resources.
OpenAI: Simplicity and Scalability
OpenAI offers one of the most robust solutions on the market, enabling the creation of powerful AI agents through the use of GPT models (such as GPT-4).
With a simple-to-use API and excellent documentation, OpenAI has become a reference for developers who want to create scalable agents with high processing capacity. Among its main advantages are:
pre-trained models with high natural language understanding capacity;
easy integration with platforms like N8N;
scalability for projects of all sizes;
support for techniques such as RAG and Function Calling;
Dify: Open source and total flexibility
Dify stands out for being 100% open source, allowing developers to have complete freedom to adapt the agent according to their needs. Dify's main features are:
open source, allowing complete customizations;
possibility of hosting on own servers, reducing expenses;
broad integrations with databases, APIs and external tools;
But how do you choose the ideal tool? Choosing the ideal tool will depend on your goals and the level of complexity of your project:
If you are looking for something practical and scalable, OpenAI may be the best choice;
for those who need advanced automations and complex integrations, N8N stands out;
If the focus is total freedom of customization and an open source solution, Dify is perfect.
And if your goal is to create complex automations with multiple integration points, N8N is the best choice. Its ability to combine automations with AI and the possibility of self-hosting make it one of the most powerful tools on the market.
Time to get your hands dirty: learn how to create your first AI Agent
If you've followed the Free AI Agent Course for Beginners | From Zero to AI Agent, it's time to put everything you've learned into practice! In this step, I'll guide you through the process of creating your first AI Agent, using accessible and efficient tools, such as N8N, OpenAI and Dify. Ready? Let's go!
1. Step 1: Defining your AI Agent front-end
The front-end is the interface of your project, the point of contact where the user interacts with your agent. In this content, we will use Telegram for its simplicity and versatility. Although it is possible integrate WhatsApp, this platform's API demands more complex processes.
So, for beginners, Telegram is the best choice. Later, you can explore the integration with WhatsApp.
2. Creating the Agent in N8n
N8N will be the main automation tool in your AI Agent. With it, you can create complex workflows without the need for advanced programming. Follow the steps below to get started:
create your free account on N8N with a 14-day free trial and credits to use the OpenAI API;
access the N8N panel and configure your credentials;
create a new workflow by clicking on “Start from scratch”;
choose your first trigger (e.g.: message received on Telegram);
add the “AI Agent” node and connect to the OpenAI GPT model.
3. Expanding the functionalities
Now that your basic AI Agent is up and running, it's the perfect time to enhance its capabilities, making it even more efficient and versatile!
Learn how to add advanced functionality that allows your agent to interact with different types of data, integrate new platforms, and provide a richer user experience.
1. Adding memory layer (WindowBufferMemory
For your AI Agent to have the ability to remember information during a conversation and maintain context between messages, it is essential to add a memory layer.
The implementation of WindowBufferMemory in N8N allows the agent to store recent interactions, ensuring more accurate responses aligned with the context of the dialogue. To implement, follow the steps below:
In N8N, add the WindowBufferMemory node to your agent flow.
configure the following parameters:
Window Size: define the number of messages the agent should remember (e.g.: 5 previous interactions);
storage method: For temporary storage, use N8N's default storage. For long-term storage, integrate with databases like Redis or Supabase;
Connect the WindowBufferMemory node to your AI Agent node so that the agent uses the history when generating responses.
To make the implementation clearer, imagine the following scenario: the user asks “What’s my appointment tomorrow?” and then simply writes “What about Friday?”.
Even without repeating the full question, the agent understands that the context is still about commitments and provides the correct answer.
Now that the agent is prepared to store contextual information, you can explore additional integrations and enhance its functionality, creating a more robust and efficient flow.
2. Integration with multiple tools (Function Calling)
To take your AI Agent to the next level, allow it to interact directly with other platforms and perform complex tasks. With Function Calling, the agent not only answers questions, but also performs practical actions across different systems. Key features you can integrate include:
Google Calendar: automatically schedule and list events;
Spreadsheets (Google Sheets/Excel): add, remove or search data in real time;
External APIs: perform queries on third-party services, such as weather forecasts, currency quotes or traffic information.
To set up these integrations, follow the steps below:
in N8N, add the node corresponding to the service you want to integrate (e.g. Google Sheets or Google Calendar);
In AI Agent, use the Function Calling function to enable the execution of automatic actions when certain commands are detected;
Create specific prompts to activate each tool, ensuring that the agent understands the user's requests. Practical examples:
“schedule a meeting for tomorrow at 2pm.”
“add the client João Silva to the contact spreadsheet.”
“send a confirmation email to [email@example.com].”
In this way, the agent becomes not only an intelligent assistant, but also an executor of complex tasks, expanding its functionalities and delivering a much richer and more dynamic experience to the user.
3. Implementing sentiment analysis
You can also enhance your AI Agent’s communication by empowering it to interpret the emotional tone of user messages and adjust its responses accordingly. This ability creates a more humanized, empathetic, and contextualized interaction.
To do this, follow the steps to implement sentiment analysis:
in N8N, add the Text Analytics node or use external APIs like Google Natural Language or IBM Watson;
connect the node to the main flow of the agent, right after receiving the user's message;
configure the node to identify emotions such as happiness, anger, sadness, or neutrality;
In the AI Agent node, create branches in the flow to adapt the agent's responses based on the identified sentiment.
If the user types, “I’m very frustrated with the service,” the agent might respond with more empathy: “I’m sorry to hear that! I’ll do my best to help you resolve the issue as quickly as possible.”
This way, the agent becomes more attentive, improving the user experience and strengthening the bond of trust.
4. Transforming audio into text (Speech-to-Text)
You can also expand your AI Agent’s accessibility by enabling it to understand voice messages. Speech-to-Text functionality allows the agent to transcribe audio into text and interact normally with the user.
To enable audio transcription in N8N, follow these steps:
add Telegram Get File node to capture the audio file sent by the user;
connect the node to OpenAI's Whisper API or Google Speech-to-Text to perform audio-to-text transcription;
send the transcribed text to the AI Agent node so that the agent can process and respond to the command normally.
With voice message understanding enabled, the user can send an audio message saying: “Schedule a meeting with Pedro tomorrow at 10 am.” The agent transcribes the audio and executes the action on the calendar, ensuring a fluid and efficient interaction.
This functionality expands the agent's possibilities of use and creates a more dynamic service experience.
5. Automatic notifications and real-time alerts
How about taking your AI Agent to a new level of efficiency with RAG (Retrieval-Augmented Generation), allowing it to search for data from external sources before generating responses? With this technique, the agent provides updated information and contextualized responses. To do this, follow these steps to configure RAG:
in N8N, add the integration node with databases, external documents (PDFs) or public APIs;
In the AI Agent prompt, instruct the agent to query external sources before generating a response to the user;
test the agent with questions that require consultation in external databases.
By adding this automation, your AI Agent gains the ability to send personalized reminders like “You have a meeting scheduled for tomorrow at 9am.”, important announcements like “There’s been a change to Friday’s event.”, and strategic promotional messages like “Unmissable offer! Up to 30% off today.”
With RAG, the agent stops being just a text generator and becomes an intelligent, real-time query tool, ideal for corporate, educational and financial sectors.
Finally, you can take your AI Agent to the next level of efficiency by implementing RAG. To set up RAG on N8N, follow these steps:
add the integration node with databases, external documents (such as PDFs) or public APIs;
configure the AI Agent prompt to instruct it to perform external queries before formulating the response to the user;
take practical tests with questions that require searching for data in real time, such as:
“What was the revenue from the last quarter?” (consulting a database);
“What is the dollar rate today?” (using financial APIs).
This feature is especially useful in corporate, educational, and financial environments where decision-making depends on accurate, timely data.
4. Testing and adjustments
Now that your agent is up and running, it’s time to test it and tweak any details to improve its performance. You can use a testing checklist to check if your agent is working properly:
Is the agent receiving messages correctly?
does it respond based on the prompt instructions?
Can you create and list events in the calendar?
Are the answers clear and accurate for the user?
If the agent is returning incorrect information, adjust the prompt to better guide responses. You can also use N8N's execution history to identify failures and test the agent with different commands to validate its flexibility.
Conclusion
By now, you’ve probably realized that creating AI Agents isn’t just a technological trend, right? Quite the opposite, it’s a real opportunity to explore new markets, automate processes and, above all, boost business in a strategic and efficient way.
Whether to improve the customer service, optimize internal flows or create scalable SaaS solutions, agents offer versatility and scalability for professionals and companies.
The best thing of all is that with No Code tools Like N8N, anyone can start this journey, even without prior programming experience. The combination of techniques such as RAG and Function Calling allows you to create powerful agents, capable of acting in different sectors and solving complex problems.
Now is the time to learn for free and get your hands dirty! In the Free AI Agent Course for Beginners, you start from scratch and create your own intelligent agent, ready to automate tasks and generate business opportunities.
The WhatsApp API is one of the main solutions for companies that want to scale their customer service and communication automation.
WhatsApp, being one of the most popular messaging apps in the world, allows direct and efficient interactions with customers. However, when it comes to automation and integration, using the WhatsApp API becomes essential.
So, there are two main options for this integration: the Official WhatsApp Business API and the Unofficial APIs. But which one should you choose?
In this article, we’ll explore the differences, advantages, disadvantages, pricing models, and costs to help you make the best decision for your project. Keep reading and find out which WhatsApp API makes the most sense for your business!
Types of WhatsApp available on the market: discover the options
Before we talk about WhatsApp APIs, it is important to understand the three main types of WhatsApp available:
Personal WhatsApp
WhatsApp Personal is the traditional version of the app, used by billions of people around the world. Intended for individual users, it does not include business-oriented features or automation.
WhatsApp BusinessVersion
Version aimed at small and medium businesses. This version has features such as a product catalog, automatic messages and business profiles, but still relies on manual interactions.
Furthermore, it allows the simultaneous use of two numbers on the same device, one in the personal application and the other in the Business application, in a completely legal manner and in line with Meta's guidelines.
WhatsApp Business API
Unlike previous versions, WhatsApp Business API is not an application, but a cloud-based solution that allows WhatsApp integration to different systems, enabling automation and personalization of interactions.
Companies that manage a large volume of messages use this API to optimize communication with customers, ensuring efficiency and scalability. With this solution, it is possible to:
automate the sending of messages and notifications;
integrate WhatsApp with CRMs, ERPs and other business platforms;
use artificial intelligence to personalize interactions.
How does the WhatsApp Business API work?
The WhatsApp Business API acts as a bridge between different systems, allowing softwares to communicate automatically. Since it is not an application installed on the cell phone, the entire operation takes place in the cloud, ensuring scalability and reliability for companies that need high-performance communication.
Although initially aimed at developers, today there are several solutions that simplify the implementation of the WhatsApp Business API, making it accessible to companies of different sizes.
Now that we understand the concept, let's explore the differences between the Official WhatsApp API and Unofficial WhatsApp APIs.
Official API vs. Unofficial API: Know the main differences
Businesses can choose between two types of WhatsApp API for integration:
Official WhatsApp Business API (provided directly by Meta or licensed companies);
Unofficial APIs (provided by third parties, without any connection to Meta, but within the law).
Next, we will understand the main differences between them.
Official WhatsApp Business API
The Official WhatsApp Business API is provided by Meta (Facebook) itself or by licensed companies. Meta recently began offering this service directly to end users, without the need for licensed intermediaries.
This API can be integrated directly via Facebook's Business Manager (BM), ensuring security and compliance with Meta's policies. Among the main features of the Official API:
integration via Facebook Business Manager;
mandatory use of pre-approved message templates to start conversations;
billing based on conversations initiated;
restriction on sending messages outside of Meta's rules;
lower risk of blocking, as long as you follow the guidelines/
Please note that Meta does not allow unrestricted sending of messages. To start a conversation, it is mandatory to use approved message templates, ensuring that the contact complies with WhatsApp policies. The template categories are:
marketing: promotions, coupons and offers;
utility: order confirmations, delivery tracking;
authentication: sending verification codes;
service: user-initiated messages at no additional cost.
After the first template is sent, the conversation can continue normally for up to 24 hours without the need for a new template.
And how does billing work on the Official API?
The pricing for the Official WhatsApp API is based on conversations initiated and varies depending on the message category:
marketing messages: about R$ 0.36 per conversation started;
utility messages: approximately R$ 0.04 per conversation initiated;
authentication messages: around R$ 0.015 per conversation initiated;
client initiated messages: at no additional cost.
Other important points about billing include the 24-hour validity for each conversation initiated by the company, which means that if it is necessary to continue the interaction after this period, a new paid template will be required.
Additionally, starting in April 2025, authentication and utility messages sent within this 24-hour window will no longer be charged.
Unofficial APIs
Unofficial APIs are solutions offered by third parties, with no direct relationship with Meta. Although they are not licensed, many of these APIs are completely legal and follow security standards. The main characteristics of Unofficial APIs include:
simplified integration via QR Code;
allows sending messages without the need for pre-approved templates;
fixed price per integrated WhatsApp number;
greater flexibility for shipping and automation;
higher risk of ban in case of spam or inappropriate use.
Unlike the Official API, which requires template approval and follows strict rules, Unofficial APIs allow messages to be sent freely, without category restrictions or prior approval. This allows for more dynamic contact with customers, ideal for companies that need freedom in communication.
And how does the billing work? Unofficial APIs?
The pricing of Unofficial APIs varies depending on the provider. Some operate with fixed monthly plans, while others charge per connected WhatsApp number.
some companies offer plans starting from R$99 per integrated WhatsApp number;
others offer packages for multiple numbers, reducing the cost per account;
There are open-source options, which can be used free of charge, but require their own infrastructure.
Despite the freedom offered by Unofficial WhatsApp APIs, it is essential to consider the risks involved. Since there is no direct link to WhatsApp, these APIs are more prone to blocking, especially when used for mass sending without the proper consent of users.
Furthermore, Meta support and warranty are not available, which means that any technical issues or blockages will depend solely on the API provider.
Sending messages without the recipients' authorization can also result in restrictions, compromising the company's continued communication with its customers.
So which API to choose?
Choosing between the Official WhatsApp API and Unofficial WhatsApp APIs depends on your business needs. While the Official API provides greater security and compliance, Unofficial APIs offer more freedom and predictable costs.
Evaluate your options based on your message volume, need for automation, and level of risk you are willing to take.
Artificial intelligence is transforming the way we interact with technology, and AI agents are one of the most powerful advancements in this area. However, to make these agents truly effective, it’s essential to train them with data specific to your business.
In this article, we will explore how to create an AI agent using the RAG technique (Retrieval-Augmented Generation) to train models with custom information. You will learn three practical ways to implement this in your own project.
Ready? Happy reading!
What is an AI agent and how does it work with RAG?
Before we get into the practical part, it is important to understand the concept of an AI agent and how it can be improved using RAG.
Basically, an AI agent is a system that can interpret commands, process information, and generate responses autonomously. To do this, it needs three fundamental elements:
AI model: the agent is based on models such as GPT, Llama or Claude, responsible for interpreting and generating text based on learned patterns;
Base prompt: these are the instructions that define how the agent should behave and structure its responses;
memory: essential for AI to remember previous interactions. Some agents have both short-term and long-term memory, allowing the conversation to continue.
In addition to these features, an AI agent can be even more efficient when using the RAG (Retrieval-Augmented Generation) technique, as we mentioned earlier. This means that, instead of relying exclusively on the model's prior knowledge, it can query external databases, such as documents, PDFs, Notion pages, or spreadsheets.
In this way, an agent trained with RAG becomes an expert in specific content, ensuring more precise and contextualized responses.
Method 1: Creating an agent with Dify
Now that you understand the basics, let's get to the practical part: how to create an AI agent trained with your own data!
One of the easiest and most effective ways to create a RAG-trained agent is by using Difyi. This tool allows you to integrate knowledge bases into your assistant quickly and intuitively.
To train your agent at Dify, follow the step by step below:
access the “Knowledge Base” tab within the Dify platform;
upload your documents, such as PDFs, HTML files, spreadsheets or web pages;
Dify processes the files and transforms them into numeric vectors, converting the textual content into a format that AI can interpret efficiently.
This process is known as embedding, in which the tool structures the data on a vector basis, allowing the AI to search and retrieve the most relevant information whenever a question is asked.
Additionally, Dify makes it easy to create virtual databases by organizing knowledge into chunks of information. This way, when a user asks the agent a question, the agent quickly identifies which chunk of text best fits the desired answer.
With Difyi, you can create specialized agents for different purposes, such as:
customer support assistants, who access FAQs and technical manuals;
sales agents, which use strategic information to personalize approaches.
The best part? Dify automates this entire process behind the scenes, making implementation simple and practical.
Method 2: Creating an agent with OpenAI Assistants and RAG
Another efficient way to train an AI agent with RAG is to use OpenAI Assistants. This solution allows you to create custom assistants, define specific behaviors, and incorporate documents so that the AI can query and respond accurately.
Unlike Dify, which automates much of the process, OpenAI offers greater control over the agent’s settings. To create your assistant using this tool, follow the steps below:
access the OpenAI platform and go to the “Assistants” tab;
create a new wizard, defining a name, description and specific instructions;
choose an AI model, such as GPT-4 Turbo, to ensure more complete and contextual answers;
Upload files that he will use as reference, such as technical manuals, internal documents, or knowledge bases.
When documents are added to the platform, OpenAI transforms this content into a vector database. This way, the agent can consult the information whenever necessary, without relying solely on the model's pre-trained knowledge.
This allows it to provide more personalized and up-to-date responses without requiring a complete AI re-processing. Additionally, OpenAI manages all the infrastructure needed to store and retrieve this information, making it easy to implement for those who don’t want to set up their own database.
One of the main advantages of this approach is its ease of implementation, as OpenAI takes care of the technical part, making the process simple and intuitive. In addition, the model guarantees high accuracy, combining the power of GPT-4 Turbo with specific information about your business, making the assistant much more effective.
If your goal is to create a specialized AI agent without having to set up an advanced technical environment, OpenAI Assistants can be a great choice.
Method 3: Creating an agent with N8N and Supabase
The third way to create an AI agent trained with RAG is by using the integration between N8N and Supabase. This approach allows greater control over the data and optimizes the search for relevant information within the vector database.
While tools like Dify and OpenAI Assistants simplify the process, using N8N in conjunction with Supabase offers more versatility and reduces operational costs by allowing the framework to be fully configured and managed within your own environment.
To create an AI agent trained with this combination, follow the steps below:
configure the vector database in supabase to store the reference documents;
upload the files that the agent will use as a knowledge base, such as manuals, FAQs or technical ebooks;
integrate Supabase with N8N to enable AI to query data and provide contextualized answers;
develop automated flows in N8N to structure agent interactions with users;
optimize agent responses by ensuring that they can access the most relevant blocks of information within the database.
But why use N8N and Supabase? with RAG?
Unlike other solutions, this approach allows for an advanced level of customization and control over the vector database. When a user asks the agent a question, it fetches the most relevant vector of data from Supabase, ensuring that the answer is based on the stored documents.
Additionally, N8N allows you to connect the AI agent to different applications, such as Whatsapp, Slack and Google Drive, expanding the possibilities of use and automation. This flexibility makes the model ideal for companies that need a highly specialized agent.
Among the main advantages of this implementation, the following stand out:
greater control over datas, allowing adjustments and customizations as needed;
cost reduction, as Supabase replaces paid solutions for vector storage;
advanced automation, with intelligent flows and integrations in N8N;
scalability, allowing the knowledge base to grow according to business needs;
greater efficiency, as the agent accesses information directly from the vector database, without relying solely on the AI model.
If you are looking for flexibility and cost reduction, N8N + Supabase is a powerful solution for training specialized AI agents with RAG.
Conclusion
Training an AI agent with your own data is an essential strategy for obtaining more accurate responses aligned with the context of your business. With RAG, you can transform internal files and documents into structured knowledge for AI, optimizing processes and improving the user experience.
If you want to dive deeper into the topic and learn how to create your own AI agents, check out the complete N8N course at NoCode Startup and take your automation to the next level!
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