How to Build a Chatbot: Your No-Code Guide

October 10, 2024
5 min

Estimated reading time: 8 minutes

Want to create your own chatbot? You’ve made a smart choice! Chatbots are becoming a crucial tool for businesses, improving customer interactions, automating tasks, and boosting sales. And the best part?

Thanks to modern no-code platforms, creating a chatbot is accessible to everyone. So you don’t need coding skills, years of technical experience, or superpowers — just a little creativity and a clear goal.

Why create a chatbot for your website?

Chatbots are more than just a trendy accessory — they’re powerful tools that deliver real results. As a result, many companies are using chatbots to:

Speed up customer responses: Instant responses lead to greater customer satisfaction.
Handle multiple conversations at once: Chatbots can handle multiple queries simultaneously, reducing waiting times.
Increase sales by up to 67%: By interacting with customers, chatbots can lead to more conversions.
Create interactive and engaging websites: Chatbots add an element of interactivity that keeps users engaged for longer.
Improve mobile experiences: Mobile-optimized chatbots help users navigate and get answers on the go.
Recover abandoned shopping carts: Remind users about their forgotten purchases and encourage them to complete transactions.
Explore the future of conversational interfaces: As AI continues to evolve, chatbots will become even more integral to customer interactions.

For e-commerce businesses, chatbots are game-changers. After all, they can handle up to 69% of customer queries from start to finish, reducing the costs of customer service in 30% and improving the overall user experience.

What can your custom chatbot do?

By creating your own chatbot, you unlock a world of possibilities. Your bot can:

Trigger actions based on user behavior: for example, starting a conversation when a visitor lands on a specific page or adding items to a shopping cart.
Integrate seamlessly with your technology stack: Whether connecting to your CRM, email marketing, or social media platforms, chatbots offer endless integration opportunities.
Personalize customer experiences: Use customer data to personalize conversations and offer relevant recommendations.
Give you full control over your conversation flow: map every interaction and ensure your chatbot delivers exactly the experience you want.

It’s no wonder that interest in chatbots has skyrocketed recently. Businesses are recognizing their value, and so can you. But here’s the question: how do you actually build an AI-powered chatbot?

Building a Chatbot: AI vs. No-Code Platforms

If you’re thinking about building a machine learning-powered bot from scratch, it can be quite challenging. Natural Language Processing (NLP) and AI algorithms require significant resources, expertise, and data — something most companies don’t have readily available.

This is why using third-party chatbot platforms is a smarter and faster choice. Therefore, platforms like Dify, n8n or Supabase use large datasets of real conversations to help you build highly functional AI chatbots in minutes.

How to Create a Chatbot in 8 Simple Steps

create a chatbot 02 2 — Chatbot messenger icons set with technical support symbols isometric isolated vector illustration

Here's a simple step-by-step guide to help you create your first chatbot with ease.

Step 1: Define the purpose of your chatbot

What is the primary purpose of your chatbot? Is it customer support, lead generation, or something else? Defining your chatbot’s role will help you shape its functionality and personality.

Questions to ask:

What are the most common questions my customers ask?
Should the bot automate certain processes, such as reservations or handling FAQs?
Do I need features like routing to human agents or saving abandoned carts?

Step 2: Choose where your chatbot will appear

Where do your customers interact most? Is it your website, Facebook, WhatsApp, or another platform? Make sure the chatbot platform you choose integrates well with these channels. Popular platforms: WordPress, Shopify, WhatsApp, Facebook Messenger, Instagram.

Step 3: Select a platform to build your chatbot

There are two main categories: AI frameworks and platforms. NoCode. Therefore, for most companies, a no-code platform is the best bet.

After all, it offers simple setup, pre-built templates, and all the functionality you need without the technical headaches.

Step 4: Create the conversation flow

This is where the fun begins! Use the visual editor provided by your platform to map out conversation paths. Set up triggers (e.g. a visitor lands on a certain page) and ultimately decide how the conversation progresses using decision trees, actions, and responses.

Step 5: Test, test, test

Before you go live, test your bot’s responses in different scenarios. After all, most platforms offer visualization features so you can see exactly how your bot interacts with users in real time.

Step 6: Train your bot with NLP (optional)

If you want your bot to understand natural language, you’ll need to feed it relevant data. So train it using common phrases, keywords, and customer queries to ensure it can accurately answer a wide range of questions.

Step 7: Collect feedback

After launching, don’t forget to collect user feedback. Use surveys or star ratings to evaluate your chatbot’s performance. After all, user feedback is a valuable resource for improving your bot over time.

Step 8: Analyze and optimize

Monitor your chatbot’s performance with analytics. Keep an eye on key metrics like user engagement, conversion rates, and drop-off points. Then, refine your chatbot’s flow and responses to continually improve the user experience.

Do's and Don'ts of Creating a Chatbot

create a chatbot 03 1 — How to Build a Chatbot: Your No-Code Guide 5

Do:

Add personality: Give your chatbot a unique tone that aligns with your brand, whether it’s professional, fun, or casual.
Provide human assistance when needed: If a query is too complex for your bot, escalate it to a human agent for a smoother customer experience.
Keep it simple: Simple conversation flows are more effective and easier for users to follow.

Don't do:

Don’t let conversations reach dead ends: always provide options for users to get help or restart the conversation.
Don’t overcomplicate the bot: The more complex the flow, the harder it becomes for users to effectively engage. Keep it manageable and intuitive.

Creating a chatbot is easier with NoCode

Building a chatbot has never been easier, thanks to no-code platforms that eliminate the need for technical knowledge. So whether you’re looking to improve customer experience, streamline business processes, or increase sales, a chatbot can make all the difference.

So what are you waiting for? Come be a part of the MasterClass AI Agent Manager and learn more about the NoCode world!

Known as “Castelo”, he discovered the power of No-Code when he created his first startup entirely without programming – and that changed everything. Inspired by this experience, he combined his passion for teaching with the No-Code universe, helping thousands of people create their own technologies. Recognized for his engaging teaching style, he was awarded Educator of the Year by the FlutterFlow tool and became an official Ambassador for the platform. Today, his focus is on creating applications, SaaS and AI agents using the best No-Code tools, empowering people to innovate without technical barriers.

Also visit our Youtube channel

Learn how to create AI Applications, Agents and Automations without having to code

What is an AI agent?

For an even more practical introduction, check out the AI Agent and Automation Manager Training from NoCode StartUp, which teaches step by step how to structure, deploy and optimize autonomous agents connected with tools such as N8N, Make and GPT.

One AI agent is a software system that receives data from the environment, interprets this information according to previously defined objectives and executes actions autonomously to achieve these objectives.

It is designed to act intelligently, adapting to context, learning from past interactions, and connecting to different tools and platforms to perform different tasks.

How Generative AI Agents Work

According to IBM, generative AI-based agents use advanced machine learning algorithms to generate contextualized responses and decisions — this makes them extremely efficient in personalized and dynamic flows.

Generative AI agents use large-scale language models (LLMs), such as those from OpenAI, to interpret natural language, maintain context between interactions, and produce complex, personalized responses.

This type of agent goes beyond simple reactive response, as it integrates historical data, decision rules and access to external APIs to perform tasks autonomously.

They operate on an architecture that combines natural language processing, contextual memory and logical reasoning engines.

This allows the agent to understand user intent, learn from previous feedback, and optimize its actions based on defined goals.

Therefore, they are ideal for applications that require deeper conversations, continuous personalization and autonomy for practical decisions.

Watch the free video from NoCode StartUp and understand from scratch how a conversational and automated AI agent works in practice:

Difference between chatbot with and without AI agent technology

While the terms “chatbot” and “AI agent” are often used interchangeably, there is a clear distinction between the two. The main difference lies in autonomy, decision-making capabilities, and integration with external data and systems.

While traditional chatbots follow fixed scripts and predefined responses, AI agents apply contextual intelligence, memory, and automated flows to perform real actions beyond conversation.

Traditional chatbot

A conventional chatbot operates on specific triggers, keywords, or simple question-and-answer flows. It usually relies on a static knowledge base and lacks the ability to adapt or customize continuously.

Its usefulness is limited to conducting basic dialogues, such as answering frequently asked questions or forwarding requests to human support.

Conversational AI Agent

An AI agent is built on a foundation of artificial intelligence capable of understanding the context of the conversation, retrieving previous memories, connecting to external APIs, and even making decisions based on conditional logic.

In addition to chatting, it can perform practical tasks — such as searching for information in documents, generating reports or triggering flows in platforms such as Slack, Make, N8N or CRMs.

This makes it ideal for enterprise applications, custom services, and scalable automations.

For an in-depth analysis of the concepts that differentiate rule-based automations and intelligent agents, it is also worth checking out the official MIT documentation on intelligent agents.

Comparison: AI agent, chatbot and traditional automation

To delve deeper into the theory behind these agents, concepts such as “rational agent” and “partially observable environments” are addressed in classic AI works, such as the book Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig.

Types of AI Agents

AI agents can be classified based on their complexity, degree of autonomy, and adaptability. Knowing these types is essential to choosing the best approach for each application and to implementing more efficient and context-appropriate solutions.

Simple reflex agents

These agents are the most basic, reacting to immediate stimuli from the environment based on predefined rules. They have no memory and do not evaluate the history of the interaction, which makes them useful only in situations with completely predictable conditions.

Example: a home automation system that turns on the light when it detects movement in the room, regardless of time or user preferences.

Model-based agents

Unlike simple reflex agents, these maintain an internal model of the environment and use short-term memory. This allows for more informed decisions, even when the scenario is not fully observable, as they consider the current state and recent history to act.

Example: a robot vacuum cleaner that recognizes obstacles, remembers areas already cleaned and adjusts its route to avoid repeating unnecessary tasks.

Goal-based agents

These agents work with clear goals and structure their actions to achieve these objectives. They evaluate different possibilities and plan the necessary steps based on desired results, which makes them ideal for more complex tasks.

Example: a logistics system that organizes deliveries based on the lowest cost, time and most efficient route, adapting to external changes, such as traffic or emergencies.

Utility-based agents

This type of agent goes beyond objectives: it evaluates which action will generate the greatest value or utility among several options. It is indicated when there are multiple possible paths and the ideal is the one that generates the greatest benefit considering different criteria.

Example: a content recommendation platform that evaluates user preferences, schedule, available time and context to recommend the most relevant content.

Learning agents

They are the most advanced and have the ability to learn from past experiences through machine learning algorithms. These agents adjust their logic based on previous interactions, becoming progressively more effective over time.

Example: a virtual customer service agent who, throughout conversations, improves their responses, adapts the tone and anticipates doubts based on the most frequently asked questions.

To understand how the use of AI is becoming a key factor in global digital transformation, McKinsey & Company published a detailed analysis on trends, use cases and economic impact of AI in business.

AI Agent Use Cases

Companies like OpenAI have been demonstrating in practice how agents based on LLMs are capable of executing complete workflows autonomously, especially when integrated with platforms such as Zapier, Slack or Google Workspace.

The application of artificial intelligence agents is rapidly expanding across various sectors and market niches.

With the evolution of no-code tools and platforms such as N8N, make up, Dify and Bubble, the creation of autonomous agents is no longer restricted to advanced developers and has become part of the reality of professionals, companies and creators of digital solutions.

These agents are especially effective when combined with automation tools, enabling complex workflows without the need for code. Below, we explore how different industries are already benefiting from these intelligent solutions.

Marketing and Sales

In the commercial sector, AI agents can automate everything from the first contact with leads to the generation of personalized proposals.

Through platforms like N8N, it is possible to create flows that collect data from forms, feed CRMs, send personalized emails and track the customer journey.

Additionally, these agents can analyze user behavior and adapt nurturing approaches based on previous interactions.

Service and Support

Companies that handle high volumes of interactions benefit from AI agents trained based on internal documents, FAQs, or databases.

With Dify and Make, for example, you can build assistants that answer questions in real time, automatically open tickets, and notify teams via Slack, email, or other integrations.

Education and Training

In the educational field, agents can be used to guide students, suggest content based on individual progress and even correct tasks in an automated way.

This automation illustrated below shows how AI agents can be practically implemented using N8N. In the flow, we have a financial agent personalized that converses with the user, accesses a Google Sheets spreadsheet to view or record expenses, and responds based on defined logic, allowed categories, and contextual validations.

The agent receives commands like “Show me my expenses for the week” or “Record an expense of R$120 on studies called 'Excel Course'”, and performs all actions automatically, without human intervention.

Automation flow with AI agent in N8N for expense control via Google Sheets

AI Agent FAQs

What can I automate with an AI agent?

AI agents are extremely versatile and can be used to automate everything from simple tasks — such as responding to emails and organizing information — to more complex processes such as reporting, customer service, lead qualification, and integration between different tools.

It all depends on how it is configured and what tools it accesses.

What is the difference between an AI agent and a customer service bot?

While a traditional bot answers questions based on keywords and fixed flows, an AI agent is trained to understand context, maintain memory, and make autonomous decisions based on logic and data. This allows it to take practical actions and go beyond conversation.

Do I need to know how to program to create an AI agent?

No. With no-code tools like N8N, Make, and Dify, you can create sophisticated agents using visual flows. These platforms allow you to connect APIs, build conditional logic, and integrate AI without having to write a line of code.

Is it possible to use AI agents with WhatsApp?

Yes. With platforms like Make or N8N, you can integrate AI agents into WhatsApp using third-party services like Twilio or Z-API. This way, the agent can interact with users, answer questions, send notifications, or capture data directly from the messaging app.

Why Learn to Build AI Agents Now

Mastering the creation of AI agents represents a competitive advantage for any professional who wants to stand out in the current market and prepare for the future of work.

By combining no-code tools with the power of artificial intelligence, it becomes possible to develop intelligent solutions that transform operational routines into automated and strategic flows.

These agents are applicable in different contexts, from simple tasks such as organizing emails, to more advanced processes such as generating reports, analyzing data or providing automated service with natural language.

And the best part: all of this can be done without relying on programmers, using accessible and flexible platforms.

Get started today with AI Agent Manager Training, or deepen your automation expertise with the N8N Course to create agents with greater integration and data structure and take the first step towards building more autonomous, productive and intelligent solutions for your routine or business.

What is an LLM?

LLM stands for Large Language Model. It is a type of artificial intelligence model trained on huge volumes of textual data, capable of understanding, generating and interacting with human language in a natural way. Famous examples include:

GPT-4 (OpenAI)
Claude (Anthropic)
Gemini (Google)
Mistral
Perplexity IA

These models function as “artificial brains” capable of performing tasks such as:

Text generation
Automatic translation
Sentiment classification
Automatic summaries
Image generation
Automated service

How do LLMs work?

In simple terms, LLMs are built on Transformer neural networks. They are trained to predict the next word in a sentence, based on large contexts.

The more data and parameters (millions or billions), the more powerful and versatile the model becomes.

Own LLMs vs. API usage: what do you really need?

Building your own LLM requires robust infrastructure, such as vector storage, high-performance GPUs, and data engineering. That's why most professionals opt for use ready-made LLMs via APIs, like those of OpenAI, Anthropic (Claude), Cohere or Google Gemini.

For those who don't program, tools like Make, Bubble, N8N and LangChain allow you to connect these models to workflows, databases, and visual interfaces, all without writing a line of code.

Additionally, technologies such as Weaviate and Pinecone help organize data into vector bases that improve LLM responses in projects that require memory or customization.

The secret is to combine the capabilities of LLMs with good practices in prompt design, automation and orchestration tools — something you learn step by step in AI Agent Manager Training.

Difference between LLM and Generative AI

Although they are related, not all generative AI is an LLM. Generative AI encompasses many different types of models, such as those that create images (e.g. DALL·E), sounds (e.g. OpenAI Jukebox), or code (e.g. GitHub Copilot).

LLMs are specialized in understanding and generating natural language.

For example, while DALL·E can create an image from a text command, such as “a cat surfing on Mars,” ChatGPT, an LLM — can write a story about that same scenario with coherence and creativity.

Examples of practical applications with NoCode

The real revolution in LLMs is the possibility of using them with visual tools, without the need for programming. Here are some examples:

Create a chatbot with Dify

As Dify Course, it is possible to set up an intelligent chatbot connected to an LLM for customer service or user onboarding.

Automate tasks with Make + OpenAI

Node Makeup Course You learn how to connect services like spreadsheets, email, and CRMs to an LLM, automating responses, data entry, and classifications.

Building AI Agents with N8N and OpenAI

O Agents with OpenAI Course teaches how to structure agents that make decisions based on prompts and context, without coding.

Advantages of LLMs for non-technical people

Access cutting-edge AI without having to code
Rapid testing of product ideas (MVPs)
Personalization of services with high perception of value
Optimization of internal processes with automations

LLMs and AI Agents: The Future of Interaction

The next evolutionary step is the combination of LLMs and AI agents. Agents are like “digital employees” that interpret contexts, talk to APIs and make decisions autonomously. If you want to learn how to build your agents with generative AI, the ideal path is AI Agent Manager Training.

May 23, 2025
3 min

Neto Camarano

What is ElevenLabs AI? The AI-Powered Voice Revolution

We are living in an era where texts, images and videos can now be created by artificial intelligence. But there is one element that is gaining strength as a competitive advantage: the voice.

Whether in podcasts, institutional videos, tutorials or even automated service, the ability to create realistic artificial voice is changing how companies and creators communicate. And in this scenario, the ElevenLabs AI emerges as one of the global protagonists.

What is ElevenLabs?

O ElevenLabs is one of the neural speech synthesizers most advanced on the market. With its technology AI voice cloning and AI-powered text to speech, allows you to create realistic voices in multiple languages, with natural intonation, dynamic pauses and surprising emotional nuances.

Key Features:

Human-quality Text to Speech
Conversational AI with support for interactive agents
Studio for longform audio editing
Speech to Text with high accuracy
Voice Cloning (Instant or Professional)
Sound Effects Generation (Text to Sound Effects)
Voice Design and Noise Isolation
Voice Library
Automatic dubbing in 29 languages
Robust API for automations with tools like N8N, Make, Zapier and custom integrations

ElevenLabs FAQ

Find out more about the company and news from ElevenLabs directly at official website of ElevenLabs and see the API documentation.

Does ElevenLabs have an API?

Yes, ElevenLabs has a complete API that allows you to integrate speech generation with automated workflows.

With this, it is possible to create applications, service bots, or content tools with automated audio.

Discover the Make Course from NoCode Start Up to learn how to connect the ElevenLabs API with other tools.

Are ElevenLabs voices copyright free?

AI-generated voices can be used commercially, as long as you respect the platform's Terms of Use and do not violate third-party rights by cloning real voices without authorization.

Is it possible to use ElevenLabs for free?

Yes. ElevenLabs offers a free plan with 10,000 credits per month, which can be used to generate up to 10 minutes of premium quality audio or 15 minutes of conversation

This plan includes access to features like Text to Speech, Speech to Text, Studio, Automated Dubbing, API, and even Conversational AI with interactive agents.

Ideal for those who want to test the platform before investing in paid plans.

What is the best alternative to ElevenLabs?

Other options include Descript, Murf.ai and Play.ht. However, ElevenLabs has stood out for its natural voice, advanced audio editing features with AI, API integration and support for multiple languages.

Their paid plans start from US$ 5/month (Starter) with 30 thousand monthly credits, and go up to scalable corporate versions with multiple users and millions of credits.

See all available plans on the ElevenLabs website. However, ElevenLabs has stood out for the naturalness of its voice and the quality of its API.

How does ElevenLabs work?

You submit a text, choose a voice (or clone one), and AI converts that text into realistic audio in seconds. It can be used via the web dashboard or via API for automated workflows.

Examples of using ElevenLabs AI in practice

1. Video and podcast narration

Ideal for creators who want to save time or avoid the costs of professional voiceovers.

2. Automated service with human voice

Turn cold bots into realistic, empathetic voice assistants.

3. Generating tutorials and training with audio

Companies and CLT professionals can create more engaging internal materials.

4. Applications that “talk” to the user

With tools like Bubble, FlutterFlow or WebWeb, it is possible to integrate AI voice into apps.

How to integrate ElevenLabs with NoCode tools

N8N + ElevenLabs API

Allows you to automate voice generation based on dynamic data using visual workflows in N8N. It is ideal for creating processes such as audio customer service responses, automated voice updates, and more.

Discover the N8N Course from NoCode Start Up

OpenAI Agents + ElevenLabs

With the use of AI agents, it is possible to create voice-responsive systems, such as a virtual attendant that speaks to the customer based on a dynamic prompt.

See the Agents with OpenAI Course

Bubble/FlutterFlow + ElevenLabs

Use the API to insert audio into your apps with interaction triggers or dynamic events.

ElevenLabs and NoCode: Open the door to creating experiences with voice AI

AI-generated voice is already a powerful, accessible and potential-rich reality. ElevenLabs is not just a tool, but an engine for creating immersive, automated and more human experiences.

If you want to learn how to integrate these possibilities with NoCode and AI tools, NoCode Start Up has the ideal paths:

May 20, 2025
4 min