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How to create an app with or without code

How to create an application with or without code

Estimated reading time: 12 minutes

If you are thinking of having your own app, you are definitely wondering how to create an app. and If you are thinking that it is necessary to be a master of programming, we can already tell you that there are other possibilities, discover what is needed to create an app

Today there are several platforms that allow the creation of apps without the need to write a line of code (learn more about what is no-code). So, if you have a good idea in your head but don't know how to program, you can put it into practice anyway!

In this article you will discover the essential steps of how to create an app and be successful in the market. 

How to create an application in code
How to create an app with or without code 4

What are the steps to create an application?

7 Steps on how to create an application

Define your goals

Answer the question: what problem am I solving and what will be the benefit that users will have when using my application?

Do a market survey

Evaluate the market before leaving for the hand in the mass definitively. Conduct a market analysis focused on the niche you intend to operate.

Search existing solutions

Carry out a more detailed study of your competitors, knowing their strengths and weaknesses in depth.

Set goals metrics

Define goals that support the tracking of development results and the application itself.

Decide on your app's features

You don't need to start with a super complete version, so think about what the main features will be that you will offer your users.

Create an MVP

Bet on a minimal model to validate your product and confirm that it is really scalable in the market.

guarantee the quality

Evaluate your app's proper quality parameters for proper functioning and perform the appropriate tests.

Define your goals

create a free app​
How to create an app with or without code 5

if you want to know how to create an app, the first step is to know what your goals are. 

That is, what do you want to achieve with the development of this app. It is at this point that you will think of an outline of your project.

What will the area be? Health, wellness, finance, lifestyle, leisure. Think about what you want to do and who you want to offer this application to.

Answer the question: what problem am I solving and what will be the benefit that users will have when using my application? Work on it, because, at the end of the day, your app it needs to be useful for something in the person's daily life. 

Do a market survey

Even if you have an interesting and purposeful idea, you need to evaluate the market before going for real. 

This is even more important when your main goal with all of this is to make money – which turns out to be most people's main goal. 

Therefore, you must carry out a market analysis focused on the segment of apps and aimed at the niche you intend to act.

From there you will be able to have a more complete view of the target audience, demand and the like.

You can even develop a app super cool, but it won't have the visibility it deserves if there isn't a market to download your program. 

That is, it is necessary to unite the best of both worlds: an interesting idea + a market that has demand. 

Bearing in mind that it is interesting to evaluate the market in general and then focus more on the area of application development. 

Many times it may seem not to have much demand thinking about the app occasionally, but the market in general is large. By developing a good solution you can grow a lot. 

Practical Example:

Imagine you want to create an appointment scheduling app for beauty professionals, such as hairdressers, manicurists, and makeup artists. Your market research should start with questions like:

  • Who are the main users of this type of app? Independent beauty professionals and their clients.
  • Do these professionals already use any apps? Identify competitors like “Bling” or “Shedul”, which already serve this public.
  • What are the pains of these professionals? Difficulty in organizing schedules, lack of a simple way to communicate with customers and manual payment management.
  • Is the market growing? Data shows that the beauty and wellness sector continues to expand, with more professionals moving into self-employment.

Based on these answers, you will have a clear view of your target audience, competition and existing demand.

This prevents you from investing time and money in an idea with no market and increases your chances of success.

Search existing solutions

During this time you can carry out a more detailed study of your competitors.

One of the big mistakes of those who are starting in the market is not looking at their competitors. 

From this assessment you will be able to assess what are the strengths and weaknesses of each one. Absorb everything that is interesting and bring it to your application (but, if possible, in an improved way) and bring a solution for each negative point that was raised.

In addition to downloading and frequently using each app of the competitor, it is essential to evaluate the comments of the users.

This turns out to be easy, because in the app store itself there are open comments from users. From this assessment it is possible to find feedbacks valuables of what you should and shouldn't do with your app.

It is also valid to say that this analysis of competitors must be constant, as the tendency is for them to innovate and seek to improve their products.

If you pay attention, you have a greater dimension of how they are using new technologies. And, remember: never underestimate your competitors, no matter how inferior they may seem. 

Common Applications in the Beauty and Appointment Services Niche:

How to create a scheduling app​
How to create an app with or without code 6
  1. Fresha: Scheduling platform for beauty and wellness professionals, with payment integration and inventory management.
    • Strong Point: Simple interface and complete management.
    • Weak Point: Fees apply for use of some features.
  2. Bling: Financial management and scheduling app, very popular among microentrepreneurs.
    • Strong Point: Integrated financial control.
    • Weak Point: Complex interface for new users.
  3. Easy Agenda: Simplified scheduling solution, ideal for freelancers.
    • Strong Point: Simplicity and accessibility.
    • Weak Point: Few customization options.
  4. Booksy: Focused on appointments for beauty professionals, with integrated marketing resources.
    • Strong Point: Marketing and customer loyalty resources.
    • Weak Point: High costs for small businesses.
  5. StyleSeat: Aimed at beauty professionals in the US, with integrated scheduling and payment.
    • Strong Point: Payment integration directly into the app.
    • Weak Point: No support in Portuguese.

How to Analyze These Apps:

  • Download and use each app: Evaluate user experience, navigation and functionality.
  • Read user reviews: Identify recurring complaints and see what users praise most.
  • Test customer support: Check the speed and efficiency of the service, which is a competitive advantage.
  • Analyze the business model: Check if the app is free, has premium plans or charges a commission.

With this information, you can create an application that not only offers the best features of your competitors, but also solves the main pain points they leave open.

Set goals metrics

This step is very important not only to know how to create an app, but for any type of endeavor you intend to engage in in your life – and it goes even for personal goals.

When we do not define our goals, when they are generalist or when they are only valid for the final result, most likely you will be discouraged with the project, in addition to being left without adequate direction.

It is very important that you set goals for where you want to go and also bring the way in which you intend to do this. 

Remembering that goals not written in stone can be changed over time to what makes the most sense.

Even at the beginning of the business it is common to set goals that do not correspond to reality because there is a lack of experience in that market. 

So before creating an app, learn how to set your goals so that they help you and not the other way around. 

Decide on your app's features

Once you have a more established and solid idea, it's time to start thinking how to create an app

The first step is not to start developing, but to think about the resources your producer will have. 

You don't need to start with a super complete version, so think about what the main features will be that you will offer your users.

Having this well defined is important to know where to start your application development project. 

Create a minimum viable product (MVP)

You will hardly develop a product that will be perfect in the first version, even if you know all the steps of how to create an app

And that doesn't just apply to apps, but to any type of project as well.

To minimize errors or wrong strategies, it is always more interesting to bet on a minimal model to validate your product and confirm that it is really scalable in the market. 

Test your own app 

It is very important that you have a structured process for carrying out tests, because that way you will be able to discover which are the bugs and details that can be improved to bring better user experience.

Being a program it will always be subject to the bugs, but you should always try to resolve them as quickly as possible so that users can browse your website normally again. app.

These were some considerations about creating applications for mobile devices or even other devices.

Quality assurance

Have a guarantee of the quality of your product. Your application it really must be good and solve the pain that it proposes so that, in fact, it has the due prominence in the market. 

And what would be a good app? Leaving aside the question of its objective, for it to be considered of quality there are some parameters to take into account, such as:

  • Ease of access 
  • lightweight app
  • Run on different systems 
  • be intuitive 
  • be responsive 
  • Have one design nice 
  • Possibility of integrations 
  • Follow all the rules of the LGPD (General Data Protection Law) 
  • Facilitate the user experience 
  • And stuff like that

If your app If you have all this and still a great idea, then it will definitely be well accepted in the market. 

Ways to create an app

Already knowing the essential steps of how to create an application, you may still be wondering how what are the possibilities to actually create your application.

Hire self-employed professionals

You can choose to hire independent professionals to run part of the application or to fully create it.

Hire a specialist company

Such companies have great know-how in creating applications, whether simple or complex. This option tends to be more expensive than the others.

Develop yourself with tools in code

Today you no longer need to know a programming language to develop your own application.

making use of no-code tools you can create your app yourself in a few weeks instead of months and save a lot of resources.

Know more about the ways to create an application in our article.

Now that you know how to create an app, how about starting to better structure your idea to get it off paper?

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More Articles from No-Code Start-Up:

Artificial intelligence has advanced rapidly and AI agents are at the heart of this transformation. Unlike simple algorithms or traditional chatbots, intelligent agents are able to perceive the environment, process information based on defined objectives and act autonomously, connecting data, logic and action.

This advancement has driven profound changes in the way we interact with digital systems and carry out everyday tasks.

From automating routine processes to supporting strategic decisions, AI agents have been playing fundamental roles in the digital transformation of companies, careers and digital products.

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
What are AI Agents? Everything You Need to Know 13

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.

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

AI Agent Manager Training
AI Agent Manager Training

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.

Further reading

Large Language Models (LLMs) have become one of the most talked-about technologies in recent years. Since the meteoric rise of ChatGPT, generative AI-based tools are being explored by entrepreneurs, freelancers, CLT professionals, and tech-curious individuals.

But why is understanding how LLMs work so important in 2025? Even if you don't know how to program, mastering this type of technology can open doors to automation, the creation of digital products, and innovative solutions in various areas.

In this article, we will explain in an accessible way the concept, operation and real applications of LLMs, focusing on those who want to use AI to generate value without relying on code.


What is an LLM

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.

Read more: Transformers Explained – Hugging Face

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

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.

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
What is ElevenLabs AI? The AI-Powered Voice Revolution 17

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
What is ElevenLabs AI? The AI-Powered Voice Revolution 18

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

NoCode tools
What is ElevenLabs AI? The AI-Powered Voice Revolution 19

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:

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