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What is MVP and why is it important for your startup?

what is mvp

Estimated reading time: 9 minutes

Have you ever had a brilliant idea that could revolutionize your field of activity, but found yourself not knowing how to turn it into reality, without spending a lot of money and resources? O MVPMinimum Viable Product – is a strategy that can completely change the way you develop products and drive your startup. 

O MVP is the most simplified version of a product or part of it, created with as few resources as possible. This version seeks to deliver the essence of the idea and its main value proposition, with the aim of validating if the idea is really interesting and if it is worth investing more resources in it.

In this article, we will explore the meaning and importance of MVP for your startup. For those who want make money online, knowing this method is essential. 

Keep reading to understand better and start implementing this strategy in your startup today! 

What is MVP? 

MVP is the acronym for Minimum Viable Product in English and means Minimum Viable Product in Portuguese.

As the name suggests, this is a practice that consists of creating a test version of the product, with only the minimum functionalities necessary for it to fulfill its function. From this test, it is possible to observe in practice the product efficiency, usability and market acceptance

It also makes it possible to compare the competition, obtaining partnerships and attracting investors. If the product is successful at this stage, it will be further developed and improved to officially reach the market. 

Now, think for a moment: How many times have you had an idea that seemed incredible, but when it was put into practice, it didn't generate the expected return? This is exactly what the MVP seeks to avoid. 

Instead of spending months or even years developing a complete product, you create an MVP to test the market, collect real user feedback, and make adjustments as needed.

Most known types of MVP

There are several types of MVP, each with its specific characteristics and approaches. Let's explore some of the best known:

Prototype

The MVP Prototype is often used for physical products, for example: smartphones, automobiles, electronics, among others. In this application, a simplified version of the product is created, which can be a real or simulated example, to be subjected to user testing. 

This type of MVP is ideal for obtaining detailed feedback on user interaction with the product.

Double

Already the MVP Double consists of creating two similar versions of the product, are tested simultaneously using the A/B methodology

This approach allows you to compare results and choose the version that best meets users' needs, helping to optimize investment.

Smoke

In turn, the MVP Smoke It is done with the intention of evaluating the interest of potential customers in the product or service. It is possible to put this strategy into practice through a landing page, for example. 

In this space, the company can talk about its concepts and operations and stimulate users' interest. From this interaction, it is possible to measure the page conversion rate and have a more realistic idea about the product.

Wizard of Oz

At MVP Wizard of Oz, customers interact with a simulated product interface, believing they are using an automated solution. However, behind the scenes, actions are performed manually. 

This type of MVP allows you to test the viability of the idea, without the need to develop a completely automated solution.

Concierge

The MVP Concierge is a strategy in which the entrepreneur himself performs the service manually, offering personalized attention to customers. 

Although it is limited in scalability, it is great for validate demand and understand user needs directly.

What is the best type of MVP? 

To answer this question, first of all, one must analyze the resources that the project has. Each type of MVP has different requirements, and this must be taken into account. 

The second item to be analyzed must be the product or service being tested. For example, if you are developing an automobile, it is essential to carry out prototype testing. But, if you are creating a social network, the Magical MVP of OZ could be exactly what you need.

Below we will guide you step by step when putting the MVP into practice! 

Woman programming MVP

How to make your MVP?

Now that you understand the types of MVP, let's explore the process of creating an effective MVP. See the step by step:

Identify the problem

The first step to creating a successful MVP is to clearly identify the problem your product will solve. Understanding the pains and needs of your target audience is crucial. Therefore, focus on these two steps: 

  • Understanding pain and needs: analyze the market and identify the difficulties your audience faces. This will ensure your MVP meets real demand.
  • Defining personas: create ideal customers that represent your target audience. Go beyond demographics and explore the specific problems they face.

Don't hesitate to dedicate a lot of time to this initial step, as it is what will dictate the quality of your service and what needs to be analyzed in each test. 

Analyze the competition

Before proceeding, it is important to understand the competitive landscape in which your product will be launched. So, study competitors offering similar solutions. Identify your strengths, weaknesses and market approaches and how you can best address each of these points. 

Identify gaps in competitor offerings. What can your product offer that is unique and best? 

Idealize the product

Now that you understand the problem, it's time to ideate how your product will solve this demand in an innovative way. At this point, it is important to specify the characteristics and functionalities of the product and determine how it will meet the needs of the personas. 

Also, it is necessary to establish the value proposition of the product. Ask yourself: How does it differentiate itself from the competition? Why would personas choose your product?

Consider demand

Assessing demand for your product is essential to ensure your MVP has acceptance potential. Therefore, carry out acceptance tests such as surveys and surveys to assess market receptivity to your product concept. 

Furthermore, create landing pages or use other strategies to capture leads and assess people's real interest.

Use programming in code

In the MVP development stage, you can explore the advantages of no-code tools, which offer a visual and simplified approach to creating applications and softwares. 

These tools allow you to develop prototypes and functional products without the need for in-depth programming knowledge. Here are some advantages of no-code tools applied to the MVP creation process:

  • Rapid development;
  • Iterative prototyping;
  • Cost reduction;
  • Greater autonomy;
  • Data integration;
  • Concept validation;
  • Design flexibility.

Some of the no-code tools available include Webflow, WordPress, Bubble, Bildr, Soft, AppGyver, FlutterFlow, Glide, Xano, Airtable, Zapier and Make.

Find out what the code is by clicking here.

Build, test and improve

With the MVP ready, develop it as planned, keeping it simple and focused on solving the problem. Conduct rigorous testing with an initial group of users, collect and evaluate feedback

Based on user feedback, make improvements to the MVP, adding necessary features and adjusting weaknesses. 

By following these steps, from problem identification to continuous iteration, you will build a solid MVP, paving the path to a successful and competitive product.

Importance of MVP for startups

Now that you understand how to create and develop an MVP, you can understand why it is so important to ensuring the success of any startup. 

MVP allows you to test your idea in the real market before investing significant resources. This process avoids unnecessary spending on products that may not meet users' needs. 

Plus, with an MVP in hand, you can collect feedback directly from users, identifying strengths, weaknesses and opportunities for improvement. This guides future development, ensuring your solution evolves in line with customer expectations.

So, if you are about to launch a startup or develop a new product, MVP is your ideal path. 

Start right now

To put the development of an MVP into practice, you can count on No-Code Startup's free courses!

As FlutterFlow course, you learn how to build apps for iOS and Android, without the need to write code. It's a great way to apply a Wizard of Oz MVP, for example. No prior knowledge is required and all classes are available free of charge.

Now, if you prefer to master the creation of softwares and web applications to implement your MVP, the bubble course stands out as the best choice. In addition to being free, it offers the necessary foundation to start your studies in the area of programming.

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Artificial intelligence (AI) is reshaping the way the financial sector operates, from risk analysis to the automation of complex processes. More than a trend, AI has become a strategic tool for financial institutions that want to increase their efficiency, reduce costs and offer personalized experiences. Within this scenario, the use of AI agents for finance has been gaining ground as a practical and accessible application for companies of all sizes.

Financial dashboard with automated charts and visuals representing artificial intelligence

AI Software Development in the Financial Sector

Creating AI-based solutions in the financial context requires robustness, security, and adaptability. Developing this type of software requires an architecture that is prepared to handle large volumes of data, continuous learning, and the ability to provide accurate insights.

In addition, systems need to be able to handle sensitive data, integrate with multiple sources (such as banks, brokerages, and ERPs), and adapt quickly to regulatory changes in the industry. Flexibility and modularity are core elements of any AI architecture for finance.

Integration with Existing Infrastructures

Much of AI’s success in the financial sector depends on its integration with legacy systems. This includes internet banking platforms, CRMs, payment gateways, and compliance tools. Using NoCode platforms such as make up or N8N allows you to create effective connections without the complexity of traditional development.

By the way, if you want to experience in practice how to integrate financial flows with AI, No-Code Start-Up provides a free N8N course with full video on YouTube. It's a great opportunity to explore real automations and understand how to structure secure and intelligent integrations in an accessible way.

With this approach, banks and fintechs can activate intelligent flows based on real data, such as automatic sending of alerts, personalized segmentations and recommendations based on consumer behavior.

Challenges in AI Development for the Financial Sector

Despite the enormous potential, there are challenges that need to be considered. Among the most relevant are:

  • Data quality: models are only effective if fed by clean and organized data.
  • Explainability: It is essential to understand how the AI arrived at a particular recommendation.
  • Cultural resistance: Traditional teams may resist adopting automation and algorithm-based decisions.

As highlighted by Deloitte, the combination of data governance, team training and ethical monitoring of AI is essential to mitigate risks and generate consistent results.

Security and Regulatory Compliance

The financial sector is one of the most regulated in the world. Therefore, all AI applications must comply with standards such as LGPD, GDPR and Central Bank regulations.

The adoption of good practices Data Privacy by Design, end-to-end encryption and role-based access control are just some of the basic requirements. Platforms such as Xano offer robust infrastructure with a focus on security for those who want to develop financial backends with AI.

Digital security illustration with padlock and financial data, symbolizing protection and compliance in AI application

Software Scalability and Resilience

As AI becomes a critical part of operations, it is necessary to ensure that systems are scalable and resilient. This means being able to grow as demand dictates, without compromising performance or security. Cloud computing and the adoption of microservices are essential strategies in this journey.

Companies like Goldman Sachs and Bank of Brazil have already demonstrated, in different contexts, how AI models can be deployed gradually, safely testing hypotheses before scaling to the entire operation.

AI Agents for Finance: Use Cases and Applications in the Financial Sector

1. Automated credit analysis

Companies like Credits use AI to evaluate hundreds of variables — including banking history, spending habits, and public data — to offer personalized credit. This reduces default rates and expands access to credit in a fairer way. According to McKinsey, automation can reduce analysis time by up to 70%.

2. Fraud prevention

O Bradesco and other institutions have implemented machine learning models that detect fraud based on behavioral patterns. When a transaction deviates from the pattern, the system triggers an automatic block or sends an additional verification to the user. According to Visa, the use of artificial intelligence helps prevent fraud totaling approximately US$14T25 billion.

3. Automated investment management

Robo-advisors like the ones from XP Investments use algorithms that analyze investor profiles, financial goals and market conditions to assemble and rebalance portfolios autonomously. CB Insights highlights that these systems are democratizing access to quality financial services, previously restricted to large investors.

4. AI-powered customer service

O Itau has incorporated AI into its digital channels, allowing customers to renegotiate debts, request second copies of bills or consult invoices using natural language. This reduces response time, improves customer experience and frees up human teams for more complex cases. According to Accenture, up to 80% of first-level banking interactions can now be automated using artificial intelligence.

5. Cash flow forecast

Financial management startups use AI agents for finance that integrate data on accounts payable and receivable, seasonality and market trends to predict cash flow for the coming months with high accuracy. Based on this information, more assertive decisions can be made. Harvard Business Review reinforces that this approach reduces the margin of error in financial projections and improves strategic planning.

The Role of AI Agents for Finance

Among all the applications, the AI agents for finance stand out for their versatility and accessibility. They function as intelligent “copilots”, performing tasks such as:

  • Automatic generation of financial reports
  • Sending alerts about targets or deviations
  • Predictive profitability analysis

Using platforms such as Dify and OpenAI, it is possible to configure these agents with natural language, making them easier to use even for those without technical training. This expands access to data intelligence in the financial sector.

The Future of AI in the Financial Sector

Artificial intelligence in the financial sector is no longer a distant promise — it is present in strategic decisions, customer service, and risk management. The adoption of technologies like AI agents for finance represents a leap forward in digital maturity. As technical challenges are overcome and platforms become more accessible, companies of all sizes will be able to use AI not only to automate, but to evolve.

Organizations that master the use of AI ethically, safely, and strategically will be ahead in delivering value and conquering the market. The future of finance is predictive, integrated, and data-driven — and it starts now. Want to learn how to build your own AI-powered financial agents without coding? Access the AI Agent Manager Training and discover the most practical way to apply all this in your context.

How AI is changing the market can be observed in practically all sectors of the economy, and this change is intensifying every day. Artificial intelligence (AI) is being recognized as a disruptive force that is profoundly reshaping the global market. From simple tasks to complex decisions, it has been integrated into processes in various sectors, transforming the way people work, consume and manage businesses.

Furthermore, when observing the effects of this transformation, it becomes clear how much the job market is being reconfigured: new opportunities arise, some professions lose ground and others adapt or are reborn with the support of technology, which demonstrates how AI is changing the market in a broad and profound way.

How AI is changing the job market

AI is accelerating the automation of repetitive and operational tasks. AI systems are already being used to efficiently perform:

  • Customer service with chatbots.
  • Predictive data analysis for sales and marketing.
  • Automated financial and audit processes.
  • Inventory control and logistics.

These changes not only reduce operational costs, they also require the workforce to be retrained for new roles, which reinforces how AI is changing the job market with great intensity.

Representation of people and artificial intelligence collaborating in different professions

Professions affected by artificial intelligence

According to PwC's report on the future of work (source), it is estimated that up to 30% of human tasks could be automated by the mid-2030s. This data shows, in practice, how AI is changing the job market and skills requirements.

Some of the roles most impacted by AI include:

  • Telemarketing operators
  • Administrative assistants
  • Data Analysts (some tasks being replaced by generative AI)

On the other hand, new functions emerge, such as:

  • Prompt Engineers
  • Automation experts with NoCode
  • Conversational Experience Designers
  • Intelligent Agent Managers

Those AI agents, for example, have been increasingly used in companies seeking to automate decisions and perform tasks with minimal human intervention. According to an analysis of the The Verge, large companies such as OpenAI, Google and DeepMind are investing heavily in the development of these systems, which can already act independently in complex corporate processes. They are designed to operate autonomously, learn continuously and integrate with other technologies — which makes them key players in the ongoing digital transformation.

What's Happening Now: How AI is Changing the Marketplace in Numbers

The AI market is experiencing exponential growth. The sector is estimated to surpass US$ 500 billion in value by 2027. There is a global race for innovation, with startups, large companies and governments investing heavily in:

  • Generative models (like ChatGPT)
  • Robotic Process Automation (RPA)
  • Artificial intelligence applied to health, education, law and agribusiness

This movement demonstrates how AI is being positioned as a strategic asset for growth and competitiveness.

Suggested reading:

AI Agent and Automation Manager Training

What are the negative aspects of AI in the job market?

Despite promising advances, important challenges also arise:

  • Structural unemployment: functions terminated without sufficient time for requalification
  • Digital inequality: not everyone has access to technological education
  • Technological dependence: companies become hostages of platforms and algorithms
  • Ethical and privacy issues: inappropriate use of data and biased automated decisions

These factors require public policies, business leaders and civil society to debate limits, transparency and responsibilities in the use of technology.

People using AI tools in a modern workplace

Opportunities and the future of work with AI

The key is in the conscious adaptation. The future of work will be driven by:

  • Continuous learning and professional requalification
  • Mastery of AI tools and NoCode platforms
  • Creating new business models based on data and automation
  • Development and management of autonomous AI agents

Increasingly, professionals and companies will need to adopt a stance proactive and experimental, turning AI into an ally.

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AI and entrepreneurship: new market frontiers

Artificial intelligence is not only transforming the traditional job market, it is also paving the way for new business models. Digital entrepreneurs are using AI to create scalable products such as intelligent assistants, recommendation systems, and data-driven SaaS platforms. No-code tools combined with AI agents are enabling the emergence of lean, highly automated, and highly personalized startups.

A great example is the AI-based micro-SaaS, which solve very specific problems and can be created by a single person. Platforms like Bubble, FlutterFlow and Make, integrated with OpenAI models, make this scenario not only possible, but accessible.

For those who wish to explore this new territory, we recommend SaaS IA NoCode Training, designed to transform ideas into digital products using the power of artificial intelligence.

How AI is changing the market and shaping the future

Artificial intelligence is changing the market in an irreversible way. It is not only a technological revolution, but also a social, professional and economic transformation. The question is no longer “if” AI will impact your work, but “how will you position yourself in this new era”.

The good news is that there have never been so many accessible tools for those who want to learn AI in practice.

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Artificial intelligence (AI) is no longer a distant promise. It is already transforming the way solo lawyers and small law firms operate. With affordable tools, it is possible to automate repetitive tasks and focus on what really matters: winning more clients and delivering high-quality service. AI agent for lawyer is the key to this revolution, offering practical solutions to everyday challenges. Each AI agent for lawyers acts as a virtual legal assistant, ready to optimize your routine.

What is an AI Agent and How Can It Help Independent Lawyers?

One AI agent for lawyer is an automated system that performs specific tasks on its own, based on predefined commands and machine learning. For lawyers, this means:

  • Reduction of time spent on manual and bureaucratic tasks.
  • 24/7 support with virtual assistants who answer common questions.
  • Greater productivity, with a focus on strategic activities.
  • Possibility of customizing legal flows according to the area of activity.

These agents can be integrated with various systems and platforms, enabling everything from the automatic drafting of contracts to the management of deadlines and hearings. In addition, they allow for the analysis of documents with greater speed and precision, reducing errors and rework. The great advantage is that, with no-code tools such as Agents with OpenAI Course and Free Dify Course, any lawyer can create their own AI agents without having to program.

Lawyer using AI agent to automate legal tasks in the office

Real Examples of Legal Automation with AI Agents for Lawyers

1. Automatic Generation of Petitions and Contracts

With tools like make up integrated into the Google Docs, it is possible to automate the creation of petitions. Imagine filling out a form and having the document ready in minutes.

2. Personalized Legal AI Agents

Much more than a simple chatbot, a AI agent for lawyer is able to continually learn from interactions, refining its responses and becoming more effective over time. Using ChatGPT integrated into the Dify, you can create an agent who not only answers common questions about labor rights or procedural deadlines, but also identifies service patterns and suggests improvements in responses. This agent can be trained with data from your own office, offering a highly personalized and efficient service, constantly evolving according to your client's needs.

3. Review and Analysis of Legal Documents

AI tools allow you to perform automatic readings, generate summaries and highlight important points in contracts and processes.

Legal document automation with AI agent assisting lawyers

AI Solutions for Lawyers: What You Need to Know

  • Dify: Creates tailor-made legal assistants.
  • make up: Automates service flows and document generation.
  • Agents with OpenAI: Develop custom agents for specific tasks.
  • N8N Course: Powerful tool for creating complex legal automation flows.

Relevant Tools in the Lawyer AI Agent Market

Document Review and Analysis

  • Kira Systems – extracts and analyzes complex contracts.
  • Luminance – automated review with AI, used in due diligence.
  • LegalSifter – reviews contracts and suggests improvements based on AI.

Automated Legal Research

  • JusIA – legal questions, analyze references and create document
  • LegalAI – write objections, initial petition with AI.
  • CaseText – AI-powered legal research (English).
  • LexisNexis – global AI-powered legal research platform.
  • Westlaw – advanced legal research, powered by AI.

Legal Document Automation

  • LawGeex – automatically reviews contracts, with AI.
  • DocuSign CLM – complete automation of contracts.

Legal Chatbots and Customer Service

  • DoNotPay – chatbot that solves simple legal questions (English).
  • IBM Watson Legal – AI solutions and chatbots for the legal sector.

Office and Process Management

  • ProJuris – Brazilian legal software with AI and automation.
  • Advbox – automation of flows and digital legal management.

Predictive Analysis and Jurimetrics

Law Firm Integrated AI Agent Workflow

Building an AI Legal Agent Using N8N and Dify

To create a more robust lawyer AI agent, you can integrate the N8N, Dify and ChatGPT. Check out this guide:

  1. Map Your Office's Needs: Define what functions the bot needs to have: answer questions, send documents, schedule appointments.
  2. Create FAQs and Flows: List frequently asked questions and response paths. In Agents with OpenAI Course you learn how to model these flows.
  3. Configure Dify: In the dashboard, create your bot based on the questions and answers, adjusting the behavior.
  4. Use N8N for Integration: Connect Dify to other platforms like WhatsApp, Google Calendar or Docs. In N8N Course you learn how to create these integrations.
  5. Automate Repetitive Tasks: Use N8N to trigger automatic responses, save data, and send alerts.
  6. Test and Improve: Put the bot into action, collect customer feedback, and optimize the system.

With this integration, your legal chatbot will not only answer questions, but also perform automatic tasks, increasing your level of service and productivity.

Will AI Replace Lawyers? No, But It Will Replace Those Who Don’t Use AI

Artificial intelligence is here to be an ally, not a replacement. Today, thousands of lawyers are already using AI agents to optimize their day-to-day activities. Each AI agent for lawyers is designed to assist in specific legal tasks, without replacing human critical reasoning and strategy. For example, law firms are automating the generation of standard contracts, reducing document production time by up to 70%. Lawyers are also using AI agents for lawyers to review large volumes of documents in due diligence processes, finding critical information in minutes. In addition, legal chatbots, such as AI agents for lawyers, allow you to serve customers 24/7, answering simple questions and directing them to specialized assistance when necessary.

Learn How to Create Your Own AI Agents and Multiply Your Results

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