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Supabase Backend – Everything you need to know

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Estimated reading time: 9 minutes

Building scalable and secure applications today requires a robust backend infrastructure. However, for many developers, managing the complexities of servers, authentication systems, and storage can be a daunting task. This is where Supabase comes into play.

Supabase is an open-source Backend as a Service (BaaS) platform that aims to simplify backend development. Providing a PostgreSQL database, real-time data synchronization, user authentication, and file storage through a simple and intuitive API.

In this guide, we'll explore everything you need to know about Supabase, its key features, and how it can help developers streamline their workflow.

What is Supabase?

Supabase is a powerful, open-source alternative to Firebase, offering a database PostgreSQL managed. In order to combine real-time features, user authentication, file storage and even serverless functions.

The platform is designed with the goal of simplifying backend development while providing scalability and security. Unlike traditional backends, Supabase handles much of the heavy lifting, offering a managed backend with minimal configuration.

Developers can focus on building their frontend applications while leveraging Supabase's backend capabilities through an API.

developer supabse

Why choose Supabase?

Supabase’s main appeal lies in its ability to provide developers with a robust backend that doesn’t require complex configuration or management. Traditionally, developers would have to deal with multiple configurations separately.

Supabase brings all of these features together into one cohesive package, reducing the need to juggle multiple services and tools. Another significant benefit is its open source nature.

While Firebase is a closed ecosystem with proprietary technology, Supabase is built with the best tools open source, primarily PostgreSQL. This means you get the flexibility and transparency of open source software without sacrificing the benefits of a managed service.

Now, let’s dive deeper into the key features of Supabase.

1. Leveraging the PostgreSQL database

At the heart of Supabase is PostgreSQL, one of the world's most popular relational database systems. PostgreSQL is a database that offers flexibility, scalability, and performance.

Supabase's decision to use PostgreSQL as its backend database allows developers to leverage the full power of SQL while benefiting from a stable and scalable infrastructure.

Some of the key features of Supabase's managed PostgreSQL database include:

  • Advanced queries: Supabase supports complex SQL queries including joins, aggregations, and even full-text search.
  • JSON support: PostgreSQL supports JSON data types, allowing you to work with both structured and unstructured data in the same database.
  • Row-level security: With Supabase, you can implement fine-grained row-level access control, ensuring that users can only access the data they are authorized to see.

Additionally, the PostgreSQL database is fully integrated with the Supabase API, which means that every table you create in your database automatically generates RESTful endpoints to perform CRUD operations.

Therefore, this Instant API feature is a huge time saver for developers who would otherwise need to manually create endpoints to interact with their data.

supabase postgre sql

2. Seamless API Integration with Supabase

One of the main strengths of this tool is its API-first approach. Developers can interact with the backend exclusively through a RESTful API. This makes it easy to integrate Supabase with different frontend technologies like React, Vue, Angular, and Next.js.

This decoupling of the frontend from the backend allows for greater flexibility, making it easier to swap or update frameworks without having to overhaul the entire backend. This way, with the Supabase API, you can perform CRUD operations, manage user authentication, and handle file uploads and downloads. All without having to write complex server-side code.

The API is also well documented, making it easy for developers to get started. So whether you’re an experienced developer or just starting out, the straightforward documentation and SDKs make Supabase accessible and quick to implement.

3. User authentication made easy

Implementing user authentication is one of the most time-consuming and error-prone aspects of backend development. Supabase simplifies this process by providing a comprehensive, integrated authentication system.

This way, developers can implement secure user registration, login, and session management with just a few lines of code. Supabase supports multiple authentication methods, including:

  • Email/Password: Traditional login with email and password.
  • Social Logins: Authentication through popular services like Google, GitHub, and others.
  • Magic Links: Password-free login via email.

In addition to handling login and registration, Supabase's authentication system also supports password resets, email verification, and JWT-based session management.

supabase user login

4. Real-time data synchronization

Real-time functionality is becoming increasingly important in modern applications. Supabase excels in this area by providing real-time data synchronization through PostgreSQL replication.

So with Supabase, you can easily build applications that react to database changes in real time. For example, if you're building a collaborative text editor, you can use the WebSocket-based event system to ensure that all users see the updates.

This feature is possible without any complex setup or configuration. So once your database is connected to Supabase, you can start listening for changes in real time using the client libraries.

5. Hassle-free file storage

In addition to handling database operations, Supabase also offers file storage. This feature is especially useful for applications that need to store and manage user-generated content.

Supabase’s storage system is secure and comes with built-in access control policies. You can create public and private buckets to manage your files. Additionally, Supabase’s API allows you to easily upload, download, and delete files programmatically.

supabse dataset

6. Supabase Edge Functions for Serverless Execution

The tool also supports Edge Functions, which are lightweight serverless functions that run at the edge, close to your users. These functions offer low-latency execution, making them ideal for tasks that need to be executed quickly and efficiently.

So with Supabase edge functions, you can write custom server-side logic in JavaScript or TypeScript and deploy it directly to Supabase. This feature is especially useful for tasks like payment processing, sending transactional emails, or data transformations.

7. Scalability and security

As your application grows, Supabase scales with you. Because Supabase is built on PostgreSQL, it is designed to handle large volumes of data and high levels of concurrent traffic.

Security is another important consideration for any backend, and this tool implements best practices for data encryption, authentication, and access control. So, whether you’re managing sensitive user data or protecting file uploads, Supabase ensures your application remains safe and secure.

supabase security

Conclusion

Supabase is an incredibly powerful tool for developers who want to simplify backend development while still having access to a scalable, secure, and feature-rich infrastructure. Offering a managed PostgreSQL database, user authentication, real-time sync, file storage, and cutting-edge features, this tool provides a comprehensive solution for building modern applications.

So, if you’re looking for an open-source alternative or simply want to simplify your backend development, Supabase is definitely worth exploring.

Finally, if you want to know how to get the most out of this tool, you need to be part of the NoCodeIA Training! Come and discover everything about the world NoCode with us!

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Neto Camarano

Neto specialized in Bubble due to the need to create technologies quickly and cheaply for his startup. Since then, he has been creating systems and automations with AI. At the Bubble Developer Summit 2023, he was listed as one of the greatest Bubble mentors in the world. In December, he was named the largest member of the global NoCode community at the NoCode Awards 2023 and first place in the best application competition organized by Bubble itself. Today, Neto focuses on creating AI Agent solutions and automations using N8N and Open AI.

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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.

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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.

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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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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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