Welcome

What is Mindbricks?

Mindbricks is not just another AI code generator—it's a paradigm shift in backend development.

Designed for professionals building real-world systems, Mindbricks combines AI-driven design with a robust, ontology-based architecture to deliver production-ready microservices at scale. "AI in Mindbricks focuses on designing structured backend architectures; Genesis turns these blueprints into fully operational code." At the heart of the platform lies the Mindbricks Pattern Ontology (MPO), a semantic framework that guides AI agents to produce consistent, modular, and maintainable systems without the chaos typically associated with generative AI.

Your backend is not just imagined—it's compiled. Mindbricks uses Genesis, a deterministic engine that transforms the AI-designed semantic blueprint into real, testable, and deployable code. This ensures that every line is not only syntactically valid but also structurally sound.

The AI agent is the project development leader: it takes your natural language requirements, asks clarifying questions, and produces the complete architectural blueprint of your project. You always retain full control—every pattern instance the AI creates can be reviewed, edited, or extended manually—but the primary philosophy of the platform is AI-led design with human oversight, not the other way around.

Whether you're launching a startup or scaling an enterprise system with dozens of microservices, Mindbricks offers a fully managed development experience—complete with AI-driven architecture design, automated code generation, infrastructure-ready deployment scripts, and zero guesswork.

This is backend development, reimagined for the AI era.

How Mindbricks Works

Mindbricks reimagines backend development by turning natural language business requirements into production-grade systems. The AI agent leads the process end to end—from understanding your intent to producing a deployable backend. Here's how it works in practice:

At the core of Mindbricks is the Mindbricks Pattern Ontology (MPO)—a structured set of reusable, meaningful building blocks. Instead of writing low-level configurations, developers define data models, routes, permissions, validations, and logic using these patterns. This creates a blueprint that is readable, modular, and semantically rich.

1. Describe Your Business to the AI Agent

You start by telling the AI agent what you want to build—in plain language. Describe your product, your users, your business rules, and your goals. The AI agent acts as a senior backend architect: it asks clarifying questions, identifies domain boundaries, and progressively refines its understanding of your system.

You speak like a founder or product owner—the AI handles the technical translation.

2. AI Designs the Architecture Using Patterns

Based on your requirements, the AI agent produces a complete backend architecture using the Mindbricks Pattern Ontology (MPO)—a structured set of reusable, meaningful building blocks. Instead of writing low-level configurations, the AI defines data models, routes, permissions, validations, and logic as pattern instances. This creates a blueprint that is readable, modular, and semantically rich.

The AI agent also serves as the primary method to update, extend, or evolve your project design. Describe what needs to change, and the AI modifies the pattern graph accordingly.

3. Compile with Genesis

Once the design is complete, Genesis, the Mindbricks compiler, transforms the ontological model into fully typed, deterministic backend code. This includes:

• RESTful APIs

• Data access layers

• Validation logic

• Event handling

• Internal service communication

• Test scripts

• Full API Documentation

The result is high-quality codebases with zero boilerplate and maximum structure.

4. Deploy with One Click—or Your Own Way

Mindbricks offers fully managed, one-click deployment to a production-grade Kubernetes cloud. As soon as your services are generated, they can be instantly deployed, monitored, and scaled without needing to configure infrastructure or manage containers yourself.

Behind the scenes, Mindbricks handles:

• Kubernetes orchestration

• Secure secrets and environment isolation

• Auto-scaling and health checks

• Build and deployment pipelines

• TLS, domain, and routing setup

For teams with existing DevOps infrastructure, Mindbricks is equally flexible—you can export the generated services, Dockerfiles, and deployment scripts to integrate seamlessly into your own CI/CD pipelines and cloud environments.

Whether you want speed and simplicity or complete control, Mindbricks adapts to your workflow.

5. Evolve with AI

Mindbricks doesn't stop at initial design. As your product evolves, the AI agent remains your primary tool for extending and refining the architecture:

• Describe new features in natural language and the AI adds services, data objects, and APIs

• Request changes to existing logic—the AI updates patterns while preserving consistency

• Version-controlled migrations track every evolution of your architecture

• Service lifecycle operations, permission updates, and integration changes are all AI-driven

You can always switch to manual editing for fine-grained control. The platform supports both AI-led and hands-on workflows seamlessly.

Working with Patterns

At the heart of Mindbricks lies a powerful idea: you build backends by composing patterns, not writing boilerplate.

Mindbricks introduces a formal, modular ontology called the Mindbricks Pattern Ontology (MPO). These patterns act as high-level, structured blueprints that define your backend's:

• Data models

• Business logic

• Access rules and permissions

• Routes and response behavior

• External API interactions

• Deployment and runtime behaviors

Each pattern is designed to be intuitive yet precise. For instance:

• Define a DataObject to represent a table or collection

• Attach BusinessApi patterns to specify how it's accessed

• Apply Validations, Permissions, or AggregatedData to shape logic

• Add FetchOptions or ResponseSettings to extend it

The AI agent creates and orchestrates these patterns based on your natural language input, producing a pattern graph that fully describes your backend. You can then review, refine, or extend the patterns manually if needed.

AI-Led, Fully Controllable

The AI agent is the primary creator and editor of patterns. It translates your business requirements into structured, verifiable pattern instances that the Genesis engine can compile into production code. This is the recommended and most efficient way to work with Mindbricks.

At the same time, patterns are readable, reusable, and editable by humans. You can create them visually through the Mindbricks Studio UI or edit them manually via structured editors. This ensures no architectural lock-in—you always have full ownership and control over your project.

This approach brings a radical benefit: AI outputs are no longer free-form and brittle—they're structured, verifiable, and deterministic. Patterns bridge the gap between high-level design and low-level code, enabling the Genesis engine to generate safe, scalable, and fully deployable microservices.

Understanding the Genesis Engine

Genesis is the core engine that transforms Mindbricks patterns into production-ready backend code. It doesn't guess or improvise—it compiles.

Every pattern in Mindbricks belongs to a pattern class. These classes carry structured meaning, relationships, and logic that describe how the backend should behave. When the design is finalized, Genesis takes over and performs three critical steps:

1. Class Interpretation It reads each pattern instance (like DataObject, BusinessApi, Permission, etc.) and understands their semantic role within the service.

2. Template Compilation Genesis maps each pattern class to deterministic, production-grade code templates—ranging from database models and controllers to service logic and deployment files. These templates are not static; they are smartly parameterized and context-aware.

3. Compositional Logic It resolves pattern interactions. For example, if a RouteValidation pattern depends on a FunctionCall, which in turn accesses a ForeignFetch, Genesis wires them together correctly. It ensures all logic flows match the semantic structure defined in the pattern graph.

The result? Genesis compiles your service into a fully structured, modular microservice—including:

• Type-safe backend code

• Validated API routes

• Session-aware authentication logic

• Deployment scripts and Dockerfiles

• Documentation and Postman collections

All derived from the pattern graph that the AI agent (or you) designed.

This compilation process eliminates code entropy, maximizes consistency, and allows your architecture to evolve safely with each change.

Effortless Deployment, Enterprise-Grade Infrastructure

With Mindbricks, deploying your backend is not a process—it's a single action. Behind that one click lies a full-scale infrastructure orchestration built for professional systems.

Fully Managed Kubernetes Cloud

Mindbricks deploys your services into dedicated Kubernetes clusters, provisioned and isolated per user. Your services run securely, with auto-scaling enabled by default to adapt to real-time load—no ops knowledge required.

Default Services: Auth, BFF, and Notifications

Every project is scaffolded with essential foundation services:

• Auth: A secure, fully integrated authentication service with token and session support.

• BFF (Backend-for-Frontend): A dynamic proxy service that consolidates service responses for frontend consumption.

• Notifications: A message service that delivers real-time updates via email, SMS, or in-app channels.

Robust Architecture Components

Mindbricks doesn't just deploy microservices—it wires them into a high-performing, event-driven architecture:

• PostgreSQL (MongoDB support coming soon): Your relational data model is automatically applied, with all tables created and fully connected to APIs.

• Redis: Instantly provisioned as a high-speed cache layer.

• Kafka: Used for internal event streaming and cross-service communication.

• Elasticsearch: Acts as a dedicated MCMQ server (Micro Command Macro Query)—Mindbricks' improved take on CQRS. It handles complex aggregation queries and high-throughput read models.

Zero-Knowledge Required

You don't need to configure Dockerfiles, Kubernetes manifests, Helm charts, or VPCs. Everything from container builds to database initialization is handled by Mindbricks in a secure, fully abstracted pipeline.

Want to take control? Mindbricks also offers hooks to integrate your own CI/CD pipelines and take over the infrastructure if needed.

Building Your First Microservice

At the heart of every application in Mindbricks is a microservice—a focused, autonomous unit responsible for managing a specific business domain. Whether you're designing a product catalog, user profile system, or payment processor, the AI agent creates each microservice with the logic, data, and APIs it needs to function independently.

Start by Describing Your Domain

Tell the AI agent what your service needs to do. For example: "I need a product catalog service where vendors can list items with prices, categories, and inventory tracking." The AI agent translates this into a complete service design with data objects, business APIs, permissions, and event definitions.

Data Model

Every microservice is built around Data Objects, which represent the tables or collections that store and organize your domain's data. These objects aren't just schemas—they're active entities, enriched with built-in business rules, permissions, validations, and dynamic relationships.

The AI agent automatically:

• Defines fields, types, defaults, and constraints

• Connects objects to each other (foreign keys or service-wide references)

• Controls access using ownership, roles, or custom conditions

Routes and Behavior

The AI agent generates fully functional REST routes for each data object. These include:

• BusinessApi with customizable logic

• Custom endpoints using edge controllers or AI-enhanced filters

Each route is automatically wired to the data object and exposed through the service's public API.

Enriched Logic with Patterns

The AI agent applies advanced patterns as needed based on your requirements:

• ForeignFetch or FunctionCall for fetching external data before writing
• Validation, MScript, or HookFunction for input validation and transformation
• AggregatedCrud for updating child records alongside a parent object

Everything follows a pattern-first design so the Genesis compiler can generate precise code and keep the structure maintainable.

Connected, Secure, Scalable

Each microservice automatically:

• Authenticates requests via the shared Auth service

• Publishes/consumes events through Kafka

• Logs structured data and exposes observability hooks

• Gets deployed with load balancing, scaling, and storage included

You can build one microservice or a hundred—the experience remains consistent, scalable, and fast.

Connecting Microservices and Events

In a modern backend architecture, services rarely operate in isolation. They must collaborate, respond to each other's changes, and maintain consistency across boundaries. Mindbricks embraces this reality by making both synchronous communication and event-driven design first-class citizens of the platform.

Service-to-Service Communication Made Simple

Mindbricks allows microservices to communicate securely and declaratively. Using the InterserviceCalls pattern, one service can invoke logic exposed by another—without worrying about low-level request handling, token management, or error propagation. Authentication is handled transparently, and service-level permissions are enforced automatically based on your configuration.

This makes cross-service orchestration seamless and consistent, even in complex workflows.

Events as a First-Class Pattern

Mindbricks services are not only connected—they're reactive. Every service can emit domain events when something meaningful happens: a user signs up, a payment is received, or a profile is updated.

By default, Mindbricks provisions a dedicated Kafka cluster for every deployed project. Events can be published automatically based on pattern configurations, using triggers like afterCreate, afterUpdate, or custom script-based logic. Other services can subscribe to these events and react accordingly.

This enables true decoupling: a service emits what happened, not who needs to know.

Merging Distributed Data with MCMQ

While interservice calls and event flows are essential, making too many synchronous requests between services can create tight coupling and performance bottlenecks. Mindbricks introduces MCMQ (Micro Command Macro Query) — an evolution of the CQRS pattern — to solve this problem.

With MCMQ, each service manages its own write model and event stream, but contributes read-side projections to a shared, denormalized aggregation layer powered by Elasticsearch. This allows the system to:

• Merge distributed data across services for fast, complex queries

• Avoid chaining interservice calls for every piece of data

• Serve the frontend (BFF) with pre-joined, optimized views

For example, a customer dashboard might need order history from one service, support tickets from another, and real-time delivery status from a third. With MCMQ, all this data is aggregated and queryable through a single Elasticsearch query—no roundtrips required.

The aggregation logic is defined declaratively using Mindbricks patterns, and updates are propagated through the event system.

Declarative Event Definition

Events in Mindbricks are not hardcoded in business logic. Instead, they are defined declaratively:

• Specify when the event should be emitted (e.g. afterUpdate)

• Define what data it should include using MScript

• Choose how it should be delivered (internal Kafka, notification system, or future external buses)

These definitions live in your patterns, version-controlled alongside your architecture, and can evolve with your product logic.

Real-Time Messaging and Notifications

Some events aren't just for systems—they're for people. Mindbricks comes with a built-in Notification Service that turns system events into messages sent to users via:

• Email

• SMS

• Push notifications

• In-app messages

This service listens to your defined events and translates them into user-facing messages using templates and rules defined in your project ontology.

Tracing, Contracts, and Observability

Mindbricks automatically logs interservice calls and emitted events, letting you trace the lifecycle of a request across the system. Microservices expose OpenAPI contracts, making integrations verifiable and accessible for both humans and AI agents. Future updates will introduce full distributed tracing via OpenTelemetry, retry strategies, and more robust observability features.

Conclusion

Mindbricks transforms backend systems from isolated services into a coordinated, intelligent network. With declarative event handling, interservice orchestration, the MCMQ query layer, and built-in messaging infrastructure, your architecture becomes both reactive and highly efficient—ready for real-world scale.

Testing and Monitoring

Mindbricks equips you with a complete suite of testing and monitoring tools to ensure your backend is robust, verifiable, and production-ready. Whether you're testing your APIs manually, integrating automated validation, or tracking system health in real time, everything is designed to work out of the box.

Multiple Interfaces for API Testing

APIFace – The AI-Enhanced Testing Playground

Mindbricks introduces APIFace, a modern alternative to Swagger UI. Designed with developer experience in mind, APIFace:

• Provides a visual, human-readable interface for testing all routes of a service

• Supports header management, request context emulation, and AI-powered suggestions

• Allows authenticated testing using your current login session

• Integrates tightly with the semantic patterns of your service, highlighting available route features, validations, and role permissions

Whether you're testing a GET route with a deep query filter or simulating a complex transaction on a child object, APIFace helps you understand, test, and debug efficiently—without switching tools or writing manual requests.

Swagger UI

Mindbricks automatically generates Swagger documentation for every service, compatible with OpenAPI standards. You can:

• Browse endpoints in a structured format

• Execute requests with real parameters

• Download the Swagger schema for external integration

This is perfect for teams that rely on existing Swagger-based workflows or need to generate client SDKs.

Postman Collections

For more advanced testing scenarios, Mindbricks also generates full Postman collections for every service:

• Includes pre-configured routes with parameters and authentication

• Can be imported directly into your CI/CD pipeline

• Useful for load testing, regression testing, and API contract verification

Declarative Testing and Validations

Mindbricks allows you to define validations and test logic right within your route definitions:

• Layer-specific route validations using MScript

• Stored assertions to be reused in other logic (e.g. permissions, roles)

• Test endpoints and mock setups for simulating edge cases or sandbox flows

These declarative tests help enforce correctness across services while preserving maintainability.

Live Logs and Service Visibility

All services are deployed with live, structured logging, accessible via the dashboard:

• Inspect HTTP request logs by service and route

• View trace-level logs for events, database operations, and interservice calls

• Filter logs by user, session, or error type

• Export logs for external monitoring tools or audits

Health Checks and System Probes

Behind the scenes, Mindbricks configures:

• Health checks for every microservice instance

• Readiness probes for Kubernetes auto-scaling and routing

• Alerting hooks for system failure detection

You can extend these with custom logic to validate external APIs, cache layers, or domain-specific metrics.

Future Enhancements (Coming Soon)

Mindbricks is preparing a set of observability features including:

• Distributed tracing via OpenTelemetry

• Performance dashboards per route and service

• Real-time alerts for latency, error spikes, and business rule violations

• Session-level replay and audit trails for critical workflows

Summary

Testing in Mindbricks isn't just about sending requests—it's about understanding, validating, and continuously improving your backend system. With Swagger, Postman, and APIFace working in harmony, you get intuitive access and professional-grade visibility for every route you deploy.

Security and Compliance

Mindbricks is built with a security-first philosophy, enabling you to launch scalable microservice backends with enterprise-grade protection from day one. From authentication to encryption, and from role-based access to PCI compliance, Mindbricks gives you both out-of-the-box security and deep customization options.

Role-Based Access Control (RBAC)

Every service in Mindbricks supports fine-grained authorization:

• Define roles such as admin, editor, viewer, or custom ones like vendor or moderator

• Assign permissions to routes, actions, and even field-level operations

• Integrate dynamic conditions via MScript, enabling complex access logic (e.g. "users can only update their own data unless they are supervisors")

These rules are enforced automatically during code generation and runtime.

Secure Authentication

Mindbricks includes a built-in Auth service that powers all login, session, and token management:

• Supports JWT-based login with secure cookie storage or header tokens

• Enables login via email/password, mobile OTP, social login (Google, Apple), or enterprise SSO

• Allows session introspection, revocation, and two-factor authentication using configuration only

You can also override the default auth behavior with your own service or plug into external identity providers.

Field-Level and Contextual Protection

Sensitive fields (e.g. passwords, payment tokens, emails) can be:

• Automatically excluded from responses

• Encrypted before storage (optional encryption strategies)

• Controlled with response-level permissions, even within nested objects

You can also define read-only or immutable fields that cannot be updated once created, unless escalated privileges are granted.

PCI & Compliance Readiness

Mindbricks is designed to align with modern compliance requirements:

• Generated code avoids anti-patterns that would break PCI-DSS, GDPR, or SOC2 guidelines

• Authentication sessions can be stored in Redis, enabling centralized session invalidation

• Audit trails, field masking, token expiration, and traceability are supported at the pattern level

If you're building a product with financial, healthcare, or legal implications, Mindbricks helps keep your architecture compliant by default.

Secure Code Generation

The Genesis engine ensures that the generated backend:

• Follows least privilege access principles

• Prevents parameter injection, unauthorized field overwrites, and ID spoofing

• Injects input validation, ownership verification, and anti-tampering hooks into generated routes

You never have to worry about introducing vulnerabilities by forgetting to write security logic—the code is secure by construction.

Service Isolation and Network Security

When deployed on the Mindbricks cloud:

• Every microservice runs in an isolated Kubernetes namespace

• Internal communication is encrypted via mTLS

• Each service has scoped permissions to databases, queues, and caches

• Public endpoints are protected by rate limiting, firewalls, and API gateway policies

Private services and internal-only endpoints are fully supported.

Secure Interservice Communication

Mindbricks uses Kafka and interservice calls for distributed workflows:

• You can define which services can publish/subscribe to specific topics

• All event data is typed and validated via the pattern layer

• Services authenticate their requests using internal service tokens, automatically rotated and secured

Summary

Mindbricks doesn't just offer security as a checkbox—it deeply embeds it into your system's architecture. Whether you're building internal tools or publicly exposed SaaS platforms, Mindbricks ensures your services are trustworthy, secure, and built to meet modern compliance standards.

Extending with Custom Logic

While Mindbricks provides an extensive set of patterns and configuration options, real-world projects often require custom behaviors that go beyond declarative design. Mindbricks allows you to seamlessly extend your backend using JavaScript-based modules, executed at defined hook points or custom controllers — without breaking the generated architecture.

Lib Functions: Reusable Logic Blocks

You can define Lib functions as standalone JavaScript modules that are:

• Injected into validation scripts, computed fields, fetches, and hooks

• Automatically available across all services (Project Library) or scoped to individual services (Service Library)

• Fully testable, versioned, and managed within the platform

Example:

// lib/capitalizeFirstLetter.js
module.exports = function capitalizeFirstLetter(str) {
  return str.charAt(0).toUpperCase() + str.slice(1);
}

Use it in your validation or MScript logic like:

this.formattedName = capitalizeFirstLetter(this.name);

Edge Functions: Full Control with Custom Controllers

When you need total freedom, use Edge Controllers:

• Define your own REST endpoint (/custom/report)

• Implement any logic, query, or API call using plain JavaScript

• Access the request, session, DB layer, Redis, or any custom module

Example:

// lib/helloWorld.js
module.exports = async (request) => {
  return {
    message: `Hello, ${request.session.userId}`,
    time: new Date().toISOString(),
  };
}

Edge functions are ideal for:

• Building custom reporting endpoints

• Integrating with third-party APIs

• Handling legacy workflows or complex multi-tenant logic

AI-Aware Extensibility

All custom logic is integrated into the Mindbricks ontology:

• Your functions appear in autocomplete suggestions

• AI agents can reason about your logic and include it in validations or fetches

• Hooks and edge controllers are versioned and safely recompiled with each deployment

Asset & Template Support

Need to send a PDF, generate HTML, or include custom files?

• Upload assets (e.g. logos, credentials, HTML snippets)

• Use EJS templates to generate dynamic content from data

• Integrate with rendering engines or mailers via custom logic

Seamless Deployment

Whether you're adding a hook or a full edge function, all custom modules are:

• Automatically bundled into the deployment package

• Tested and verified during build

• Observable in logs and debug traces

You get the power of full-stack extensibility without sacrificing structure, security, or consistency.

Summary

Mindbricks gives you the best of both worlds: a powerful AI-driven design system to build scalable services, and an escape hatch to write code when you need it. Whether you're embedding custom validators, transforming queries, or building high-performance endpoints — your logic fits naturally into the system.

From Vision to Production

Mindbricks is not just a tool — it's a complete philosophy for building, scaling, and evolving professional backends with confidence and clarity.

By placing AI at the center of the development process and combining it with semantic pattern design, deterministic code generation, and managed deployment, Mindbricks enables you to:

• Design with AI — describe your business in natural language and let the AI agent architect your entire backend

• Develop faster — instant code and schema generation from the AI-designed pattern graph

• Evolve continuously — update and extend your project by talking to the AI agent, or fine-tune manually

• Deploy smarter — one-click Kubernetes infrastructure with enterprise-grade security

• Scale securely — isolated multi-service environments with built-in auth, RBAC, and compliance

• Extend freely — hooks, edge controllers, and fully customizable libraries when you need custom code

• Monitor continuously — built-in API testing and observability tools

Whether you're launching your first microservice or orchestrating a platform with dozens of services, Mindbricks adapts to your ambition — eliminating boilerplate and empowering your team to focus on what truly matters: your product logic and business goals.

Ready to Start?

The fastest way to build with Mindbricks:

1. Create a project and describe what you want to build

2. Let the AI agent design your services, data models, APIs, and permissions

3. Review and refine — adjust anything the AI produced, or extend it manually

4. Preview the generated code and test your APIs live

5. Deploy instantly — or export to integrate with your own pipelines

From idea to infrastructure, from prototype to production — Mindbricks is your launchpad.

Welcome to the future of backend development.