🔎 Overview

The MindBricks Design Service is the foundation of project generation within the MindBricks ecosystem.
It is responsible for defining, storing, and orchestrating the data required to create projects, whether they are generated by AI, manually, or through a hybrid approach.

The Design Service acts as a bridge between AI-powered services, user inputs, and project-facing interfaces, ensuring:

• Consistency → Data and metadata are centralized, so services and clients always consume the same definitions.

• Scalability → Projects can grow without breaking structure since everything is version-controlled.

• Collaboration → Both humans and AI agents can contribute to building or extending a project.

In short, the Design Service provides the blueprints that power the generation of applications, services, APIs, and admin panels across the platform.

🏗️ How It Works

The Design Service defines how project data is structured, displayed, customized, and updated.
It integrates tightly with other core services to allow projects to evolve dynamically.

Key Responsibilities

1. Data Orchestration

   • Collects and manages all project metadata (services, objects, APIs, configurations).

   • Ensures that this metadata is always aligned with AI outputs and manual updates.

2. Frontend Integration

   • The Project Panel (frontend) consumes definitions from the Design Service.

   • This allows dynamic rendering of menus, layouts, and project data without hardcoding.

3. AI Collaboration

   • Works with AI agents through the APD Service to generate or enhance projects.

   • AI can propose new services, objects, or APIs, which are validated and stored in the Design Service.

4. Built-in Services

   • When a project is created, the Design Service automatically provisions essential services like:

     • Authentication → Default identity and access management.

     • BFF (Backend-for-Frontend) → Standardized API gateway for UI.

     • Notification → Event-based messaging support.

   • Each built-in service is fully customizable and can be extended for specific project needs.

5. Custom Service Creation

   • Beyond the built-ins, clients can define additional services.

   • Services can be:

     • AI-generated (designed by agents based on natural-language requirements).

     • Manually created (designed directly by the client or developers).

6. Deployment Readiness

   • The Design Service structures all definitions so the Project Deployer can:

     • Generate backend services.

     • Generate APIs automatically.

     • Configure and deploy the Admin Panel.

     • Provide a preview environment before final deployment.

🔄 Data Lifecycle

The lifecycle of project data within the Design Service ensures traceability and consistency.

1. Creation

   • A new project is created → built-in services are provisioned.

   • Client defines new services → either manually or via AI prompts.

2. Validation

   • AI-generated or manually added services are validated.

   • Metadata structure ensures compatibility with deployers and admin panels.

3. Versioning

   • Every update increments the service version.

   • Versioning ensures that historical compatibility is preserved while projects evolve.

4. Preview

   • Clients can preview their projects before deployment.

   • Preview relies on the Design Service as the single source of truth.

5. Deployment

   • The Project Deployer consumes structured definitions.

   • Services, APIs, and UI panels are generated automatically.

📊 Service Architecture

🚀 Capabilities

With the Design Service, you can:

1. Generate Projects with AI

   • Fully leverage AI agents to create projects end-to-end.

   • Example: “Create a SaaS project with authentication, subscription, and reporting modules.”

2. Update Existing Projects with AI

   • Use AI to refactor or extend a project.

3. Create Projects Manually

   • Start from zero and build step by step.

   • Example: Define objects, APIs, and services directly in the Project Panel.

4. Adopt a Hybrid Workflow

   • Combine AI automation with manual customization.

   • Example: Let AI scaffold a service, then update structures manually.

5. Maintain Project Versioning

   • Every update increments the service version.

   • Example: Service Auth v1.0.0 → updated to Auth v1.0.1 → safely consumed by deployers.

🧩 Example Workflow

1. User creates a new project.

2. Design Service provisions built-in services: Auth, BFF, Notification.

3. User asks AI: “Create a todo manager application”

4. AI generates the service definition, objects, and endpoints.

5. Design Service validates and stores this definition.

6. User manually adds a Reporting service with custom objects.

7. Project Panel updates automatically to reflect both services.

8. Project Deployer consumes the definitions → generates backend, APIs, and admin panel.

9. User previews the project → validates flows.

10. Deployment proceeds with version-controlled artifacts.

🛡️ Benefits

• Centralized Control → All data and metadata flow through one hub.

• Flexibility → AI, manual, or hybrid workflows supported.

• Scalability → Supports complex projects with multiple interdependent services.

• Reliability → Versioning ensures safe updates and backward compatibility.

• Speed → Rapid prototyping and deployment reduce time-to-market.

✅ Conclusion

The MindBricks Design Service is the backbone of project generation within the platform.
It combines AI innovation, manual customization, and structured orchestration to enable:

• Faster development.

• Consistent architecture.

• Scalable, version-controlled deployments.

By using the Design Service, teams can go from idea → definition → preview → deployment seamlessly, all within the MindBricks ecosystem.