AI-Powered Diagram-Driven Microservices Generator

Describe your business requirements to AI, and QuickCode turns them into production-ready enterprise microservices deployed to the cloud.

  • Production-ready microservices from DBML, DSL, and AI-assisted design
  • CQRS or Service per module, regenerate safely without losing custom code
  • Gateway, identity, admin portal with live reports, and GitHub Actions deploy to Google Cloud Run
How it works
Illustration of building cloud microservices with QuickCode
AI Project Analysis
Describe your product in natural language (optional PDF). QuickCode builds a reviewable module plan, then generates DBML per module.
Module AI Assist
Add a module or update one bounded context with AI — refine schema and DSL without rerunning a full project analysis.
Safe Regeneration
Change DBML and regenerate. Generated code updates while your custom logic stays in partial classes.
Deploy to Cloud Run
Each project gets its own GitHub Actions pipeline to Google Cloud Run — gateway, portal, and module APIs.
Start from a prompt
Describe your system in plain language. QuickCode Studio analyzes it into modules, you review the plan, then each service schema is generated.
Describe your microservice system
Model: …
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Iterative generation loop

Analyze, Design, Visualize, Code, Deploy

Run the same workflow again whenever you extend your system, with QuickCode.

Analyze
Design
Visualize
Code
Deploy

Explore a real generated project — microservices, gateway, identity, GitHub Actions, and cloud deploy config — before you start building.

Explore Generated Repository

Extend your schema and regenerate; custom code stays protected.

Create project with AI Create project with AI
AI project setup
Add module with AI Add module with AI
AI module generation
QuickCode Studio workspace QuickCode Studio workspace
Studio workspace
AI-Powered DBML Generation
Describe your business requirements in plain language or edit DBML in QuickCode Studio. AI (Gemini, OpenAI, Claude) proposes modules and schemas; you refine tables, relationships, and QuickCode DSL before generation.
Google Gemini
OpenAI
Anthropic Claude
ERD diagram and QuickCode DSL ERD diagram and QuickCode DSL
DBML, DSL & APIs
Every table gets automatic CRUD. In DBML table Notes, QuickCode DSL adds custom Query (read), Update, and Delete operations, each line becomes a REST endpoint with handlers and Dapper SQL.
Query, Update, Delete in Notes
Query: filters, joins, reports, and existence checks. Update: named state changes (e.g. mark paid, reduce stock). Delete: conditional or bulk deletes beyond delete-by-id CRUD.
CQRS or Service
Choose CQRS + Mediator or Service architecture per module.
Microservice modules in Studio Microservice modules in Studio
Full Microservices Stack
Every project includes per-module APIs, YARP gateway, admin portal, and IdentityModule, users, roles, and permissions without duplicating auth modules in your DBML.
GitHub Actions workflows GitHub Actions workflows
CI workflows
GitHub Actions pipeline run GitHub Actions pipeline run
Deploy pipeline
Generation pipeline in Studio Generation pipeline in Studio
Studio pipeline
GitHub & Cloud Run
Generated code is pushed to GitHub under QuickCodeNet/{project-name}. GitHub Actions build containers and deploy to Google Cloud Run, gateway, portal, and module APIs.
GitHub
Cloud Run

Microservices Architecture

Platform overview

QuickCode targets a modular microservice layout: each bounded context becomes an API with its own database, fronted by a single gateway and optional admin UI, not a monolith with shared tables.

  • Gateway & portal: External APIs via YARP; admin users use the generated portal.
  • Isolated services: One deployable API and database per module, plus central identity.
  • Delivery: Generated CI/CD (e.g. GitHub Actions) toward runtimes such as Google Cloud Run.
QuickCode reference microservices architecture diagram