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, and GitHub Actions deploy to Google Cloud Run
Try generating with AI
Try generating with AI
DBML to Production Code
Draw your schema, add DSL annotations — QuickCode generates endpoints, handlers, and optimized Dapper SQL.
Safe Regeneration
Update your DBML and regenerate. Generated code updates while your custom logic stays untouched in partial classes.
Deploy to Cloud Run
Each project gets its own GitHub Actions pipeline deploying to Google Cloud Run — isolated builds, minimal setup.
Admin Portal
Group-based authorization, table-level permissions, and configurable endpoint workflows — without writing a line of UI code.
Generate Microservice with AI
Describe your system in plain language. AI will split it into modules and generate scalable DBML schemas for each service database.
Design, Visualize, Code, Deploy
How QuickCode works—from your first AI prompt or DBML diagram through generated microservices, built-in gateway and identity, and automated deployment to production.
AI-Powered DBML Generation
Describe your backend 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
Why QuickCode is Different
QuickCode ships a full generated codebase—not one-off AI snippets. Add your own endpoints, services, and business logic on top; when you change DBML and regenerate, the fresh generated layer and your additions still work together.
Extend Generated Code
Use partial classes and your own files for custom behavior. QuickCode regenerates .g.cs scaffolding—it does not overwrite the code you added.
Regenerate Without Starting Over
Update tables, DSL, or modules in Studio, then regenerate. New APIs and migrations land while your extensions stay in place and keep compiling.
Real APIs from Day One
Working endpoints with Dapper and real database operations—not empty Swagger mocks.
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.
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 & 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
See the Full Walkthrough
This is the short version. For every step—from project setup and Studio to generation pipelines and the live demo—open How It Works or try the demo from the menu.
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.
- Entry points — Admin users use the portal; external systems call APIs through the gateway (YARP).
- Identity — Central authentication service and dedicated identity store for users, groups, and tokens.
- Generated services — One deployable API per module, each with an isolated database (no cross-module DB coupling).
- Events — Kafka-backed listener coordinates workflows and cross-service reactions without tight synchronous coupling.
- Observability — Logs and metrics flow into Elasticsearch/Kibana for operations and troubleshooting.
- Delivery — Generated repos include CI/CD (e.g. GitHub Actions) toward container runtimes such as Google Cloud Run.
The diagram shows component types and data flow—not individual module names from a sample project.