Explore a real generated project — microservices, gateway, identity, GitHub Actions, and cloud deploy config — before you start building.
Explore Generated RepositoryAI-Powered Microservices Development Platform
Design your system with AI, generate production-ready services, orchestrate business workflows, and deploy — while keeping architecture, code and documentation in sync.
- AI analysis with versioned project and module contexts
- DBML/DSL first, then workflows from the same analysis
- Ask Your Project on analysis, contexts, and DBML with edits via diff
- Generate per module (.NET or Node.js), deploy, regenerate safely
Start from a prompt
We’ll draft modules and open questions, then refine the plan with you. Generate the stack when it’s ready.
How you build with QuickCode Studio
Describe and analyze, ask about the plan, shape schema and workflows, then generate per module and deploy.
Describe & analyze
Write a brief (optional PDF). AI builds a project analysis plus a separate analysis and context for each module. Versions are saved so you can reopen and reanalyze.
Ask Your Project
Ask about analysis, contexts, architecture, and DBML. QuickCode AI can change the schema with confirm → diff → save, and guide new workflows.
Schema & DSL
Generate DBML from the plan or edit by hand. Tables get CRUD; QuickCode DSL in Notes becomes custom REST endpoints. DBML is versioned.
Workflows after DBML
Analysis suggests business flows early. After DBML exists, create Kafka or endpoint workflows yourself or with AI. Each workflow is versioned and deployable.
Generate & regenerate safely
Per module: .NET or Node.js, Service or CQRS, SQL Server, MySQL, or PostgreSQL. Change later, regenerate; your custom code stays.
Deploy on Cloud Run
Watch GitHub Actions and Cloud Run in QuickCode Studio as gateway, portal, and module APIs go live together.
Continuous Improvement
Analyze → Schema → Workflows → Generate → Deploy
Review the plan in QuickCode Studio, refine DBML and workflows, then ship and loop back anytime.
Describe the product, get the analysis
Start on the homepage or in QuickCode Studio with Project with AI. Describe what you want to build. AI turns that brief into a Project Analysis: modules, architecture, open questions, and project context. It also prepares a separate analysis and context for each module. Every analysis version is saved so you can reopen, compare, and reanalyze when the brief changes.
Project and module analysis
One plan for the whole product, plus a dedicated analysis and context per module so each service stays grounded in the same brief.
Open questions & notes
Answer only the gaps that would change the schema or module map. Add notes for v1 constraints, then update the plan before you generate DBML.
Versioned analyses
Switch between saved analysis versions to compare plans and continue from an earlier understanding of the product.
Workflow suggestions early
During analysis, AI proposes business workflows you may need later. You review the suggestions now; you write the workflows after DBML exists.
Ask Your Project anytime after the project exists
Once the project is created, use QuickCode AI next to DBML and ERD. Ask about the analysis, module contexts, architecture, data model, business rules, and APIs. Answers use your project, not generic chat. Ask AI to change DBML (confirm → diff → save), or to guide you when you need a new workflow.
Grounded in analysis and context
Answers use Project Analysis, per-module contexts, architecture, DBML, DSL rules, and generated APIs when they exist.
Schema changes with a diff
Ask for a table or column change: confirm the named module, preview the line diff and token usage, then save when it looks right.
Workflow guidance
When a new business flow is needed, Ask AI can explain steps or adjust YAML for the selected workflow.
Generate DBML and DSL from the plan
When the analysis looks right, generate per-module DBML in the background, or edit schemas by hand in QuickCode Studio. AI drafts tables and relations; you refine columns, Refs, and QuickCode DSL. Every table gets automatic CRUD. Custom Query, Update, and Delete lines in table Notes become REST endpoints. DBML revisions are versioned like analyses.
Anthropic
OpenAI
Google Gemini
DeepSeek
Groq
NVIDIA
Query, Update, Delete in Notes
Query: filters, joins, reports, and existence checks. Update: named state changes. Delete: conditional or bulk deletes beyond delete-by-id CRUD.
Edit diagram or text
Work in the DBML editor or the ERD; they stay in sync. Restore earlier DBML drafts from revision history when an experiment goes the wrong way.
Write workflows after DBML
Analysis already suggested the business flows. After DBML exists, create those workflows in QuickCode Studio: Kafka event workflows for async work, and endpoint workflows that ship with your APIs. Write them yourself or with Ask AI. Describe the business flow, review it, run it — without hand-writing orchestration services.
Suggestions first, write later
You get workflow ideas on the analysis Workflows tab while the plan is forming. Creation and editing wait until the modules have DBML.
Kafka and endpoint kinds
Event-based Kafka flows coordinate modules asynchronously. Endpoint workflows become callable API sequences when you generate.
Versioned workflows
Edit YAML or diagrams, Ask AI beside a selected flow, and keep every revision so you can restore or compare.
Choose stack per module, then generate
Generate production-ready microservices from the design. Choose stack per module — .NET or Node.js, Service or CQRS, SQL Server, MySQL, or PostgreSQL in the same solution. Gateway, Identity, Swagger, and an admin portal ship as source code you own. Change the design later and regenerate safely: custom code stays.
Per-module stack
One module .NET + CQRS + PostgreSQL; another Node.js + Service + SQL Server. Mix what fits each bounded context.
Safe regeneration
Refresh the generated layer when schema or workflows change. Your custom code remains.
No vendor lock-in
Output is standard best-practice code you keep even if you leave QuickCode.
Push to GitHub and deploy on Cloud Run
Watch GitHub Actions and Cloud Run in QuickCode Studio as gateway, portal, and module APIs go live together. From describe to a running URL — generate, containerize, deploy, and observe.
End-to-end pipeline
Solution generation, migrations, Docker, registry, and Cloud Run — visible in Studio as the system comes up.
Gateway + modules together
APIs and portal go live as one deployable system, not a folder of sample code.
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.
- Events: Kafka-backed listener service coordinates async workflows across modules.
- Observability: Gateway health checks plus logs and metrics in Elasticsearch/Kibana.
- Delivery: Generated CI/CD (e.g. GitHub Actions) toward runtimes such as Google Cloud Run.