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AI application templateHigh confidence

AI app architecture template

Start from a production-aware AI app brief for chatbots, document assistants, knowledge search, and RAG products. Generate provider-specific architecture, cost ranges, Terraform, CLI commands, diagrams, and deployment workflow guidance.

Generates forAWSAzureGCP
Generate this architectureView sample output
Sample architecture diagram

Service flow generated from this template

Public preview
1

Upload and ingest

Store documents, validate file types, and queue extraction jobs.

2

Process embeddings

Chunk content, generate embeddings, and track usage per tenant.

3

Retrieve context

Search a vector index with tenant filters and relevance controls.

4

Generate response

Call the LLM layer, stream output, and record conversation history.

What this template generates

RAG architecture summary
Object storage and ingestion queue
Vector database and retrieval layer
LLM inference service selection
Cost estimate by main service
Terraform starter modules
CLI provisioning commands
GitHub Actions deployment workflow
Terraform preview
module "ai_app" {
  source      = "./modules/ai-app"
  provider    = var.cloud_provider
  region      = var.primary_region
  environment = "production"
}

module "vector_search" {
  source    = "./modules/vector-search"
  tenant_id = var.tenant_id
}
CLI preview
clouddesign generate --template ai-app --provider aws
terraform init
terraform plan -var-file="production.tfvars"
Production review

Risks this template calls out

1

Embedding costs can grow quickly without content deduplication and upload limits.

2

Tenant isolation must be enforced on every vector query to prevent cross-customer data leakage.

3

Large files need async processing and retry queues so extraction does not block the user request.