Google Cloud
AI & ML
intermediate
Document AI Pipeline — Google Cloud Architecture Template
Uploaded documents trigger a pipeline that runs OCR, entity extraction, and translation, storing structured results in Firestore.
Why this architecture works
- A storage-triggered function makes the pipeline fully event-driven with no polling.
- Chaining Vision OCR, Natural Language, and Translation composes managed APIs instead of training custom models.
- Structured extraction results in Firestore are instantly queryable by downstream apps.
- Pub/Sub document events let auditing and analytics subscribe without touching the pipeline.
- Per-stage logging pinpoints exactly which document failed at which step.
What's inside (9 resources)
Inbox Bucket
gcp-cloud-storage
Pipeline Trigger Fn
gcp-cloud-functions
Doc Events
gcp-pub-sub
Vision OCR
gcp-vision-ai
Entity Extraction
gcp-natural-language
Translation
gcp-translation
Extracted Data
gcp-firestore
Cloud Monitoring
gcp-monitoring
Pipeline Logs
gcp-logging
From template to running infrastructure
- Open this template in the CloudForge visual designer (free account, no credit card).
- Customize resources, names, and connections on the drag-and-drop canvas — or ask Vani, the AI architect, to adapt it.
- Generate production-ready Terraform or Pulumi in one click.
- Review the plan diff and security scan, then deploy with human approval.