Cloud Document AI API

Name: Cloud Document AI API API
Brand: Cloud Document AI API
Availability: InStock

✓ Official Vendor SpecAI/MLVisionoauth220 EndpointsREST

For Agents

Extract structured fields, tables, and entities from PDFs, scans, and images using Google's pretrained or custom Document AI processors with optional human review.

Quickstart

Get started with Cloud Document AI API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"extract structured data from a document"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Cloud Document AI API API.

Process a single document synchronously through a Document AI processor and receive extracted entities, tables, and form fields

Submit batch document processing jobs across Cloud Storage inputs and poll long-running operations until completion

Deploy, undeploy, enable, or disable processor versions to control which model serves production traffic

Trigger human review on low-confidence extractions via reviewDocument and retrieve the reviewer-corrected output

GET STARTED

Start building with Cloud Document AI API API

Explore with Jentic

View OpenAPI Document

Use for: I need to extract line items and totals from a vendor invoice PDF, Parse fields from a scanned identity document, Submit a batch of 10,000 receipts for processing and wait for completion, Deploy a new processor version so it starts serving requests

Not supported: Does not handle document storage, document signing, or generating new documents — use for extracting structured fields from existing documents only.

Google Cloud Document AI parses structured information from unstructured and semi-structured documents using Google's pretrained processors for forms, invoices, receipts, contracts, and identity documents. The API supports synchronous processing for single documents and batch processing for large volumes, with optional human-in-the-loop review for low-confidence extractions. Custom processors can be deployed, undeployed, evaluated, and trained against project-specific document types. Operations are scoped under projects, locations, and processors with full lifecycle management for processor versions.

Use Cases

Patterns agents use Cloud Document AI API API for, with concrete tasks.

★ Invoice Data Extraction at Scale

Accounts payable teams use Cloud Document AI to extract supplier name, invoice number, line items, tax, and totals from incoming PDF and image invoices, replacing manual keying. The Invoice processor handles multi-page documents, multi-currency totals, and tabular line-item parsing in one synchronous call, with batchProcess for nightly runs over Cloud Storage. Typical end-to-end automation, including downstream ERP posting, takes a few weeks rather than building OCR pipelines from scratch.

Call POST /v1/{processor}:process on the Invoice processor with a base64-encoded PDF and return the extracted total amount and line items as JSON.

ID and Form Verification for Onboarding

Fintech and marketplace onboarding flows route uploaded identity documents and signed forms through Document AI to extract names, dates of birth, document numbers, and signature blocks. Low-confidence extractions are routed to human reviewers via reviewDocument, with the corrected output returned for downstream KYC. This replaces brittle template-based OCR and reduces manual review queues by routing only ambiguous fields to humans.

Process a passport image with the ID processor, and if confidence on the document number is below 0.9, submit a reviewDocument request and poll until the human-corrected output is returned.

Custom Processor Training for Domain Documents

Teams with proprietary document types (loan applications, medical claims, leases) train custom Document AI processors on labelled examples and evaluate accuracy against held-out sets. The API supports creating processor versions, evaluating them with metrics endpoints, and deploying the best-performing version to production. Initial training and evaluation can be wired into an MLOps pipeline in a few days.

Train a new processor version against the labelled dataset in gs://my-bucket/labels, evaluate it, and if the F1 exceeds 0.92, call deploy on the new version.

Agent-Driven Document Workflows via Jentic

AI agents that need to reason over uploaded documents call Cloud Document AI through Jentic to extract structured data before answering questions or taking actions. The agent searches for the right processor operation by intent, loads the request schema, and submits the document, all without browsing Google Cloud documentation or hard-coding endpoint paths.

Search Jentic for 'extract fields from a document', load the documentai process operation schema, and execute it against an uploaded contract PDF.

Key Endpoints

20 endpoints — google cloud document ai parses structured information from unstructured and semi-structured documents using google's pretrained processors for forms, invoices, receipts, contracts, and identity documents.

METHOD

PATH

DESCRIPTION

POST

/v1/{+name}:batchProcess

Submit batch document processing across Cloud Storage inputs

POST

/v1/{+name}:deploy

Deploy a processor version to serve production traffic

POST

/v1/{+name}:disable

Disable a processor without deleting it

POST

/v1/{+humanReviewConfig}:reviewDocument

Send a document for human review

GET

/v1/{+name}

Get a processor, processor version, or operation by name

POST

/v1/{+name}:cancel

Cancel an in-flight long-running operation

POST

/v1/{+name}:batchProcess

Submit batch document processing across Cloud Storage inputs

POST

/v1/{+name}:deploy

Deploy a processor version to serve production traffic

POST

/v1/{+name}:disable

Disable a processor without deleting it

POST

/v1/{+humanReviewConfig}:reviewDocument

Send a document for human review

GET

/v1/{+name}

Get a processor, processor version, or operation by name

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Google Cloud service account JSON keys are stored encrypted in the Jentic vault. Agents receive scoped, short-lived OAuth access tokens with the cloud-platform scope; the raw service account key never enters the agent context.

Intent-based discovery

Agents search by intent (e.g. 'extract fields from an invoice') and Jentic returns matching Document AI operations with their input schemas, so the agent calls the right processor without browsing the discovery document.

Time to first call

Direct Document AI integration: 3-7 days for OAuth, processor selection, batch operation polling, and error handling. Through Jentic: under 1 hour for synchronous extraction; batch flows the same day.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Cloud Storage API

Stores input documents and batch output JSON for Document AI

Use alongside Document AI when source documents live in GCS or batch outputs need to land there

Complementary

Cloud KMS API

Manages encryption keys for documents at rest

Use when documents contain PII or PHI and need customer-managed encryption keys

Alternative

Vertex AI API

General-purpose ML platform for custom vision and text models

Choose Vertex AI when the document type does not match a Document AI processor and a fully custom model pipeline is needed

FAQs

Specific to using Cloud Document AI API API through Jentic.

What authentication does the Cloud Document AI API use?

Document AI uses Google OAuth 2.0 with the cloud-platform scope. Tokens are obtained from a service account or end-user OAuth flow and passed in the Authorization header as a Bearer token. Through Jentic, the service account JSON is stored encrypted in the credential vault and never enters the agent context.

Can I run a custom document parser on the Cloud Document AI API?

Yes. Create a processor of a custom type, train a processor version against labelled examples, and call deploy on the version that wins evaluation. The same /v1/{+name}:process endpoint serves custom and pretrained processors.

What are the rate limits for the Cloud Document AI API?

Default quotas are 600 process requests per minute per processor and a per-project page-processing quota that varies by region. Quotas can be increased from the Cloud Console quotas page. Batch operations consume the same page-level quota over the course of the job.

How do I batch-process invoices with Cloud Document AI through Jentic?

Search Jentic for 'batch process documents', load the schema for POST /v1/{+name}:batchProcess, and execute it with a Cloud Storage input prefix and output bucket. Jentic returns the long-running operation name; poll the operations.get endpoint for completion.

Is the Cloud Document AI API free?

No. Pricing is per-page and varies by processor type. The first 1,000 pages per month for some processors are free. See https://cloud.google.com/document-ai/pricing for current rates.

How do I trigger human review for a low-confidence document?

Call POST /v1/{+humanReviewConfig}:reviewDocument with the inline document or Cloud Storage URI. The endpoint returns a long-running operation; when it resolves, the human-corrected document JSON is available at the operation response.