Google Cloud Document AI API
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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.
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.
Install Jentic One Beta
Connect the Cloud Document AI API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Cloud Document AI API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
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Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Cloud Document AI 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
List available processor types per location and create processors for invoices, receipts, contracts, or custom schemas
Evaluate a processor version against a labelled dataset and retrieve precision, recall, and F1 metrics
Use Cases
Patterns agents use Cloud Document AI 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
POST
/v1/{+name}:cancel
Cancel an in-flight long-running operation
Why 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.
FAQs
Specific to using Cloud Document AI 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.
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