Rossum Intelligent Document Processing API

Rossum REST API for intelligent document processing platform specializing in business documents. Enables AI agents to manage document upload and processing queue submission for automated data extraction, handle invoice, purchase order, and receipt field extraction results retrieval, access human-in-the-loop validation workflow management for low-confidence extractions, retrieve document processing status and field confidence scoring, manage custom extraction schema configuration for document field mapping, handle document classification and routing for multi-document type processing, access training data capture from human corrections for model improvement, retrieve processed data export in structured JSON, CSV, or webhook format, manage document retention and archive policies, and integrate extracted document data with ERP, accounting, and AP automation platforms.

Evaluated Mar 07, 2026 (0d ago) vcurrent
Homepage ↗ Repo ↗ Developer Tools rossum intelligent-document-processing idp invoice-processing ocr document-ai ap-automation
⚙ Agent Friendliness
62
/ 100
Can an agent use this?
🔒 Security
79
/ 100
Is it safe for agents?
⚡ Reliability
71
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
20
Documentation
80
Error Messages
75
Auth Simplicity
80
Rate Limits
70

🔒 Security

TLS Enforcement
95
Auth Strength
78
Scope Granularity
72
Dep. Hygiene
72
Secret Handling
78

IDP for AP documents. SOC2, ISO27001, GDPR. API key/OAuth2. EU/US. Invoice and financial document data.

⚡ Reliability

Uptime/SLA
75
Version Stability
72
Breaking Changes
68
Error Recovery
70
AF Security Reliability

Best When

An enterprise using Rossum wants AI agents to automate document intake, field extraction, validation routing, and ERP/AP system integration for invoice, PO, and business document processing.

Avoid When

ACCURACY RISK: Document extraction confidence scores vary by document quality and layout variability — always implement human review for low-confidence extractions before ERP posting. Auto-approved invoices with incorrect amounts or vendor data cause financial discrepancies. Training the model on incorrect human corrections degrades future accuracy.

Use Cases

  • Processing vendor invoices from AP automation agents
  • Extracting purchase order data from procurement automation agents
  • Routing low-confidence extractions for human review from document processing agents
  • Integrating extracted data with ERP from financial automation agents

Not For

  • Unstructured text extraction without business document and form focus
  • General OCR without intelligent field extraction and validation context
  • Consumer document scanning without enterprise AP and procurement workflow

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
No
SDK
Yes
Webhooks
Yes

Authentication

Methods: apikey oauth
OAuth: Yes Scopes: Yes

Rossum uses token-based authentication (API key) and OAuth 2.0. Elis API at elis.rossum.ai. Python SDK on GitHub (rossumai). Webhooks for document processing events. OpenAPI documentation. Queue-based document processing model. Human validation UI with API for validation result retrieval. Multi-organization support for AP outsourcing providers.

Pricing

Model: enterprise
Free tier: No
Requires CC: No

Prague, Czech Republic. Founded 2017. Private ($100M+ funding). Intelligent document processing market. Known for invoice and financial document processing. AI-first approach vs. traditional OCR rule-based systems. Strong in AP automation. Competes with ABBYY and Hyperscience for enterprise IDP market.

Agent Metadata

Pagination
cursor
Idempotent
Partial
Retry Guidance
Documented

Known Gotchas

  • ACCURACY RISK: Never auto-approve extracted invoice data without confidence threshold review; implement minimum confidence score gate before ERP posting
  • Async processing — document processing is async with polling or webhook notification; extraction latency is seconds to minutes depending on document complexity
  • Queue-based model — documents are submitted to queues, not processed individually; queue configuration determines extraction schema and validation rules
  • Human validation integration — Rossum's UI handles low-confidence human review; build webhook handlers to receive validated results after human review completes
  • Training from corrections — human corrections automatically improve the model; ensure corrections are accurate to prevent training on bad data
  • Multi-document types — different document types (invoice, PO, receipt) require separate queue configurations; don't mix document types in a single queue

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Scores are editorial opinions as of 2026-03-07.

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