QVeris
Run a task

AI Document Analysis Agent with OCR and File Intelligence

Give your AI agent the ability to read, parse, extract, and reason over real-world documents through one unified capability routing layer.

Browse Document Analysis Tools →Read the MCP Server Docs ↗
document_analysis.preview
📄Scanned PDFOCR required
🧾Invoicefield extraction
📊Report with tablestable parsing
📝Contractclause detection
📋Formfield mapping
🖼ScreenshotOCR + classify
AI document analysis workflow for OCR, extraction, validation, and structured output

Why Document Agents Need More Than an LLM

Large language models can reason over text, but real-world document analysis involves scanned PDFs, invoices, contracts, reports, receipts, forms, tables, screenshots, and mixed-format files. An LLM alone cannot perform OCR on a scanned image, extract structured fields from a table-heavy PDF, or validate whether a document is missing required information.

QVeris connects your AI agent to real document intelligence capabilities — OCR, PDF parsing, table extraction, document classification, field extraction, and file analysis — through one unified capability routing layer, powered by the Model Context Protocol (MCP). The agent discovers the right tool, inspects its schema and cost before calling, and receives structured, review-ready output.

scanned PDFsinvoicescontractsreportsreceiptsformstablesscreenshotsmixed-format files

What QVeris Enables for Document Analysis Agents

Three capabilities every document intelligence agent needs to handle real-world files at scale.

🔍

Discover the Right Document Capability

Not every document is the same. QVeris lets the agent discover the most relevant OCR, parsing, or extraction capability for the specific file type, structure, and task — instead of hardcoding one tool.

📋

Inspect Schema Before Execution

Before calling a capability, the agent inspects required inputs, supported file types, output fields, cost signals, and provider metadata. No blind calls to unknown document tools.

⚡

Call and Return Structured Intelligence

The agent calls the selected capability and receives structured output — extracted fields, table data, classification labels, risk notes, and missing-field flags — ready for downstream review or automation.

Document input
→
QVeris Discover
→
Inspect schema
→
Call capability
→
Structured intelligence

Example Workflow: Invoice Analysis from Start to Finish

How an AI agent uses QVeris to process a real-world document through seven structured steps.

Example Agent Prompt
"Analyze this vendor invoice, extract the key fields, check for missing information, and summarize any payment risks."

Identify the document type

The agent inspects the file and determines it is a vendor invoice in PDF format requiring OCR and field extraction.

Discover relevant OCR or document parsing capabilities

The agent queries QVeris for capabilities matching the document type, file format, and extraction goals.

Inspect schema, inputs, outputs, latency, and cost

Before calling, the agent inspects required parameters, supported file types, expected output fields, and billing signals.

Call the selected capability

The agent executes the OCR and field extraction capability with the inspected parameters.

Extract structured fields

The agent receives structured output containing vendor name, invoice number, dates, amounts, line items, and payment terms.

Reason over missing fields and risks

The agent checks the extracted fields, identifies missing or low-confidence items, and flags potential payment risks.

Generate a review-ready brief

The agent compiles a structured brief with all extracted fields, risk notes, missing-field flags, and suggested actions for human review.

Example Structured Output

Illustrative output from a document analysis agent. Not extracted from a real private document or invoice.

invoice_analysis_output.json
{ "document_type": "vendor_invoice", "file_format": "scanned_pdf", "capabilities_used": ["ocr_processing", "field_extraction", "document_classification"], "extracted_fields": { "vendor": "Example Vendor Ltd.", "invoice_number": "INV-EXAMPLE-001", "invoice_date": "YYYY-MM-DD", "due_date": "YYYY-MM-DD", "total_amount": "Example amount", "currency": "Example currency", "payment_terms": "Net 30 — illustrative" }, "missing_fields": ["PO number not detected", "Tax ID field unclear"], "risk_notes": [ "Due date is within 7 days — prioritize review.", "Payment terms field is low confidence — verify manually." ], "suggested_action": "Review missing fields, confirm payment terms with vendor, and approve or escalate.", "review_required": true }

This is an illustrative example. It does not represent real private documents, invoices, or customer data. All extracted outputs should be reviewed and verified by qualified humans before use in financial or legal workflows.

Common Use Cases for Document Analysis Agents

Six real-world document intelligence workflows powered by QVeris capabilities.

🧾

Invoice Processing

Extract vendor, amounts, dates, line items, and payment terms from invoices. Flag missing fields and payment risks for review.

📝

Contract Review

Identify parties, effective dates, obligations, renewal terms, and risk clauses from contract documents through structured extraction.

📊

Financial Report Analysis

Parse tables, extract key metrics, and structure financial data from reports, filings, and statements for downstream analysis.

📄

Receipt and Expense Extraction

Process receipts, extract merchant, date, amount, and category, and route structured output to expense management workflows.

📋

Form Processing

Map form fields to structured data, handle checkboxes, signatures, and handwritten inputs, and export for database insertion.

🛡

Compliance Document Review

Classify documents, extract compliance-relevant fields, and flag missing or inconsistent information for auditor review.

Why Use QVeris for Document Intelligence Agents

RequirementHardcoded document APIsQVeris capability routing
Tool discoveryDevelopers manually search and wire each OCR or parsing provider✓Agent discovers relevant document capabilities based on the file type and task
Schema inspectionDeveloper reads and maintains provider-specific docs✓Agent inspects inputs, outputs, cost signals, and supported formats before calling
Multi-format supportEach file type may require a separate integration✓Agent can route different document types to different capabilities through one layer
Structured outputOften raw text or provider-specific JSON✓Structured output with fields, risks, missing flags, and suggested actions
Usage visibilitySpread across multiple provider dashboards✓Usage history and credits ledger in one place

Example Agent Prompt

How a developer might instruct an AI agent to use QVeris for document analysis.

Agent Instruction
"You are a document analysis agent with access to QVeris capabilities. When you receive a document: 1. Identify the file type and classification. 2. Use QVeris to discover the best OCR, parsing, or extraction capability. 3. Inspect the capability schema before calling. 4. Call the capability and extract structured fields. 5. Check for missing or low-confidence fields. 6. Flag risks and suggest next steps. 7. Return a review-ready structured brief. Never present extracted data as verified truth without human review."

Who This Is For

🤖

AI Agent Builders

Developers building intelligent document processing agents that need OCR, parsing, and extraction capabilities beyond what an LLM provides.

🏢

Operations and Finance Teams

Teams processing invoices, receipts, contracts, and reports who need structured, review-ready output without manual data entry.

⚖

Legal and Compliance Teams

Professionals reviewing contracts, regulatory filings, and compliance documents who need structured extraction with audit trails.

🧩

Platform and Workflow Builders

Teams embedding document intelligence into larger automation pipelines, dashboards, or enterprise workflows through a unified API layer.

Continue Exploring QVeris

Frequently Asked Questions

What is an AI document analysis agent?
An AI document analysis agent is an agent workflow that uses external tools and structured capabilities — such as OCR, PDF parsing, table extraction, and document classification — to analyze real-world documents and return structured, review-ready output.
How does QVeris help with document analysis?
QVeris acts as a capability routing layer. Instead of hardcoding every OCR or parsing provider, the agent discovers the right capability through QVeris, inspects its schema and cost before calling, and receives structured output for downstream use.
What file types can a document analysis agent handle?
Through QVeris, agents can discover capabilities for scanned PDFs, images, screenshots, invoices, contracts, reports, receipts, forms, tables, and mixed-format files — routing each to the most appropriate capability.
Is QVeris a standalone OCR tool?
No. QVeris is a capability routing network that connects AI agents to verified external tools, APIs, and services — including OCR and document intelligence capabilities from third-party providers.
Can document analysis outputs be used without human review?
No. All extracted outputs should be reviewed and verified by qualified humans before being used for financial, legal, compliance, or other high-stakes decisions.
Does the agent inspect tools before calling them?
Yes. The QVeris workflow enables agents to inspect schemas, required parameters, supported file types, cost signals, and provider metadata before executing any capability call.

Start Building Your Document Analysis Agent

Use QVeris to give your AI agent access to OCR, document parsing, table extraction, and file intelligence capabilities — all through one unified layer.

Try Document Analysis in QVeris →Read the REST API Docs ↗