Turn PDFs, invoices, contracts, receipts, and scanned forms into structured data with i10X AI agents—combining OCR, layout understanding, and workflow automation to reduce manual entry and speed up document processing.
i10X replaced our messy OCR-plus-spreadsheet stack and cut invoice processing from 12 hours a week to under two.
Hours saved weekly10+
Sarah Kline
Accounts Payable Manager
Tool-switching fatigue was burning $4k a month in labor until i10X automated document extraction into one seamless workflow.
Monthly labor cost cut$4,000
Marcus Hale
Operations Director
Onboarding packets once took 45 minutes across three tools; i10X extracts every field in seconds and slashed cycle time 80%.
Onboarding cycle-time drop80%
Priya Shah
Head of People Ops
Cosa può fare l'agente per Office & Productivity
Un Superagent, con sub-agenti specializzati per ogni attività.
You upload PDFs, scans, invoices, contracts, or forms; i10X prepares them for accurate AI processing.
2
Choose Extraction Goals
You select the data fields you need; i10X configures the Super Agent to match your document type.
3
Let i10X Extract
You start the run; i10X reads layouts, captures tables and key-values, then structures the results.
4
Review And Refine
You check outputs and corrections; i10X learns from feedback to improve future extractions.
A chi è rivolto
Pensato per le attività concrete che le persone svolgono davvero.
Accounts Payable Specialist
Attività gestite dall'agente
Extract vendor names, invoice numbers, dates, line items, taxes, and totals from PDFs or scans.
Match invoice fields against purchase orders, receipts, or approval rules.
Flag missing, duplicate, or low-confidence values for quick human review.
Export clean invoice data to CSV, ERP, or accounting workflows.
Risultato: Invoice work shifts from copy-paste marathons to exception handling, giving AP specialists cleaner queues and faster payment cycles.
Finance Operations Manager
Attività gestite dall'agente
Turn batches of invoices, receipts, and statements into standardized finance datasets.
Validate totals, payment terms, supplier IDs, and cost-center fields before posting.
Monitor extraction exceptions and recurring error patterns across document queues.
Prepare structured data for month-end reporting and audit trails.
Risultato: Month-end stops feeling like a document chase; finance leaders get cleaner data, fewer bottlenecks, and better control over throughput.
Contract Manager
Attività gestite dall'agente
Extract parties, effective dates, renewal terms, obligations, values, and termination clauses from contracts.
Tag contract metadata and classify documents by agreement type or risk category.
Compare extracted clauses against playbook rules or required fields.
Create searchable summaries for legal, procurement, and sales teams.
Risultato: Contract review starts with the facts already surfaced, so legal teams spend less time hunting clauses and more time managing risk.
HR Operations Coordinator
Attività gestite dall'agente
Capture employee details, tax fields, IDs, signatures, dates, and policy acknowledgements from onboarding forms.
Convert scanned HR documents into structured records for HRIS upload.
Check forms for missing fields, expired documents, or inconsistent employee information.
Organize employee files by document type, status, and completion stage.
Risultato: New-hire paperwork becomes a guided data flow, freeing HR coordinators to focus on people instead of form cleanup.
Insurance Claims Processor
Attività gestite dall'agente
Extract claimant details, policy numbers, incident dates, medical codes, receipts, and supporting evidence from claim packets.
Classify incoming claim documents by type, urgency, and required next action.
Validate key fields against policy rules and flag inconsistencies for adjusters.
Summarize claim files so reviewers can assess cases faster.
Risultato: Claim files arrive pre-sorted and pre-read, helping processors move faster while keeping suspicious or incomplete cases visible.
Logistics Documentation Coordinator
Attività gestite dall'agente
Extract shipment IDs, bill of lading details, customs fields, weights, SKUs, addresses, and delivery dates.
Normalize data from carrier forms, commercial invoices, packing lists, and proof-of-delivery scans.
Check documents for missing compliance fields or mismatched shipment information.
Send structured logistics data into TMS, ERP, or tracking spreadsheets.
Risultato: Shipment paperwork turns into usable data before delays pile up, giving logistics teams more control over compliance and delivery updates.
Superagent rispetto agli strumenti singoli
Funzionalità
Superagent
Strumenti singoli
Setup and workflow build
One agent can ingest documents, extract fields, validate outputs, and trigger follow-up workflow steps from a single configured flow.
Teams often configure a parser, an OCR/extraction tool, a workflow tool, and destination-system mappings separately.
Tools required to complete the process
Combines OCR-style extraction, AI reasoning, validation, routing, and downstream task automation in one platform.
Typically requires multiple products: OCR/document parser, data validation layer, automation platform, integration connector, and reporting tool.
Data handoff and schema consistency
Uses one shared workflow context and output schema across document intake, review, CRM/ERP updates, and notifications.
Field names, confidence scores, and validation rules must be mapped between tools, increasing reconciliation and maintenance work.
Exception handling and human review
Can route low-confidence fields, missing values, or policy exceptions to a human reviewer inside the same automation flow.
Exceptions are commonly handled through a separate queue, spreadsheet, ticketing tool, or manual email process.
Cost model and scaling
Platform pricing is consolidated around the agent/workflow rather than separate subscriptions for OCR, parsing, automation, and integration tools.
Costs can stack across per-page extraction fees, automation runs, connector subscriptions, and seats for review or admin tools.
Esempi di workflow
Prompt reali da copiare nell'agente qui sopra.
Free AI Document Extraction: Invoice-to-JSON Extraction Workflow
You are an AI document extraction agent. Extract structured data from the uploaded invoice document and return ONLY a valid JSON array. Do not include markdown, explanations, or extra text. If a field is missing, use null. If confidence is low, include the best value and a confidence score.
Task: Process the invoice PDF/image and extract invoice header data, vendor details, buyer details, line items, taxes, totals, payment terms, and due dates.
Required JSON array schema:
[
{
"document_type": "invoice",
"file_name": "<source file name>",
"extraction_confidence": 0.0,
"vendor": {
"name": null,
"address": null,
"tax_id": null,
"email": null,
"phone": null
},
"buyer": {
"name": null,
"address": null,
"tax_id": null,
"email": null,
"phone": null
},
"invoice": {
"invoice_number": null,
"purchase_order_number": null,
"invoice_date": null,
"due_date": null,
"currency": null,
"payment_terms": null
},
"line_items": [
{
"description": null,
"quantity": null,
"unit_price": null,
"discount": null,
"tax_rate": null,
"line_total": null
}
],
"totals": {
"subtotal": null,
"tax_total": null,
"shipping": null,
"discount_total": null,
"grand_total": null,
"amount_due": null
},
"validation_checks": {
"line_items_sum_matches_subtotal": null,
"subtotal_plus_tax_matches_total": null,
"missing_required_fields": []
}
}
]
Rules:
1. Normalize dates to YYYY-MM-DD when possible.
2. Normalize monetary values as numbers without currency symbols.
3. Preserve all invoice line items in order.
4. If multiple invoices are uploaded, return one object per invoice in the JSON array.
5. Return valid parseable JSON only.
Free AI Document Extraction: Contract Metadata and Clause Extraction Workflow
You are an AI contract document extraction agent. Analyze the uploaded contract or agreement and return ONLY a valid JSON array containing structured contract metadata, parties, important dates, financial terms, obligations, and key clauses. Do not include markdown or commentary. If information is not present, use null. If multiple contracts are provided, return one JSON object per contract.
Required JSON array schema:
[
{
"document_type": "contract",
"file_name": "<source file name>",
"extraction_confidence": 0.0,
"contract_metadata": {
"contract_title": null,
"contract_type": null,
"effective_date": null,
"execution_date": null,
"expiration_date": null,
"renewal_terms": null,
"governing_law": null,
"jurisdiction": null
},
"parties": [
{
"party_name": null,
"party_role": null,
"address": null,
"signatory_name": null,
"signatory_title": null
}
],
"financial_terms": {
"contract_value": null,
"currency": null,
"payment_terms": null,
"late_payment_terms": null,
"fees_or_penalties": null
},
"key_clauses": {
"termination": null,
"confidentiality": null,
"indemnification": null,
"limitation_of_liability": null,
"intellectual_property": null,
"data_protection": null,
"non_compete_or_non_solicit": null,
"dispute_resolution": null,
"force_majeure": null
},
"obligations": [
{
"responsible_party": null,
"obligation_summary": null,
"deadline_or_frequency": null,
"source_section": null
}
],
"risk_flags": [
{
"risk_type": null,
"risk_summary": null,
"severity": "low|medium|high|null",
"source_text": null
}
]
}
]
Extraction rules:
1. Extract exact clause summaries without inventing terms.
2. Include source section names or headings when available.
3. Normalize dates to YYYY-MM-DD where possible.
4. Preserve party names exactly as written.
5. Return valid JSON only.
[
{
"document_type": "contract",
"file_name": "service_agreement.pdf",
"extraction_confidence": 0.91,
"contract_metadata": {
"contract_title": "Master Services Agreement",
"contract_type": "Services Agreement",
"effective_date": "2026-01-01",
"execution_date": "2025-12-18",
"expiration_date": "2026-12-31",
"renewal_terms": "Automatically renews for successive one-year terms unless either party gives 60 days written notice.",
"governing_law": "New York",
"jurisdiction": "State and federal courts located in New York County, New York"
},
"parties": [
{
"party_name": "Acme Operations Inc.",
"party_role": "Client",
"address": "410 Industrial Parkway, Dallas, TX 75201",
"signatory_name": "Jordan Lee",
"signatory_title": "Chief Operating Officer"
},
{
"party_name": "Brightline Consulting LLC",
"party_role": "Service Provider",
"address": "88 Madison Avenue, New York, NY 10016",
"signatory_name": "Priya Shah",
"signatory_title": "Managing Partner"
}
],
"financial_terms": {
"contract_value": 120000,
"currency": "USD",
"payment_terms": "Invoices payable within 30 days of receipt.",
"late_payment_terms": "1.5% per month on overdue balances.",
"fees_or_penalties": null
},
"key_clauses": {
"termination": "Either party may terminate for material breach after 30 days notice and failure to cure.",
"confidentiality": "Parties must protect confidential information for 5 years after termination.",
"indemnification": "Provider indemnifies Client for third-party claims arising from Provider negligence or willful misconduct.",
"limitation_of_liability": "Liability capped at fees paid in the previous 12 months, excluding confidentiality and indemnity obligations.",
"intellectual_property": "Client owns deliverables upon full payment; Provider retains pre-existing materials.",
"data_protection": "Provider must maintain reasonable administrative, technical, and physical safeguards.",
"non_compete_or_non_solicit": null,
"dispute_resolution": "Good-faith negotiation followed by binding arbitration.",
"force_majeure": "Performance excused for events beyond reasonable control."
},
"obligations": [
{
"responsible_party": "Service Provider",
"obligation_summary": "Submit monthly status reports by the fifth business day of each month.",
"deadline_or_frequency": "Monthly",
"source_section": "Section 3.2 Reporting"
}
],
"risk_flags": [
{
"risk_type": "Liability cap exception",
"risk_summary": "Confidentiality and indemnity obligations are excluded from the liability cap.",
"severity": "medium",
"source_text": "excluding confidentiality and indemnity obligations"
}
]
}
]
Free AI Document Extraction: Multi-Document OCR Cleanup and Structured JSON Export Workflow
You are an AI document extraction and OCR cleanup agent. Process the uploaded scanned forms, receipts, reports, PDFs, or images and convert them into a clean structured JSON array. Return ONLY valid JSON. Do not include markdown, notes, or explanations.
Goal: Identify each document type automatically, extract text blocks, tables, key-value pairs, and any detected handwritten or low-confidence fields. Use a flexible schema suitable for mixed document batches.
Required JSON array schema:
[
{
"file_name": "<source file name>",
"document_type_detected": null,
"language_detected": null,
"page_count": null,
"overall_confidence": 0.0,
"pages": [
{
"page_number": 1,
"ocr_text_cleaned": null,
"key_value_pairs": [
{
"key": null,
"value": null,
"confidence": 0.0
}
],
"tables": [
{
"table_title": null,
"columns": [],
"rows": [
{}
],
"confidence": 0.0
}
],
"checkboxes_or_selection_fields": [
{
"label": null,
"selected": null,
"confidence": 0.0
}
],
"handwritten_fields": [
{
"field_label": null,
"transcribed_value": null,
"confidence": 0.0
}
],
"low_confidence_items": [
{
"field_or_text": null,
"reason": null,
"confidence": 0.0
}
]
}
],
"normalized_output": {
"dates": [],
"amounts": [],
"names": [],
"addresses": [],
"emails": [],
"phone_numbers": [],
"identification_numbers": []
},
"recommended_human_review": false
}
]
Rules:
1. Keep the original meaning but clean OCR errors where obvious.
2. Maintain table row and column structure as accurately as possible.
3. Use null for missing values and empty arrays when no items are found.
4. Set recommended_human_review to true if any critical field has confidence below 0.75.
5. If the batch contains multiple files, return one object per file in the JSON array.
6. Return valid JSON only.