Products · Extract
Define a schema. Get structured JSON. Every time.
Submit any document with a field schema. ParseAI Extract returns consistent, typed JSON with confidence scores on every field. No templates, no regex, no retraining.
Request
POST /v1/extract
document: <file>
schema: {
"vendor_name": "string",
"invoice_date": "date",
"total_amount": "number",
"gst_number": "string"
}
Response
{
"vendor_name": {
"value": "Acme Corp Ltd",
"confidence": 0.98
}
"invoice_date": {
"value": "2026-04-12",
"confidence": 0.96
}
"total_amount": {
"value": 47295.00,
"confidence": 0.99
}
"gst_number": {
"value": "27AABCU...",
"confidence": 0.72
}
}
Schema in Plain English
Define field names like vendor_name, invoice_date. No regex, no templates.
Typed JSON Output
Every extraction returns the same structure. Plug directly into your database or application.
Confidence on Every Field
Know exactly which fields to trust and which need review. Built-in quality signal.
Approach
You define the fields. We find them.
Traditional OCR tools extract everything and leave you to parse the output. Extract works the other way: you tell ParseAI which fields you need, and the API returns only those fields, consistently structured every time. If you need vendor_name, invoice_date, and total_amount from every invoice, that is all you get back. No post-processing step, no field-matching logic, no brittle template rules.
Define your schema
Write a JSON object with field names as keys and types as values. Field names can be natural language: vendor_name, invoice_total, patient_date_of_birth. ParseAI understands what you are asking for. No code mapping required.
Submit the document
POST any document along with your schema. PDFs, scanned images, photos, multi-page documents: Extract handles the same formats as Parse. Batch processing is available for high-volume workflows.
Receive structured JSON
The response contains exactly the fields you defined. Each field includes a value and a confidence score between 0.0 and 1.0. High-confidence fields auto-process. Low-confidence fields are flagged for your pipeline to handle.
Quality signals
Know exactly what to trust.
Every field in Extract's response includes a confidence score. This is not a generic accuracy estimate. It is a per-field signal that tells your system exactly where human review or fallback logic is needed.
Score 0.90 and above
Auto-process
Field extracted cleanly. Use directly.
Score 0.70 to 0.89
Review recommended
Field found, some uncertainty. Check before use.
Score below 0.70
Route for human review
Field ambiguous or missing. Flag in your pipeline.
Why this matters in production
Most extraction systems return a single accuracy number for the entire document. Extract returns a confidence score for every individual field. This means your pipeline can auto-process invoices where all fields score above 0.9 and route only the exceptions for review, typically less than 10 percent of documents in a well-tuned workflow.
Applications
What teams integrate with Extract
Invoice and Accounts Payable
Define a schema with vendor name, invoice number, line items, tax amounts, and bank details. Extract returns the same structure for every invoice regardless of vendor format. Feed directly into ERP systems or accounting software without any transformation layer.
KYC and Identity Documents
Extract PAN number, Aadhaar number, full name, date of birth, and address from identity documents. Handles the variation across state-issued documents, laminated cards, and scanned photocopies with equal accuracy.
Lending and Loan Underwriting
Pull monthly income, employer name, account number, and closing balance from bank statements and salary slips. Extract handles the format differences across 50+ Indian banks in a single schema call.
Insurance Claim Processing
Extract policy number, claim amount, incident date, and hospital details from claim forms and medical bills. Confidence scores identify fields where the claim form was incomplete or illegible, routing those cases for adjuster review automatically.
FAQ
Common questions
How do I define a schema?
A schema is a JSON object where keys are field names and values are data types: string, number, date, or boolean. Field names can be descriptive natural language. ParseAI maps document content to your field names based on semantic understanding, not keyword matching.
What happens when a field is not found in the document?
If a field defined in your schema is not present in the document, Extract returns null for that field's value along with a confidence score of 0.0. Your pipeline can handle missing fields with explicit null checks rather than relying on error states.
Can I extract line items or repeated fields?
Yes. Specify array types in your schema for repeated fields. Extract will return all instances of line items, transactions, or any repeated structure in the document. Contact us for documentation on array schema syntax.
How does Extract handle multi-page documents?
Extract processes all pages of a document as a single unit. If a field spans multiple pages, such as a total on page 3 that references line items on pages 1 and 2, Extract understands the cross-page context and returns the correct value.
Built for
The extraction layer for your data stack
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