Structured Data Extraction
From Any Document
Define the fields you need once — no code, no templates — and Parsli's AI turns PDFs, scans, images, and emails into clean, typed, schema-validated data. JSON for your systems, rows for your spreadsheets, bills for your books.
No credit card required · 10 free pages to start
What is structured data extraction?
Structured data extraction is the process of converting unstructured documents — PDFs, scans, photos, emails — into structured data: named, typed fields and rows that software can use. An invoice PDF is unstructured; "vendor: Acme Corp, invoice_number: INV-2041, total: 1362.50, line_items: [...]" is structured. The document doesn't change; what changes is that its contents become addressable — you can query them, validate them, sync them, and build on them.
Most business data still arrives in unstructured form, locked inside documents that only humans could read. Structured data extraction is the unlock: instead of a person reading each document and re-typing its contents into a system, an AI engine reads it and emits the structured record directly. Everything else on this platform — routing, table extraction, matching — builds on this step.
Define your schema — no code, no templates
The schema is your contract with the engine: the exact fields you want back, with names, types, and plain-English instructions. Parsli's visual schema builder lets anyone define one in minutes — no code, and crucially, no templates. You never draw zones on a sample document or train on labeled examples; you describe the data, and the AI finds it on any layout.
Fifteen field types cover real documents: text, numbers, dates, currencies, booleans, lists, nested objects, and full tables for line items. Each field takes a natural-language instruction ("the total after tax, not the subtotal") that steers the extraction. One schema handles every vendor's layout — including formats you've never seen — because the engine reads meaning, not positions. When your needs change, edit the schema and the next document follows it.
How the engine reads documents — grounded, or it abstains
Parsli's extraction engine reads the full page the way a person does — text, layout, tables, handwriting — and maps what it finds onto your schema. Built-in AI OCR means scans, photos, and faxes go through the same path as native PDFs, and handwritten documents are read by the same engine.
The engine is built on a strict rule: extracted values must be grounded in the document. If a field genuinely isn't on the page, the honest answer is empty — not a plausible-looking guess. Alongside that, every field carries a confidence score, so your workflow can auto-accept what the engine is sure of and route what it doubts to human review. Structured output you can't trust is just noise with a schema; grounding and confidence are what make the output safe to build on.
From unstructured documents to clean destinations
Extraction ends where your systems begin. Every extracted record is schema-validated, typed, and delivered wherever the data needs to live: appended as rows to Google Sheets, downloaded as Excel/CSV, posted as bills with line items to QuickBooks, Xero, or ZohoBooks, fanned out through Zapier, Make, or Power Automate, synced to Airtable, or pushed as JSON to your own endpoint via webhooks. One extraction, any number of destinations — browse them all at /integrations.
For developers: structured extraction as an API
Everything the no-code builder does is available as a REST API: POST a document with a schema ID, get typed JSON back — fields, tables as arrays, confidence scores, and metadata. Synchronous responses for interactive flows, webhook callbacks for batch pipelines. Auth is a bearer token; endpoints and examples live in the docs.
Where structured extraction fits
Structured extraction is the foundation layer of the Parsli platform — the other capabilities compose on top of it:
- Document routing decides which schema each incoming document should be extracted against.
- Table extraction is structured extraction applied to repeating rows — line items, transactions, order tables.
- Data matching compares structured records against your POs, ledgers, and master data.
- Vertical workflows package it end to end: accounts payable, bookkeeping practices, and logistics.
- Document-type pages show the schemas in action: invoices, bank statements, PDFs, and more at /document-types.
Code Example
Any document → typed JSON
const response = await fetch('https://api.parsli.co/v1/extract', {
method: 'POST',
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
'Content-Type': 'application/json'
},
body: JSON.stringify({
parser_id: 'your_parser_id',
file_url: 'https://example.com/document.pdf'
})
});
const { data } = await response.json();
// data.fields — typed, schema-validated, grounded in the document:
// {
// vendor: "Acme Corp",
// invoice_number: "INV-2041",
// issue_date: "2026-07-14",
// total: 1362.50,
// line_items: [ ... ] // tables come back as JSON arrays
// }No credit card required · 10 free pages to start
FAQ
Frequently asked questions
What is structured data extraction?
It's converting unstructured documents — PDFs, scans, images, emails — into structured data: named, typed fields and table rows that software can query, validate, and sync. Instead of a person reading a document and re-typing its contents, an AI engine reads it and emits the structured record directly.
What's the difference between structured and unstructured data?
Structured data has a defined shape — fields with names and types, rows with columns — so software can process it directly (a database record, a JSON object, a spreadsheet row). Unstructured data has no machine-readable shape: a scanned invoice, a photographed receipt, an email. Extraction is the bridge from one to the other.
How is this different from OCR?
OCR converts an image of text into characters — output is still unstructured text. Structured data extraction goes further: it understands what the text means and maps it into your schema's fields and tables. Parsli includes AI-powered OCR as the reading step, then does the structuring on top — see /ocr-software for the OCR layer itself.
Do I need templates or training data?
No. You define a schema — field names, types, and plain-English instructions — and the AI locates those fields on any layout semantically. No zone drawing, no labeled samples, no per-vendor setup, and nothing breaks when a sender redesigns their document.
How does Parsli avoid making data up?
The engine is built grounded-or-abstain: extracted values must come from the document, and a field the engine can't find comes back empty rather than guessed. Every field also carries a confidence score, so low-confidence values can be routed to human review instead of flowing into your systems.
What document formats can I extract from?
PDFs (native and scanned), images (JPEG, PNG, TIFF), Word and Excel files, and emails with attachments. Scans, photos, faxes, and handwritten documents run through the same AI engine as clean digital files.
What output formats do I get?
Typed JSON via the API and webhooks, rows in Google Sheets or Excel/CSV, bills with line items in QuickBooks, Xero, or ZohoBooks, and records in Airtable or any app reachable through Zapier, Make, or Power Automate.
Is there a free tier?
Yes — 10 free pages on signup, no credit card required. Define a schema, extract a few real documents, and check the output against the source before deciding anything.
Explore
The rest of the platform
One pipeline from intake to delivery — each capability builds on the others.
AI Document Routing: Classify, Split, and Send Every Document to the Right Workflow
AI document routing that classifies, splits, and sends every document to the right parser and destination — email intake, multi-invoice PDF splitting, no template training. Start free.
Learn moreMatch extracted document data against your records — automatically
Extract document data, validate it against your records, review exceptions, and sync clean results to QuickBooks, Xero, or Sheets. How 2-way and 3-way matching works — and how to automate it.
Learn moreTable & Line Item Extraction Software
Extract every row from every table — invoice line items, multi-page tables, borderless layouts, even handwritten tables — straight to Excel, Google Sheets, QuickBooks, or JSON via API. Start free.
Learn moreRelated Resources
Works with your stack
Route extracted data straight into the tools your team already uses.
Google Sheets
Paste one IMPORTDATA formula into a Google Sheet and every document Parsli parses — PDFs, invoices, bank statements, emails — appears as a new row. No Zapier, no middleware, no manual export.
QuickBooks Online
The native QuickBooks Online integration AP teams use as a QuickBooks receipt scanner and bill-posting engine. AI reads invoices, vendor bills, and card receipts, then creates QuickBooks Bills, Expenses, or Invoices with the source PDF attached. No Zapier middleware, no per-vendor templates. Connect over Intuit's official OAuth in under 60 seconds.
Xero
Stop manually entering invoices into Xero. Parsli extracts vendor, amount, date, and line items from any invoice format and pushes the data to Xero via Zapier or Make — automatically.
Zapier
Use Parsli to extract structured data from any email or document, then use Zapier to route the data to any of 5,000+ apps — CRMs, databases, project tools. Parsli replaces Zapier's built-in Email Parser with real AI; Zapier handles the routing.
Webhooks
Full webhook support for both sending documents to Parsli and receiving extracted data. Connect to any HTTP endpoint with standard authentication options.
REST API
A developer-friendly REST API for extracting structured data from documents. Send files, receive typed JSON. Standard HTTP conventions with Bearer token authentication.
Documents we parse
Document types this capability handles out of the box.
Stop re-typing documents into systems
Define your schema in minutes and turn your first documents into structured data today. 10 free pages on signup — no credit card required.
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