Simply drag and drop your PDF file. Supports documents up to 1GB in size.
Advanced vision models analyze each page, extracting text, images, and understanding layouts.
Receive clean, structured markdown with embedded diagrams and preserved formatting — or validated JSON matching your schema.
"I need the tenant's full name, unit number, monthly rent amount, security deposit, lease start and end dates, whether pets are allowed, the landlord name, and any special clauses or addendums."
unit_4B_lease.pdf
smith_renewal_2025.pdf
sublease_agreement.pdf
...any document type
{
"tenant_name": "John Smith",
"unit_number": "4B",
"monthly_rent": 2500,
"security_deposit": 2500,
"lease_start": "2024-01-01",
"lease_end": "2025-12-31",
"pets_allowed": false,
"landlord_name": "Greenfield Properties",
"special_clauses": ["No subletting",
"60-day renewal notice"]
}
✓ Schema validatedInvoice, contract, report
Extract structured data
Format for your systems
Data in your database
Result: Zero manual data entry — PDFs become structured database records automatically
Pillar articles on schema-validated extraction, MCP, and RAG—plus vertical workflows for invoices, leases, contracts, and call archives.
Why schema-at-generation beats post-hoc cleanup for invoices, leases, and regulated forms.
Connect schema-validated JSON to downstream systems with tenant-scoped MCP servers.