Synthetic test data
Reference moduleCynsta turns sensitive PDFs into realistic synthetic document sets for OCR, extraction, QA, demos, and vendor testing, with validation records showing original private values were removed before generation.
Problem
Document-heavy AI systems need realistic examples: personal files, wealth records, case materials, onboarding packets, financial statements, and other sensitive documents. Production data is too risky for broad testing, while handmade mock data is too small, too clean, and too unlike the work the system will actually see.
Where it fits
Use the Synthetic Document Twin Generator when teams need realistic replacement PDFs, machine-readable truth, and validation records for OCR, extraction, form-filling, compliance, regression, demos, and vendor testing.
How it works
01
Normalize PDFs or page images, classify page roles, extract layout, and map every coordinate.
02
Detect sensitive entities, generate masks, remove private pixels locally, and block generation until privacy validation passes.
03
Place coherent synthetic values with deterministic rendering or masked image patches while preserving layout.
04
Rebuild a safe PDF and export truth, validation, redaction, and audit metadata for downstream tests.
What this can support
Detect sensitive entities, generate masks, remove private pixels locally, and validate the safe base before generation.
Create document-level synthetic entity graphs so names, IDs, dates, accounts, totals, and relationships stay coherent across pages.
Render typed fields deterministically and use image patches only where realism matters, such as handwriting, stamps, signatures, and degraded scans.
Export a rebuilt synthetic PDF with truth.json, redaction, validation, and inspection reports.
Who it is for
We can decide whether this building block fits your workflow, deployment boundary, and review requirements.