The Unstructured Data Problem Is Growing
AI, Agentic AI, machine learning, and increasingly automated application workflows require larger and more diverse collections of real-world data.
At the same time, privacy regulation, enterprise security policy, data sovereignty rules, and distributed teams are making production data harder to access than ever.
The result: a growing gap between the data teams need and the production data enterprises can safely provide.
Two Paths to Compliant Unstructured Data
Pick based on what you have and what you need — or combine both for an end-to-end workflow that evolves production data into scalable synthetic data.
Start with Real Data
Use the fidelity of existing production documents while permanently removing the PII/PHI that prevents them from being safely used.
- Maximum fidelity to production documents
- Existing layouts, formats and naturally occurring edge cases
- Auditable, on-prem PII/PHI removal
- Production-derived data for testing or AI training
Create the Data You Need
Move beyond what happens to exist in production — generate documents, images and unstructured content with fully controlled conditions.
- Unlimited data volume on demand
- Positive and negative scenarios
- Rare edge cases that don't exist in production
- Structured and unstructured data stay logically connected
Sensitive fields — SSN, name, address, control number — are permanently removed from a real document, leaving a safe, reusable template with the original layout intact.
That template is combined with Design-Driven structured data to generate unlimited realistic variants at scale — no two documents exactly alike.
When to Use What
Picking the right path — Redact, Generate, or both — depends on what you have and what you need.
Redact → Generate: The Combined Workflow
Use real data as a seed. Expand it synthetically. Get a corpus that's both realistic and limitless.
Real layouts, real edge cases — preserved by Redact.
Unlimited volume and variants — added by Generate.
On-prem throughout. No PII ever leaves your boundary.
Preserve Reality — Or Design What Doesn't Exist
UDA-Redact
Preserves what production has already given you — real layouts, real edge cases, real structure — safely.
UDA-Generate
Creates what production cannot — unlimited volume, rare scenarios, and conditions designed on demand.
Connect Unstructured and Structured Data
Enterprise workflows rarely operate on a document alone. UDA-Generate combines unstructured templates with GenRocket's Design-Driven structured synthetic data so related information stays consistent across a complete test or AI training scenario.
Real Results, Real Enterprises
How Fortune 100 and Fortune 500 organizations use UDA-Redact, UDA-Generate, and the combined workflow today.
Unlocking Handwritten Clinical Data for Agentic AI Testing
Physician notes held valuable test signal but also PHI that couldn't enter AI testing environments — and fully synthetic text couldn't replicate real handwriting and clinical complexity. UDA-Redact de-identified typed and handwritten PHI while preserving handwriting, terminology, and structure.
Expanding Laboratory Requisition OCR Test Coverage
OCR systems had to hold up against faxes, blur, stains, and handwriting on scanned lab requisitions — without training on PHI-laden production samples. UDA-Generate produced production-faithful synthetic requisitions plus 30+ negative variants with known expected values.
Securing Loan Document Testing at Scale
Mortgage notes, deeds of trust, credit reports, and appraisals carried NPI that blocked reuse for QA — and generic templates lacked production realism. UDA-Redact turned real loan documents into reusable templates; UDA-Generate populated them with realistic synthetic values at scale.
From Production Dependency to Enterprise Data Provisioning
GenRocket's Unstructured Data Accelerator extends Enterprise Data Provisioning beyond structured databases.
Organizations can begin with production-derived documents made safe through UDA-Redact, transition those documents into reusable templates, and ultimately create controlled synthetic data with UDA-Generate.
Ready to Put Your Unstructured Data to Work?
Use either solution independently — or combine them into one end-to-end workflow.
UDA-Redact
Make real-world unstructured data safe to use.
UDA-Generate
Create the unstructured data production cannot provide.