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How to Get Realistic Documents for Testing and AI — Without the Privacy Risk

From enterprise applications and intelligent document processing to AI and agentic systems

Live — virtual discussion Recorded — podcast release

The documents enterprises run on — claims, records, invoices, statements — are packed with PII, PHI, and other sensitive production data. That data cannot be handed to QA teams, AI trainers, or development environments. Yet those are exactly the teams that need realistic documents at volume to test applications and train models. The live discussion covers how teams close that gap.

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Hosted by Dave Zwicker, CMO, GenRocket · featuring Ullas Kumar, AI Practice Lead, GenRocket
The unstructured data challenge

The data teams need is the data they can't use

Every enterprise runs on documents like these — and every one of them is a testing and privacy problem.

Insurance claim form, healthcare record, invoice, contract, and bank statement
Claim forms · healthcare records · invoices · contracts · statements
  1. 01 Production documents are a liability Claims, records, invoices, and statements are packed with PII and PHI. Every copy that reaches a development, test, or training environment is exposure.
  2. 02 Redaction has to be intelligent — and irreversible Sensitive values have to be found wherever they hide in a document, then replaced in a way that cannot be reversed, so the document is immediately safe to use.
  3. 03 Redaction alone is not the answer Synthetic generation replaces the values with controlled data, giving teams the precise cases they need for testing and model training — at any volume.
Secure. Compliant. On-demand.
The right data for development, testing, AI and beyond — generated, not copied.
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The big picture

Learn How to Overcome These Challenges

How to apply unstructured data redaction and unstructured synthetic data generation to make these critical production documents secure, available, and valuable for any test or training requirement.

45 minutes. Real answers. Bring your hardest questions.

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Who you'll hear from

Two perspectives on the same shift

Ullas Kumar
Ullas Kumar
AI Practice Lead, GenRocket

As AI Practice Lead at GenRocket, Ullas focuses on secure enterprise data access for AI, data, QE, and security teams — the sanitization, synthesis, and provisioning of structured and unstructured data across financial services, insurance, and healthcare.

From enterprise systems and quality engineering at Infosys to product initiatives at Google, and as Co-Founder of GritWorks.ai, he brings a practical view of how enterprise data architectures are evolving for intelligent document processing, AI, and agentic applications.

Dave Zwicker
Dave Zwicker
CMO, GenRocket

Dave has spent his career at the intersection of enterprise technology, product strategy, and market development, including Vice President of Solutions Marketing roles at large global technology companies and growth leadership at emerging ones.

For the past decade at GenRocket he has led both marketing and product management across test data management, synthetic data generation, data privacy, and enterprise data provisioning — a broad view of how data requirements are shifting across testing, IDP, AI, and agentic systems.

~45 min + live Q&A · Live video call (recorded)
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