With GenRocket’s Unstructured Data Accelerator (UDA), enterprises can now bridge Intelligent Document Processing (IDP) with Design-Driven Synthetic Data Generation — gaining complete control over document data in any volume, variety and format.
90% of enterprise data is unstructured — buried in PDFs, scans, and handwritten forms that drive everyday operations across industries like banking, insurance, and healthcare.
These documents are essential to business, yet they’re also the hardest to test, validate, and manage safely.
UDA changes that — enabling enterprises to generate realistic, compliant, and referentially accurate document data that transforms how teams test and trust unstructured information.
Turning Document Complexity into Data Confidence.
Think bank statements aligned with account data, mortgage packets linked to loan systems, EOBs and claims forms tied to patient records, or invoices and purchase orders connected to ERP test data — all generated synthetically, safely, and with full referential integrity.
Introducing GenRocket’s Unstructured Data Accelerator (UDA)
GenRocket is pleased to introduce the Unstructured Data Accelerator (UDA) — the newest innovation within the Quality Evolution Platform (QEP).
UDA extends Design-Driven Synthetic Data into the unstructured domain — enabling enterprises to design, generate, and validate documents, images, voice, and video data with the same precision and governance that define GenRocket’s structured data solutions.
Unifying Every Form of Data
Today, 90% of enterprise information is unstructured.
Yet, testing and AI training pipelines remain fragmented — with structured data widely used and unstructured data left behind.
UDA closes that gap.
By combining structured data design with dynamic, template-driven document generation, UDA enables organizations to achieve full data integrity, traceability, and compliance across every data type.
A New Standard for Synthetic Document Data
UDA expands QEP’s synthetic data capabilities to power document-centric workflows across key industries such as financial services, insurance, and healthcare — where accuracy, compliance, and scale are non-negotiable.
With UDA, teams can:
Generate production-safe documents — invoices, claims, contracts, statements, and IDs — all designed to reflect real-world business rules.
Preserve referential integrity between document content and structured data sources, maintaining consistency across workflows.
Simulate real-world imperfections — blurred scans, handwriting variations, missing signatures, torn edges, or stamped markings — to validate AI and automation performance.
Support compliance-driven testing and training across highly regulated sectors like banking and healthcare, without ever exposing sensitive production data.
Last week, we introduced the GenRocket Quality Evolution Platform (QEP) — our next step in bridging traditional Test Data Management (TDM) with design-driven synthetic data and AI orchestration. QEP empowers enterprises to unlock the full value of synthetic data — helping teams test safer, faster, and smarter by design.
For years, teams have relied on traditional TDM methods — copying and masking production data to feed testing environments. Now traditional TDM is falling short of compliance needs, and it’s struggling to match the speed, scale, and complexity of today’s DevOps-driven delivery cycles.
QEP changes that. It extends the foundation of traditional TDM into a modern, synthetic-first framework — enabling enterprises to evolve test data management without disruption and move toward full automation:
GenRocket is pleased to announce the Quality Evolution Platform (QEP) — marking the next stage in enterprise test data evolution. QEP brings together synthetic data generation, privacy protection, and quality engineering in a unified framework designed to help organizations and enterprises move beyond the limits of production data.
From Legacy TDM to Synthetic-First: What Changes with QEP
QEP modernizes traditional Test Data Management (TDM) by combining proven capabilities with new levels of automation and synthetic data design and deployment. The platform enables teams to generate test data that is purpose-built, compliant, and instantly available — removing long refresh cycles and the need for production data in lower environments.
The QEP platform incorporates a TDM bridge capability which allows customers to continue with familiar test data management processes like full database masking and subsetting; they follow a synthetic data transformation journey to lockdown data privacy, improve data quality, and more fully automate the data delivery process.
We’re excited to announce the availability of GenRocket’s Unstructured Data Accelerator (UDA) for controlled beta testing. UDA is a new solution accelerator that expands the GenRocket platform beyond structured synthetic data into the world of unstructured data in the form of PDF documents, images, audio files, as well as unstructured text, sensor data and event streams.
How it works
A common use case that demonstrates the value of UDA is the ability to simulate PDF documents. Starting with a sample document, like a bank statement, UDA converts unstructured media into a PDF template and combines it with structured synthetic data with the required variety and volume. This allows synthetic documents to be generated using both positive and negative scenarios to produce comprehensive training and test data at scale.
Why is this important?
Enterprises often rely on document-heavy workflows that must operate accurately and efficiently. These systems must be trained and tested with high volumes of quality data without exposing sensitive customer or patient information. With GenRocket’s UDA solution, synthetic documents can be generated with positive and negative scenarios in terms of image recognition.
In the case of online check deposits, are the checks aligned properly, is handwritten data in the right location on the check, and is the handwriting even legible? UDA can simulate these conditions while, at the same time, generating data variations to validate the numerical values on the checks match their hand-written equivalents. This level of control over data quality allows systems to be trained and tested with unmatched speed and accuracy.
Typical use cases include:
Generating synthetic PDFs like bank statements, contracts, invoices, and claims packets.
Producing synthetic ID cards for onboarding and facial recognition workflows.
Creating synthetic audio clips to train and test customer service and compliance systems.
With UDA, organizations can accelerate testing, eliminate compliance risk, ensure full coverage, and boost the accuracy of AI/ML models — all while integrating seamlessly into CI/CD pipelines.
UDA is Now Available for Beta Testing
The Unstructured Data Accelerator is now available for beta testing to organizations that align with our beta testing objectives. Attached is a comprehensive overview of the UDA solution. If your project is addressing some of the challenges and use cases described in the document, we would be happy to discuss your participation in our beta program.
Please contact your GenRocket account director for additional information. They will schedule a discovery session with our UDA experts to discuss your specific use case and how GenRocket can meet your requirements.
The Banking, Financial Services and Insurance (BFSI) sector is undergoing massive digital transformation—from modernizing core banking systems to deploying AI for fraud detection and personalization.
One thing holds it all together: test data. And traditional test data management can’t keep up.
Cloning and masking production data is time-consuming, risky, and rarely provides the variety needed for modern testing or AI model training. GenRocket changes that.
GenRocket’s platform generates synthetic data on demand, giving BFSI teams the ability to create secure, production-safe test data tailored for every use case—from system integration testing to AI/ML training. All without accessing a single byte of real customer data.
That’s because GenRocket synthetic data generation approach is driven by metadata not the use of real production data.
With GenRocket, dev & test teams transform the use of test and training data as they:
Stay Compliant: Meet privacy mandates like GDPR, GLBA, and PCI DSS
Test with Confidence: Design rule-based data for edge cases, high-volume loads, or rare event simulations
Accelerate AI: Train smarter models with large-scale, bias-free, production-safe datasets
One Platform: Combine traditional TDM functions (copy/mask/subset) with next-gen synthetic data in one solution to allow a graceful transformation
Whether you’re validating core banking systems, transactional workflows, payment fraud detection systems, or loan underwriting processes—GenRocket gives you the power to test and train at scale, securely and efficiently.
Learn how BFSI organizations are accelerating innovation with GenRocket:
Healthcare organizations face mounting challenges in testing complex Electronic Health Record (EHR) and Electronic Medical Record (EMR) systems for interoperability, compliance, and performance. As digital health platforms evolve to include APIs, microservices, and a growing array of third-party integrations, traditional masked production data no longer meets the need for secure, scalable, and accurate testing.
GenRocket’s synthetic data platform offers a transformative approach called Design-Driven Synthetic Data. It provides dynamically generated test data tailored to real-world scenarios and precise test case objectives. And with 100% HIPAA compliant test data, GenRocket empowers healthcare organizations to test every workflow—without risking patient privacy. Unlike masked production data, synthetic data ensures full referential integrity, covers edge cases and regulatory complexities, and integrates seamlessly into CI/CD pipelines.
FHIR, HL7, and X12 standards each present unique testing challenges. GenRocket supports them all with a flexible architecture that generates compliant, high-quality synthetic data for testing EHR/EMR modules, revenue cycle systems, and interoperability APIs. This future-ready approach aligns with CMS, HIPAA, and GDPR requirements while accelerating QA and reducing bottlenecks.
As AI and machine learning gain prominence in healthcare, synthetic data isn’t just about compliance—it’s about innovation. From training algorithms for early diagnosis to simulating complex clinical workflows, GenRocket’s platform future-proofs your testing strategy while delivering unmatched data quality and speed.
Synthetic data isn’t optional—it’s essential. Learn how GenRocket is redefining EHR and EMR testing for a connected, compliant, and data-driven future.
The convergence of Test Data Management (TDM) and Artificial Intelligence (AI) is rapidly transforming enterprise test and training data provisioning strategies. Traditionally, TDM aims to enhance software quality and compliance, while AI demands vast datasets for accurate training and intelligent predictions. Despite distinct goals, both require secure, realistic, and context-specific data.
GenRocket’s latest blog, “How Design-Driven Synthetic Data Enables the Convergence of Test Data Management with AI,” explores how its innovative approach bridges this gap. GenRocket’s platform uniquely empowers organizations to design and deploy synthetic data tailored precisely for TDM and AI/ML requirements. This ensures data is both fit-for-purpose and compliant with regulatory standards.
Key highlights from the blog:
Unified Data Provisioning: GenRocket generates synthetic data that simultaneously supports rigorous software testing and sophisticated AI model training, breaking down traditional data silos.
Enhanced Data Quality: The platform’s design-driven methodology guarantees data realism, maintains referential integrity, and aligns precisely with test or training scenarios.
Regulatory Compliance: Synthetic data generation adheres strictly to data privacy laws, mitigating the risks associated with using sensitive production data.
Organizations seeking to optimize their data provisioning strategies and leverage the combined strengths of TDM and AI will find GenRocket’s approach invaluable.
Explore the full article to discover how design-driven synthetic data can revolutionize your organization’s data strategy.
A leading UK-based health and life insurance provider transformed its Test Data Management by adopting GenRocket’s Design-Driven Synthetic Data platform. Faced with the complexity of testing in a microservices environment, the insurer struggled with slow, inconsistent, and manual test data creation. Traditional approaches couldn’t keep up with the demands of dynamic, API-driven workflows—and posed increasing risks under GDPR.
By transitioning its TDM approach to GenRocket, the insurer now generates real-time synthetic test data that is injected directly into microservices via API—no data storage required. Using GenRocket’s self-service Test Data Cases, testing teams simulate complex insurance workflows, including lifestyle-based underwriting, claims adjudication, and wellness reward programs—all with full referential integrity and automated data delivery.
The result? Test data provisioning dropped from days to seconds. Test coverage expanded to include edge cases and business rules, while full GDPR compliance was achieved by eliminating the use of production data entirely.
This case study illustrates how Design-Driven Synthetic Data is reshaping quality engineering—empowering teams to test faster, smarter, and more securely in today’s agile and regulated environments.
Design-Driven Synthetic Data is Changing the Traditional Test Data Paradigm
Enterprises are reaching a tipping point. As data privacy concerns rise and production data becomes increasingly inaccessible for development and testing, organizations are seeking smarter, more secure alternatives. The latest blog from GenRocket dives into this shift—and why design-driven synthetic data is taking center stage.
Leading organizations are reducing their dependance on masked production data for testing due to data privacy concerns and data quality limitations. As Quality Engineering teams turn their focus to the use of synthetic data, many are embracing the innovative concept of design-driven synthetic data. This approach allows teams to generate exactly the data they need, tailored to their testing scenarios, with full control over variety, volume, format and complexity.
The result? Higher test coverage, faster delivery cycles, and dramatically improved software quality—all while staying compliant with evolving data protection regulations.
Discover why traditional Test Data Management approaches are being phased out in favor of scalable, intelligent synthetic data generation. Learn how GenRocket’s platform empowers DevOps teams to provision precise, rule-based test data on demand—and why this evolution is essential for the advancement of quality engineering.
Read the full blog article to explore how design-driven synthetic test data is unlocking speed, safety, and innovation across the software development lifecycle.
