A New Chapter in Test Data
Test data has always been the unsolved challenge of quality engineering. For years, teams relied on copies of production data—masked, subsetted, and manually augmented—to approximate real-world conditions. But that approach came with problems: it was slow, costly, and fraught with compliance risks. GenRocket introduced the concept that data could be intentionally designed to fit exact testing needs. This innovation put teams in control of what their data looked like and how it behaved.
Yet even as organizations embrace design-driven data, another challenge emerges: the process of defining requirements and translating them into usable test data can be time‑consuming and complex. The AI Orchestrator was created to solve this last mile challenge—turning the power of design-driven data into a simple, efficient, and conversational experience.
Why Conversational Matters
A common question is why not simply rely on AI models to generate synthetic data directly. The answer lies in what design-driven data from GenRocket provides beyond what generative AI can offer:
Security – GenRocket’s intelligent data generators operate in a locked-down environment, avoiding the risks inherent in generative AI models.
Accuracy without Hallucination – AI models can hallucinate or create invalid outputs. GenRocket eliminates this risk by basing every dataset on defined rules and schemas.
Referential Integrity – Complex data models require relationships and dependencies to remain intact. GenRocket ensures these connections are preserved across all datasets.
Efficiency and Reuse – Data designs are captured as executable test data cases that can be shared, reused, version-controlled, and executed on demand.
Design-driven data picks up where generative AI leaves off. AI Orchestrator leverages conversational simplicity to define requirements, while GenRocket’s intelligent data generators guarantee the quality, control, and governance enterprises require.
When data is designed, every attribute, constraint, and relationship must be specified. Traditionally, this required specialized knowledge of tools, schemas, and data models. With AI Orchestrator, those barriers disappear. Testers, developers, and QA engineers can describe what they need in plain language, and the AI Orchestrator takes care of the translation.
This means design-driven data becomes accessible to more people, not just specialists. Coverage expands because more scenarios can be expressed quickly. And time to delivery shrinks because requirements can be captured in minutes, not hours or days. The result is a natural extension of design-driven data: one that combines precision with simplicity.
How the Orchestrator Works
At its core, AI Orchestrator serves as the interpreter between human intent and executable test data. The process follows four steps:
- Plain Language Input – A user specifies the data they need using a natural language expression such as “10,000 credit card accounts with 25% flagged as delinquent.”
- Interpretation – The Orchestrator uses a chatbot interface that can operate in two modes:
- Deterministic, algorithmic guidance for organizations that prefer strict, rules‑based processes and want to restrict the use of AI within their organization.
- AI‑assisted design through an embedded LLM for faster, conversational interactions to support a wider range of use cases and scenarios.
- G‑Storyboard Translation – Conversational data requirements are converted into G‑Cases, GenRocket’s executable test data scripts.
- Execution and Delivery – APIs and industry-standard interfaces deliver the data into CI/CD pipelines, test automation frameworks, and development environments in real time.
AI Orchestrator: Design Driven Synthetic Data – Simplified
- Novice users will be guided by a deterministic algorithm to design data
- G-Storyboard will translate natural language requirements into G-Cases
- Intermediate and Advanced users will use a fully conversational approach
- AI can initially be turned off to meet customer security requirements
Built‑In Governance and Control
Enterprises need both speed and assurance. The AI Orchestrator provides dual operating modes so organizations can decide how much AI to allow. In deterministic mode, AI suggestions are off, and all design steps are explicitly approved by users. In AI‑assisted mode, the AI Orchestrator accelerates workflows with conversational guidance while staying within enterprise‑defined guardrails.
Audit trails, role‑based access, and data privacy safeguards ensure that the AI Orchestrator can be trusted in even the most regulated industries. No production data is ever used; all test data is generated with governance built in.
Extending to Automation
The same architecture that powers conversational design-driven data also enables seamless integration with third‑party test automation platforms. Using APIs and other industry-standard interfaces, external tools can request synthetic data on the fly as they generate test cases. The AI Orchestrator interprets these requests, creates the necessary G‑Cases, and delivers data instantly – allowing test data and test automation to run in parallel inside CI/CD pipelines.
Beyond Testing: Training Data Solutions
The AI Orchestrator is not limited to testing alone. It also supports the creation of training data for AI and machine learning initiatives. Organizations can generate:
GenRocket Technology – Ensures Accurate Al Deployment
- Rule‑based training data for applications such as fraud detection systems.
- Statistical training data using metadata profiles to match population distributions.
- Unstructured training data that blends structured tables with documents, forms, images, or audio/video.
- AI‑driven training data that unites test automation requirements with both tabular and textual datasets.
Enterprise Scale, Enterprise Trust
Large organizations require solutions that scale globally and operate securely. The AI Orchestrator was designed with enterprise‑class governance from the ground up: every action and every user role is controlled while every dataset is generated with privacy protections in place. Whether serving a single team or a worldwide QA organization, the AI Orchestrator provides consistency and trust.
Why Enterprises Choose AI Orchestrator
- Conversational workflows that make design-driven data accessible and intuitive.
- Total coverage of test scenarios, delivered in real time to pipelines and frameworks.
- Governance that matches organizational policies with dual operating modes.
- Integration with automation tools, cloud environments, and DevOps ecosystems.
- Global scalability designed for distributed teams and complex enterprises.
Bring Conversation to Test Data Design
With AI Orchestrator, design-driven data evolves from a specialized task into a natural, conversational process. It combines the rigor of structured data creation with the simplicity of plain language input, delivering the best of both worlds. Modern enterprises can accelerate quality, reduce risk, and empower more teams to participate in testing—without adding complexity.
It’s not just automation. It’s test data you can talk to.