Synthetic Clinical Data Generation
Validate Without Risk.
TrialGen™ enables the generation of statistically consistent, regulation-aware synthetic clinical datasets — allowing teams to test, validate, and refine workflows without exposure to sensitive patient data.
Built to support clinical trial simulations, system testing, and validation workflows across the clinical data lifecycle.
TrialGen™ Platform · Challenge → Solution → Outcomes
Clinical Validation is Limited by Data Access
Clinical data teams often face a fundamental constraint — real data is either unavailable, restricted, or incomplete when it's needed most.
This creates critical bottlenecks:
- Validation workflows are delayed due to lack of usable datasets
- Testing is performed on limited or non-representative data
- Edge cases and anomalies remain untested until late stages
- Privacy and compliance risks restrict data usage across teams
TrialGen™ removes this dependency — enabling teams to simulate realistic clinical datasets on demand and validate systems before real data is available.
Understand the Approach →As a result, systems are often validated reactively, not proactively.
Safe, Scalable Testing for Clinical Data Systems
TrialGen™ creates high-fidelity synthetic datasets that replicate the structure, relationships, and statistical behavior of real clinical trial data — without compromising privacy or compliance.
It enables teams to simulate real-world scenarios across study design, data validation, and regulatory workflows, ensuring systems perform as expected before live deployment.
Key Capabilities:
Realistic Data Simulation
Generate SDTM datasets aligned with CDISC structures, domains, and cross-domain relationships
Validation Readiness
Test validation rules, workflows, and pipelines under simulated trial conditions
Privacy-Safe Data Environments
Eliminate dependency on sensitive or restricted datasets during development and testing
Scenario-Based Testing
Simulate edge cases, anomalies, and complex data interactions for robust system validation
The AI Reads. Deterministic Code Writes.
A protocol goes in. A verified synthetic SDTM package comes out — every value traceable to its source, reproducible byte for byte, with no patient data used or stored anywhere in the system.
Protocol In
CDISC USDM maps deterministically, with no model. PDF extraction ties every claim to a quote and page — unsupported claims block approval
Evidence-Based AE Profile
An AI evidence agent ranks published safety data from DailyMed, ClinicalTrials.gov and FAERS — it never invents an incidence rate
Signed Human Approval
A reviewer resolves gaps and signs the study spec and AE profile. Unapproved input is refused, never filled with a default
Offline Generation
No model, no network. One clinical history per subject keeps AEs, dosing, labs and visits consistent — same seed, same data
Verified SDTM Package
30+ domains as Define-XML 2.1, Dataset-JSON, SAS XPT and CSV — MedDRA-coded AEs, CDISC Library terminology, row-level lineage and a truth manifest
- ADaM generation — analysis datasets derived from the SDTM, driven by the SAP as a second source document
- Near-synonymous AE terms — richer handling of complex AE descriptions; today ambiguous terms go to human review, never guessed
From Data Constraints to Data Readiness
TrialGen™ integrates seamlessly into clinical data workflows — supporting simulation, validation, and system readiness across the lifecycle.
- Clinical trial simulation before study execution
- System and pipeline testing (EDC, SDTM, downstream workflows)
- Validation framework testing (including Kwalify™)
- Training and sandbox environments
Use Cases
- Accelerates development and validation cycles
- Improves confidence in data and system behavior
- Enables proactive, not reactive, validation
- Scales across studies, domains, and complexity levels
Value
TrialGen™ transforms synthetic data from a workaround into a strategic validation layer.