Synthetic Data for Responsible, Smarter Model Development, Benchmarking, and Red-teaming
Build better AI responsibly. Our synthetic data platform generates privacy-safe, bias-aware datasets that fuel innovation while ensuring compliance. From edge-case coverage to robust model evaluations, we help teams accelerate development without compromising on fairness, trust, or governance.
"Companies prefer buying synthetic data because of the hidden costs of building it yourself."
Product Management, AWS SageMaker
Key Challenges
| Challenge | Description |
|---|---|
| Data Scarcity & Imbalance | Rare or underrepresented cases limit robustness and create gaps in model performance across different demographics and scenarios. |
| Privacy & Compliance Barriers | Regulated sectors (finance, healthcare, insurance) face strict restrictions on PHI/PII use, limiting data sharing and collaboration. |
| Responsible AI Risks | Without proper controls, synthetic data can amplify bias or degrade fairness, leading to discriminatory AI systems. |
| Edge-Case Blind Spots | Real data often misses rare but critical failure scenarios, leaving models vulnerable to unexpected behaviors. |
| Evaluation Gaps | Lack of transparent validation pipelines hinders trust and adoption of AI systems in production environments. |
| Governance Debt | Enterprises need lineage, auditability, and explainability to meet regulatory demands and maintain stakeholder trust. |
Our Solutions
| Solution | Description |
|---|---|
| Privacy by Design | Differential privacy, k-anonymity, and built-in PHI/PII sanitization for GDPR, HIPAA, SEC, and CCPA compliance. |
| Responsible AI Guardrails | Fairness checks, bias-balancing synthesis, and drift detection for continuous monitoring and ethical AI development. |
| Scenario Simulation | Generate complex edge-cases and multi-agent workflows to test safety and resilience of AI systems. |
| Evaluation as a Service | Synthetic benchmarks for factuality, toxicity, bias, and fairness with human-in-the-loop verification. |
| Audit-Ready Lineage | Metadata, contribution reports, and one-click compliance exports for auditors and regulatory compliance. |
| Seamless Integration | APIs and connectors fit natively into ML pipelines (Snowflake, Databricks, SageMaker) for easy adoption. |
Use Cases
| Use Case | Description |
|---|---|
| Fraud Detection & AML | Simulate rare fraud patterns responsibly without exposing customer PII |
| Healthcare & Life Sciences | Generate HIPAA-compliant synthetic EHRs for safe triage and clinical trials |
| Model Evaluation & Auditing | Stress-test fairness, bias, and harmful outputs with synthetic eval suites |
| API/Function-Calling for LLMs | Build structured, bias-balanced training data for safer AI agents |
| Government & Public Sector | Enable responsible citizen analytics without privacy compromise |
Key Benefits
| Benefit | Description |
|---|---|
| Scalable & Cost-Effective | Eliminate dependence on expensive real data collection while maintaining quality |
| Built-in Privacy & Compliance | Datasets are inherently safe, meeting regulatory standards out of the box |
| Fairness & Inclusivity | Synthetic boosters ensure underrepresented groups are accurately modeled |
| Robustness & Safety | Edge-case and drift simulation strengthens resilience against failures |
| Auditability & Trust | Metadata lineage and compliance exports provide full transparency |
| Future-Proof AI | Responsible, hybrid data strategy (real + synthetic) ensures sustainable model quality |
"We strive to start each relationship with establishing trust and building a long-term partnership. That is why, we offer a complimentary dataset to all our customers to help them get started."
Ready to Get Started?
Contact our team to learn how we can help your tech organization develop AI systems that meet the highest standards.