Synthetic Data for Model Development, Benchmarking, and Red-Teaming
Generate training data, evaluation sets, and adversarial test cases for LLMs and ML systems—without scraping, licensing headaches, or privacy risk.
"Companies prefer buying synthetic data because of the hidden costs of building it yourself."
Product Management, AWS SageMaker
Key Challenges
| Challenge | Description |
|---|---|
| Training Data Bottlenecks | Quality labeled data is expensive and slow to collect. Public datasets are overused, and scraping raises legal and ethical concerns. |
| Evaluation Blind Spots | Models fail silently on edge cases, adversarial inputs, and demographic slices that aren't well-represented in test sets. |
| Red-Teaming at Scale | Manual red-teaming doesn't scale. Teams need systematic ways to probe for jailbreaks, hallucinations, and harmful outputs. |
| Data Licensing & IP Risk | Using scraped or licensed data creates legal exposure. Synthetic alternatives sidestep these issues entirely. |
| Reproducibility & Versioning | Training runs are hard to reproduce when data sources change or disappear. Synthetic pipelines offer deterministic, versionable datasets. |
Our Solutions
| Solution | Description |
|---|---|
| Custom Training Data Generation | Generate domain-specific datasets for fine-tuning: structured outputs, function-calling examples, multi-turn dialogues, and more. |
| Evaluation Suite Builder | Create targeted test sets for specific capabilities, failure modes, or demographic coverage—then version and reuse them. |
| Automated Red-Teaming | Generate adversarial prompts, jailbreak attempts, and edge cases systematically to stress-test model safety. |
| Bias & Fairness Testing | Synthesize balanced test sets across demographics, languages, and scenarios to catch disparities before deployment. |
| Pipeline Integration | APIs and connectors for Snowflake, Databricks, SageMaker, and CI/CD workflows—generate data where you need it. |
Use Cases
| Use Case | Description |
|---|---|
| LLM Fine-Tuning | Generate instruction-following datasets, function-calling examples, and domain-specific training data |
| Targeted Evaluation | Spotted an issue in production? Generate test cases for that specific failure mode in minutes, not weeks of data collection |
| Red-Teaming & Safety | Systematically probe for jailbreaks, prompt injections, and harmful outputs |
| RAG & Search Testing | Create synthetic document corpora and query sets to evaluate retrieval pipelines |
| Agent & Tool-Use Testing | Generate multi-step scenarios to test AI agents with tool access and complex workflows |
Key Benefits
| Benefit | Description |
|---|---|
| Ship Faster | Unblock training and eval pipelines without waiting on data collection or labeling |
| Reduce Legal Risk | No scraping, no licensing disputes, no PII exposure |
| Catch Failures Early | Systematic edge-case coverage finds problems before users do |
| Reproducible Experiments | Deterministic data generation makes runs comparable and auditable |
| Scale Red-Teaming | Automate adversarial testing instead of relying on manual review |
"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.