Synthetic Data & Intelligence Pipelines
Generating, validating, and deploying domain-specific datasets. Built on NVIDIA NeMo DataDesigner. Apache 2.0 foundation, production-grade output.
What We Build
End-to-end synthetic data infrastructure -- from generation through validation to deployment.
Synthetic Training Data
High-fidelity synthetic datasets for model fine-tuning and evaluation. Domain-specific generation with statistical validation to ensure distribution alignment with real-world data characteristics.
Distillation Pipelines
Knowledge distillation from large foundation models into smaller, deployable models. Automated pipeline for generating teacher-student training pairs with quality filtering and decontamination.
Domain-Specific Datasets
Custom datasets built for vertical-specific ML applications. Financial services, healthcare compliance, organizational behavior, and defense -- each domain has unique data requirements and privacy constraints.
Privacy-Preserving Copies
Synthetic replicas of sensitive datasets that preserve statistical properties while eliminating PII. Train models on realistic data without regulatory risk or privacy exposure.
Domain Pipelines
Financial Data
Synthetic trade histories, risk scenarios, and market microstructure data. Generate realistic order books, portfolio return distributions, and stress test scenarios that capture tail-risk behavior. Used internally by Helena's trading algorithms and available for enterprise ML teams building financial models.
Organizational Data
Agent interaction patterns, delegation logs, and communication graph data. Derived from real ALCUB3 runtime telemetry, anonymized and augmented to create training sets for coordination protocol research and org design optimization.
Compliance Data
HIPAA and GDPR-safe synthetic records for healthcare, insurance, and regulated industries. Statistically faithful replicas of patient records, claims data, and financial disclosures that pass regulatory audit while containing zero real PII.
Custom Datasets
On-demand generation for enterprise ML teams. Specify your schema, distribution requirements, and privacy constraints -- we generate production-grade synthetic datasets validated against your real data characteristics. From 10K rows to 10B+.
Infrastructure
Production synthetic data infrastructure built on open-source foundations.
- Generation NVIDIA NeMo DataDesigner -- Apache 2.0 licensed synthetic data generation framework with configurable pipelines and quality metrics
- Validation ALCUB3 Agent Runtime -- automated quality validation using multi-agent review loops that check statistical fidelity, decontamination, and domain accuracy
- Storage BigQuery -- per-division datasets with row-level access controls, versioned schemas, and automated retention policies
- Delivery GCS buckets with signed URLs for enterprise delivery, plus direct BigQuery access for internal consumers
Built on Open Foundations
Our synthetic data infrastructure is built on NVIDIA NeMo DataDesigner, released under Apache 2.0. We contribute improvements upstream and publish our domain-specific pipeline configurations for the community. We believe synthetic data tooling should be open -- the competitive advantage is in domain expertise and validation, not the generation framework.
Need domain-specific data?
If you're building ML models and need high-quality synthetic training data, let's talk.