Supervised model training
Create and manage labeled datasets for classification, regression, or forecasting tasks.
LLM fine-tuning
Curate conversational, structured, or synthetic datasets to refine large language models.
Feature store population
Transform raw data into reusable, versioned, production-ready feature sets for downstream pipelines.
Balanced dataset generation
Automatically rebalance datasets across key attributes (e.g., class labels, geographies, demographics) to reduce model bias and improve generalization.
Multi-source feature stitching
Join records across disparate systems using entity resolution or embedding-based matching to build richer training datasets.
Synthetic data generation
Use generative models or rule-based approaches to create synthetic datasets when real data is scarce, sensitive, or imbalanced.
Scott Stafford
Chief Enterprise Architect at Ping
CEO
InsureTech Company
Felix Baldauf-Lenschen
CEO and Founder
