Automatic data rebalancing, upsampling, bootstrapping for backtesting, and data imputation to increase the performance of machine learning models by up to 15% without changing model structure.
Generation of high-quality synthetic data for data monetization with third-party firms that preserves statistical properties of original data and is free of sensitive PII/SPI data.
Create reusable data transformation workflows to shorten data collection lead time. Iteratively set up and test ML pipelines with Airflow, GCP Cloud Composer, Dbt, Spark, and other ETL tools instead of waiting for the lengthy original data access procedures.
Synthetic high-quality database generation for functional validation, performance, and integration testing in the cloud for Snowflake, GCP, Amazon Redshift, and Microsoft Azure.
DevOps-friendly API-driven framework to create tabular data and databases in minutes using YAML config files and Python DSL.
Improved data quality
Our machine learning models learn statistical properties in a table and across tables to help create high-quality data, often better than production data.
Guaranteed compliance
"Data as Code" approach enables you to codify complex compliance requirements into concrete data transformations.
Fast and easy deployments
Simple API to integrate into your CI/CD or data pipeline, both on-premise and cloud. Supports all relational databases and data governance platforms (BigID, Collibra, Zaloni) and deployments using Kubernetes, OpenShift, and Docker.
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