YData offers a data‑centric AI platform called Fabric that automates data profiling, quality assessment, and synthetic data generation for structured datasets. The platform provides drag‑and‑drop pipelines, extensive connectors, and privacy‑compliant synthetic data modules to help enterprise data science and engineering teams accelerate model development while improving data quality and meeting compliance requirements.
Funding
$2.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
2OFFFounders
Product
Problem
Data science teams often struggle with low-quality, incomplete, or privacy‑restricted datasets, leading to slow model development, biased outcomes, and compliance risks. Manual data profiling, cleaning, and augmentation are time‑consuming and require specialized expertise.
Solution
YData provides a data‑centric AI platform—Fabric—that automates data profiling, quality assessment, and synthetic data generation for structured data. The platform ingests data from a wide range of sources, creates detailed data quality reports, and offers drag‑and‑drop pipelines to clean, enrich, and version data assets. Synthetic data modules generate privacy‑compliant tabular, time‑series, and multi‑table datasets that preserve statistical properties while eliminating personally identifiable information. Integrated development environments (Jupyter, VS Code) and scalable Lab workspaces let data scientists experiment and iterate quickly. All components can be deployed on cloud or on‑premises, ensuring data never leaves the organization’s infrastructure.
Target Audience
Primary customers are data science, analytics, and data engineering teams in enterprises that need to accelerate AI model development while ensuring data quality and regulatory compliance.
Features
- Automated data catalog with 20+ connectors for files, databases, and cloud storage
- One‑click data profiling that detects quality issues, missing values, and PII, producing interactive reports
- Synthetic data generation for tabular, time‑series, and multi‑table data with configurable privacy‑utility trade‑offs
- Drag‑and‑drop Pipelines for reproducible data preparation, version control, and monitoring
- Scalable Lab environments offering CPU/GPU resources and pre‑installed data science libraries
- SDK and API for programmatic access to profiling, synthesis, and pipeline orchestration
- SOC 2 Type 2 compliance and on‑premises/kubernetes deployment options for strict security requirements