AI/MLDevelopersDevOpsNewsOpen Source

Generative AI Startup DataCebo Raises $8.5 Million In Seed Funding

DataCebo’s first product - SDV Enterprise - takes Synthetic Data Vault (SDV) to the next level by providing every team with synthetic data 1000x faster with 10x the quality.
Monika ChauhanBy 303 views
funding, growth
0

DataCebo emerged with SDV Enterprise, a commercial offering of the popular open source product, Synthetic Data Vault (SDV). The company has raised $8.5 million in seed funding co-led by Link Ventures and Zetta Venture Partners. Uncorrelated Ventures also participated. The company plans to use the funding to advance its product and to build a go-to-market team.

With SDV Enterprise, developers can easily build, deploy and manage sophisticated generative AI models for enterprise-grade applications when real data is limited or unavailable. SDV Enterprise’s models create higher quality synthetic data that is statistically similar to original data so developers can effectively test applications and train robust ML models. SDV Enterprise is currently in beta with the Global 2000.

DataCebo co-founders Kalyan Veeramachaneni (CEO) and Neha Patki (vice president of product) created SDV when at MIT’s Data to AI Lab. SDV lets developers build a proof-of-concept generative AI model for small tabular and relational datasets with simple schemas and create synthetic data. SDV has been downloaded more than a million times and has the largest community around synthetic data. DataCebo was then founded in 2020 to revolutionize developer productivity at enterprises by leveraging generative AI.

Key features include:

  • Scalability: developers can train a generative AI model with much larger datasets and complex schemas with hundreds of interconnected tables
  • Deep Data Understanding: developers can train models that understand the deeper meaning behind real world data concepts like the structure of a phone number and which geographical areas it represents
  • Programmability: developers can fine-tune the generative AI model stack using low-code APIs by supplying their data schema, business logic and evaluation criteria
  • Integration: developers can deploy synthetic data applications by ingesting and exporting data in a variety of different formats
  • Management: developers can manage multiple synthetic data applications, track changes and update their generative AI models as their applications grow and change

You may also like