
DatologyAI builds tools to automatically select the best data on which to train deep learning models. Our tools leverage cutting-edge research—much of which we perform ourselves—to identify redundant, noisy, or otherwise harmful data points. The algorithms that power our tools are modality-agnostic—they’re not limited to text or images—and don’t require labels, making them ideal for realizing the next generation of large deep learning models. Our products allow customers in nearly any vertical to train better models for cheaper.

DatologyAI builds tools to automatically select the best data on which to train deep learning models. Our tools leverage cutting-edge research—much of which we perform ourselves—to identify redundant, noisy, or otherwise harmful data points. The algorithms that power our tools are modality-agnostic—they’re not limited to text or images—and don’t require labels, making them ideal for realizing the next generation of large deep learning models. Our products allow customers in nearly any vertical to train better models for cheaper.
What they do: Automated AI data curation tools to select, augment, and batch training data for deep-learning models
Headquarters: Redwood City, California, United States
Founding team highlights: Co‑founders include Ari Morcos (CEO), Bogdan Gaza (CTO), Matthew Leavitt (CSO)
Funding: Raised a Seed (~$11.65M) and a $46M Series A (total ≈ $57.5M)
Employees: Approximately 48
AI training data quality and curation for deep learning models.
Technology, Information and Internet
$11.65M
Seed included participation from Radical Ventures, Conviction Capital, Quiet Capital and prominent angel backers
$46M
Series A led by Viviana Faga and Astasia Myers from Felicis with participation from prior investors and strategic backers
“Backed by top-tier VCs and prominent AI/tech angels (including Jeff Dean, Yann LeCun, Geoffrey Hinton, Adam D'Angelo), indicating strong investor confidence in AI research-driven teams”
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