Companies want to train their own large models on their own data. The current industry standard is to train on a random sample of your data, which is inefficient at best and actively harmful to model quality at worst. There is compelling research showing that smarter data selection can train better models faster—we know because we did much of this research. Given the high costs of training, this presents a huge market opportunity. We founded DatologyAI to translate this research into tools that enable enterprise customers to identify the right data on which to train, resulting in better models for cheaper.
Our team has pioneered deep learning data research, built startups, and created tools for enterprise ML.
Following our $11.65M Seed round last September, we've raised a $46M Series A led by Felicis Ventures. Our investors include Radical Ventures, Amplify Partners, Microsoft, Amazon, and notable angels like Jeff Dean, Geoff Hinton, Yann LeCun and Elad Gil.
With over $57.5M in total funding, we're rapidly scaling our team and compute resources to revolutionize data curation across modalities. Join us in pushing the boundaries of what's possible in AI!
