We make training better AI models fast and affordable
Companies waste massive compute budgets training on low-quality, repetitive data. DatologyAI automatically curates your datasets so you can train faster, achieve better performance, and deploy smaller models.
Models are what they eat—and most are stuck training on terrible data. While frontier labs invest billions in curation, everyone else relies on massive, low-quality datasets, missing the potential of their own. DatologyAI was built to change that, delivering better model performance to every company, not just those with big budgets and specialized teams.
Models are what they eat—and most are stuck training on terrible data. While frontier labs invest billions in curation, everyone else relies on massive, low-quality datasets, missing the potential of their own. DatologyAI was built to change that, delivering better model performance to every company, not just those with big budgets and specialized teams.
How we’re solving the data problem at scale
DatologyAI was founded to help AI models select the best data to be trained on. We democratize data curation, allowing every company to easily train its own custom model on the right data without needing to invest massive resources.
Our platform leverages cutting-edge research to identify redundant, noisy, or otherwise harmful data points, and manage the entire process from data in blob storage to the dataloader used for training code.
DatologyAI delivers fully automated, scalable data curation allowing customers to optimize training efficiency, maximize performance, and reduce compute costs with data curation that’s easy to implement and to generalize across models.
Meet the team behind DatologyAI
Co-Founder, CEO
Ari Morcos
Former FAIR@MetaAI and DeepMind, Best Papers at both NeurIPS and ICLR, leading expert in data research for deep learning, PhD in neuroscience from Harvard.
Co-Founder, CTO
Bogdan Gaza
Former CTO and co-founder of Moonsense, 10+ years infrastructure engineering and management experience at Amazon and Twitter.
Co-Founder, CSO
Matthew Leavitt
Former Head of Data Research at MosaicML (acq Databricks), FAIR@MetaAI, PhD in neuroscience from McGill.
Jack Urbanek
Founding Member of Technical Staff
Amro Abbas
Founding Member of Technical Staff
Fan Pan
Founding Member of Technical Staff
Pratyush Maini
Founding Member of Technical Staff
Alvin Deng
Member of Technical Staff
Josh Wills
Member of Technical Staff
Aldo Carranza
Member of Techinical Staff
Paul Burstein
Member of Technical Staff
Jacqueline Liu
Lead Talent Partner
Haoli Yin
Member of Technical Staff
Ricardo Monti
Member of Technical Staff
Kaleigh Mentzer
Member of Technical Staff
Cody Blakeney
Member of Technical Staff
Parth Doshi
Member of Technical Staff
Luke Merrick
Member of Technical Staff
David Schwab
Member of Technical Staff
Zhengping Wang
Member of Technical Staff
Vineeth Dorna
Member of Technical Staff
Tiffanie Pham
Talent Partner
Haakon Mongstad
Member of Technical Staff
Brett Larsen
Member of Technical Staff
Kylie Clement
Director of Sales
Elise Clark
Business Operations Lead
Darren Teh
Member of Technical Staff
Spandan Das
Member of Technical Staff
Liz Gatapia
Product Designer
Rishabh Adiga
Member of Technical Staff
Alex Fang
Member of Technical Staff
Jeremy Custenborder
Member of Technical Staff
Better data, better
models, better business
Data quality is the difference between superior models and
expensive disappointment. That belief shapes what we build.
Customer-obsessed
We tune our curation approach based on your specific needs and the types of models you're training. We know training on bad data wastes enormous compute budgets and delays your progress. You deserve a partner who puts your success first, every time.
Experiment relentlessly
We approach every data problem as data scientists. We test hypotheses and measure results, and only ship what the data supports. Every training run moves you closer to production-ready results.
Bold bets, fast learning
We commit real resources to audacious ideas, learn from what doesn't work, and iterate quickly. Our cutting-edge research, 80% of which is novel and unpublished, gives you access to techniques that get better results, faster.
Curated data. Your edge
DatologyAI works with open source or proprietary datasets to increase training value. Let's discuss how we can help you achieve better model performance, train faster, and reduce costs.