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Datoric develops custom datasets for robotics, treating research, collection, verification, and production as one continuous process. We work closely with frontier model teams to turn emerging limitations and model failures into testable data hypotheses, and run exclusive collections for specific data methods based on results.
Data is collected through private invite-only applications separated by modality, customer, and trust level. Every submission remains linked to the contributor, device, task, session, consent, rights, and processing history that produced it, giving our internal QA and fraud models the context to detect problems that may appear legitimate in the finished file.
Once a collection method is validated, we scale it up as a solution to the model failure and it also becomes a reusable data recipe for future custom projects or independently collected, rights-cleared data products.
Previously Flow AI, Arzule
CEO @ Datoric. Prev Quant & SWE Intern. Math/Econ/CS @ UChicago; Bypassed Google OAuth 2.1 and QA systems on data annotation platforms to automate training tasks in 2023, then disclosed the loopholes to the affected platforms.
CTO @ Datoric. Prev AI/ML & SWE Intern. Math & CS & Robotics at NYU. Created a bounding box algorithm for more efficient labeling as Head of Data for the NYU Robotics Team.
Founded 2025 · Source
Source-based assessment · automated evidence rules
The retained text does not establish product availability or customer use.
Maturity is an evidence-based assessment, not a success rating. It may understate the current stage where evidence is limited. Coverage records documented areas, not a complete or independently audited company history.
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