Company profile · 3 min read
Datoric: provenance-first training data for robotics and physical AI
Custom and catalogued robotics data from egocentric video, human demonstrations, tactile data, and teleoperation with consent and provenance records.
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
Published · Updated
Datoric is a training-data R&D company for robotics and physical AI. The buyer decision is whether a model team needs custom, rights-cleared human demonstrations and egocentric data with provenance, consent, and quality controls—not just another scraped dataset.
What it does
Datoric describes custom collection for voice models, robotics, and world models, with private invite-only applications separated by modality, customer, and trust level. Each submission is linked to contributor, device, task, session, consent, rights, and processing history. The current site emphasizes robotics datasets, human manipulation, egocentric video, and custom robot teleoperation (Datoric homepage; Datoric about page).
| Fact | What the public sources say |
|---|---|
| Buyer | Teams building voice models, robots, and physical-AI systems |
| Data | Egocentric video, human demonstrations, tactile or action data, and robot teleoperation |
| Quality model | Collection specifications, provenance, consent, rights, fraud detection, and task-specific QA |
| Public scale claim | The YC launch says Datoric works with more than 300,000 active contributors |
| Founders | Jeffrey Lin and Nikhil Reddy |
Why it fits
The valuable distinction is provenance at collection time. A model team can buy a file that looks clean but cannot explain how it was captured, whether the contributor consented to the use, or what task the recording represents. Datoric is trying to make those facts part of the dataset so a buyer can review the data before training and reuse a validated collection method.
The scale and revenue claims are company-reported. A buyer should ask for sample records, consent and licensing terms, contributor verification, demographic coverage, sensor metadata, acceptance criteria, duplication controls, and the exact rights granted for model training. The public site describes licensed and ethically sourced data, but no dataset audit was performed.
The founders bring relevant data and security context. YC describes Reddy as a quantitative and software engineer who disclosed annotation-platform loopholes, and Lin as a robotics and data-labeling engineer who built bounding-box tooling (YC company profile).
Short version: Datoric is a strong fit when the hard part is getting trustworthy physical-world data. Start with a small collection specification and review the provenance record before scaling contributors.
Sources checked — 2026-09-19
Cohort context
Datoric is listed in Summer 2026. In our 2026-09-18 directory snapshot, 119 of 232 listed companies in that cohort have YC’s primary industry label B2B (51.3%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.
Public website snapshot
Observed 2026-09-19T16:18:42.426Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.
| Signal | Homepage observation |
|---|---|
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Not observed in this response |
| Pricing link | Not observed in this response |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |
Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.
About the author
Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).
I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.
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