da
ta
saur
/ dat-uh-sawr /
A recently discovered creature, capable of churning through data at remarkable speed.
A key distinguishing trait of the human species is our ability to invent and utilize tools to give us an evolutionary advantage.
Datasaur was founded on the premise that building the right tools can further the adoption and democratization of artificial intelligence.
Companies both large and small have re-invented the wheel in building their own ad hoc data labeling tools.
We strive to combine all the industry’s best practices and create a machine learning platform so our clients can focus on doing what they do best.
Meet our founder
Ivan Lee is the CEO and Founder of Datasaur.ai. He graduated with a Computer Science B.S. from Stanford University. He was chosen for the selective Mayfield Fellows entrepreneurship program in 2010. Ivan went on to found Loki Studios, an iOS game studio. After raising institutional funding from DCM's A-Fund and launching a profitable game, Loki was acquired by Yahoo.
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"We compared Datasaur to 55 other options, and in that exhaustive comparison -- we found Datasaur to have the most complete suite of tooling."

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"Datasaur enabled us to automate our entire QA pipeline, we know what has been labeled (and the quality of each label) every 5 minutes without touching anything. It's all automated."

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"Integrating the platform with our AWS environment has been seamless, providing us with scalable data labeling capabilities."

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"We found the entire platform incredibly intuitive and easy to navigate. Onboarding was smooth and we were able to quickly adopt their automation tooling which was very important for us when considering a labeling platform."

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"We [Consensus] had a very complex and specific set of annotation needs. Datasaur was able to address those needs efficiently and effectively all while maintaining the personal touch you would expect from a start-up."

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"Our experience was pretty great. I enjoyed my time on Datasaur."

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"Instead of manually creating each project, we’re able to automate the project creation. Instead of manually scrolling through hundreds of medical labels, we can rely on search functions. This has saved admins and team members a lot of time in their project workflows."

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Learn about the latest in NLP and LLM

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This month's updates bring more control to your labeling workflows, sharper audio tooling, and deeper connectivity with cloud storage. Whether you're working with text spans, audio timestamps, or building RAG-powered applications, these improvements are designed to reduce friction and keep your teams moving faster.

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March 2026 Feature Updates: Better Guidance, Smarter Reviews, and Deeper Insights
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This month’s updates introduce ML-assisted selective labeling, granular time-tracking metrics, and enhanced label guidance to boost precision and workflow efficiency. Additionally, new analytics for pre-labeled data and domain security controls provide teams with deeper visibility and stronger workspace protection.

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February 2026 Updates: Smarter Redaction, Easier Search, and Faster Exports
February 2026 Updates: Smarter Redaction, Easier Search, and Faster Exports
March 10, 2026

February updates bring improvements that make annotation workflows faster and easier, including smarter PII redaction, improved search for span labeling, and better export organization. New integrations and usability enhancements also help teams move from model training to deployment more quickly while simplifying large project management.

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