About | NLP Labeling

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."

"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."

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

"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."

"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."

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

"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."

Learn about the latest in NLP and LLM

June 2026 Feature Updates: More Flexible Labeling, Better Audio Workflows, and Easier Sync Management

July 9, 2026

June's releases focused on giving teams more flexibility in how they structure projects, more control over labeling workflows, and less friction when managing audio and synced content. Across Data Studio and LLM Labs, the biggest improvements this month help teams adapt workflows more easily while keeping day-to-day operations smoother.

NLP Labeling

Less Clicking. More Transcribing.

June 11, 2026

Transcription is a game of momentum. Whether you're reviewing interviews, customer calls, research recordings, media content, or AI training data, productivity often comes down to how long you can stay focused on the audio itself. Every interruption—reaching for a mouse, navigating menus, or switching between tools—breaks concentration and slows the review process. That's why we've introduced new workflow enhancements designed to help transcriptionists and reviewers spend less time interacting with the interface and more time focusing on the content.

NLP Labeling

May 2026 Feature Updates: Richer Audio Labeling, Smarter Predictions, and More Labeling Control

June 8, 2026

May’s updates bring meaningful improvements across Data Studio, from a significantly expanded Audio Labeling experience to smarter search tools and more control over automated predictions. Whether you’re working with multi-channel audio, running large-scale labeling jobs, or protecting sensitive data, this month’s releases are designed to give you more precision and flexibility in your workflow.

NLP Labeling

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