Supahands started in 2014 in the personal concierge industry. We helped our users complete requests or tasks virtually – we became their proverbial hands! A few years (and many experiments) later, we found our niche in image annotation for machine learning.
In 2018, Supahands moved from serving everyday people like you and me, to data scientists and machine learning engineers in deep tech. In just a few years, we’ve completed millions of image annotations for AI/ML teams around the world.
We no longer just lend our users a helping hand.
Our mission is to solve data labeling’s last mile problem. Trust. We help our users turn unlabeled data to trusted data with a single, easy-to-use data labeling platform for quality insights.
Our goal is to continually simplify the data labeling experience and give anyone the opportunity to get their image data labeled and understood – no code required. Through a collaborative feedback loop with our trained annotators, we help our users surface label noise and edge cases to iterate with quality insights.
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If there's one thing that makes SUPA stand out, it's their commitment to providing consistent support throughout the data labeling process. The team actively and efficiently engaged with us to ensure any ambiguity in the dataset was cleared up.
Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.
I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.
The annotators were really quick. I would upload and 5 minutes later - 10 images done. I checked 5 minutes later - 100 images done.
The team at [SUPA] has been very professional & easy to work with since we started our collaboration in 2019. They've provided us with good quality judgments to train, tune, and validate our Search & Recommendations models.
Let us walk you through the entire data labeling experience, from set up to export
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