SuperAnnotate enhances AI performance by prioritizing high-quality data studies, confirming that data set quality surpasses size in importance for optimal results
Vahan and Tigran Petrosyan, brothers, knew how hard it was to handle a lot of data while they were in college teaching algorithms. One thing Vahan did for his Ph.D. study on image segmentation was to make a tool for managing data.
After a few years, Vahan understood that developers and businesses would gladly pay for this tool. To build it, the brothers set up a business called SuperAnnotate.
“As models and multimodal AI became more popular in 2023, the need for high-quality datasets grew. This is because each organization had different use cases that needed different kinds of data,” Vahan said. “We saw a chance to make a platform that is easy to use and doesn’t require a lot of code, like a Swiss Army Knife for teaching modern AI.”
SuperAnnotate helps people make and keep track of big data sets for teaching AI. Companies like Databricks and Canva use it. The company started making labeling software, but now it also makes tools for improving, testing, and analyzing data sets.
Users can connect data from local sources and the cloud to SuperAnnotate’s tool to make data projects they can work on with others. Users can compare the performance of models based on the data used to train them from a dashboard. When the models are ready, they can be deployed to different settings.
SuperAnnotate also gives businesses access to a market where people can bid on jobs to annotate data. Annotations are usually textual labels that show what something means or parts of data that models learn from. They help models distinguish between things, places, and ideas.
To be honest, there are a lot of negative Reddit posts about how SuperAnnotate treats the data annotators it uses. It’s common for annotators to complain about poor communication, unclear standards, and low pay.
For its part, SuperAnnotate says it pays its annotators the going rate and that its requirements are standard in the field. We asked the company for more information about how it does business, and we’ll make changes to this article if we hear back.
In the field of AI data management, there are a lot of rivals, such as new companies like Scale AI, Weka, and Dataloop. But San Francisco-based SuperAnnotate has been able to hold its own. It just raised $36 million in a Series B round led by Socium Ventures, with Nvidia, Databricks Ventures, Play Time Ventures, and Defy.vc also investing.
SuperAnnotate has now raised a total of just over $53 million. The new money will be used to add to its current team of about 100 people, research and develop new products, and get more companies to become SuperAnnotate customers.
Vahan said, “We want to build a platform that can fully adapt to the changing needs of businesses and offer a lot of customization in data fine-tuning.”
