NEW! Request Early Access: Semantic Search for Unstructured Data
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NEW Data Discovery

Natural language search for unstructured data

Be the first to try our new data discovery product using semantic search and similarity to explore your unstructured data and curate datasets for labeling.

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Curate better datasets. Build better models.

Index, visualize, and operationalize your unstructured data

Connect and explore unstructured data from cloud buckets in the launching pad and go-to interface for all of your data development efforts.

Search and categorize your data using natural language and similarity

Leverage generative AI to search data semantically and by object similarity, in order to further understand and refine datasets. For example, if your existing model is not performing well for a certain class of data, you can identify more samples for expansion.

Curate datasets and seamlessly add tasks to Label Studio Enterprise projects

Select sample data objects through your discovery workflow to be directly exported as tasks in new or existing Label Studio Enterprise projects for labeling or review.

Request Early Access

Bootstrap training data for new models, and identify data for model expansion and fine-tuning. Data Discovery is currently in Private Preview—join the waitlist to get a demo or request early access.

Frequently Asked Questions

How does the semantic search functionality work?

Semantic search allows you to use real language to search through your unstructured data. You can use phrases like "pictures of oranges" or "three zebras" and using the power of vector databases and foundation models, the Data Discovery tool will return every instance that it finds.

How do foundation models and generative AI support Data Discovery?

Foundation models sit at the very core of Data Discovery. Using foundation models, we index and automatically categorize all the unstructured data you connect the system to. It then renders that data searchable, catalogable, and ultimately, labelable.

Can I use Data Discovery for managing data even if I don’t plan on labeling?

While Data Discovery was designed with dataset development workflows in mind, it can also be used for any application where indexing and understanding the semantic relationships between various items of unstructured data would be helpful.