Teach your AI to solve your information extraction task.
Powered by active learning, automatic machine learning, and in-house foundation models.
Automatically assign a class (a.k.a. label) to a text document. You teach the model how to predict the correct class from a custom set of classes (e.g. “Biology“ and “Physics”) that you define during the teaching process. Great for sentiment analysis, content moderation, or topic identification.
Automatically assign a set of labels to a text document. Each document can be given one label, multiple labels, or no label at all. It can be used for topic classification when multiple topics can be present in the same document.
Identify and classify any kind of entities or concepts mentioned in a text document. You teach the model how to recognize entities from a custom set of entity types (e.g. “Person” or “Address”) that you define during the teaching process. Entity Detection can also be used to extract paragraph-length spans.
Drastically reduce the amount of labels necessary by building on top of large language models.
Analyze large volumes of data using topic modeling to understand, extract themes, and categorize documents effectively.
Speed-up labeling by letting the model identify the most informative documents.
Talk to us to access our un-released foundation models, and get in-house model customization by our model experts.
