High-performance content classification driven by machine learning. Automatically add tags to all your documents based on their full content, not just the metadata. Classifier uses artificial intelligence and machine learning to automatically detect the characteristics of documents. Each tag is associated with a unique signature that is subsequently used to select which tags to apply to new documents. Ongoing quality control provides a feedback loop that adjusts incorrect tagging, increasing precision over time.

Training phase: In the learning phase, Classifier creates its semantic signature repository. By providing a subset of already tagged documents, you can greatly accelerate this phase. If such a set does not exist, a web-based workbench guides you through the initial tagging. Antidot’s Active Learning technology drastically reduces the effort by guiding you in the selection of a representative sample of documents.

Classification phase: Once trained, you can inject into Classifier your full document corpus for initial, batch tagging. Then, on an ongoing basis, every new document is processed in real-time. Tagging of each document takes only a few milliseconds, making it easy to integrate classification into any business or IT process.