Adlib has developed a new multifaceted approach to classifying unstructured and semi-structured content and extracting data from that content. This new approach leverages several recent innovations in AI, ML and computer vision that enable the platform to solve unique content problems—all without customization, coding or expensive, lengthy projects.
Identifying, classifying, categorizing, and separating different document types is a human labor-intensive process. Imagine the possibility of software being able to do this for you and do it with a higher level of precision. Our intelligent document classification solution can provide this for many different document types.
Ninety percent of enterprise data is typically unstructured, unmanaged, and spread across repositories and file shares—leaving many organizations scrambling to keep up with everyday processes vs. harnessing the full strategic value of their data. Before extraction and deeper analysis can occur, the documents must be identified, analyzed, and classified. For enterprises saddled with vast volumes of data, however, legacy and semi-automated methods of classification are not fit for the task at hand.
Leverage unsupervised machine learning for document clustering and semi-supervised rule building to define a document training set to be leveraged in the automated document classification of a larger document collection. Companies can easily organize, prioritize, and leverage the data that exists across the enterprise.
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