There is simply too much data at your organization for manual categorization processes to be efficient. Yet, proper Records Management and Information Governance require that you know exactly what sort of documents you’re dealing with.
Enter Auto-Categorizer. Using advanced statistical modeling to read and categorize documents automatically, Auto-Categorizer solves the problem of data overload by leveraging the power of machine learning. Your documents and email, along with their associated metadata, are evaluated, categorized and processed all without human intervention. Welcome to the age of smart machines.
BridgeLogiQ’s Auto-Categorizer performs sophisticated, automated categorization of structured and unstructured data files. BridgeLogiQ learns to make decisions based on a combination of word matching, phrase matching, metadata matching, and knowledge accumulated in a training process for text matching. This learning and decision making functionality makes BridgeLogiQ well-suited to solve particular problems, such as searching through unstructured data for association with the appropriate category.
Feith’s Auto-Categorizer application is a fully-integrated and fully-automated auto-categorization solution arranged in a “waterfall” architecture that ensures both electronic and paper records are acquired and ingested securely and efficiently based on your industrial, organizational, or governmental requirements.
Feith’s Auto-Categorizer trains the machine to recognize your document category type employing the Bayes Theorem to create a trainable and learning search and categorization engine.
Auto-Categorizer provides a powerful and flexible categorization engine.
The Auto-Categorizer engine has the ability to return 90%+ accuracy, which will put a large dent in your manual categorization workload.