Automate Knowledge Management with Autoprofiling

For decades, legal knowledge management has faced a seemingly insurmountable problem: accurately profiling the legal documents users store in their Document Management System (DMS) with rich, meaningful metadata—while simultaneously not burdening lawyers and support staff with tedious data entry.

Enter NetDocuments' newly debuted autoprofiling capabilities. With autoprofiling, customers can establish automated triggers to identify documents with incomplete profiles, and then automatically run a workflow in the background that fills in metadata elements like document types, descriptions, practice areas, parties, jurisdiction, and much more.

What sets these new autoprofiling capabilities apart from traditional machine learning approaches is that NetDocuments workflows can be tailored to exactly the metadata a firm cares about, without any pretraining of models or need to provide example documents. For example, autoprofiling workflows can extract profile data about parties, effective dates, and renewal periods from contracts, while pulling out information about court, case number, and judge from litigation filings. The options for metadata extraction are nearly limitless.

For customer interested in learning more about autoprofiling, see our support center article (login required) which provides comprehensive information on setup and implementation.