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.

How Progressive is Your Classification Solution?


Utilizes limited ECM capabilities to aid in document classification—barcoded templates, tedious data entry, or other manual and semi-automated legacy methods are typically employed.


Leverages 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 entire enterprise.

Extract Intelligence from Unstructured Data

Using advanced document conversion, clustering, and rules-based classifiers, Adlib’s Progressive Document Classification solution allows enterprises to:

  • Automatically process massive volumes of unstructured data—creating actionable insights and revealing hidden value and risk.
  • Take documents from multiple lines of business, remove the ROT, and ensure that valuable assets (contracts, agreements, reports, and regulatory submissions) are grouped, enhanced, and easily searchable.
  • Eliminate the cost, risk, and inconsistency of manually processing rapidly growing volumes and varieties of documents and data.
  • Leverage a flexible and configurable system that learns and adapts as your business needs evolve.
  • Group or cluster similar documents to enable targeted extraction of key data elements that can be added as metadata, validated against other systems, etc.
  • Identify sensitive or pertinent data to manage compliance risk effectively (e.g. identify PII) or accelerate business decisions (e.g. process claims faster).
  • Mitigate risk by identifying classified documents and applying appropriate security measures or redacting confidential information.

Information Governance & Classification

Adlib data management experts discuss how applying Progressive Classification technology allows organizations to see data clearly, make more informed business decisions, and implement data classification solutions as an ongoing information governance initiative.

Progressive Classification?: Achieving Intelligent Information Governance and Content Clarity

Learn how enterprises are achieving intelligent information governance and content clarity with Progressive Document Classification.

Success Stories

Global insurance company executes digital transformation project and overcomes content migration hurdles.

Global Insurance Company Executes Digital Transformation Project and Overcomes Content Migration Hurdles

Aerospace Company Gets Defensible

Leading Oil and Gas Company Takes Steps to Gain Control of Their Data

Danish municipality meets regulatory requirements by rapidly archiving large volumes of documents

Acquisition-Triggered Migration Project Enables a New Capability for Archiving and Search


Energy Company Achieves Classification Success

Mapping Unstructured Content: The Key to Real Classification (Webinar)

Mapping Unstructured Content: The Key to Real Classification (Webinar)

Effective Classification Techniques (White Paper)

Effective Classification Techniques (White Paper)

Effective Examination of Content-Enabled Intelligent Classification (White Paper)

Effective Examination of Content-Enabled Intelligent Classification (White Paper)