Automatically categorize your records

You have a lot of data, and it’s hard to know what to do with it all. Not only is it hard to know where to start, but trying to manually categorize everything is just too time consuming and expensive.  Feith Auto-Categorizer takes the pain out of records management by automatically categorizing your documents for you. Using powerful rules, workflows, and AI text analysis, this proprietary software tool can read and categorize documents without human intervention. This not only saves you time and money, but also ensures that your records are properly classified and stored in accordance with regulations.

Saves time spent sorting through unorganized data

Make sense of your unstructured content, documents, email, and data, so automated retention is possible.

Facilitates better information governance

Enterprise Information Governance is impossible when you don't know what you have and where you have.

Helps ensure compliance with regulations

Apply document and user permission controls based on the category, content and metadata of your records.

Enhances data discovery and retrieval

Categorizing records makes it simple to integrate them into structured discovery processes and other workflows.

Automatically classifies by content and purpose

Leverage cutting-edge technology to know what's in your records, and where they live in your repositories.

Minimizes the risk of human error in categorization

Become compliant with Record Retention rules faster, with lower costs, and eliminate human error.

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 scanned paper records are acquired and ingested securely and efficiently based on your industrial, organizational, or governmental requirements.

The Market-Leading Records Categorizer

Today, almost every organization is struggling with the problem of data overload. The goal of Records Management (RM) is to ensure the efficient and effective handling of records throughout their life cycle, but this becomes increasingly difficult when you have too much data to deal with. This is where Feith Auto-Categorizer comes in. Using advanced statistical modeling to read and categorize documents automatically, Auto-Categorizer solves the problem of data overload by leveraging the power of the Feith Platform. Your documents and email, along with their associated metadata, are evaluated, categorized and processed all without human intervention.

With Auto-Categorizer, you can automatically categorize your records, improving efficiency, reducing costs, and providing better compliance with regulations. Automated categorization can save organizations time and money by reducing the need for manual sorting and categorization of records. It can also help to ensure that records are properly classified, marked and stored in accordance with Federal regulations.

The Basics of Auto-Categorization

Records management (RM) is the process of organizing, storing, retrieving, and disposing of corporate records and information. The goal of RM is to ensure the efficient and effective handling of records throughout their life cycle. To determine the lifecycle of a record requires knowing a record’s “category”, or what type of record it is. Knowing a records type helps us know how long to keep it — an employment application may be kept 2 years, while a contract may be kept until it expires or 20 years.

Categorization is the process of identifying and grouping records according to their content and purpose so that they can be managed as a unit. Unfortunately, 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.

Now you can perform sophisticated, automated categorization of the structured and unstructured data in your repositories. Feith 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 Feith well-suited to conquering unstructured data repositories, like email, network drives, SharePoint and more.

How it works

Once it's categorized