Service Provider Completes “Second Request”
with Large Document Volume

The Challenge

Ahead of a large merger, the client of a service provider received a “second request” from the Federal Trade Commission and the Antitrust Division of the Justice Department. These requests investigate mergers to ensure there are no anti-competitive concerns.


With approximately 10-14 million documents touched by this second request, a manual redaction process would have required up to nine months. However, the provider needed to shorten that.


The end client also wanted manual reviews performed on responsive documents and to apply special redaction criteria to the metadata. These files included images, PDFs, and every major Microsoft Suite file, including native Excels. Lastly, the first delivery deadline for produced files was a mere three days after the first effective document drop.



  • Government-issued “Second Request”

  • 1st Deadline 3 Days After Kickoff

  • Approximately 10-14 Million Docs

The Solution

Being newer users of Blackout, this provider worked closely with Milyli to master job-building during the condensed time frame. It continued to collaborate with Milyli support to flag poorly scanned documents and manage an extensive tagging structure for the reviewed documents.


In imaged files or Excels, Blackout was able to redact all the various forms of personally identifiable information (PII) documents. That included Social Security numbers, account numbers, routing numbers, names, license plate information, and more.


Using Blackout’s automated redaction functionality on the documents that did not need manual review shaved months off the project. This allowed the provider to keep up with round the clock requests from a particularly involved client and deliver thoroughly reviewed documents for this important government request.

Information Blackout Can Flag

  • Personal Identification Numbers

  • Account IDs

  • Credit Card Numbers

  • Dates

  • Contact Information

    • Emails​

    • Phone Numbers

    • Addresses

  • Charts

  • Pivot Table Data

  • Embedded Objects

  • Notes/Comments

And much more.

Blackout uses regular expression (regex) text strings to find terms in documents that match the regex pattern so that they may be marked up, including the ability to redact or markup partial words and phrases. The user’s review team writes these with or without Milyli's assistance and can load them directly into Blackout or import them in bulk.


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