Communication Intelligence by TCDI

Communication Intelligence is TCDI's mobile and chat data processing tool that alleviates the challenging, inefficient, and error-prone method of reviewing thousands of interspersed text conversations most often displayed from Cellebrite or Excel formatted reports. Results are loaded to Relativity or RelativityOne, enabling users to benefit from Relativity features like dashboard, highlighting, redactions and Active Learning among others.

Convert Mobile and Chat Data to Streamline your review process.

Communication Intelligence groups messages together based on underlying periods of activity between individuals. The resulting documents, rendered in user friendly “chat bubble” style with emojis and image thumbnails, allows reviewers to zero in on relevant content without losing context. The platform is agnostic toward device type, chat application and forensic software, enabling it to work with virtually any data source, including Cellebrite reports, Slack Exports, social media, and more. Its powerful processing engine provides comprehensive across-device threading and de-duplication, even in the face of inconsistent contact names and network time stamps.

Key features of Communication Intelligence include:

  • Logically groups individual messages into conversation segments for easier, faster review. This overcomes many of the obstacles presented by other approaches, which either require each message be reviewed as a separate document, or consolidate potentially years of messages into a single sprawling transcript.
  • Individual messages and attachments are all preserved as family members, making it easy to review at the conversation level and produce at the message level.
  • Works with chat formats SMS, MMS, Whatsapp, Facebook Messenger, Twitter, and other formats rendered in XML or JSON containing sender, recipient and date metadata, regardless of the device type and version the content was extracted from.
  • Presents messaging content in a user friendly “chat bubble” format, displaying each unique message in the same manner a reviewer would see it on the device itself.
  • Robust de-duplication and threading, both within and across devices, using advanced data normalization techniques to resolve across-device inconsistencies.
  • Provides textual definitions of emojis for easy indexing and searching along with an index of emojis contained on each device for fast identification of messages containing those emojis.
  • Helps reduce the risk of missing information and results in significant improvement of reviewing, searching, producing, and referencing key conversations.