Technology-Assisted Document Review
Supervision capabilities are greatly enhanced by incorporating language detection, personally identifiable information, and sentiment analysis on top of the basic search criteria.
Time and Labor Inefficiency
Out of say 1000 documents, reviewers may need to manually go over 10% of them until our AI can understand the type of information that the organization is looking for and begin pulling documents that follow the approved structure. This is done by recognizing similar semantics used across messages and gaining approval by the reviewers if certain messages fit the criteria the investigation requires. If approved, the AI will continue pulling similar documents, but if not the AI will refine its search to pull more relevant documents.
Manually reviewing documents when met with investigations and reviews is a tedious, frustrating process that relies heavily on hours dedicated by employees. This energy could instead be deployed toward more meaningful projects and creativity within an organization. Maintaining an engaging, uplifting environment is important, and cannot exist if employees feel as though their time could be spent in more worthwhile ways.
How does Semantics AI work?
By recognizing the language used in the documents reviewers have approved, our AI will begin its own search for similar documents. For example, if the document contains information pertaining to an internal investigation about trade secrets and particular employees, our AI will capture documents that contain language indicative of that situation. First, reviewers will manually review a certain number of documents so that the AI can understand the requirements needed. Once the AI has had substantial data to learn from, it can begin its own search and pull relevant documents. If, however, the AI has pulled a certain document that does not match the criteria, reviewers can disapprove it, and the AI will refine its search further in order to pull the most accurate data possible.