Technology Assisted Review

Technology Assisted Review (TAR) is a powerful tool that helps businesses and organizations streamline the search and analysis of digital data in legal proceedings. TAR uses advanced algorithms and machine learning techniques to identify relevant data more quickly and accurately than traditional manual review methods.

Streamlining the Search and Analysis of Digital Data in Legal Proceedings

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 essential and cannot exist if employees feel as though their time could be spent in more worthwhile ways.

There are several benefits to using TAR in legal proceedings:

Increased efficiency:
TAR can help reduce the time and cost associated with manual review by quickly and accurately identifying relevant data. This can help businesses and organizations meet their legal obligations more efficiently and respond to legal requests.

Improved accuracy:
TAR can help improve the accuracy of the review process by using advanced algorithms and machine-learning techniques to identify relevant data. This can help businesses and organizations meet their legal obligations more effectively and reduce the risk of missing essential data.

Enhanced consistency:
TAR can help ensure consistency in the review process by using standardized criteria and algorithms to identify relevant data. This can help businesses and organizations meet their legal obligations more effectively and reduce the risk of subjectivity in the review process.

Solution

Grotabyte offers a range of TAR solutions that can help businesses and organizations streamline the search and analysis of digital data in legal proceedings.

Supervision capabilities are greatly enhanced by incorporating language detection, personally identifiable information, and sentiment analysis on top of the essential search criteria.

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 from the reviewers if specific messages fit the criteria the investigation requires. If approved, the AI will continue pulling similar documents; if not, the AI will refine its search to show more relevant documents.

By recognizing the language used in the documents reviewers have approved, our AI will begin its search for similar documents. For example, if the document contains information about an internal investigation of 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. Once the AI has actual data to learn from, it can begin its search and pull relevant documents. If, however, the AI has pulled a specific document that does not match the criteria, reviewers can disapprove it, and the AI will refine its search further to pull the most accurate data possible.

Contact us today to learn more about how our TAR solutions can help your business or organization improve the efficiency, accuracy, and consistency of the review process.