AI algorithms and machine learning (ML) automatically filter out irrelevant documents, streamlining the review process by highlighting only the relevant ones. Natural language processing (NLP) helps in understanding document content and extracting and categorizing important information.

Supervised learning allows for the classification of new documents based on past cases and document tags. By training on a small set of tagged documents, the system can automatically tag the remaining unclassified documents.

Automatically detects and extracts key information, including people, organizations, and dates, from large documents using text recognition.

Automatically detects and safeguards documents with personal information, effectively preventing data breaches.

Processes and translates documents in multiple languages to address global eDiscovery needs.

The data visualization feature enables quick identification of key insights like patterns, trends, and anomalies in documents and data. It also helps in identifying critical case points and assessing risks through data analysis models, facilitating the creation of effective strategies.

Interactive AI helps in learning from eDiscovery data and searching for specific information. It also swiftly reviews document contents and provides summaries of key issues.