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Electronic Discovery

Early Case Assessment

Early Case Assessment (ECA) provides visibility into the size and scope of your case by quickly identifying important information about your data such as file formats, volume, date ranges, etc, while examining custodians, search terms, and concepts. ECA seeks to identify the key issues in a legal matter, the keywords and search terms relevant to those issues, estimated e-discovery costs if the complaint is pursued and the overall legal liability of the case.

 

Email Threading
Email threading identifies all emails that are part of the same chain. This feature also identifies all emails which are subset emails to later emails in the same chain. These subset emails can be excluded from main review.

 

Near Duplicate Detection

Multiple versions of documents are frequently distributed across an organisation. Near Duplicate Detection (NDD) identifies all similar documents across the dataset and allows you to review them together. This feature can massively speed up review for datasets with large amounts of near duplicates.

 

Keyword expansion

Identifying the right key words to search on is a critical part of any case. Through keyword expansion, text analytics empowers you with immense investigative power to widen searches and draw in more relevant documents quicker in the review process by teasing relevant terms out of key documents.

Search on keywords of your choice from any document, and from those terms text analytics can provide a list of terms that are conceptually related based on the unique language in your data set. By finding related terms from other documents, you can discover unexpected or hidden words, such as project code names and company or industry jargon, and ensure you aren’t overlooking anything important to your case.

 

Concept searching
Words that hold very similar meanings, such as “cold” and “frigid,” or words with multiple meanings, such as “leaves,” can skew results of traditional keyword searches and slow down the review process. Concept searching is another text analytics feature that helps overcome obstacles in standard searching techniques.

Concept searching goes beyond keywords to find documents based on ideas rather than specific terms. This allows you to identify important documents and follow an investigatory pattern, locating relevant documents even without knowledge of the specific terms, phrases, jargon, or code words that may be used in other documents.

 

Categorisation
Categorisation allows subject matter experts to automatically group unreviewed documents into categories they define themselves with the issues coded in a small manually reviewed set. You can also use categorisation to determine if documents are most likely to be responsive or non-responsive.

 

Include similar documents
As a valuable QA tool before sending out a discovery, this feature can assist locating privilege documents and prevent them from being inadvertently disclosed. The system learns the concept of a privilege document by analysing the human identified privileged documents, and then reviews proposed discovery. Analytics will identify suspected privileged documents can then be manually reviewed before disclosure.

 

Clustering
Text analytics helps get the most important groups of documents to review teams as soon as possible and batch documents by conceptual similarity for faster, more consistent coding. With a feature called clustering, you can organise and prioritise your review much earlier in a case.

Clustering automatically identifies and groups documents with similar concepts. It labels those groups by the most prevalent ideas in each one and visually represents how the groups relate to one another. Unlike a concept search, the user provides no input as to what they’re looking for—there’s no need for subject matter experts to identify example documents.

 

Technology-assisted review
Technology/Computer-assisted review helps you accelerate your review process by amplifying your team’s efforts across any substantial document set. Text analytics (categorisation) is one of the three key elements of computer-assisted review, which also includes statistical validation and, most importantly, actual humans.

In computer-assisted review, senior reviewers code documents in the system in the form of seed sets, and the system applies their decisions to the rest of the document universe through an iterative workflow managed by the review team. The end result is a less costly and tedious Electronic Discovery experience.

 

Virtual Data Rooms

High Availability

24x7x365 service availability with fully redundant servers, automatic failover, load balancing and real-time geo-replication, allowing access to our system from anywhere, at any time, even if a disaster affects one of the data centres.

 

Privacy + Security Compliance

HIPPA BAA, ISO/IEC 27001 and 27018, IRS 1075, CJIS Security Policy and CSA STAR

 

Document security

Dynamic watermarks, document expiry, restricted viewing, 256-Bit SSL encryption, document access restrictions, data backup, virus scanning and on save/on open encryption.

 

Encryption

AES-256 disk encryption, Transparent Data Encryption, TLS/SSL and IPsec ensures there is no time when your data is unencrypted, whether at rest or in motion.

 

Accessibility + Security

Multiple-factor verification, granular permission settings, two-factor authentication, mobile device application(s), multi-language support and plugin-free.

 

Data Centre Security

Certifications in SOC 2, ISO 27001, SSAE 16 and physical data centre security.

 

Logs + Reporting

Audit Logs, time-stamping, detailed activity reports, document version control and notifications.

Data in the Blockchain

Sign and store files in the blockchain

Any document, contract or agreement can be signed legally and the record stored securely on the blockchain.

 

Distributed ledger

The distributed storage of your files on multiple nodes, ensures no single point of failure or vulnerability.

 

Encrypted Access

Access to your records are cryptographically encrypted.