Draft Salient Characteristics.pdf

Rela vityOne eDiscovery Pla orm (Brand Name or Equal)

SALIENT CHARACTERISTICS

For an ‘or equal’ product to be considered technically acceptable, the eDiscovery solu on must have the following salient characteris cs. See FAR Clause 52.211-6, Brand Name or Equal for proposing ‘or equal’ products

FedRAMP Authorized to Operate (ATO) o A cloud compu ng pla orm with FedRAMP Moderate authoriza on, hosted on

Microso Government Cloud, ensuring isolated and secure handling of government data.

o Seamlessly connects with Microso 365 Government for managing and discovering data.

o U lizes the scalable resources of Microso Government Cloud to manage and process extensive data and complex tasks.

High Security and Compliance Standards o Secure handling of data with features such as legal hold management, audit trails, and data integrity protec on to meet rigorous compliance standards and safeguard custodian data.

Machine Learning Features o Adapts and improves with user input and data to enhance accuracy over me.

o Automates the process of tagging and organizing documents to streamline eDiscovery workflows.

o Machine learning algorithms to priori ze and categorize documents based on relevance and context.

o Algorithm that updates every 30 minutes

Comprehensive pla orm that provides an end-to-end solu on for data collec on, processing, review, and produc on, integra ng all aspects of eDiscovery in one pla orm.

o Tools for collec ng data from various sources to include Microso 365, OneDrive, Outlook, SharePoint, and Teams.

o Ability to collect directly into pla orm

Box – collect data from Box cloud storage sources. You can also collect files from Box directly into pla orm.

Google Workspace – collect documents from Chat, Drive, Gmail, and Groups.

iManage – collect document and email data from iManage applica on.

Microso 365 – collect documents from custodian’s OneDrive account, custodian’s Outlook mailbox, archived mailbox, calendar, and contacts list, and custodian’s Team account.

Slack – collect short messages from within organiza on or custodian’s Slack Enterprise applica on.

Processing func onality that allows users to ingest raw data directly into workspace for eventual search, review, and produc on without the need for an external tool.

Email threading feature which iden fies email rela onships, and then extracts and normalizes email metadata. An email thread is a single email conversa on that starts with an original email, the beginning of the conversa on, and includes all of the subsequent replies and forwards pertaining to that original email. The analy cs engine uses a combina on of email headers and email bodies to determine if emails belong to the same thread. Analy cs allows for data inconsistencies that can occur, such as me stamp differences generated by different servers.

The Analy cs engine then determines which emails are inclusive, meaning that it contains unique content and should be reviewed. This process includes the following steps at a high level:

o Segment emails into the component emails, such as when a reply, reply-all, or forward occurred.

o Examine and normalize the header data, such as senders, recipients, and dates. This happens with both the component emails and the parent email, whose headers are usually passed explicitly as metadata.

o Recognize when emails belong to the same conversa on, referred to as the email thread group, using the body segments along with headers, and determine where in the conversa on the emails occur. Email thread groups are created using the body segments, email from, email to, email date, and email subject headers.

o Determine inclusiveness within conversa ons by analyzing the text, the sent me, and the sender of each email.

Import/export feature that allows for “drag and drop” processing of PST files, which uploads, processes, and publishes data in one place.

Automated redac on capabili es to easily remove sensi ve informa on and PII.

Keyword expansion and concept searching to discover unexpected or hidden words.

Search engine that provides advanced search func onality such as proximity, stemming, and fuzzy searches across any field type.

Ability to iden fy documents that are nearly iden cal, such as mul ple versions or dra s of the same document.

Ability to filter to search for items. The filters search for values in the fields on the ac ve view, and across all records available in the searchable set. Filters are also available for item lists on tabs and pop-up windows.

Tagging to allow customiza on of a category or manual categoriza on of a document.

Mass edi ng is the ability to perform ac ons on a set of documents at once instead of performing the same ac on for each individual document.

Produc on that can be completed without needing to leave the pla orm, once finished with review.

Ability to create templates for produc ons that can be used on a repeated basis.

Ability to turn files into an image and simple PDF.

Transcripts applica on that allows users to upload, review, code, and annotate transcript files in the pla orm.

Produc on sets that allow for bates stamping and produc on of placeholders if the files are too large for imaging.

Expor ng of Vaughn Indices with all required metadata and tagged exemp ons.

Persistent highlight sets apply term highligh ng to assist with document review in the Viewer. If a document contains any of the terms specified in a set, the list of terms and the number of mes they appear in the document appear under the set. If a document contains no terms from the set, the set in the tree cannot expand or collapse. The na ve images also will highlight the terms if they appear in the document. The documents are navigable by highlighted terms.

Batches split a sta c set of documents into mul ple document sets based on criteria set by a system admin. With the appropriate permissions, users can then check out these batches and assign themselves documents. Batching can bring structure to managing a large-scale document review with mul ple reviewers. You can generate batches automa cally or manually. Automa c document assignment reduces the chances of accidental redundancy in the review process.