Choose from one of the three different cost calculators below to estimate the total spend for eDiscovery processing and hosting. One has been created to estimate total costs associated with an Early Case Assessment workflow, one for a traditional processing and hosting workflow, and one that leverages predictive coding. Access each one either by clicking on the learn more links below, or from the menu available when you hover your mouse over the HOME link at the top of this page. To learn more about D4 select one of the options under DISCOVER MORE.
The ECA workflow assumes that you'll want to maintain an active "well" that you can go back to at any time but have a need to segregate data selected for review from data that was initially searched and culled. The ECA pricing model carries a one time fee for ingesting the source data into a pre-review workspace and carries a low monthly storage rate. After iterative culling is completed selected documents are promoted to a full review workspace and carry a slightly higher monthly storage rate. This calculator projects total processing and hosting costs for the first month and for the life of the project.
This workflow assumes that either all data processed and received will need to be promoted directly to a review workspace or that search terms and filters have already been decided. Charges for processing are split between a one-time charge for all compressed data that is ingested, and a one-time charge for all extracted data that results after the filters have been applied. Any load-ready data that does not require processing is imported into the review environment and carries a one time charge. Standard monthly hosting storage and user license fees apply. This calculator projects total processing and hosting costs for the first month and for the life of the project.
A standard ECA workflow is leveraged to reduce the initial volume of discovered data. The resulting set is then promoted and indexed for predictive analytics. A series of review batches are presented to a small expert team of reviewers to train the active machine learning. Once the training is complete a targeted subset of records are isolated for eyes-on attorney review. Option to leverage structured analytics to enable near-duplicate detection, email threading, categorization, foreign language detection, concept clustering and communication analysis.